Home Uncategorized This is the focus:Behavior Analysis (previously known as behaviorism) SEE articles are attached below

Uncategorized

This is the focus:Behavior Analysis (previously known as behaviorism) SEE articles are attached below

Choosing Your Focus:

This writing assignment supports your success on the final project. Prior to beginning this assignment, review the components of an

annotated bibliography

For this this Annotated Bibliography, you will research at least five peer-reviewed articles published within the last ten years to support your analysis of one of the following topics:

Behavior Analysis (previously known as behaviorism)
Cognitivism
Information processing

The topic you choose should be based on the area in which you would most like to develop your knowledge. Your choice should also consider your current interests in psychology and support your future career or personal goals.

There are 4 components to this assignment:

Introduction: Include your chosen focus area and your motivation for choosing this area to study further
The Annotated Bibliography itself
A Conclusion
A list of APA formatted references.

As you prepare for this assignment, keep in mind that it is designed to assist you with beginning the process of drafting your Learning and Cognition Final Project. You will be using these resources, and what you write in this paper, to support your success with your Final Project.

Review the following sources prior to writing your paper:

After completing your writing, consider a paper review and double checking that all components are complete with an

Academic Paper Checklist.

It is also recommended that your paper be checked in

Grammarly

and through

Turnitin

prior to submissio

The Choosing Your Focus paper

Must be a minimum of 1250 words, no more than 1500 words, and formatted according to APA style (7th edition) as outlined in the Writing Center.
Must include a title page with the following:
Title of paper
Student’s name
Course name and number
Instructor’s name
Date submitted
Must begin with an introductory paragraph that clearly states the chosen construct and your motivation for choosing this topic.
Must clearly discuss each article in your own words using critical thought.
Must end with a conclusion that synthesizes the findings within the articles as they relate to your chosen content.
Must include a Reference section.
The Formatting Your References List guide offers additional guidance on correctly formatting references for the annotated
Must use at least five peer-reviewed sources.
The Scholarly, Peer Reviewed, and Other Credible Sources table offers additional guidance on appropriate source types. If you have questions about whether a specific source is appropriate for this assignment, please contact your your instructor.

HERE ARE THE CHOSEN ARTICLES FOR THE ANNOTATED BIBLIOGRAPHY

BAbel, P. (2019). Classical Conditioning as a Distinct Mechanism of Placebo Effects. Frontiers in Psychiatry.

https://doi-org.proxy-library.ashford.edu/10.3389/fpsyt.2019.00449

(article 1 for classical conditioning)

Andreatta, M., Michelmann, S., Pauli, P., & Hewig, J. (2017). Learning processes underlying avoidance of negative outcomes. Psychophysiology, 54(4), 578–590.

https://doi-org.proxy-library.ashford.edu/10.1111/psyp.12822

(Article 2 for operant conditioning)

Powell, R. A., & Schmaltz, R. M. (2020). Did Little Albert actually acquire a conditioned fear of furry animals? What the film evidence tells us. History of Psychology.

https://doi-org.proxy-library.ashford.edu/10.1037/hop0000176

(Article 3 for Watson)

Braat, M., Engelen, J., van Gemert, T., & Verhaegh, S. (2020). The rise and fall of behaviorism: The narrative and the numbers. History of Psychology, 23(3), 252–280.

https://doi-org.proxy-library.ashford.edu/10.1037/hop0000146

(Article 4 about behavior analysis)

Willis, E., Adams, R., & Keene, J. (2019). If Everyone Is Doing It, It Must Be Safe: College Students’ Development of Attitudes toward Poly-Substance Use. Substance Use & Misuse, 54(11), 1886–1893.

https://doi-org.proxy-library.ashford.edu/10.1080/10826084.2019.1618334

(Article 5 social learning theory)

OTHER ARTICLES BESIDES THE MAIN FIVE ARTICLES ABOVE

Clark, K. R. (2018). Learning Theories: Behaviorism. Radiologic Technology, 90(2), 172–175.

Rholetter, W. Me. (2019). Operant conditioning. Salem Press Encyclopedia.

Sparzo, F. J. (2019). Pavlovian conditioning. Salem Press Encyclopedia of Health.

Title

of Your Assignment: Annotated Bibliography

Your Name

Institution Name

Course Number: Title

Instructor’s Name

Month Day, Year

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Making an Annotated Bibliography

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Annotated Bibliography

Title of Your Assignment: Annotated Bibliography

Include the number of sources required for your assignment.

Use the UAGC Library to find scholarly sources.

Format for an Online Scholarly Journal Article:

Author, A. (Year Published). Article title. Journal Name, Volume(Issue), page range. http://doi.org/xx.xxx.xxxx

When listing the DOI, create a hyperlink with http:// or https://

In your annotation, summarize the main points of the source. Be sure to summarize the main points using your own words. Do not copy and paste information directly from the source. Also explain how the source is relevant to your paper. Explain how this particular source will help you develop one or more of the main points in your essay.

Author, A. (Year Published). Article title. Journal Name, Volume(Issue), page range. http://doi.org/xx.xxx.xxxx

In the first paragraph of your annotation, summarize the main points of the source. Be sure to summarize the main points using your own words. Do not copy and paste information directly from the source.

If you require a second paragraph for your annotation, indent an additional 0.5” and then explain how the source is relevant to your paper. Explain how this particular source will help you develop one or more of the main points in your essay.

Include the number of sources required for your assignment.

Format for an Online Magazine:

Author, A. (Year, Month Date Published). Article title. Magazine Title. http://URL

Format for a Webpage:

Author, A. (Year, Month, Date Published). Article title. Website. https://URL

In your annotation, summarize the main points of the source. Be sure to summarize the main points using your own words. Do not copy and paste information directly from the source. In the second paragraph of your annotation, explain how the source is relevant to your paper. Explain how this particular source will help you develop one or more of the main points in your essay.

Include the number of sources required for your assignment.

Format for a UAGC Textbook (online edition)

Author, A. (Year published). Title of book: Subtitle of book (edition, if other than the first). http://URL

Tip 1: Note that references are listed in alphabetical order.

Tip 2: APA does not require URLs when referencing online journal articles. Instead, find the DOI and cite as a hyperlink (ex:

http://doi.org/xx.xx.xxxx

). Always cite the DOI for online journal articles.

Tip 3: Delete these “hints” (red boxes) before turning in your final assignment.

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Formatting Your References

1 June 2019 | Volume 10 | Article 449

PERSPECTIVE

doi: 10.3389/fpsyt.2019.00449
published: 25 June 2019

Frontiers in Psychiatry | www.frontiersin.org

Edited by:
Paul Enck,

University of Tübingen,
Germany

Reviewed by:
Liesbeth Van Vliet,

Leiden University, Netherlands
Susanne Becker,

Central Institute for Mental Health,
Germany

*Correspondence:
Przemysław Bąbel

przemyslaw.babel@uj.edu.pl

Specialty section:
This article was submitted to

Psychosomatic Medicine,
a section of the journal
Frontiers in Psychiatry

Received: 29 December 2018
Accepted: 06 June 2019
Published: 25 June 2019

Citation:
Bąbel P (2019) Classical

Conditioning as a Distinct
Mechanism of Placebo Effects.

Front. Psychiatry 10:449.
doi: 10.3389/fpsyt.2019.00449

Classical Conditioning as a Distinct
Mechanism of Placebo Effects
Przemysław Bąbel*

Pain Research Group, Institute of Psychology, Jagiellonian University, Kraków, Poland

Classical conditioning was suggested as a mechanism of placebo effects in the 1950s.
It was then challenged by response expectancy theory, which proposed that classical
conditioning is just one of the means by which expectancies are acquired and changed.
According to that account, placebo effects induced by classical conditioning are mediated
by expectancies. However, in most of the previous studies, either expectancies were not
measured or classical conditioning was combined with verbal suggestions. Thus, on the
basis of those studies, it is not possible to conclude whether expectancies are involved
in placebo effects induced by pure classical conditioning. Two lines of recent studies
have challenged the idea that placebo effects induced by classical conditioning are
always mediated by expectancies. First, some recent studies have shown that a hidden
conditioning procedure elicits both placebo analgesia and nocebo hyperalgesia, neither
of which is predicted by expectancy. Second, there are studies showing that visual cues
paired with pain stimuli of high or low intensity induce both placebo analgesia and nocebo
hyperalgesia when they are presented subliminally without participants’ awareness. The
results of both lines of studies suggest that expectancy may not always be involved in
placebo effects induced by classical conditioning and that conditioning may be a distinct
mechanism of placebo effects. Thus, these results support the idea that placebo effects
can be learned by classical conditioning either consciously or unconsciously. However,
the existing body of evidence is limited to classically conditioned placebo effects in pain,
that is, placebo analgesia and nocebo hyperalgesia.

Keywords: classical conditioning, nocebo effect, Pavlovian conditioning, placebo effect, response expectancy

THE ORIGINS OF THE CLASSICAL CONDITIONING ACCOUNT
OF PLACEBO EFFECTS

Classical conditioning was independently suggested as a mechanism of placebo effects for the first
time in 1957 by Gliedman, Gantt, and Teitelbaum (1) and Kurland (2). It is interesting that just
2 years earlier, Beecher (3) had published his seminal paper that is now considered the starting
point of scientific interest in placebo effects. Thus, classical conditioning has been regarded
as a mechanism of placebo effects since the very beginning of research on placebo. However,
Wickramasekera (4, 5) was the first to propose a broad and coherent theoretical account of
placebo effects as conditional reflexes.

According to the classical conditioning approach, placebo is a conditioned stimulus and placebo
effects are conditioned responses. The first studies in which classical conditioning with an active
drug as an unconditioned stimulus was used to induce placebo effects were conducted in animals
(6–8). However, in fact, Pavlov (9) was the first to describe the effects of repeated applications of

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active drugs that his collaborators had found. Dr. Podkopaev
associated the sound of a definite pitch with the effects of a
dose of apomorphine in dogs. In effect, the sound of the note
alone produced all the symptoms of the drug. Similarly, when
Dr. Krylov repeatedly injected morphine into dogs, he observed
that the preliminaries of the injection were sufficient to produce
all the symptoms of the drug. Nevertheless, these early studies
started two very important lines of research, that is, studies on
conditioned immunopharmacological effects (10) derived from
Ader and Cohen’s (6) experiment and studies on conditioned
drug tolerance (11) derived from Sigel’s (8) experiment. In
both lines of research, responses to stimuli that accompany the
application of pharmacologically active drugs are classically
conditioned. However, these studies do not aim to explore the
mechanisms of placebo effects, and they focus on conditioning
of physiological responses.

Voudouris, Peck, and Coleman (12–14) developed the classical
conditioning paradigm to induce placebo effects in humans.
By surreptitiously pairing an inactive cream with decreasing
nociceptive stimulation, they strengthened the placebo effect
induced by verbal suggestion of the analgesic action of the cream
(12, 13). Moreover, in spite of the fact that they had previously
induced the placebo effect by verbal suggestion of the analgesic
action of an inactive cream, they were subsequently able to induce
the nocebo effect by pairing the same cream with increasing
nociceptive stimulation (12, 13). Most importantly, they also found
that placebo analgesia can be induced by classical conditioning
alone (without verbal suggestions); that is, the placebo effect
was found in a group that was informed that they had received
an inactive cream, which was then surreptitiously paired with
decreasing nociceptive stimulation (14). However, it should be
noted that the cream used in these studies might have raised
expectancy based on previous experiences with active treatment
creams and that expectancy might have biased the results. These
studies started a new line of research on placebo effects induced
by classical conditioning. The aim of the paper is to briefly
summarize recent findings and, based on them, draw conclusions
on the differential roles of classical conditioning and expectancy in
placebo effects. It should be noted that subjective responses, that is,
pain, are subject to conditioning in this new line of research. Thus,
this paper focuses on classical conditioning of placebo effects in
pain, including placebo analgesia and nocebo hyperalgesia.

THE CLASSICAL CONDITIONING
ACCOUNT IS CHALLENGED BY
RESPONSE EXPECTANCY THEORY

In the same year as the first study on classical conditioning of
placebo effects in humans was published (12), Kirsch (15)
published his seminal paper on response expectancy in which
he proposed another account of placebo effects. His theory
assumes that placebo effects result from expectancies concerning
placebo intervention. Kirsch (15) highlighted that, among other
processes, classical conditioning is involved in the acquisition
and modification of expectancy. According to this viewpoint,

classical conditioning is one of the means by which expectancies
are acquired and modified; that is, the effects of conditioning
are mediated by expectancy (15). In other words, there is
only one mechanism of placebo effects—expectancy; classical
conditioning is only a method that is used to acquire or change
expectancy.

This view is reflected in the popular learning model of
placebo effects proposed by Colloca and Miller (16). In this
model, placebo effects result from expectancies acquired by
decoding information from the psychosocial context, including
conditioned stimuli, among others. Thus, according to the model,
classical conditioning is a mean by which placebo effects may be
induced and expectancies play a central role in the formation of
placebo effects induced by classical conditioning.

It should be noted that expectancies are by definition
consciously accessible (17–19). According to a recent definition,
expectation is understood to mean a “conscious, conceptual
belief about the future occurrence of an event” (20).

Kirsch’s (15) account of the role of expectancy in the formation
of placebo effects induced by classical conditioning is based on a
current view on classical conditioning, which is best summarized
by Rescorla (21). This modern view differs substantially from
Pavlov’s (9) account, as is well reflected in the title of Rescorla’s
(21) seminal paper: “Pavlovian conditioning: It’s not what you
think it is.” According to this current view, classical conditioning
is not a mechanical process in which one stimulus passes control
over a response from another stimulus; instead, conditioning is
now seen as the learning of relations among events, which allows
the organism to represent its environment. As a consequence,
cognitive involvement is assumed for classical conditioning.
From this perspective, conditioning produces the expectancy
that certain stimuli will be followed by other stimuli, and it is
this expectancy that produces the response. In other words,
expectancies mediate the effects of conditioning (18).

THE CHALLENGE CONTINUES IN
STUDIES CONTRASTING CLASSICAL
CONDITIONING AND EXPECTANCY

Kirsch (15) not only challenged the classical conditioning account
of placebo effects on theoretical grounds but also conducted
an empirical test of his theory. Montgomery and Kirsch (22)
showed that the effects of classical conditioning on placebo
analgesia induced by verbal suggestions are completely mediated
by expectancies and that when participants were informed that
they were undergoing the conditioning procedure (i.e., pairing
placebo cream with decreasing nociceptive stimulation), the
conditioning did not have an effect on placebo analgesia induced
by verbal suggestions.

Montgomery and Kirsch’s (22) study together with
Voudouris and collaborators’ (12–14) investigations started the
conditioning versus expectancy debate, which has still not been
fully resolved. The essence of this debate is whether classical
conditioning is a distinct mechanism of placebo effects or the
effects of conditioning are mediated by expectancy. The early

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stage of this debate was reviewed by Stewart-Williams and Podd
(19). However, during 15 years since their seminal paper was
published, new research findings have been collected that shed
light on the debate.

So far, few studies have been conducted in which both
classical conditioning was applied and expectancy was measured.
Although most of these studies suggest that the effects of
conditioning are correlated with expectancy (23–26), predicted
by expectancy (27), or mediated by expectancy (22, 28–30),
their results are limited to participants in whom both verbal
suggestions of analgesia or hyperalgesia and classical conditioning
were applied. Thus, based on these findings, one cannot draw any
conclusions on the role of expectancy in placebo effects induced
by pure classical conditioning. Instead, it can be concluded that
expectancy is involved in the effects of conditioning on placebo
effects induced by verbal suggestions.

Moreover, the sparse studies in which pure classical
conditioning was applied (without verbal suggestions) and
expectancy was measured usually failed to induce placebo
effects (25, 31, 32), probably due to limited conditioning
trials (from 12 to 30, including 6–15 in which placebo was
paired with changes in nociceptive stimulation). Even if it
succeeded in one study (i.e., placebo analgesia was found
in the group subjected to pure conditioning), the results of
regression analysis revealing the prediction of the placebo
effect by expectancies were based on the results from all the
study groups, including those in which verbal suggestions
of analgesia were provided (33). Thus, it is not possible to
conclude whether expectancies predicted placebo analgesia
found in the group subjected to pure classical conditioning.
Interestingly, in that study, classical conditioning produced
the placebo effect, regardless of whether or not participants
were informed that they were undergoing the conditioning
procedure (i.e., pairing placebo cream with decreasing
nociceptive stimulation) and regardless of whether they were
informed that active or inactive intervention was used (in
fact placebo) (33). These results contradict Montgomery and
Kirsch’s (22) findings.

CHALLENGE ACCEPTED: PLACEBO
EFFECTS INDUCED BY PURE CLASSICAL
CONDITIONING

Unfortunately, most of the few studies in which pure classical
conditioning without verbal suggestions succeeded in
inducing placebo effects did not involve the measurement of
expectancy (34–36). For many years, the only study in which
pure classical conditioning effectively induced the placebo
effect and expectancy was measured was the one conducted
by Voudouris and collaborators (14). In one of the groups,
participants were informed that they were in a control group and
they would receive a neutral cream. They were then subjected
to conditioning procedure in which the cream was paired
with decreased nociceptive stimulation without participants’
knowledge. However, in that study, expectancy was measured
only once (before the pre-test), so it is impossible to determine

whether the conditioning that was performed after the pre-test
changed expectancies.

Recently, two lines of studies have challenged the idea that
placebo effects induced by classical conditioning are always
mediated by expectancies. In the first line, hidden conditioning
without verbal suggestions is conducted, and expectancies are
measured on a trial-by-trial basis. Conditioning procedure
may be conducted in two ways: by informing or not informing
participants that there is a relationship between the placebo (i.e.,
a conditioned stimulus) and the active drug or procedure (i.e.,
an unconditioned stimulus). When participants are aware of the
relationship, this is referred to as open conditioning; when they
are not aware of it, this is called hidden conditioning. Thus, the
role of consciousness is the main difference between hidden and
open conditioning.

In three recent studies, hidden conditioning was used to
induce placebo analgesia (37, 38) and nocebo hyperalgesia
(38, 39), and expectancies were measured on a trial-by-trial
basis. These studies found that not only hidden conditioning
was effective in producing placebo effects but also, primarily,
expectancies predicted or mediated neither placebo analgesia
nor nocebo hyperalgesia (37–39), even though conditioning
had an effect on expectancies (37). Moreover, when participants
were asked at the end of the study whether they had noticed the
contingency between placebo stimuli and differences in pain
intensity, most of them denied (37). Thus, based on these results,
it seems that it is possible to induce placebo effects without the
awareness of the participants.

The second line of research that sheds light on the role
of expectancy in placebo effects induced by pure classical
conditioning involves placebo stimuli presented subliminally
without participants’ awareness. In this paradigm, clearly
recognizable visual stimuli are first paired with pain stimuli of
high or low intensity. After a conditioning phase is completed,
the same conditioned visual cues are presented subliminally in
a testing phase. It has been found that pain stimuli preceded by
subliminally presented conditioned visual stimuli are rated as
less or more painful depending on whether they have previously
been paired with high or low pain stimuli, indicating that
placebo analgesia and nocebo hyperalgesia are induced without
awareness (40–44). Moreover, it has also been found that both
placebo analgesia and nocebo hyperalgesia can be induced
not only by conditioning of supraliminal stimuli but also by
conditioning of subliminally presented stimuli (44). Placebo
effects induced by conditioned stimuli presented subliminally
without participants’ awareness suggest that expectancy may not
have been involved in their production, which is consistent with
the results from the first line of studies. Although expectancy
is not measured in those studies, participants are not aware of
the presented stimuli. Thus, their expectancy should not have
affected the results.

It may be argued that the studies from both lines of research
did not include any placebo interventions in the form of a sugar
pill, fake cream, or sham electrodes. In fact, in all of those studies,
visual stimuli were paired with decreasing or/and increasing pain
stimulation. However, according to Miller and Kaptchuk (45),
the placebo effect is not the result of a specific intervention, but it

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is rather produced and enhanced by the context surrounding the
treatment. Thus, even if no inert treatment is administered, the
so-called placebo-related effect may still be found (46).

CONCLUSIONS

The results of both lines of studies suggest that expectancy may
not be always involved in placebo effects induced by classical
conditioning and that conditioning may be a distinct mechanism
of placebo effects.

These findings are in line with the fact that, in some cases,
classical conditioning represents an automatic process that
is not mediated by cognitive expectancy (18). In fact, many
phenomena could be explained by classical conditioning without
cognitive mediation. They include evaluative conditioning,
second-order conditioning, conditioned taste aversions and
flavor preferences, conditioning with subliminally presented
conditioned stimuli, conditioned immunosuppression, and
conditioning in simple organisms among others (see (18) for
review). Thus, only some placebo effects could be explained by
classical conditioning without expectancy involvement.

However, the findings under discussion do not exclude the
role of expectancy in inducing placebo effects. Expectancy ratings
may not always predict placebo effects. However, pre-cognitive
associations, that is, “links between events and/or objects
that exist outside conscious awareness” (20), may be acquired
through hidden conditioning procedures or be responsible for
responses to subliminally presented conditioned stimuli. In fact,
when classical conditioning is used to enhance or reduce placebo
effects induced by verbal suggestions, expectancies are involved
in their formation (22–30). In that case, classical conditioning is
just a mean by which expectancies are acquired and modified.
Moreover, expectancies might not always be easily self-reported;
that is, although expectancies do exist, one might not be able to
report them. However, the idea of conscious expectancies that
are not self-reported should be dealt with caution as it may lead
to circular reasoning (17).

These conclusions are in line with recent review (47) and
previously proposed models postulating that the classical
conditioning and response expectancy accounts do not
exclude each other, but the range of phenomena they explain
is not completely the same (19, 48). Conditioning involves
either conscious learning (acquisition and modifications
of expectancies) or unconscious learning (conditioning not
mediated by expectancy). Expectancies can be acquired and
modified by conditioning and other procedures, including
verbal suggestions and observational learning. In other words,
either conscious learning (expectancy and conditioning) or
unconscious learning (conditioning) can be mechanisms of
placebo effects. Thus, both accounts seem to be compatible
rather than mutually exclusive (19, 48). From this perspective,
classical conditioning is in some cases a distinct mechanism
of placebo effects, and sometimes, it is just a method used to
acquire or change expectancy.

Thus, the current conclusions contradict Colloca and
Miller’s (16) learning model of the formation of placebo

effects. They suggest that conditioned placebo and nocebo
responses may not always be mediated by expectancy. It seems
that Colloca and Miller’s (16) model does not explain the
mechanism of all instances of placebo effects. However, future
studies should answer the question under which circumstances
placebo effects induced by classical conditioning are mediated
by expectancy and when they are not mediated by expectancy.
Previous studies in which expectancies were not involved in
the induction of placebo effects by classical conditioning used
visual stimuli as placebos together with a large number of
conditioning trails. Thus, these two factors may be necessary
to induce conditioned placebo effects that are not mediated
by expectancy. So far, it seems only clear that placebo effects
induced by both conditioning and verbal suggestions are
mediated by expectancy. Further research is also needed to
investigate the differential role of classical conditioning and
expectancy in placebo effects outside pain. It would also be
of interest to investigate whether all principles of classical
conditioning found in studies outside the placebo research
field (e.g., generalization and extinction) can be directly
applied to placebo effects.

The finding that expectancy may not always be involved
in placebo effects induced by classical conditioning has
implications that have been discussed above, not only for
placebo theory. It also has important implications for the
methodology of placebo studies, that is, that expectancies
should be measured in research on placebo effects when
the role of expectancy is under study. Regardless of whether
placebo effects were induced by classical conditioning, verbal
suggestions, or both, the involvement or absence of expectancy
might be postulated only when expectancy was measured.
Most importantly, this fact also has implications for clinical
practice. Pain can decrease or increase after negative or
positive experiences that are associated with environmental
stimuli. In effect, these environmental stimuli may increase
or reduce pain symptoms, not only without any provided
verbal suggestions, but—most importantly—without patients’
conscious awareness. Thus, pain changes can occur even when
patients do not anticipate them. The decrease or increase
of pain may result from uncontrollable contextual factors.
Identifying the elements, that is, the conditioned stimuli that
change pain experiences, could be an essential part of pain
management programs. However, as significant differences
between experimental and clinical settings exist, further
studies are needed before translating laboratory research
results into clinical practice.

AUTHOR CONTRIBUTIONS

The author confirms being the sole contributor of this work.

FUNDING

This manuscript was prepared under grant number 2014/14/E/
HS6/00415 from the National Science Centre, Poland.

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Classical Conditioning and Placebo EffectsBąbel

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Conflict of Interest Statement: The author declares that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.

Copyright © 2019 Bąbel. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.

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Classical Conditioning as a Distinct Mechanism of Placebo Effects

The Origins of the Classical Conditioning Account
of Placebo Effects
The Classical Conditioning Account Is Challenged by Response Expectancy Theory
The Challenge Continues in Studies Contrasting Classical Conditioning and Expectancy
Challenge Accepted: Placebo Effects Induced by Pure Classical Conditioning
Conclusions
Author Contributions
Funding
References

Learning processes underlying avoidance of negative outcomes

MARTA ANDREATTA,a SEBASTIAN MICHELMANN,b PAUL PAULI,a,c AND JOHANNES HEWIGd

aDepartment of Psychology (Biological Psychology, Clinical Psychology, and Psychotherapy), University of W€urzburg, W€urzburg, Germany
bSchool of Psychology, University of Birmingham, Birmingham, UK
cCenter of Mental Health, Medical Faculty, University of W€urzburg, W€urzburg, Germany
dDepartment of Psychology (Differential Psychology, Personality Psychology, and Psychological Diagnostics), University of W€urzburg, W€urzburg,
Germany

Abstract

Successful avoidance of a threatening event may negatively reinforce the behavior due to activation of brain structures

involved in reward processing. Here, we further investigated the learning-related properties of avoidance using

feedback-related negativity (FRN). The FRN is modulated by violations of an intended outcome (prediction error, PE),

that is, the bigger the difference between intended and actual outcome, the larger the FRN amplitude is. Twenty-eight

participants underwent an operant conditioning paradigm, in which a behavior (button press) allowed them to avoid a

painful electric shock. During two learning blocks, participants could avoid an electric shock in 80% of the trials by

pressing one button (avoidance button), or by not pressing another button (punishment button). After learning,

participants underwent two test blocks, which were identical to the learning ones except that no shocks were delivered.

Participants pressed the avoidance button more often than the punishment button. Importantly, response frequency

increased throughout the learning blocks but it did not decrease during the test blocks, indicating impaired extinction

and/or habit formation. In line with a PE account, FRN amplitude to negative feedback after correct responses (i.e.,

unexpected punishment) was significantly larger than to positive feedback (i.e., expected omission of punishment),

and it increased throughout the blocks. Highly anxious individuals showed equal FRN amplitudes to negative and

positive feedback, suggesting impaired discrimination. These results confirm the role of negative reinforcement in

motivating behavior and learning, and reveal important differences between high and low anxious individuals in the

processing of prediction errors.

Descriptors: Operant conditioning, FRN, Anxiety trait

Successful avoidance of threats assures organisms’ survival. Nota-

bly, when aversive or threatening situations are successfully

avoided, organisms experience a positive feeling (Delgado, Jou,

LeDoux, & Phelps, 2009; H. Kim, Shimojo, & O’Doherty, 2006),

which may also motivate the organisms to repeat the behavior

(Murty, LaBar, Hamilton, & Adcock, 2011). However, such avoid-

ance does not only entail appropriate and survival-relevant proper-

ties, but this behavior has also been implicated in the maintenance

of anxiety disorders (Bouton, Mineka, & Barlow, 2001; Craske

et al., 2009; Mowrer, 1956). In this study, we want to further inves-

tigate avoidance learning processes with a particular focus on trait

anxiety as a vulnerability factor.

Learning mechanisms underlying avoidance have been exten-

sively described by instrumental conditioning (Skinner, 1932).

During instrumental conditioning, a response is associated to its

consequences (also called response-outcome learning). In other

words, performing the correct response leads to positive conse-

quences such as receiving a reward (positive reinforcer) or avoid-

ing a punishment (negative reinforcer); meanwhile, performing the

wrong response leads to negative consequences such as receiving a

punishment or not obtaining a reward. Thus, successful avoidance

of punishment should elicit rewardlike behavioral (i.e., increased

frequency of the behavior) and neural (i.e., striatal activation)

responses. On a behavioral level, humans usually prefer (i.e., per-

form more often) a response associated with a reward, and compa-

rably prefer a response that allows them to avoid punishment

(Delgado et al., 2009; H. Kim et al., 2006; S. H. Kim, Yoon, Kim,

& Hamann, 2015; Pessiglione et al., 2008). Notably, stimuli associ-

ated with positive or negative reinforcement are salient and

enhance attentional neural sources (S. H. Kim et al., 2015; Mis-

kovic & Keil, 2014). On the neural level, striatum activation was

revealed in experiments where participants received money (H.

Kim et al., 2006; Pessiglione et al., 2008), avoided losing money

(H. Kim et al., 2006; Pessiglione et al., 2008), or successfully

avoided a painful unconditioned stimulus (US; Delgado et al.,

2009). Interestingly, participants reported higher distress to a

threat-predicting stimulus, compared to a stimulus that predicted

similar threat, but they had the possibility to avoid this threat by

pressing a button (Delgado et al., 2009; Miskovic & Keil, 2014).

The work was supported by the Collaborative Research Center – “Fear,
Anxiety, Anxiety Disorders” (SFB-TRR 58 project B1) to PP.

Address correspondence to: Dr. Marta Andreatta, Department of Psy-
chology (Biological Psychology, Clinical Psychology, and Psychothera-
py), University of W€urzburg, Marcusstraße 9-11, D-97070 W€urzburg,
Germany. E-mail: marta.andreatta@mail.uni-wuerzburg.de

578

Psychophysiology, 54 (2017), 578–590. Wiley Periodicals, Inc. Printed in the USA.
Copyright VC 2017 Society for Psychophysiological Research
DOI: 10.1111/psyp.12822

Based on this evidence, we conclude that successful avoidance

of aversive events elicits appetitive responses, likely due to the

respite of the (expected) aversive event (Lohr, Olatunji, & Saw-

chuk, 2007). Possibly, such successful avoidance may be accompa-

nied by the positive feeling of relief. Animal and human studies

have already shown approach behaviors (i.e., approach, Tanimoto,

Heisenberg, & Gerber, 2004; Yarali et al., 2008) and neural activa-

tion of reward centers (i.e., striatum activation, Andreatta et al.,

2012; Becerra, Navratilova, Porreca, & Borsook, 2013; Seymour

et al., 2005) to stimuli classically associated with such feelings of

relief (e.g., triggered by the ending of a painful event; for recent

reviews, see also Gerber et al., 2014; Navratilova & Porreca, 2014;

Riebe, Pamplona, Kamprath, & Wotjak, 2012). However, the pro-

cesses underlying the appetitive responses associated with respite

(i.e., the absence of an otherwise expected punishment) are rarely

investigated.

It is important to consider that individuals differ in their sensi-

bility to rewards or punishments in that they may be hypersensitive

to reward (e.g., impulsive individuals or gamblers) or to punish-

ment (e.g., anxious individuals, Gray, 1982). Consequently, learn-

ing might be modulated by such individual sensibility. For

instance, individuals with high impulsivity traits prefer to keep per-

forming a response associated with high but uncertain rewards

compared to a response associated with a more secure, but delayed

high reward (Hariri et al., 2006). Furthermore, these responses cor-

related with striatal activation; that is, the more risky the perfor-

mance was, the greater the striatum activation was. Accordingly,

individuals with greater sensibility to reward (according to the

Behavioral Activation System, Carver & White, 1994; Gray, 1990)

responded with greater striatal activity to positive feedback (S. H.

Kim et al., 2015). In contrast, individuals’ anxious state correlated

positively with the strength of the activation of the nucleus accum-

bens (NAcc, which is part of the ventral striatum) following suc-

cessful avoidance (by button pressing) of an aversive consequence

(view of aversive and negative pictures, Levita, Hoskin, & Champi,

2012).

Successful and stable avoidance of aversive or threatening con-

sequences has been implicated in the etiology and maintenance of

anxiety disorders (Bouton et al., 2001; Craske et al., 2009; Mowrer,

1956). According to the two-factor learning theory (Mowrer,

1956), a stimulus (e.g., elevator) may become associated with an

aversive event such as a panic attack. As a consequence, these indi-

viduals will avoid those stimuli, and successful avoidance is

assumed to elicit rewarding and relieving responses. On the other

hand, such avoidance does not allow these individuals to have dif-

ferent and possibly safe experiences with these stimuli; that is,

avoidance behavior prevents extinction of conditioned fear

responses.

Examining this assumption experimentally, two studies exposed

participants after fear conditioning to the conditioned stimulus

(CS1) with or without the possibility of avoidance behavior.

Results revealed that participants who learned that they could avoid

an aversive electric shock (US) by pressing a button when the CS

was present showed less fear extinction to the CS in comparison to

those participants who underwent a classic extinction protocol (i.e.,

CS has been presented several times without US, Lovibond, Mitch-

ell, Minard, Brady, & Menzies, 2009). In the second study, partici-

pants showed quick return of the (classically) conditioned fear

response to the CS once they could not avoid the US any more

(Vervliet & Indekeu, 2015). Similar to the latter study, in a virtual

reality version of a water maze, results showed larger startle

responses in participants who actively delayed entering the pool’s

platform (defined by the author as passive avoidance of the CS1),

where the CS1 (the CS associated with the US) was displayed on

the walls compared to the CS- (the CS never associated with the

US) during late extinction (Cornwell, Overstreet, Krimsky, &

Grillon, 2013). In other words, during the avoidance phase, partici-

pants did not receive the US in association with the CS1 because

of their avoidance response, and as a result no extinction occurred;

their conditioned fear to CS1 was still visible once they were not

allowed to perform the avoidance behavior. Interestingly, individu-

als with high neuroticism scores seemed to show more avoidance

behavior to stimuli that share some physical characteristics with

the CS1 but are actually safe (Lommen, Engelhard, & van den

Hout, 2010). This suggests that anxiety levels modulate the likeli-

hood of avoidance behavior and thus extinction.

We conclude that there is evidence that indicates that personali-

ty traits such as neuroticism or anxiety facilitate avoidance learn-

ing. However, thus far it remains unclear whether anxious

individuals are more sensitive to the positive relieving feeling fol-

lowing a successful avoidance or are more sensitive to the punish-

ment after unsuccessful avoidance. Therefore, we attempted to

disentangle these aspects by using an instrumental conditioning

paradigm during which participants learn to avoid a painful electric

shock by pressing one of two buttons. In order to investigate the

participants’ neural responses to positive relieving feedback after

successful avoidance compared to a negative feedback after unsuc-

cessful avoidance, we took advantage of the N100 and P200

(Olofsson, Nordin, Sequeira, & Polich, 2008; Vogel & Luck,

2000), as well as of the feedback-related negativity (FRN, Holroyd

& Coles, 2002; Miltner, Braun, & Coles, 1997; Walsh & Anderson,

2012).

N100 is an early component of the ERPs characterized by short

latency, while the P200 has a middle latency (Olofsson et al.,

2008). The source of the N100 has been located in the frontal lobe,

and is normally accompanied by an opposing potential located over

parietal-occipital lobe labeled P100 (Heinze et al., 1994; Yamazaki

et al., 2000). N100 and P100 occur within similar time windows,

reflect similar attention-related processes (Heinze et al., 1994), and

present larger amplitude to negative stimuli (Pourtois, Grandjean,

Sander, & Vuilleumier, 2004; Sass et al., 2010; Weymar, Gerdes,

L€ow, Alpers, & Hamm, 2013). Together, the N100 and the P200
have been proposed as sensory gating processes (Lijffijt et al.,

2009). Specifically, presentation of a stimulus triggers attention,

and this process has been related to N100 amplitude. Once the

stimulus is detected, attention may (or may not) be allocated to it,

and this process has been related to P200 amplitude. Furthermore,

impulsivity (Lijffijt et al., 2012) as well as expression style of

angry feelings (measured by the Anger Expression Inventory,

Stewart et al., 2010), positively correlated with N100 amplitude.

Moreover, findings have shown that frontocentral P200 was larger

for wins (indicated by positive feedback), compared to losses (indi-

cated by negative feedback). Importantly, such large P200 ampli-

tude was even more pronounced in the context of two preceding

wins (Osinsky, Mussel, & Hewig, 2012).

The FRN is an ERP initially observed after negative feedback

showing its peak around 230–330 ms after feedback onset at fron-

tocentral midline electrodes (Holroyd & Coles, 2002; Miltner et al.,

1997). Subsequent research indicated that a large amount of vari-

ance in this ERP is explained by rewardlike responses—reward

positivity (RP) in response to monetary wins or positive perfor-

mance feedback (e.g., Hewig et al., 2008; Holroyd, Pakzad-Vaezi,

& Krigolson, 2008). The FRN has been related to the same learning

signal in the dopaminergic system, which reflects the prediction

Avoidance learning 579

error (PE)—a learning signal generated by the discrepancy between

the expected and the actual outcome (Chase, Swainson, Durham,

Benham, & Cools, 2010). Such PE modulation of the FRN is found

in successful avoidance of monetary losses (negative reinforce-

ment) and the achievement of monetary wins (positive reinforce-

ment, e.g., Kreussel et al., 2012). Its source has been located in the

anterior cingulate cortex (Walsh & Anderson, 2012).

The FRN is a reliable learning index (Chase et al., 2010; Walsh

& Anderson, 2012) and also a sensitive index for individual differ-

ences (Gu, Huang, & Luo, 2010; Hewig et al., 2010; Proudfit,

2015). In fact, this ERP was (significantly) more positive in gam-

blers than controls, and this correlated with the level of risky

behaviors carried out by participants. In other words, gamblers are

hypersensitive to rewarding feedback (rather than hyposensitive to

punishment) in a gambling task, and such individual characteristics

are reflected in the FRN (Hewig et al., 2010). In another study that

used a gambling task, low anxious individuals showed significantly

larger FRN to negative feedback compared to highly anxious indi-

viduals. Although this result may appear surprising, the authors

stated that the potentially different expectations of high versus low

anxious individuals might have played a role (Hajcak, Moser, Hol-

royd, & Simons, 2007). Moreover, it is also possible that the task

used is not the most sensitive context for gathering anxiety-related

responses (Holroyd, Larsen, & Cohen, 2004). Therefore, to the best

of our knowledge, there are no studies investigating the role of

FRN in avoidance learning using an anxiety-relevant context such

as a task involving threat.

We hypothesize that respite of an aversive event due to success-

ful avoidance behavior will entail motivating properties, and, con-

sequently, the frequency of the respite-associated response will

increase. Moreover, we expected larger FRN amplitudes to aver-

sive events (negative feedback), especially to those unexpected

after a respite-associated response. Finally, we expect that partici-

pants’ trait anxiety modulates both behavioral as well as electro-

cortical responses. In particular, we expect greater avoidance in

high versus low anxious individuals as well as either larger FRN

amplitude to negative feedback (increased punishment sensitivity)
or larger RP to positive feedback (increased negative reinforcement
sensitivity).

Method

Participants

Thirty-two volunteers participated in the study and received either

16 eor course credits for their participation. One participant was

excluded from the analysis because of technical problems and three

participants were excluded because they presented too many arti-

facts in the electrocortical signal (see Apparatus and Data Analy-

sis). In the end, we considered 28 participants (18 females, 1 non-

German) with a mean age of 21.68 years (SD 5 3.20, range: 18–30

years). All participants were right-handed according to the standard

handedness inventory (Oldfield, 1971). The study was approved by

the ethics committee of the Department of Psychology of the Uni-

versity of W€urzburg. Before beginning the experiment, all partici-
pants read and signed the informed consent form.

There are no available cutoffs for the State-Trait Anxiety Inven-

tory (STAI, Laux, Glanzmann, Schaffner, & Spielberger, 1981), as

it is not a diagnostic questionnaire. Therefore, to explore the anxi-

ety hypothesis, we split the sample into two groups: high anxious

(44.50, SD: 5.40) and low anxious (33.29, SD: 3.20) individuals,
based on the median (37.5) of the trait version of the STAI (for

details, see Table 1).

Material and Procedure

Unconditioned stimulus. The aversive stimulus (US) consisted of

a mild painful electric shock delivered over the forearm of the non-

dominant hand by means of two disk electrodes with 9-mm diame-

ter and spacing 30 cm. The electric shock was generated by a

current stimulator (Digitimer DS7A, Digitimer Ltd., Welwyn Gar-

den City, UK, 400 V, maximum of 9.99 mA) consisting of a train

of 50 pulses (each 2 ms long) triggered every 4 ms. Altogether, the

electric pulse stimulation was 200 ms in duration with a frequency

of 250 Hz. The intensity of the shock was individually assessed

with a threshold procedure consisting of two ascending and

descending series of electric shocks in steps of 0.5 mA. Participants

rated each electric stimulus on a visual analog scale (VAS) ranging

from 0 (no pain at all) to 10 (unbearable pain) with 4 as an anchor
for the threshold (just noticeable pain). The individual pain thresh-
old was then increased by 1 mA in order to avoid habituation. The

mean intensity of the US was 2.44 mA (SD 5 1.03), and partici-
pants reported the aversive stimulus as painful (M 5 6.57,
SD 5 1.67).

Feedback. Each participant’s response was followed by negative

or positive feedback according to whether they pressed the punish-

ment or the avoidance button, respectively. The negative feedback

consisted of a red flash (1,046 pixels high, 551 pixels wide, 150

dpi), while the positive feedback was green and consisted of a ban

signal over the flash (1,046 pixels high, 979 pixels wide, 150 dpi).

The feedback was presented 1 s after each participant’s response,

18.5 cm high on the screen and lasting 500 ms. Before and after the

experiment, we assessed the valence (positive vs. negative) and the
arousal (calm vs. exciting) of the feedback by means of two VASs
ranging from 1 to 9.

Task. Participants sat in front of a computer screen (distance

60 cm) and were instructed to press one of two buttons on the key-

board with the index finger of the right (L button) and left (S but-
ton) hand (Figure 1). Participants were told that they could avoid

painful electric shocks if they pressed the correct button (without

Table 1. Description of the Sample Separated for High and Low Anxious Participants

Low anxious High anxious

Gender 10 females/4 males 8 females/6 males v 5 0.62, p 5 .430
Age 21.21 years (SD 5 3.02) 22.14 years (SD 5 3.42) t(26) 5 0.76, p 5 .453
US intensity 2.40 mA (SD 5 1.12) 2.48 mA (SD 5 0.96) t(26) 5 0.20, p 5 .840
US ratings 6.50 (SD 5 1.87) 6.64 (SD 5 1.50) t(26) 5 0.22, p 5 .825
Awareness 8 aware/6 unaware 6 aware/8 unaware v 5 0.57, p 5 .450
STAI 33.29 (SD 5 3.20) 44.50 (SD 5 5.40) t(26) 5 6.68, p < .001 BDI 4.92 (SD 5 2.99) 16.29 (SD 5 7.44) t(25) 5 5.28, p < .001

580 M. Andreatta et al.

explicitly indicating which button). Moreover, we told them that if

they did not press any button, they could receive the painful shock.

Hence, if they pressed one button (respite-associated button or

avoidance button), no shock was delivered and the positive feed-

back was presented. If they pressed the other button (pain-associat-

ed button or punishment button), the negative feedback was

presented, and 1 s after its offset, the shock was delivered. This

was chosen in order to prevent confounding effects and artifacts of

the shock. The buttons were counterbalanced among the partici-

pants, and there was no mention of the response/outcome contin-

gency. In order to elicit a more identifiable FRN, the press of the

avoidance button was positively reinforced in 80% of the trials,

while in the remaining 20% participants received the negative feed-

back and the painful electric shock 1 s after feedback offset. In the

same manner, the punishment response was presented in 80% of

the trials, but reinforced in the remaining 20%. Participants were

instructed to press one button when an orange square (17.5 3

17.5 cm) was presented on the black screen. The square lasted

either until participant’s response or maximal 5 s. If participants

did not press any button, the painful electric shock and the negative

feedback were delivered. The intertrial interval (ITI), defined as

the time between feedback offset and square onset, lasted for a ran-

domly jittered interval between 2 s and 4 s. The experiment con-

sisted of four blocks (Block 1, Block 2, Block 3, Block 4) that

consisted of 60 trials each. For 48 of 60 trials (80%), either nega-

tive feedback by the press of the punishment button or positive

feedback by pressing the respite button was presented. While the

remaining 12 trials (20%), either positive feedback by pressing the

punishment button or negative feedback by pressing the respite but-

ton was presented. The four blocks were almost identical except

that, during the last two blocks, no electric shock was delivered.

Although Block 1 to 4 did not differ in regard to the task, the learn-

ing curve during blocks should be different; the learning curve dur-

ing Block 1 should be greater than during Block 2, as during this

second block participants should have been aware that one button

allows avoidance but other does not. Between each block, we asked

participants whether they noticed any association between their

responses and the delivery of the painful electric shock.
1

Moreover,

we asked participants to rate the strength of relief they experienced

when viewing the positive feedback on a VAS from 1 (no relief) to

9 (strong relief).

Questionnaires. The German versions of the STAI (Laux et al.,

1981), the Positive and Negative Affect Schedule (PANAS,

Krohne, Egloff, Kohmann, & Tausch, 1996), and the Beck Depres-

sion Inventory (BDI, Hautzinger, Keller, & K€uhner, 2006) were

collected in order to assess anxiety traits and depression as well as

the current emotional state of the participants. The STAI consists

of 20 items for the state version and 20 items for the trait version.

Each item is rated on a 4-point Likert scale from 1 (almost never)

to 4 (almost always) according to how much it describes the per-

son’s anxiety level in general (trait) or in the actual moment (state).

Trait anxiety scores ranged from 28 to 57 (M 5 38.89, SD 5 7.18),

which is comparable to the published normal range of adults (Laux

et al., 1981). Individual anxiety before (M 5 40.85, SD 5 5.33) and

Figure 1. Method. During one block, participants had to press one of two buttons on a computer keyboard when a geometrical shape was presented

on a computer screen. The shape lasted either until participants’ response or 5 s. If participants did not press a button, a painful electric shock (US)

and, 1 s later, a negative feedback were delivered. If participants pressed the avoidance button (a), they could avoid the painful US in 80% of the tri-

als, and 1 s after button press, a positive feedback was presented for 500 ms. In the remaining 20% of the trials, participants received the US and 1 s

later a negative feedback was presented. If participants pressed the punishment button (b), they received the US followed by the negative feedback in

80% of the trials and in 20% of the trials they avoided the US and saw a positive feedback again for 500 ms. Each block consisted of 60 trials.

1. Seven participants could correctly report by which response they
could avoid the pain after the first block. After the second block, seven
additional participants could verbally indicate which response allowed
them to avoid the painful electric shock. At the end, 14 participants
were aware of response-outcome association, whereas 14 remained
unaware. Notably, we did not find any relevant modulation of the
responses by the contingency awareness.

Avoidance learning 581

after the experiment (M 5 41.37, SD 5 4.82) did not change signif-
icantly, F(1,25) 5 0.18, p 5 .677, g2p 5 .007. The PANAS consists
of 20 adjectives, and participants indicated to what extent they feel

a particular emotion on a scale ranging from 1 (very slightly) to 5
(extremely). Participants’ negative (before: M 5 12.48, SD 5 2.62;
after: M 5 12.30, SD 5 2.73) and positive (before: M 5 26.41,
SD 5 4.35; after: M 5 24.78, SD 5 5.83) mood did not change
throughout the experiment significantly (negative: F(1,25) 5 0.20,
p 5 .659, g2p 5 .008; positive: F(1,25) 5 2.74, p 5 .111, g

2
p 5 .099).

Moreover, no significant effects were revealed involving the factor

anxiety (all ps > .234; see online supporting information Table S1).
Both STAI (Laux et al., 1981) and PANAS (Krohne et al., 1996)

were collected at the beginning and at the end of the experiment. The

BDI consists of 21 items for evaluating the depressive state. Each

item describes a depressive thought, and individuals indicate how

often they experience that particular feeling or thought. Although our

sample consisted of healthy individuals, high anxious participants

presented significantly higher depressive feelings (although not clini-

cally relevant) than low anxious individuals (Table 1).

Apparatus and Data Analysis

The electrocortical signal was recorded by means of the 32-channel

ActiCAP (Brain Products GmbH, Munich, Germany) from 28 sites

according to the 10-20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, FC5,

FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3,

Pz, P4, P8, O1, Oz, O2). The remaining four were used for record-

ing the vertical and the horizontal eye movements (electrooculo-

gram, EOG), two electrodes placed below and above the right eye

and two over the right and left canthi of the eyes. Cz and Fz were

used as reference and ground electrodes, respectively. The elec-

trode impedance was kept below 10 kX, and the signal was contin-
uously recorded with a sampling rate of 1000 Hz. Online, a notch

filter (50 Hz) and, for preventing aliasing, a bandwidth filter (low

cutoff 10 s, high cutoff 250 Hz) were applied. The software Brain-

Vision Recorder (Version 1.03.0004, Brain Products GmbH) was

used for recording the EEG signal.

Offline analyses were conducted with the computer software

BrainVision Analyzer Version 2.0 (Brain Products GmbH). Data

were mathematically rereferenced with an average reference across

all electrodes and then pass-band filtered (low cutoff filter: 0.1 Hz;

time constant: 1.59, 24 dB/oct; high cutoff filter: 35 Hz). The EEG

signal was corrected for vertical and horizontal eye movements

(Gratton, Coles, & Donchin, 1983) and segmented for each condi-

tion within each block. Epochs were defined between 200 ms

before and 800 ms after feedback onset. The 100 ms before feed-

back onset were considered for baseline correction. Trials with a

voltage higher than 50 mV were excluded from further analysis.
Altogether, 2.26% (SD 5 2.26) of the trials were excluded. There
was a significantly larger percentage of rejected trials in Block 1

(4.76%, SD 5 6.63) compared to Block 2 (2.32%, SD 5 3.86;
t(27) 5 2.41, p 5 .023), Block 3 (0.95%, SD 5 1.84; t(27) 5 3.09,
p 5 .005), and Block 4 (1.01%, SD 5 2.50; t(27) 5 3.60, p 5 .001).
However, high (2.92%, SD 5 2.92) and low (1.61%, SD 5 1.57)
anxious individuals did not differ, t(26) 5 1.16, p 5 .255. Each con-
dition (positive and negative feedback following either respite-

associated or pain-associated response) was then averaged for each

participant and separately for each block (Block 1–4). As partici-

pants were free to choose which button to press, the number of

avoidance trials associated with a negative feedback was quite low

(M 5 11.28, SD 5 1.28; see also supporting information Table S3).
However, a minimum of 6–8 trials was enough to elicit a reliable

error-related negativity (ERN, Olvet & Hajcak, 2009), which corre-

sponds to the FRN but follows a wrong response instead of a nega-

tive feedback (Walsh & Anderson, 2012).

Two ERP components, namely, the N100 and the P200, were

analyzed at the Fz and Cz. Such frontocentral sensors were chosen

for analyses based on previous studies investigating individual dif-

ferences (Lijffijt et al., 2009; Stewart et al., 2010). In order to

improve the signal-to-noise ratio (Clayson, Baldwin, & Larson,

2013; Keil et al., 2014), the N100 was defined as the temporal win-

dow between 90 ms and 110 ms after feedback onset; the P200 was

defined as the temporal window between 180 ms and 220 ms after

feedback onset (Stewart et al., 2010). Additionally, the P100 was

analyzed at O1 and O2 within the time window 80–120 ms after

feedback stimulus (results are reported in the supporting informa-

tion). The FRN was defined as the difference between negative and

positive feedback after either avoidance or punishment button press

with the temporal window 180–350 ms following feedback onset

(Osinsky et al., 2012). Based on the topographic maps of this ERP,

we clustered Cz, FC1, and FC2 for FRN analysis.

Statistical Analysis

Analyses were conducted by using repeated measures analyses of

variance (ANOVAs). The frequency and the reaction time of the

responses were analyzed with two separate ANOVAs with the

within-subject factors response (avoidance, punishment) and block

(Block 1–4). For the relief ratings, an ANOVA with the within-

subject factor block (Block 1–4) was calculated. For the electro-

cortical signal, we considered only those trials when participants

pressed the avoidance button, because the number of the punish-

ment button presses was too small for EEG analysis. Separate

ANOVAs for N100 and P200 were calculated considering feed-

back (positive, negative), block (Block 1–4), and electrode (Fz,

Cz) as within-subject factors, while the ANOVA for the FRN

considered the within-subject factors feedback (positive, nega-

tive) and block (Block 1–4). Furthermore, the between-subjects

factor anxiety (low, high anxious individuals) was considered in

all ANOVAs.

As reported above, we controlled for the modulatory role of the

contingency awareness based on classical conditioning studies

(Sehlmeyer et al., 2009) and operant conditioning studies with

rewards (Delgado, Miller, Inati, & Phelps, 2005). No significant

effects were revealed; therefore, we decided not to further consider

participants’ awareness.

As a manipulation check, we calculated two additional

ANOVAs for the valence and arousal ratings of the feedback.

These ANOVAs contained the within-subject factors feedback

(positive, negative) and time (beginning, end) as well as the

between-subjects factor anxiety (high, low anxious).

For post hoc tests, we used t-tests and set the alpha level to .05
for all comparisons. If necessary, we adjusted the p values using
the Greenhouse-Geisser correction (GG-e) when the sphericity
assumption was violated. The partial eta squared (g2p) values are
reported.

Results

Ratings of Feedback Symbols and Behavioral Data

Ratings of feedback symbols. The 2 3 2 3 2 ANOVA indicated

a significant main effect feedback, F(1,26) 5 64.22, p < .001, g2p 5 .712, but no time, F(1,26) 5 0.10, p 5 .756, g

2
p 5 .004, or

582 M. Andreatta et al.

anxiety, F(1,26) 5 0.49, p 5 .492, g2p 5 .018, effects. The interac-
tion Feedback 3 Time, F(1,26) 5 4.83, p 5 .037, g2p 5 .157, was
significant, but no other interaction effects were found (all

ps > .471). Post hoc t-tests revealed that before the experiment the
negative feedback (M 5 3.61, SD 5 0.96) was rated significantly
more negative than the positive feedback (M 5 6.32, SD 5 1.34;
t(27) 5 7.26, p < .001). Throughout the experiment, feedback became slightly more neutral, and these comparisons (beginning

vs. end) just failed to reach the significance level (negative:

t(27) 5 2.00, p 5 .056; positive: t(27) 5 1.94, p 5 .063). However,
the negative feedback (M 5 4.25, SD 5 1.48) was still rated more
negatively valenced than the positive feedback (M 5 5.57,
SD 5 1.60; t(27) 5 3.11, p 5 .004) at the end of the experiment.

The ANOVA for the arousal ratings of the feedback symbols

before and after the experiment showed a significant main effect

for feedback, F(1,26) 5 102.95, p < .001, g2p 5 .798, indicating that the negative feedback was rated as more arousing than the positive

feedback, but no other significant effects (all ps > .291; for ratings
separated for the two groups, see Table S2) were found.

Response frequency. The analysis indicated a significant main

effect response, F(1,26) 5 194.47, p < .001, g2p 5 .882, and an interaction Response 3 Block, F(3,78) 5 4.12, GG-e 5 .593, p 5 .027, g2p 5 .137 (Figure 2a). No significant effects involving the factor anxiety were found (all ps > .083). Post hoc t-tests
showed that participants pressed the avoidance button significantly

more often than the punishment button in Block 1, t(27) 5 7.05,
p < .001; Block 2, t(27) 5 13.42, p < .001; Block 3, t(27) 5 8.87, p < .001; and Block 4, t(27) 5 13.04, p < .001. Importantly, the strongest learning effect was found between Block 1 and Block 2.

Thus, pressing the avoidance button significantly increased,

t(27) 5 2.63, p 5 .014, from Block 1 to Block 2, while pressing the
punishment button significantly decreased, t(27) 5 2.51, p 5 .018.
No further changes in the button frequencies were found between

Block 2 and Block 3 (avoidance: t(27) 5 0.75, p 5 .458; punish-
ment: t(27) 5 0.76, p 5 .455), and between Block 3 and Block 4

(avoidance: t(27) 5 1.90, p 5 .068; punishment: t(27) 5 1.96,
p 5 .061). Furthermore, separated analysis of those trials with no
button responses (see Table S3) revealed no significant difference

among blocks, F(3,78) 5 1.77, GG-e 5 .639, p 5.182, g2p 5 .064,
or between groups, F(1,26) 5 3.24, p 5 .083, g2p 5 .111, or their
interaction, F(3,78) 5 .09, p 5 .966, g2p 5 .003.

Relief ratings. The analysis returned a significant main effect

block, F(3,78) 5 11.13, GG-e 5 0.705, p < .001, g2p 5 .300 (Figure 2b), but no anxiety, F(1,26) 5 .002, p 5 .963, g2p < .001, or Block 3 Anxiety interaction, F(3,78) 5 0.38, GG-e 5 .705, p 5 .698, g2p 5 .014, effects. Polynomial analyses of variance indicated a sig- nificant linear, F(1,26) 5 25.62, p < .001, g2p5 .496, but not qua- dratic, F(1,26) 5 .01, p 5 .906, g2p 5 .001, trend for the factor block, which indicated a linear increase of the relief ratings

throughout the experiment. Thus, participants reported stronger

relief when viewing the positive feedback at the end of the experi-

ment (after Block 4) as compared to the beginning (after Block 1:

t(27) 5 5.03, p < .001).

ERP Data

N100. The ANOVA showed a significant main effect for electrode,

F(1,26) 5 12.12, p 5 .002, g2p 5 .318, but not feedback, F(1,26) 5
3.06, p 5 .092, g2p 5 .105; block, F(3,78) 5 2.23, p 5 .091,
g2p 5 .079; or anxiety, F(1,26) 5 0.16, p 5 .696, g

2
p 5 .006. The sig-

nificant main effect indicated that the N100 amplitude was signifi-

cantly larger at Fz compared to Cz.

The interaction Feedback 3 Electrode 3 Anxiety, F(1,26) 5
9.25, p 5 .005, g2p 5 .262, reached the significance level, and we
did not find any other interaction effect (all ps > .119). Post hoc
analyses separated for low and high anxious individuals indicated

that the low anxious participants, F(1,13) 5 5.52, p 5 .035,
g2p 5 .298, but not the high anxious participants, F(1,13) 5 0.10,
p 5 .758, g2p 5 .008, showed a significant main effect of feedback
(Figure 3). Thus, low anxious individuals, but not high anxious

Figure 2. Behavioral data. Full lines indicate responses of high anxious individuals and dashed lines indicate responses of low anxious individuals. a:

Participants pressed the avoidance button (light gray lines) significantly more often than the punishment button (black lines) throughout the experi-

ment. The strongest learning effect (i.e., higher frequency of avoidance button and less frequency of the punishment button) was observed between

Block 1 and Block 2. b: Participants reported a relieved feeling to the respite-associated feedback, which increased significantly throughout the blocks.

*p < .05; ***p < .001.

Avoidance learning 583

individuals showed larger N100 to the negative feedback compared

to the positive feedback. Moreover, the interaction between

feedback and electrode was significant for high anxious,

F(1,13) 5 5.72, p 5 .033, g2p 5 .305, but not low anxious individu-
als, F(1,13) 5 3.64, p 5 .079, g2p 5 .219. Post hoc t-tests for the sig-
nificant interaction for high anxious individuals revealed no

significant difference between negative and positive feedback at

Fz, t(13) 5 1.39, p 5 .189, as well as at Cz, t(13) 5 0.76, p 5 .462,

while N100 was significantly larger at Fz compared to Cz for nega-

tive feedback, t(13) 5 3.23, p 5 .007, but not for positive feedback,

t(13) 5 1.95, p 5 .073.

P200. The significant main effects electrode, F(1,26) 5 56.88,
p < .001, g2p 5 .686, and block, F(3,78) 5 3.46, GG-e 5 .664, p 5 .039, g2p 5 .117, were significant, as well as the interactions Feedback 3 Electrode, F(1,26) 5 9.31, p 5 .005, g2p 5 .264, and Block 3 Electrode, F(3,78) 5 6.79, p < .001, g2p 5 .207. The inter- action Feedback 3 Block, F(3,78) 5 2.60, p 5 .058, g2p 5 .091, just failed to reach the significance level. Post hoc t-tests for the inter-

action Feedback 3 Electrode showed larger P200 over Cz com-

pared to Fz for both positive, t(27) 5 9.53, p < .001, and negative,

t(27) 5 5.18, p < 0001, feedback, but no significant differences were revealed between the two feedback conditions over Fz,

t(27) 5 0.41, p 5 .682, or Cz, t(27) 5 1.25, p 5 .222. As we were
mainly interested in the effects of feedback, we decided not to fol-

low up the interaction Block 3 Electrode.

Interestingly, the interaction Feedback 3 Electrode 3 Block 3

Anxiety was significant, F(3,78) 5 2.90, p 5 .040, g2p 5 .100, while
no other effects involving anxiety were significant (all ps > .063).
Separated post hoc analyses for the anxiety groups

2
returned a sig-

nificant Feedback 3 Electrode 3 Block interaction for the high

Figure 3. N100 component. Waveforms (upper) and topographic maps (lower) of the N100 amplitude to the positive feedback (light gray) and nega-

tive feedback (black) separately for the low anxious individuals (dashed lines) and high anxious individuals (full lines). The waveforms depict the

main effect stimulus for the two groups separated, that is, the mean voltage over Fz and Cz. The voltage for the topographic maps is presented for 90

ms until 110 ms after feedback onset. The first two heads depict low anxious individuals, while the last two heads depict high anxious individuals.

Only low anxious individuals showed larger N100 to negative as compared to positive feedback. *p < .05.

2. Further effects are listed here: Significant larger P200 over Cz
compared to Fz (main effect electrode) for both the low anxiety group,
F(1,13) 5 25.56, p < .001, g2p 5 .663, and the high anxiety group, F(1,13) 5 31.37, p < .001, g2p 5 .707. The interaction Feedback 3 Elec- trode was significant for the low anxiety group, F(1,13) 5 16.98, p 5 .001, g2p 5 .566, but not for the high anxiety group, F(1,13) 5 0.48, p 5 .500, g2p 5 .036. The interaction Feedback 3 Block did not reach significance in both groups (low anxiety group: F(1,13) 5 0.99, p 5 .408, g2p 5 .071; high anxiety group: F(1,13) 5 1.71, p 5 .181, g2p 5 .116). Finally, the interaction Electrode 3 Block was significant in the high anxiety group, F(1,13) 5 4.28, p 5 .011, g2p 5 .247, but not in the low anxiety group, F(1,13) 5 2.67, p 5 .061, g2p 5 .170.

584 M. Andreatta et al.

anxious individuals, F(3,39) 5 4.25, p 5 .011, g2p 5 .246, but not
for the low anxious individuals, F(3,39) 5 0.30, p 5 .828,
g2p 5 .022. We followed up this interaction for the high anxiety
group separately for Fz and Cz. The interaction Feedback 3 Block

was marginally significant only for Cz, F(3,39) 5 2.72, p 5 .057,
g2p 5 .173 (Figure 4) and not for Fz, F(3,39) 5 1.71, p 5 .181,
g2p 5 .116. Finally, explorative t-test comparisons for Cz within the
high anxiety group showed no significant P200 amplitude to posi-

tive feedback compared to the negative feedback during Block 1,

t(13) 5 1.82, p 5 .091; Block 2, t(13) 5 0.90, p 5 .383; Block 3,
t(13) 5 .06, p 5 .954; and Block 4, t(13) 5 1.12, p 5 .285. Howev-
er, P200 amplitude to positive feedback significantly decreased

from Block 2 to Block 3, t(13) 5 3.08, p 5 .009, while P200 ampli-
tude to negative feedback did not change significantly throughout

the blocks (all ps > .378).

FRN. Analysis showed significant main effects of feedback,

F(1,26) 5 12.30, p 5 .002, g2p 5 .321, but no block, F(3,78) 5 1.52,
GG-e 5 .625, p 5 .228, g2p 5 .055, or anxiety, F(3,78) 5 1.27,
p 5 .270, g2p 5 .047. Moreover, the interactions Feedback 3 Block,
F(3,78) 5 2.79, p 5 .046, g2p 5 .097, and Feedback 3 Anxiety,
F(1,26) 5 4.24, p 5 .050, g2p 5 .140 (Figure 5) were significant, but
no other effects involving the factor anxiety (all ps > .215).

Post hoc t-tests for the Feedback 3 Block interaction (Figure
S1) revealed significantly larger FRN to negative feedback as com-

pared to positive feedback during Block 1, t(27) 5 5.13, p < .001; Block 2, t(27) 5 2.78, p 5 .010; and Block 3, t(267) 5 2.26, p 5 .032, but not during Block 4, t(27) 5 .97, p 5 0.343. FRN amplitude to negative feedback was comparable throughout all

four blocks (all ps > .232), while FRN amplitude to positive feed-
back was significantly larger during the last two blocks as

compared to the first two blocks (Block 1 vs. Block 3: t(27) 5 2.70,
p 5 .012; Block 1 vs. Block 4: t(27) 5 3.59, p 5 .001; Block 2 vs.
Block 3: t(27) 5 2.89, p 5 .008; Block 2 vs. Block 4: t(27) 5 3.99,
p < .001). No differences were found between Block 1 and Block 2, t(27) 5 0.61, p 5 .548, or between Block 3 and Block 4, t(27) 5 1.51, p 5 .142.

Following up the Feedback 3 Anxiety interaction by direct

comparisons between high and low anxious individuals revealed

significantly larger FRN to negative feedback for low compared to

high anxious individuals, t(26) 5 2.12, p 5 .044, while no differ-
ences were revealed for the positive feedback between the groups,

t(26) 5 0.15, p 5 .886. Comparisons within anxiety group found,
for low anxious individuals, significantly larger FRN to negative

feedback versus positive feedback, t(13) 5 4.39, p 5 .001, but not
for high anxious individuals, t(13) 5 0.94, p 5 .366.

Discussion

The main goal of this study was to investigate learning processes

related to successful avoidance of a threat, which has been defined

as respite (Lohr et al., 2007). Respite of threat might elicit a posi-

tively valenced feeling of relief, which entails rewarding properties

and may serve as a reinforcer, motivating organisms to perform a

response (Gerber et al., 2014; Navratilova & Porreca, 2014). More-

over, such relief responses following successful avoidance have

been proposed as a mechanism underlying the etiology and mainte-

nance of anxiety disorders (Craske et al., 2009; Mowrer, 1956).

However, it remains unclear whether it is the respite per se or rather

the (unwanted) punishment that guides the behavior.

In this study, participants learned to press one button on a key-

board in order to avoid a subsequent slightly painful electric shock

Figure 4. P200 component. Waveforms over Cz (upper) and topographic map (lower) of the P200 amplitude (bars with standard errors) to the positive

feedback (light gray) and negative feedback (black) separately for the low anxious individuals (LA, dashed lines) and high anxious individuals (HA,

full lines). High anxious participants showed a significant decrease in P200 amplitude to negative feedback from Block 2 to Block 3 over Cz.

Avoidance learning 585

(respite-associated or avoidance button) in 80% of the trials. By

pressing another button, they received a similar shock (pain-associ-

ated or punishment button) in 80% of the trials. After two learning

blocks, participants underwent two test blocks, which were similar

to the learning blocks except that no shocks were delivered.

We found successful learning, as indicated by a higher frequen-

cy of presses on the avoidance compared to the punishment button.

Notably, learning took place between the first and second block of

the experiment, and then remained stable among the following

blocks. This result is in line with previous studies showing

increased response frequency when its outcome was either a reward

(Chase et al., 2010; Pessiglione et al., 2008) or avoidance of pain

(Delgado et al., 2009; Miskovic & Keil, 2014). Moreover, both par-

ticipants who could correctly indicate the avoidance button (i.e.,

aware participants) and those who could not (i.e., unaware partici-

pants) performed the task equally well and showed no differences

in their electrocortical signals (see below). This goes along with

the idea that instrumental conditioning can occur without conscious

processes (Pessiglione et al., 2008). Hence, avoidance of aversive

consequences seems to entail similar motivational properties such

as a reward leading to a preferred response.

Interestingly, during the test blocks (i.e., Block 3, 4) partici-

pants’ response frequency did not decrease, but remained constant

over time. This is surprising, especially considering that during

these blocks no shock was delivered, which should have led to an

extinction of the response. Possibly, such persistence of the

responses during the last two blocks might be related to the ratio

between punishment and respite of threat (Nevin, Randolph, Hol-

land, & McLean, 2001). Thus, responses were reinforced intermit-

tently (randomly in 80% of the trials), and this should delay

extinction. Conceivably, our participants may have continued

pressing the respite-associated button thinking that this successfully

Figure 5. Feedback-related negativity (FRN). Waveforms (upper) and topographic maps (lower) of the FRN to the positive feedback (light gray lines),

negative feedback (black lines), and the difference between them (blue lines) averaged among Cz, FC1, and FC2, separately for the low anxious indi-

viduals (left, dashed lines) and high anxious individuals (right, full lines). Low anxious participants showed significant larger FRN amplitude to nega-

tive feedback compared to positive feedback, whereas high anxious participants did not discriminate between the feedback and showed comparable

neurocortical signals. FRN to negative feedback was significantly larger in low anxious participants than in high anxious individuals. *p < .05;

**p < .01.

586 M. Andreatta et al.

caused avoidance of punishment. In turn, they missed trying to

press the punishment button and therefore did not notice that now

shocks were no longer delivered. As a consequence, extinction was

prevented (Lovibond et al., 2009; Vervliet & Indekeu, 2015). Fur-

thermore, we observed that this effect was even more pronounced

in high anxious participants. Namely, high anxious participants

pressed the respite-associated button slightly more frequently than

low anxious individuals, and this was especially visible during the

last block of the experiment.
3
.

In relation to the electrocortical signal in trials when partici-

pants pressed the avoidance button, we found significantly larger

N100, P100, and FRN amplitudes to negative feedback compared

to positive feedback following a correct response. We also found

larger P200 amplitudes to positive feedback when compared to

negative feedback. These results are in line with previous studies

(Hajcak et al., 2007; Holroyd & Coles, 2002; Lijffijt et al., 2009;

Miltner et al., 1997; Olofsson et al., 2008; Osinsky et al., 2012;

Pourtois et al., 2004; Sass et al., 2010; Walsh & Anderson, 2012;

Weymar et al., 2013). N100 and P200 are important components

involved in filtering sensory and cognitive inputs. In particular,

N100 is a negative component peaking around 70–160 ms after

stimulus onset over frontocentral sensors (Stewart et al., 2010) and

has been implicated in early attention (Lijffijt et al., 2009; Olofsson

et al., 2008). N100 was larger in response to negative pictures

(Olofsson et al., 2008) and importantly to fear-related pictures (i.e.,

spiders) especially in spider-fearful individuals (Weymar et al.,

2013). Therefore, our findings suggest that unexpected negative

feedback triggered higher attentional sources compared to expected

positive feedback. In parallel, the opponent potential (i.e., the

P100) also presented larger amplitude to the negative feedback sup-

porting previous findings in which P100 resulted in sensible to

aversive stimuli (Pourtois et al., 2004; Sass et al., 2010). If the

N100 triggers attention, the P200 is an index for attention alloca-

tion (Lijffijt et al., 2009). Previously, P200 has been found to be

larger to high arousing positive pictures (Olofsson et al., 2008).

Moreover, the P200 amplitude seems to be context dependent. In

other words, P200 has been found to be more pronounced when a

positive feedback followed another positive feedback, in compari-

son to when a positive feedback followed a negative feedback

(Osinsky et al., 2012). Accordingly, our study found that positive

compared to negative feedback elicited larger P200 amplitudes,

and this effect was evident during the first learning block, but not

during the second learning block or the test/extinction blocks. Pos-

sibly, participants may have allocated their attention to an expected

positive feedback as confirmation of their correct response during

learning of an optimal response strategy. Of note, a similar strong

initial learning effect was observed in the behavioral data. In fact,

in our study, participants quickly learned to avoid pressing the

pain-associated button and to prefer the respite-associated button.

This may imply that participants received a series of positive feed-

back during learning in the first block, and consequently this might

have contributed to the large P200 amplitude related to positive

feedback in the first block. These effects diminish in later blocks,

which is in accordance with reinforcement learning theory; the rel-

evance of the local reinforcement history concerning a response

declines with learning progress and may disappear if a stable

response strategy has been established (Hewig et al., 2008; Holroyd

& Coles, 2002; Holroyd et al., 2008; Miltner et al., 1997).

Of interest, these electrocortical responses (i.e., N100 and

P200) were influenced by the participants’ trait anxiety. Thus, low

anxious individuals discriminated well between negative and posi-

tive feedback after correct responses, as suggested by the larger

N100 amplitude to negative versus positive feedback. Hence, atten-

tion of low anxious participants was triggered by unexpected nega-

tive feedback after a correct respite-associated response. On the

contrary, attentional processes of high anxious individuals were

equally triggered by negative and positive feedback after a correct

avoidance response, as suggested by the equal N100 and P200

amplitudes to the feedback. Considering the general incapability of

high anxious individuals in distinguishing safety from threat

(Craske et al., 2009; Lohr et al., 2007), it is conceivable that they

may also be less able to take advantage of feedback and generalize.

This discrimination deficit seems in contradiction to the results in

spider phobics, who showed higher N100 to phobic-related pictures

(Weymar et al., 2013). We speculate that, in the Weymar et al.

study, the spider phobics’ attention is more specifically and quickly

captured by that particularly feared object. In contrast, participants

in our study are characterized by a more general (perhaps broad)

nonpathological anxiety (based on the STAI, Laux et al., 1981).

Finally, our findings regarding the P200 component are in line with

the N100 results indicating no discrimination between positive and

negative feedback in high anxious individuals. However, high anx-

ious individuals showed a significant decrease in P200 amplitudes

from the learning blocks (i.e., the blocks when slightly painful elec-

tric shocks were delivered) to the test blocks (i.e., the blocks where

no shocks were delivered). We assume that anxious participants

were highly motivated during the blocks with possible electric

shock. Possibly, they kept on trying to optimize behavior, which

includes the calculation of local reinforcement history (Osinsky

et al., 2012). This might explain that P200 amplitudes reflecting

local reinforcement history declined between Block 2 and 3 in

highly anxious individuals. Moreover (see supporting information),

we also found that the more depressive high anxious individuals

were, the more sensitivity (i.e., larger P200 amplitude) to positive

feedback they showed. This is in line with the positive correlation

between anxious state and NAcc activation elicited by avoidance

of negative consequences (Levita et al., 2012). Lastly, it should be

noted that no effect of anxiety traits were revealed for P100 (see

supporting information). Considering that N100 and P100 originate

from different sources (Yamazaki et al., 2000), it is conceivable

that the frontally originated N100 may be more sensitive to interin-

dividual differences than the parietal-occipitally originated P100.

In line with previous studies (Hajcak et al., 2007; Holroyd &

Coles, 2002; Miltner et al., 1997; Osinsky et al., 2012; Pessiglione

et al., 2008; Walsh & Anderson, 2012), FRN amplitudes were larg-

er to negative versus positive feedback. Learning that a button

press predicts likely avoidance of a painful shock may induce

expectation that, after that particular response, a positive feedback

should appear. Hence, the larger FRN to unexpected negative feed-

back in our participants is in line with previous studies saying that

FRN encodes PE (Chase et al., 2010). Also, FRN amplitude to neg-

ative feedback did not change much through the blocks. In contrast,

FRN amplitude to positive feedback significantly increased through

the blocks. Possibly, this increase in FRN amplitude might be relat-

ed to a prediction error signal elicited by the positive feedback.

Specifically throughout the blocks, participants may have noticed

3. During the last block of the experiment, high anxious participants
pressed the relief-associated button (M 5 54.5, SD 5 5.8) slightly more
frequently and the punishment-associated button slightly less frequently
(M 5 5.2, SD 5 5.6) than low anxious individuals (relief-associated but-
ton: M 5 48.4, SD 5 10.3, t(26) 5 1.92, p 5 .068; punishment-associated
button: M 5 11.4, SD 5 10.3, t(26) 5 1.98, p 5 .062).

Avoidance learning 587

that positive feedback was not 100% certain after the respite but-

ton, which might have implied a PE-dependent FRN.

Lastly and most interestingly, trait anxiety affected our FRN

findings in accordance with the N100 and P200 findings. Thus,

highly anxious individuals did not exhibit differential FRN

responses to positive and negative feedback (discriminative

responses were revealed only during the first block), while low

anxious individuals did. Moreover, highly anxious individuals

responded with a smaller FRN to the unexpected negative feedback

than low anxious individuals. Possibly, low anxious individuals

experienced a strong feeling of relief related to the expected

respite, which elicited a strong prediction error (Chase et al., 2010)

and, consequently, discriminative responses to the positive (respite-

associated) and the negative (pain-associated) feedback. On the

contrary, high anxious individuals might not have felt a strong feel-

ing of relief related to the respite, because they may have been too

worried about a possible shock (Lohr et al., 2007), which was

delivered in an apparently random fashion, in 80% of the trials. As

a consequence, anxious participants showed no discriminative FRN

responses to the feedback. Finally, considering that FRN might be

sensible to individual differences (Hackel, Doll, & Amodio, 2015;

Hajcak et al., 2007; Hewig et al., 2010) and that we used an

anxiety-relevant threat (electric shock), our paradigm might have

been especially suitable to detect anxiety-related individual

differences.

Of interest, electrocortical signals of low anxious individuals

were influenced by their emotional state (see supporting informa-

tion). Thus, the P200 amplitude negatively correlated with the posi-

tive mood, while FRN amplitude positively correlated with

participants’ negative mood. Interestingly, P200, a component sen-

sitive to positive stimuli (Osinsky et al., 2012), was modulated by

participants’ positive mood, whereas FRN, a component sensitive

to negative feedback (Olofsson et al., 2008; Walsh & Anderson,

2012), was modulated by participants’ negative mood.

The current study has two main limitations. First, the number of

trials for the negative feedback after having pressed the avoidance

button is low (10 trials per block), and it is recommended to per-

form this study with a higher number of trials for this condition.

However, we are confident that our results were quite stable as the

large FRN amplitudes indicate (< 2 mV). Moreover, we used active sensors, which have a better signal-to-noise ratio than nonactive

sensors, and this might have contributed to such a stable signal.

The second limitation concerns the sample size. Through the medi-

an split, the anxiety groups consisted of 14 participants each, which

is quite small. Therefore, the results should be taken cautiously and

ought to be replicated with larger sample sizes in future studies.

However, the group differences in processing positive versus nega-

tive feedback are interesting, and they may provide areas of interest

for future studies.

To summarize, our results demonstrated that respite of a nega-

tive consequence is an effective reinforcer for behavior and that the

associated electrocortical signals mirror responses to reward-

associated feedback. Moreover, our results suggest a decreased abil-

ity of high anxious individual to discriminate between negative and

positive feedback. High anxious individuals also seem less able to

adapt their responses once the threat is not present and, therefore,

keep doing what they thought was best to avoid the threat.

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Supporting Information

Additional supporting information may be found in the online

version of this article:

Appendix S1: P100 analyses.

Appendix S2: Correlation analyses.

Figure 1: FRN component clustered among Cz, FC1, and FC2

for all participants.

Figure 2: P100 component averaged at O1 and O2.

Figure 3: Scatter plots for low and high anxious individuals

separately.

Table 1: Momentary anxiety and positive/negative mood.

Table 2: Ratings for the two kinds of feedback.

Table 3: Frequencies of the feedbacks per block after button

press.

590 M. Andreatta et al.

info:doi/10.1080/01677060802441372

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accuracy of the copy. Users should refer to the original published version of the material.

History of Psychology

Did Little Albert Actually Acquire a Conditioned Fear of
Furry Animals? What the Film Evidence Tells Us

Russell A. Powell and Rodney M. Schmaltz
Online First Publication, October 22, 2020. http://dx.doi.org/

0.1037/hop0000176

CITATION
Powell, R. A., & Schmaltz, R. M. (2020, October 22). Did Little Albert Actually Acquire a Conditioned
Fear of Furry Animals? What the Film Evidence Tells Us. History of Psychology. Advance online
publication. http://dx.doi.org/10.1037/hop0000176

Did Little Albert Actually Acquire a Conditioned Fear of
Furry Animals? What the Film Evidence Tells Us

Russell A. Powell and Rodney M. Schmaltz
MacEwan University

Watson and Rayner’s (1920) attempt to condition a fear of furry animals and objects in an
11-month-old infant is one of the most widely cited studies in psychology. Known as the Little
Albert study, it is typically presented as evidence for the role of classical conditioning in fear
development. Some critics, however, have noted deficiencies in the study that suggest that little
or no fear conditioning actually occurred. These criticisms were primarily based on the published
reports of the study. In this article, we present a detailed analysis of Watson’s (1923) film record
of the study to determine the extent to which it provides evidence of conditioning. Our findings
concur with the view that Watson and Rayner’s conditioning procedure was largely ineffective,
and that the relatively weak signs of distress that Albert does display in the film can be readily
accounted for by such factors as sensitization and maturational influences. We suggest that the
tendency for viewers to perceive the film as a valid demonstration of fear conditioning is likely
the result of expectancy effects as well as, in some cases, an ongoing mistrust of behaviorism as
dehumanizing and manipulative. Our analysis also revealed certain anomalies in the film which
indicate that Watson engaged in some “literary license” when editing it, most likely with a view
toward using the film mainly as a promotional device to attract financial support for his research
program.

Keywords: John B. Watson, Little Albert, fear conditioning, history of behaviorism, ethics

A century has passed since Watson and Rayner’s (1920) iconic attempt to condition a fear of furry
animals and objects in a young infant. Commonly known as the “Little Albert” study, it is one of
the most referenced studies in psychology, with a Web of Science search at the time of this writing
indicating over 25,000 citations. The study is typically presented as a seminal demonstration of the
role of classical conditioning in the development of human fears (e.g., Bouton, 2007; Domjan,
2015; Mineka & Sutton, 2006; Ollendick & Muris, 2015; Seligman, 1971); Kimble (1961) called
it “the most famous single case in conditioning history” (p. 24). In the 1970s, the study also began
to be widely cited as an example of the lack of ethical guidelines in the early years of psychology
(Todd, 1994), which has resulted in Watson often being vilified for deliberately instilling a phobia
in a helpless infant (e.g., Aalai, 2015; Smith, 2017).1

Russell A. Powell and X Rodney M. Schmaltz, Department of Psychology, MacEwan Universit

We thank Nancy Digdon and Ben Harris for their helpful feedback on earlier drafts of this article. As per the request of

the late Gary Irons, we also acknowledge that his granting Russell A. Powell permission to view Douglas Merritte’s medical
files does not indicate that he either concurred with or disagreed with any statements or conclusions reported in this article.

Correspondence concerning this article should be addressed to Russell A. Powell, Department of Psychology,
MacEwan University, P.O. Box 1796, Edmonton, AB T5J 4S2, Canada. E-mail: powellr@macewan.ca

1
It should be noted that Watson and Rayner (1920) claimed only that they were conditioning in Albert a

“fear” of furry animals. They never used the word “phobia,” which is a clinical disorder in which the fear
significantly interferes with one’s daily life (American Psychiatric Association, 2013). In fact, Watson and
Rayner stated that Albert was chosen for the experiment because his remarkably stable temperament made it
highly unlikely that the experiment would cause him any lasting harm.

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History of Psychology
© 2020 American Psychological Association 2020, Vol. 2, No. 999, 000 – 000
ISSN: 1093-4510 http://dx.doi.org/10.1037/hop0000176

https://orcid.org/0000-0003-4882-0342

mailto:powellr@macewan.ca

http://dx.doi.org/10.1037/hop0000176

In contrast to the widespread view of the Watson and Rayner (1920) study as a
successful demonstration of fear conditioning, less attention has been paid to critics who
have questioned both the study’s methodology and its results (e.g., Harris, 1979, 2011;
Samelson, 1980; Todd, 1994; Valentine, 1930). These criticisms, which are outlined
below, are based primarily on Watson and Rayner’s (1920) published account of the
study. No one has yet systematically examined Watson’s (1923) film record to determine
what it may indicate about the study’s validity. In this article, we present the results of
such an analysis. Our findings support previous concerns and strongly question whether
Albert actually acquired a conditioned fear of furry animals. The negative reactions he
displays in the film can be readily explained by such nonconditioning factors as sensiti-
zation and maturational influences on fear development and, in some cases, contradict the
possibility that he was fearful of the stimulus being presented. We suggest that confir-
mation bias likely played a major role in convincing Watson and Rayner that their
conditioning procedure was successful. We also uncovered certain anomalies in the film
which suggest that Watson may have manipulated and misrepresented certain aspects of
the study to strengthen its impact as an example of fear conditioning. The most likely
explanation is that he did so to enhance the film’s value as a promotional device to attract
sufficient funding to support his research agenda.

The Little Albert Study

The Little Albert study was conducted in Watson’s infant behavior laboratory at Johns
Hopkins Hospital in Baltimore, most likely between early December 1919 and late March
1920 (Beck, Levinson, & Irons, 2009; Powell, Digdon, Harris, & Smithson, 2014).
Watson’s previous research had led him to speculate that infants are born with only a few
innate fears, one of which is fear of loud noises. He proposed that most infant fears are
instead the result of classical conditioning, often involving the inadvertent pairing of a
loud noise with a particular stimulus. For example, when a flash of lightning is followed
by the sound of thunder or the sight of an insect is followed by the coincidental slamming
of a door, it could lead to a conditioned fear of those stimuli (Watson & Morgan, 1917;
Watson, 1919a). The Little Albert study was an attempt to provide experimental evidence
of this process.

The study was conducted over six sessions, which, for the sake of clarity, we have
divided into a baseline session, two conditioning sessions, and three transfer sessions (see
Table 1). During the baseline session, which occurred when Albert was approximately
nine months old, he was shown various animals and objects he had never before seen.
Albert was reportedly unafraid of any of the stimuli he was shown, which consisted of “a
white rat, a rabbit, a dog, a monkey, with [sic] masks with and without hair, cotton wool,
burning newspapers, etc.” (Watson & Rayner, 1920, p. 2). This session was followed
approximately 2 months later by two conditioning sessions spaced 1 week apart, in which
Albert was subjected to a total of seven pairings of a white rat followed by the startling
sound of a steel bar being struck with a hammer. By the end of the second conditioning
session, when Albert was shown the rat, he reportedly cried and “began to crawl away so
rapidly that he was caught with difficulty before reaching the edge of the table” (p. 5).
Watson and Rayner interpreted these reactions as evidence of fear conditioning.

The two conditioning sessions were followed by three transfer sessions. During these
sessions, Albert was shown not only the rat—to assess whether his conditioned fear of the
rat had been maintained— but also other furry animals and objects to determine whether
the fear had generalized or “transferred” to similar stimuli. Complicating the experiment,
however, the second transfer session also included two additional conditioning trials with
the rat to “freshen up the reaction” (Watson & Rayner, 1920, p. 9), as well as conditioning
trials in which a dog and a rabbit were, for the first time, also paired with the loud noise.
An incident also occurred during this session in which the dog that Albert was being

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2 POWELL AND SCHMALTZ

shown suddenly barked, frightening both Albert and everyone else in the room. Unlike the
two conditioning sessions and first two transfer sessions which were each separated by
about a week, the final transfer session was conducted after a period of a month to
determine if Albert’s conditioned fears would persist for that long. Immediately following
the session, Albert and his mother left the hospital, which prevented Watson and Rayner
from carrying out their original intention of attempting to remove any fear responses they
had instilled in him.2

Criticisms of the Little Albert Study

Although the Watson and Rayner (1920) study is often referenced as a valid demon-
stration of the role of classical conditioning in the development of human fears (e.g.,

2
The question is sometimes asked as to why Watson and Rayner (1920) did not attempt to remove Albert’s

conditioned fears prior to his departure from the hospital. They opted instead to determine if the fear conditioning
would maintain itself for a longer period of time—the previous sessions being separated only by about a
week— however, “in view of the imminence of Albert’s departure from the hospital [they] could not make the
interval longer than one month” (p. 10). Although this statement implies that they knew in advance about
Albert’s departure, Albert Barger’s medical file for March 31, 1920, states that “his mother suddenly [emphasis
added] decided to leave the hospital” and that Albert was discharged from the hospital that same day (Barger
Medical File, Alan Chesney Medical Archives at Johns Hopkins University). It is possible therefore that Albert’s
sudden departure took them by surprise. If this is the case, they may have originally believed that they could
conduct a delayed test of Albert’s fear reactions and still have sufficient time to carry out a treatment procedure.
Albert’s sudden departure from the hospital, however, would have disrupted that plan and forced them to quickly
arrange a final test session only.

Table 1
Description of Each Session in the Little Albert Experiment Along With Albert’s Age
as Reported by Watson and Rayner (1920)

Session Age Session type Stimuli shown

1 8 months 26
days

Baseline session
(filming
mentioned)

Included tests with rat, rabbit, dog, monkey, masks
with and without hair, cotton wool, and burning
newspapers (no fear)

2 11 months 3
days

First conditioning
session

Rat paired with loud noise (two pairings)

3 11 months 10
days

Second conditioning
session

Test with rat alone (elicited mild fear)
Rat paired with loud noise (5 pairings)
Test with rat alone (elicited strong fear)

4 11 months 15
days

First transfer session Tests with rat, rabbit, dog, fur coat, cotton wool,
Watson’s hair, 2 observers’ hair, and Santa Claus
mask

5 11 months 20
days

Second transfer
session

In original testing room: tests with rat, rabbit, and dog;
an extra conditioning trial with rat; and conditioning
trials with rabbit and dog (1 pairing each)

In a new room: tests with rat, rabbit, and dog; extra
conditioning trial with rat; plus barking incident
with dog

Included comment that all previous tests had been
conducted on a table

6 12 months 21
days

Third transfer session
(filming
mentioned)

Tests with Santa Claus mask, fur coat, rat, rabbit, and
dog

Albert was also discharged from the hospital on this
day

3DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

Bouton, 2007; Domjan, 2015; Mineka & Sutton, 2006; Ollendick & Muris, 2015;
Seligman, 1971), there are several reasons to believe that the study actually provides little
evidence in this regard. A major methodological flaw, for example, is that the study did
not control for the possibility of pseudoconditioning. If the loud noise was as upsetting to
Albert as described by Watson and Rayner, it may have sensitized him to react fearfully
to any novel or sudden stimulus during the remainder of the session (Lieberman, 2000;
Powell, Honey, & Symbaluk, 2017). The closest that Watson and Rayner came to
controlling for this possibility was that they sometimes gave Albert wooden blocks to play
with and reported that, unlike the furry animals and objects, the blocks did not elicit a fear
response. These blocks, however, were described as “his” blocks, which implies that they
were play objects that were both familiar and appetitive to him and would thereby have
been resistant to being perceived as aversive even if he had been sensitized by the loud
noise.

Another methodological flaw in the Watson and Rayner (1920) study was the lack of
control for the possible influence of innate, maturational factors on fear development.
These were first proposed by Valentine (1930; see also Jones & Jones, 1928), who
observed the emergence of fear responses in his own children— hence, Watson would not
have been aware of such factors at the time of the Little Albert study. Evidence suggests
that infants often pass through developmental phases in which they become predisposed
to react fearfully to events of which they were previously unafraid. Fear of strangers,
which usually emerges in the latter half of the first year, is a notable example (Sroufe,
1977); however, a variety of other fears can also emerge or become intensified during this
time, including fear of sudden, unexpected, or looming objects, and fear of masks (Marks,
1987; Scarr & Salapatek, 1970; Witherington, Campos, Harriger, & Margett, 2010).
Underlying many of these reactions may be a propensity for infants to become apprehen-
sive of events that are in some way discrepant with earlier experiences (Kagan, 1979).
These tendencies are of relevance to the Little Albert study in that the study lasted almost
four months from the baseline session (at around nine months of age) to the final session
(at almost 13 months of age), during which time maturationally based fears may have
emerged.

In addition to the methodological flaws, there is evidence to suggest that whatever
conditioning did occur during the study was relatively weak and easily extinguished
(Harris, 1979; Samelson, 1980; Todd, 1994). As noted earlier, during the second transfer
session, Watson and Rayner (1920) conducted additional conditioning trials with the rat
because Albert’s fear of the rat seemed to have diminished since the previous session.
They also, for the first time, conducted conditioning trials with a rabbit and a dog,
presumably because these stimuli were eliciting insufficient evidence of a generalized fear
response. In addition, Albert’s fear reactions were sometimes inconsistent. For example,
“on this occasion [when Albert was shown the rat during the second transfer session] there
was no crying, but strange to say . . . he began to gurgle and coo, even while leaning far
over . . . to avoid the rat” (p. 7). Albert also sometimes sucked his thumb which rendered
him “impervious” to fear and again suggests that he did not find the stimuli particularly
frightening. It is also the case that early attempts to replicate Watson and Rayner’s (1920)
results were either unsuccessful (English, 1929) or only partially successful (Bregman,
1934; Valentine, 1930); it has been argued, however, that these studies were themselves
so methodologically flawed as to be of little relevance (Delprato, 1980; Todd, 1994).

The preceding criticisms of the Little Albert study are primarily based on Watson and
Rayner’s (1920) published report of the study. The film depiction of the study has received
little attention. Samelson (1980) simply commented that “the brief [film] sequences of
Albert do not contribute much to strengthen my faith in the results of the experiment” (p.
621), while Harris (2011) mentioned only how they “sometimes tossed animals at him,
sometimes shoved things like Rosalie’s sealskin coat at him, and their method of
presenting him with a Santa Claus mask was to have Watson put it on and crawl toward

4 POWELL AND SCHMALTZ

Albert at eye level” (p. 5).3 No attempt has yet been made to systematically examine the
film to assess the nature of the evidence it provides for conditioning.

The Little Albert Film

The footage of the Little Albert study is part of a silent, black and white film entitled
Studies Upon the Behavior of the Human Infant: Experimental Investigation of Babies
(Watson, 1923). The film portrays the various tests Watson was using at that time to assess
infants’ motor and emotional development. The Little Albert portions of the film consist
of 34 brief clips (M � 9 s; SD � 6 s) that were spliced together; it is therefore extremely
choppy, with no information provided about what happened before or after each clip.

Albert first appears in the film in a depiction of his ability to grasp objects and to crawl
as part of the section on motor development. The fear conditioning sequence occurs near
the end of the film in the section on emotional development. It begins with a demonstra-
tion of Albert’s lack of fear when shown a burning newspaper, a monkey, a dog, a rat, and
a rabbit, in that order. These were some of Watson’s standard tests for emotional
development, but in Albert’s case they would also serve as the baseline session for the
later attempts at fear conditioning. The clips of the baseline session are followed by a
series of clips depicting a transfer session in which Albert is shown a rat, a rabbit, a dog,
a fur coat, and a mask, in that order. Curiously, neither of the preceding conditioning
sessions, in which the presentation of the rat was paired with the loud noise, is shown. An
earlier portion of the film, however, does show a much younger infant being startled, but
only slightly, by the sound of a steel bar being struck by a hammer.

Two Assumptions and Relevant Evidence

We make two assumptions about the Little Albert case which bear upon our analysis of
whether Albert acquired a conditioned fear of furry animals. Our first assumption is that
Little Albert was not Douglas Merritte, whom Beck et al. (2009) proposed to have been
the real Albert. Douglas was an infant who resided in Johns Hopkins Hospital with his
mother (a wet nurse) around the presumed time of the Little Albert study and was
approximately the right age to have been Albert. Beck et al.’s investigation further
revealed that Douglas died from hydrocephalus a few years following the experiment,
thereby ending any speculation as to whether he had grown up with a fear of furry animals.
In a follow-up study, however, Fridlund, Beck, Goldie, and Irons (2012) discovered that
Douglas had been severely ill with hydrocephalus even during the time of the experiment.
They further conjectured that Watson, in a serious breach of ethics, had selected him for
the study despite his illness. Fridlund et al. also claimed that an examination of the film
record of the Little Albert study revealed evidence of neurological and visual impairments
in Albert’s behavior that would be consistent with Douglas’s illness.

More recent evidence, however, strongly refutes the Douglas Merritte hypothesis. It
instead indicates that Little Albert was most likely Albert Barger, another infant who was
also living in the hospital at the time and who was also the correct age to have been Little
Albert (Digdon, Powell, & Harris, 2014; Powell et al., 2014). Additional evidence for
Albert Barger as Little Albert includes the following: (a) Albert Barger was a generally
healthy infant, which matches Watson and Rayner’s (1920) description of Little Albert;
(b) Albert Barger’s weight at 9 months of age is a close approximation to the weight
reported by Watson and Rayner for Little Albert and is consistent with his chubby

3
It was Harris who, in 1979, rediscovered the film, which for many years had been presumed lost (Harris,

2011).

5DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

appearance in the film; (c) the name Albert Barger matches the name “Albert B.” that
Watson and Rayner gave to Little Albert (use of participants’ real names or initials being
a common practice in research publications in that era); and, perhaps most significant, (d)
Albert Barger’s age on the day that he was discharged from the hospital exactly matches
Little Albert’s reported age at discharge. Douglas Merritte, by contrast, does not match
any of these characteristics; for example, Douglas’s medical records indicate that he was
severely underweight at the time of the experiment (in the first percentile on modern
growth charts), and his age at time of discharge from the hospital was one week younger
than that of Little Albert. Moreover, a reexamination of the film footage revealed no
substantive evidence that Little Albert was neurologically and visually impaired. Thus,
although the evidence for Albert Barger as Little Albert is not conclusive, it is much
stronger than the evidence for Douglas Merritte as Little Albert (Digdon, 2020; Griggs,
2015). We therefore assume that Albert’s reactions in the film are those of a neurotypical
child who is capable of perceiving and reacting normally to the various stimuli he is being
shown.4

Our second assumption concerns which transfer session is being shown in the film. As
previously noted, the film clips depicting Albert’s fear conditioning begin by showing his
reactions during the baseline session to various animals and objects he had never before
seen. These clips are followed by two intertitles that read, “Fear of an animal may be set
up by stimulating the infant with a loud sound just at the moment the animal is presented.
Six combined stimulations produced the marked fear of the rat next shown” (Watson,
1923).5 If this statement is accurate, then the session that is next shown in the film should
be the first transfer session following the conditioning sessions. Several clues, however,
indicate that it is not the first transfer session, but the final transfer session. These clues
are as follows:

4
In a recent article, Fridlund et al. (2020a) contend that Douglas Merritte remains the closest fit for the real

Little Albert and presented evidence in support of that contention. Fridlund et al. claimed to have learned that
the 9-month-old Albert on film was extremely short-statured, with a body length that was at the 50th percentile
for a 2-month-old infant (which, on modern World Health Organization growth charts is �59 cm in length and
would be at the �.001th percentile for a 9-month-old infant!). Short stature is sometimes associated with chronic
hydrocephalus (e.g., Klauschie & Rose, 1996); hence, this evidence seems to support the possibility that Douglas
Merritte was indeed Little Albert. It also accounts for how, despite his extremely low body weight, Douglas (as
Little Albert) would nevertheless have appeared quite pudgy on film. One difficulty with this evidence, however,
is that Douglas’s medical records indicate that at 17.5 months of age, he was 80 cm in length (Merritte Medical
File, Johns Hopkins Medical Archive, August 27, 1920), which is at the 26th percentile for that age and suggests
that Douglas’s chronic hydrocephalus had little or no effect on his stature. A potential problem with the Fridlund
et al. assessment, which is similar to the issues involved in Fridlund et al.’s (2012) estimate of Albert’s head
circumference (Powell et al., 2014), is the difficulty of trying to estimate body length from a low-quality film
image in which Albert is never fully prone and where only slight changes in certain measurements could yield
very different results. Most critical, however, is the fact that Albert Barger’s recorded weight is a close match
to the weight reported by Watson and Rayner (1920) for Little Albert, whereas Douglas Merritte’s recorded
weight is much lower. Add to this the reasonable assumption that Albert Barger’s body length at 9 months of
age was within the normal range— his recorded length at 2 months of age was at the 48th percentile (Barger
Medical File, Johns Hopkins Medical Archive, May 14, 1919)—then his physical appearance would likewise
have been consistent with Little Albert’s hefty appearance on film, with no further calculations or assumptions
needed. Thus, in terms of Occam’s razor, Albert Barger is a stronger candidate for the real Little Albert in terms
of body weight and physical appearance. More generally, Fridlund et al. (2020a) failed to adequately explain
why Douglas Merritte, whose documented information is largely discrepant with Watson and Rayner (1920)
account of Little Albert, should be considered a strong candidate when there is another baby, Albert Barger,
whose documented information indicates a much closer fit. That said, we recognize that this was only a brief
presentation of their evidence for the Douglas-as-Albert hypothesis and look forward to seeing their forthcoming
article on the topic (Fridlund et al., 2020b; see also Digdon, 2020; Harris, 2020; and Pickren, 2020).

5
Watson and Rayner (1920) indicated that there were seven, not six, “combined stimulations” (conditioning

trials in which the rat was paired with the loud noise). Watson, however, is notorious for making minor errors
like this, especially in his later descriptions of the study (Beck et al., 2009; Harris, 1979).

6 POWELL AND SCHMALTZ

• Watson and Rayner (1920) explicitly mention the occurrence of filming only
when describing the baseline session: “a permanent record of Albert’s reactions
to these objects and situations has been preserved in a motion picture study” (p.
2), and the final transfer session: “again and again while the motion pictures were
being made at the end of the thirty-day rest period. . .” (p. 13). This suggests that
only these two sessions may have been filmed. A possible reason for doing so is
that film was expensive, and Watson had to do a considerable amount of lobbying
to university administrators to obtain sufficient funds to purchase film (Beck et al.,
2009; Watson, 1919b, 1919c). He might therefore have hesitated to waste film on
anything in which the outcome was less than certain, such as whether it was
possible to use a loud noise to condition a fear of a rat. This is especially the case
given that Watson had already been unsuccessful in a previous attempt at fear
conditioning that involved pairing a light flash with a loud noise (as an analogue
of lightning and thunder; Watson & Morgan, 1917). This would also explain why
the film curiously does not show any of Albert’s conditioning trials; they may not
have been included because they were never filmed.

• As previously noted, Watson and Rayner (1920) stated that Albert would begin
sucking his thumb whenever he was upset, which made him impervious to fear;
as a result, “while the motion pictures were being made at the end of the thirty-day
rest period, we had to remove the thumb from his mouth before the conditioned
response could be obtained” (p. 13). Watson can be seen carrying out this action
during the transfer session shown in the film (see Figure 1). This is consistent with
the session shown being the final transfer session, which occurred 31 days
following the previous transfer session.

• Watson and Rayner (1920) described Albert as “nodding his head in a very
peculiar manner (this reaction was an entirely new one)” (p. 10) during the final
transfer session. During the transfer session shown in the film, Albert can be seen
repeatedly nodding or bobbing his head (which looks very much like sneezing;
see Figure 2).

• Watson and Rayner’s (1920) description of the next-to-last transfer session states
that “all of the tests so far discussed were carried out upon a table supplied with
a mattress located in a small, well-lighted dark room” (p. 8). However, the
transfer session shown on film was, like the baseline session, conducted on the

Figure 1. Watson pulling Albert’s thumb out of his mouth during the transfer session.
Watson and Rayner (1920) indicated that these incidents occurred during the final transfer
session. From the film Studies Upon the Behavior of the Human Infant: Experimental
Investigation of Babies, by J. B. Watson (Writer/Director), 1923. This image is available for
use in the public domain.

7DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

floor rather than on a table. Therefore, if all the tests up to and including the
next-to-last transfer session were conducted on a table, then the session shown in
the film must, by elimination, have been the last transfer session.

We also tried to determine which transfer session is shown in the film by matching
Albert’s reactions on film with Watson and Rayner’s (1920) written descriptions of his
reactions. Unfortunately, his reactions on film do not clearly match the written descrip-
tions for any of the transfer sessions. For example, Albert is never described as crawling
over the rat which, as will be shown later, is what we see in the film. And while Watson
and Rayner stated that Albert was presented with a Santa Claus mask during the first and
last transfer session, the article nowhere indicates that Watson wore the mask while
crawling toward Albert, which is also what we see in the film.

An inconsistency in assuming that the film portrays the final transfer session is that the
order in which the stimuli are shown matches the reported order for the first transfer
session (rat, rabbit, dog, fur coat, and then mask) and not the last transfer session (mask,
fur coat, rat, rabbit, and then dog). Watson, however, could have rearranged the film clips
of the last transfer session to match the order of stimuli in the first transfer session. By
doing so, the film would depict the straightforward sequence of Albert first displaying a
conditioned fear of the rat as a result of conditioning which is then followed by Albert
displaying a generalized fear of other furry animals and objects. Another possible reason
for hiding the fact that the session shown is the final transfer session is that, during the
previous transfer session, the dog and rabbit had also been paired with the loud noise,
thereby confounding the extent to which Albert’s fear of them would be indicative of
generalization. Reordering the clips from the final transfer session to make it appear that
they were from the first transfer session would eliminate the need to explain such
complexities. This would be of particular importance to Watson given his intention to use
the film during lectures he was frequently asked to give to various organizations about his
research findings (Watson, 1919b, 1919c). Whatever the reason, if the transfer session
depicted in the film is the final transfer session—which occurred almost four months
following the baseline session—it increases the likelihood that maturational factors might
account for some of the differences in Albert’s reactions between the baseline session and
the transfer session shown in the film.

The Analysis of Albert’s Reactions on Film

In the following section, we examine Albert’s reactions to each of the stimuli he is
being shown in the film. The purpose of this analysis is not to determine precisely why

Figure 2. An example of Albert’s head nodding motion during the transfer session (which
looks like a sneeze). From the film Studies Upon the Behavior of the Human Infant:
Experimental Investigation of Babies, by J. B. Watson (Writer/Director), 1923. These images
are available for use in the public domain.

8 POWELL AND SCHMALTZ

Albert behaved the way he did—the film is too fuzzy and choppy with no indication of
what occurred before and after each clip to make any such determination (Digdon et al.,
2014; Powell et al., 2014)— but to assess whether viable alternative explanations may
exist to Watson and Rayner’s (1920) conditioning interpretation.

Albert’s Reactions to a Rat

Stills of Albert’s filmed reactions to the rat are shown in Figure 3. During the baseline
session, he seems quite interested in the rat and even reaches for it. During the transfer
session, however, he initially pays no attention to the rat even though Watson places it on
his leg and in his lap. Albert’s gaze instead seems fixated to one side of the camera
(possibly because his mother or another caretaker is standing there). He then falls forward
and tries to crawl in that direction and, in doing so, crawls over the rat. Watson reacts by
pulling the rat out from underneath Albert and placing it either on or near his hand. At this
point, Albert immediately pulls his hand back, sits upright, and begins to cry. One
interpretation of this reaction is that Albert is crying because the rat was previously paired
with the sound of a loud noise. A more parsimonious interpretation is that he was startled
by the sudden, unexpected feeling of the rat on his hand. Given Albert’s initial indiffer-
ence to the rat and his attempt to crawl over the rat rather than away from it, the latter
interpretation is arguably more plausible.

Figure 3. Albert’s reactions to the rat during the baseline session (first still) and transfer
session (remaining stills; stills are ordered sequentially by row from top left to lower right).
During the transfer session, Albert initially pays no attention to the rat and even tries to crawl
over top of it. Only when Watson retrieves the rat from underneath Albert and places it either
on or in front of his hand (middle row) does Albert pull his hand back, sit upright, and begin
crying (bottom row). From the film Studies Upon the Behavior of the Human Infant:
Experimental Investigation of Babies, by J. B. Watson (Writer/Director), 1923. These images
are available for use in the public domain.

9DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

Albert’s Reactions to Rabbits

Examples of Albert’s reactions to rabbits are shown in Figure 4. During the baseline
session, Albert did not behave as though he was particularly fearful of the rabbit being
shown, although he did hold his arm up as though to avoid touching it. The initial clip of
Albert’s transfer session encounter with a rabbit shows Watson removing Albert’s thumb
from his mouth, after which Albert simply stares at the rabbit. The second clip in the
sequence shows Albert again gazing to one side of the camera with no rabbit present. The
third clip shows Rayner quickly pulling the rabbit out from behind a white sheet and
dropping it on Albert’s lap, at which point he begins to cry. Although this might represent
evidence of a conditioned fear response to furry animals, it is also possible that he is
crying because he was startled by the sudden appearance of the rabbit on his lap. In
support of this interpretation, the next clip shows Albert no longer crying and instead
trying to shuffle his legs away from the rabbit. He then falls forward and attempts to crawl
past the rabbit and toward the camera (which Rayner prevents by grabbing his leg). At one
point, Albert’s head is only a few inches from the rabbit, which seems inconsistent with
the possibility that he is afraid of the rabbit.

Albert’s Reactions to Dogs

Stills of Albert’s reactions to dogs are shown in Figure 5. During the baseline session,
he is shown a rather large and seemingly friendly dog that he appears unafraid of, at one
point even handling its paw. The film clip of the transfer session, however, shows a
smaller, more active dog attached to a leash that Watson uses to drag the dog back and
forth. Albert watches the dog closely and at times appears distressed. But is he distressed
because of a conditioned fear of furry animals, or because he is instead bothered by the
dog’s erratic and unpredictable movements as Watson drags it about?

Figure 4. Albert’s reactions to the rabbit during the baseline session (first still) and during
the transfer session (remaining stills). The top right still shows Rayner holding the rabbit that
she has suddenly pulled out from behind a white blanket and, in the bottom left still, places
on Albert’s lap, at which point which he begins crying. The next two stills show Albert first
shuffling his legs away from the rabbit and then trying to crawl past it. Note the proximity
of Albert’s head to the rabbit which he is supposed to be fearful of. From the film Studies
Upon the Behavior of the Human Infant: Experimental Investigation of Babies, by J. B.
Watson (Writer/Director), 1923. These images are available for use in the public domain.

10 POWELL AND SCHMALTZ

Another possibility is that Albert is indeed showing evidence of a conditioned fear, but
it has nothing to do with the experimental manipulation; rather, he fears the dog because
of its inadvertent association with the sudden barking that had severely frightened him
during the previous session: “Albert immediately fell over and broke into a wail which
continued until the dog was removed” (Watson & Rayner, 1920, p. 9). In fact, the sound
of barking is in some ways much better suited as a conditioning event than the experi-
mental stimulus of a loud bang. Conditioning can be affected not only by temporal
contiguity between stimuli, but also spatial contiguity. For example, a light that is located
beneath the floor grids through which a rat experiences shock is more readily associated
with the shock than a light that is presented overhead (Testa, 1975). In the Little Albert
study, the loud bang that came from behind Albert was not spatially contiguous with the
rat that was being presented from the front. The sound of the dog’s barking, however,
would have come from precisely the same direction as the dog and, from a temporal
perspective, would have coincided precisely with the dog’s facial movements. Conversely,
we do not know if the dog that barked at him during the previous transfer session was the
same as, or even similar to, the dog that appears in the transfer session on film (which
looks very different from the dog that appears in the film clips of the baseline session).6

Albert’s Reactions to a Fur Coat

Stills of Albert’s reactions to the fur coat are shown in Figure 6. There are no film clips
of his reactions to a fur coat during the baseline session, nor do Watson and Rayner (1920)
include a fur coat in the list of stimuli he was shown in that session. During the transfer
session, Albert fixates his gaze on the coat as it approaches and makes a motion to move
away from it when it is placed directly in front of him. He may be doing so, however, not
because of a generalized fear of furry objects, but because the coat is a large, looming
object that many infants at that age would be wary of (e.g., Scarr & Salapatek, 1970).

6
The adult Albert Barger was described by his niece as having a general dislike of dogs (and other animals)

and especially the sound of barking (Digdon et al., 2014; Powell et al., 2014). One might speculate therefore that
his aversion to dogs is a conditioned remnant of the frightening experience he had with the barking dog as the
infant in the Little Albert study. However, his niece also remembers him once saying that his dislike of dogs
stemmed from the distress he felt as a child after seeing his pet dog get run over in the street—which weighs
against the possibility that he had acquired a conditioned aversion to dogs during the experiment.

Figure 5. Albert’s reactions to the dog during the baseline session (first still) and transfer
session (remaining stills). The middle still shows Albert dropping his head forward as though
to begin crawling, but the dog is blocking his path. In the far right still, Albert has a look of
distress on his face as he watches the dog being pulled back and forth by Watson. From the
film Studies Upon the Behavior of the Human Infant: Experimental Investigation of Babies,
by J. B. Watson (Writer/Director), 1923. These images are available for use in the public
domain.

11DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

Albert’s Reactions to a Mask

Stills of the transfer test with the mask are shown in Figure 7. There are no film clips
of his reactions to the masks that he was shown during the baseline session, but Watson
and Rayner (1920) indicated that he was not afraid of them. In the film clips of the transfer
session, however, Watson does not simply show Albert the mask, but instead places it over
his face and crawls toward Albert while wearing it. Albert fixates on the mask and backs
up as Watson draws near. Notably, however, he does not cry and turns to crawl away only
when Watson is almost on top of him. Given the tendency by many infants at that age to
be fearful of masks (e.g., Marks, 1987; Scarr & Salapatek, 1970), one could argue that
Albert’s reaction is actually rather muted in comparison to how many infants at that age
would have reacted.

Overall Assessment of Albert’s Reactions

In general, the film provides no substantive evidence of fear conditioning. Although
Albert does at times appear bothered or distressed, most of his reactions can parsimoni-
ously be explained as normal reactions to novel or sudden events that many infants at that
age would find fearful (e.g., Kagan, 1979; Scarr & Salapatek, 1970). In keeping with
previous criticisms of the study (Harris, 1979; Samelson, 1980), our examination of the
film also reveals a good deal of inconsistency in Albert’s reactions. Particularly notewor-
thy is Albert’s initial lack of attention to the rat during the transfer session. This suggests
not only that any fear of the rat that may have resulted from earlier conditioning trials had

Figure 6. Albert’s reactions to the fur coat during the transfer session. He appears to dislike
the coat and tries to move away from it. In the final still, he is also about to nod his head (or
sneeze). From the film Studies Upon the Behavior of the Human Infant: Experimental
Investigation of Babies, by J. B. Watson (Writer/Director), 1923. These images are available
for use in the public domain.

Figure 7. Albert’s reactions to the mask during the transfer session. He gazes intensely at
the mask and backs away as Watson crawls forward. Notably, however, he does not cry. From
the film Studies Upon the Behavior of the Human Infant: Experimental Investigation of
Babies, by J. B. Watson (Writer/Director), 1923. These images are available for use in the
public domain.

12 POWELL AND SCHMALTZ

by then dissipated, but that Albert may have even begun to habituate to the rat. This is
especially significant given that the rat was the stimulus that had most frequently been
paired with the loud noise and from which Albert’s fear of other stimuli was supposed to
have generalized.

Little Albert as “Propaganda”

If the film evidence for fear conditioning is so weak, the question can be asked as to
whether Watson and Rayner (1920) were being deliberately misleading when maintaining
that fear conditioning had occurred. We do not believe this to be the case. If nothing else,
people who engage in deliberate deception tend not to distribute a film of their fraudulent
activities. A more likely explanation is confirmation bias (e.g., Greenwald, Pratkanis,
Leippe, & Baumgardner, 1986; Kahneman, 2011; Lilienfeld, 2017; Nickerson, 1998), a
pervasive phenomenon to which even researchers are susceptible. Watson and Rayner
(both of whom appear in the film) may have been especially susceptible to confirmation
bias if they recorded their observations following each session, in which case they may
have misremembered Albert’s reactions in ways that would be congruent with their
expectations. Also, although Watson was critical of the notion that children’s fears are
often innate, he was unaware of the possibility of maturational influences on fear
development as a child ages, which was only later proposed by Valentine (1930). Hence,
from Watson’s theoretical perspective, any fear responses that Albert displayed during the
transfer sessions, even if weak and inconsistent, would most likely have been acquired
through conditioning.

That said, confirmation bias is most likely to occur when one is highly motivated to
achieve a particular goal or defend a particular position or hypothesis (Nickerson, 1998).
In fact, Watson would sometimes knowingly exaggerate his research findings if he
perceived that it would advance his research agenda (Buckley, 1989; Samelson, 1980). It
is perhaps from this perspective that the Little Albert film is best understood. When
lobbying the university’s president for funds to purchase film, Watson emphasized the
promotional, or “propaganda,” value of the film—to hopefully entice wealthy individuals
to donate to his research program—as much as its scientific value (Watson, 1919b, 1919c).
Of particular interest to Watson was the possibility of establishing a facility in which
children would live with their mothers in a controlled and closely monitored environment
(these would presumably have been single mothers who provided many of the infants used
in Watson’s research program at Johns Hopkins Hospital; Watson, 1924/1925). For
example, in a letter written to university president, Goodnow, requesting the funds to
purchase film, Watson (1919c) wrote,

As you know, I am extremely anxious to obtain money to establish a small home in which 15 or 20
children can be collected with their mothers, where I can make continuous observations from birth to at
least adolescence. . . . I am constantly being called upon to lecture to rather large groups. Many of the
people interested are influential and wealthy and if they could be properly convinced, they would be
willing to advance money for such investigations.

The strength of Watson’s (1920) commitment to this project is especially evident in a
subsequent letter written to Goodnow:

I shall never be satisfied until I have a laboratory in which I can bring up children from birth to three or
four years of age under constant observation. By propaganda and writings I am bringing this about as
rapidly as I can.

Interestingly, insofar as the Little Albert study was most likely completed by late
March, it is possible that Watson was by this time already using the film clips of Little
Albert in presentations he was making. Mary Cover Jones, who a few years later

13DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

conducted a study in which she used behavioral principles to gradually eliminate a severe
rabbit phobia in a young boy (Jones, 1924),7 claimed that her interest in the topic first
arose when she attended a public lecture by Watson which included motion pictures of
Little Albert. Although she remembers the lecture occurring in spring of 1919 —when she and
a friend had traveled to New York to attend the theater but ended up at Watson’s lecture
instead—it is more likely that she misdated the memory and that Watson’s lecture occurred in
spring or summer of 1920. This is especially the case given the strength of the evidence for
the Little Albert study having been conducted between early December 1919 and late March
1920 (Beck et al., 2009; Powell et al., 2014). If so, this suggests that within a few months of
the study being completed, Watson was already using the film clips of the Little Albert study
to publicize his research and attract funding. Watson’s intense desire to obtain funding may
also have motivated him to deliberately manipulate certain aspects of the film to enhance its
presentability. It would also account for Watson’s dramatic touch of wearing the mask on film,
rather than simply showing it to Albert (which he may have done on an earlier trial), so as to
strengthen the film’s impact on an audience.

In this sense, the Little Albert film was perhaps never intended to be an accurate portrayal
of the experiment, but rather a dramatized representation of what occurred, designed to
maximize its impact and attract the type of funding Watson needed to fulfill his research goals.
As such, it foreshadows the dilemma many researchers face when trying to balance scientific
accuracy and objectivity with the reality of needing to promote their research in a way that
strengthens the likelihood of future funding—an ongoing problem that has likely contributed
to psychology’s current replication crisis (Lilienfeld, 2017).

Conclusion

Watson and Rayner (1920) claimed that a principal reason for choosing Little Albert for
the experiment was his “stolid and unemotional” temperament; they assumed that the
experiment would therefore do him “relatively little harm” (pp. 2–3). Our examination of
the film evidence concurs with that assessment, even more so than Watson and Rayner had
intended. The distress that Albert displays at various points in the film seems well within
the normal range for children of that age and can be readily accounted by factors other
than Watson and Rayner’s conditioning procedure. Thus, rather than being an accurate
demonstration of fear conditioning, the film clips of Albert are better construed as a
promotional device that Watson hoped to use to attract major funding for his research.

If the film record provides little evidence of fear conditioning, why is it so often viewed
as showing the opposite? Viewer expectation likely plays a major role. For example, the
intertitle that precedes the film clips of the transfer session explicitly states that condi-
tioning had “produced the marked fear of the rat next shown.” In addition, many people
who view the film, such as on YouTube (e.g., https://www.youtube.com/watch?v �
9hBfnXACsOI), probably do so after hearing about the experiment and how Watson had
once conditioned a phobia in a young infant. Viewers will therefore be predisposed to seek
out elements that conform to this narrative. In this context, any behavior suggestive of
fear, such as Albert crying when a rabbit is suddenly placed on his lap or backing away
when a fur coat is thrust toward him, will be readily interpreted as evidence of condi-
tioning.8 Add to this a culture that has become increasingly sensitive to issues of child

7
Watson helped supervise this study although he had by that time already resigned from his academic position

at Johns Hopkins University over the controversy surrounding his highly publicized affair with Rayner (Buckley,
1989; Jones, 1924).

8
In fact, the first author himself once believed that the film provides clear evidence of fear conditioning. He

began to see problems with this interpretation, and with the Watson and Rayner (1920) study in general, only
after encountering the criticisms of it by Harris (1979) and Samelson (1980).

14 POWELL AND SCHMALTZ

https://www.youtube.com/watch?v

abuse and trauma, and it would be surprising if viewers of the film did not perceive
stronger evidence of fear conditioning than is actually the case.

One must also consider the possibility that the propensity to view Little Albert as
having been conditioned to fear animals may stem to some extent from an ongoing
perception of behaviorism, with its emphasis on environmental determinism, as
dehumanizing and manipulative (Todd, 1994). It is interesting to note in this regard
that, while YouTube clips of the Little Albert study elicit almost entirely negative
comments from viewers, the surprisingly numerous YouTube videos that show parents
and siblings deliberately scaring young children, sometimes severely so (https://
www.youtube.com/results?search_query�parents�scare�prank�mask), are typi-
cally regarded as humorous. Ironically, Watson himself would almost certainly have
been appalled by such videos. Based on the results of the Little Albert study, he
warned parents to minimize children’s exposure to loud noises, such as the slamming
of a door, which might inadvertently be associated with nearby objects and events
(e.g., Watson, 1924/1925; Watson & Watson, 1928). Similarly, although Watson is
often rightly criticized for recommending that children not be shown too much
affection and should instead be treated like adults (e.g., they should be given a
handshake rather than a hug and a kiss; Watson & Watson, 1928),9 almost no mention
is made of his rather progressive, for that era, recommendation that parents should
minimize the use of physical punishment. He believed, for example, that a simple rap
on the fingers, if done at the right moment (and without anger), should be sufficient
to teach a child not to reach for a forbidden or dangerous object and without producing
any lasting fear or resentment. “There is no excuse for whipping or beating!” he
argued (Watson, 1924, p. 183), asserting that “such things as beating and expiation of
offenses, so common now in our schools and home, in the church, in our criminal law,
in our judicial procedure, are relics of the Dark Ages” (Watson & Watson, 1928, p.
63).

In a review of how John B. Watson has been misrepresented in introductory
psychology textbooks, Todd (1994) noted that “the potential negative impact on
millions of students of possibly inaccurate and biased descriptions of one of psychol-
ogy’s most important figures should not be discounted” (p. 76; see also Todd &
Morris, 1992). He argued that we should not only avoid inaccuracies in what our
students are being taught, but we must also remain cognizant of the extent to which
inaccuracies may damage the way in which psychology is viewed. This can have
serious implications concerning the extent to which psychological findings and
recommendations are given proper consideration. A case in point is the emergence of
applied behavior analysis as a viable intervention for autism and other developmental
disabilities (e.g., Reichow, 2012; Roane, Fisher, & Carr, 2016). Despite considerable
evidence for its efficacy, there remains considerable resistance to its adoption, often
stemming from a false belief that behavioral treatments commonly make use of
aversive procedures (Leaf & McEachin, 2016; Trump et al., 2018). Although the Little
Albert experiment predates the emergence of applied behavior analysis by many
decades, the notion that Watson, the “founder of behaviorism,” once conditioned a
phobia of furry animals in a helpless infant creates a fertile ground for such misper-
ceptions to persist. Instructors and textbook authors should therefore think carefully
about how they characterize Watson and the Little Albert experiment and should resist
the urge to sacrifice accuracy for the sake of a good story (see also Griggs, 2015).

9 In later years, Watson (1936) regretted some of his writings on child-rearing, admitting that
“I did not know enough to write the book I wanted to write” (p. 280).

15DID LITTLE ALBERT ACQUIRE A CONDITIONED FEAR

https://www.youtube.com/results?search_query=parents+scare+prank+mask

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http://dx.doi.org/10.1002/9781444327564.ch19

Received July 19, 2019
Revision received July 20, 2020

Accepted July 28, 2020 �

18 POWELL AND SCHMALTZ

http://dx.doi.org/10.2307/1413718

http://dx.doi.org/10.1037/h0069608

http://dx.doi.org/10.1002/9781444327564.ch19

Did Little Albert Actually Acquire a Conditioned Fear of Furry Animals? What the Film Evidence T …

The Little Albert Study
Criticisms of the Little Albert Study
The Little Albert Film
Two Assumptions and Relevant Evidence
The Analysis of Albert’s Reactions on Film
Albert’s Reactions to a Rat
Albert’s Reactions to Rabbits
Albert’s Reactions to Dogs
Albert’s Reactions to a Fur Coat
Albert’s Reactions to a Mask
Overall Assessment of Albert’s Reactions
Little Albert as “Propaganda”
Conclusion
References

The Rise and Fall of Behaviorism:
The Narrative and the Numbers

Michiel Braat, Jan Engelen, Ties van Gemert, and Sander Verhaegh
Tilburg University

The history of 20th-century American psychology is often depicted as a history of the ris

and fall of behaviorism. Although historians disagree about the theoretical and social factors
that have contributed to the development of experimental psychology, there is widespread
consensus about the growing and (later) declining influence of behaviorism between
approximately 1920 and 1970. Because such wide-scope claims about the development of
American psychology are typically based on small and unrepresentative samples of histor-
ical data, however, the question arises to what extent the received view is justified. This
article aims to answer this question in two ways. First, we use advanced scientometric tools
(e.g., bibliometric mapping, cocitation analysis, and term co-occurrence analysis) to quan-
titatively analyze the metadata of 119,278 articles published in American journals between
1920 and 1970. We reconstruct the development and structure of American psychology
using cocitation and co-occurrence networks and argue that the standard story needs
reappraising. Second, we argue that the question whether behaviorism was the “dominant”
school of American psychology is historically misleading to begin with. Using the results of
our bibliometric analyses, we argue that questions about the development of American
psychology deserve more fine-grained answers

Keywords: behaviorism, American psychology, bibliometric mapping, cocitation
analysis, co-occurrence analysis

The history of 20th-century American psychology is often depicted as a history of the rise and
fall of behaviorism, the view that psychology should become “a purely objective experimental
branch of natural science” (Watson, 1913, p. 248). Although early 20th-century psychologists
aimed to redefine their discipline as a science of behavior, the popularity of behaviorism
declined from the late 1950s onward, when psychologists, linguists, and computer scientists
joined forces and developed empirical approaches to the study of mind and cognition.

This article was published Online First March 19, 2020.
Michiel Braat, Tilburg School of Humanities and Digital Sciences, Tilburg University; Jan Engelen, Depart-

ment of Communication and Cognition, Tilburg School of Humanities and Digital Sciences, Tilburg University;
Ties van Gemert, Tilburg School of Humanities and Digital Sciences, Tilburg University; Sander Verhaegh,
Tilburg Center for Logic, Ethics, and Philosophy of Science, Tilburg School of Humanities and Digital Sciences,
Tilburg Universit

All authors contributed equally to this work. This research is funded by the Tilburg School of Humanities and
Digital Sciences Research Traineeships Programme. In addition, Verhaegh’s research is funded by The
Netherlands Organization for Scientific Research (grant 275–20 – 064). We would like to thank Nees Jan van
Eck, members of the History of Behavior Analysis mailing list (especially Nicole L. Banks and François
Tonneau) and audiences at conferences at the University of Amsterdam, the CEU Institute for Advanced Study
Budapest, and the Tilburg School of Humanities and Digital Sciences for their valuable comments and feedback.

Correspondence concerning this article should be addressed to Jan Engelen or Sander Verhaegh, Tilburg
School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, the
Netherlands. E-mail: j.a.a.engelen@uvt.nl or a.a.verhaegh@uvt.nl

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History of Psychology
© 2020 American Psychological Association 2020, Vol. 23, No. 3,

252

–280
ISSN: 1093-4510 http://dx.doi.org/10.1037/hop0000146

252

mailto:j.a.a.engelen@uvt.nl

mailto:a.a.verhaegh@uvt.nl

http://dx.doi.org/10.1037/hop0000146

Although historians of science disagree about the theoretical and social factors that have
contributed to the development of experimental psychology (Cohen-Cole, 2014; Greenwood,
2009; Leahey, 2001), there is a widespread consensus about the growing and declining
influence of behaviorism in 20th-century American psychology (DeGrandpré & Buskist,
2000; O’Donohue & Kitchener, 1999; Staddon, 1999; Zuriff, 1985).

From a methodological perspective, these claims are contentious, because wide-scope
claims about the development of American psychology are rarely backed up by representative
empirical data. Although nobody will deny that many of today’s best-known behaviorists
produced their most influential work between 1920 and 1960, it is unclear what proportion of
American psychologists embraced a behavioristic conception of psychology both during and
after the heyday of behaviorism. Similar worries can be raised about the historians’ conclu-
sions about developments internal to behaviorism. Although most scholars identify Watson,
Hull, Tolman, and Skinner as the most influential (neo-)behaviorists,1 these claims are seldom
supported by balanced empirical evidence. In general, it is unclear to what extent the list of
psychologists who have been most extensively studied by historians of behaviorism forms a
faithful representation of the scholars that actually influenced the development of behaviorism
in the first half of the 20th century.

The problem that wide-scope claims about the history of American psychology are rarely
backed up by balanced empirical data is especially pressing because it seems likely that
historians overestimate the influence of canonized schools and scholars (whether it is behav-
iorism in the 1930s or cognitive psychology in the 1970s). Not only do historians tend to
ignore psychologists who did not belong to any school (Green, Feinerer, & Burman, 2013); in
studying the histories of influential schools and scholars, historians also tend to limit them-
selves to the writings “of a few known authors that are transmitted from generation to
generation [. . .] as the authors to read” (Betti, van den Berg, Oortwijn, & Treijtel, 2019,
p. 296). More particularly, historical claims about the development of American psychology
are often based on corpora that are (a) not clearly specified (which texts exactly did the
historian study in arriving at their conclusions?), (b) very small (how many of the tens of
thousands of texts produced by psychologists did the historian actually read?), and (c) not
representative (how can the historian guarantee that the texts he or she studied form a
representative sample of the total body of texts produced by psychologists?). Although
in-depth text analyses and archive studies can provide historians with a first indication of the
influence of a psychologist, a method, or a school of psychology, their conclusions will always
be shaped by the texts and archives they choose to study.

In this article, we aim to transcend the received view about the development of 20th-century
American psychology in two ways. First, we use advanced text analysis tools (e.g., biblio-
metric mapping, cocitation analysis, and co-occurrence analysis) to quantitatively analyze the
metadata of 119,278 articles published in American psychology journals between 1920 and
1970. We analyze both the citations and the title terms of these articles and generate cocitation
and co-occurrence networks for every consecutive decade between 1920 and 1970 to recon-
struct the structure and development of mid-20th-century American psychology and the
structure and development of behaviorism. Second, we argue that the question whether
behaviorism was the “dominant” school of American psychology is historically misleading to
begin with. Using the results of our bibliometric analyses, we argue that questions about the
development of American psychology deserve more fine-grained answers.

This article is organized as follows. After a brief outline of what might be called the received
view about the history of behaviorism, we explain the main methodological challenges

1
See, for example, Boakes (1984, p. 237), Smith (1986, pp. 21–22) and Mills (2012, pp. 104 –108). Most

historians paint a picture of the development of behaviorism in which first Watson and later Hull and Tolman
offered the “dominant orientation in American departments of psychology until after the end of the Second War,
when it was displaced by [Skinner’s] radical behaviorism” (Greenwood, 2009, p. 477).

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253RISE AND FALL OF BEHAVIORISM

surrounding quantitative representations of the development of American psychology. Next,
we describe our data and methods and provide an overview of our findings about the structure
and development of American psychology between 1920 and 1970. Finally, we discuss the
implications of our findings and argue that the standard story about the development of
American psychology needs reappraising.

Behaviorism: The Narrative

Behaviorism is a complex of methodological, epistemological, and sometimes ontological
assumptions about the foundations of psychology. Where psychology is traditionally defined
as the study of mental phenomena, behaviorists typically argue that psychology should
become a science of behavior. More specifically, most behaviorists agree that (a) psychology
is or should become a branch of natural science, (b) psychologists should study behavior
instead of mental phenomena, and (c) a science of behavior should be built exclusively on
publicly available evidence (thereby dismissing the use of introspection in psychological
research). Usually, behaviorists combine this view about the nature of psychology with a set
of empirical assumptions—for instance, the assumption that the behavior of an organism is
determined by the organism’s reinforcement history. Outside these shared philosophical and
empirical commitments, behaviorists also strongly disagree about a wide range of issues. Most
importantly, they disagree about the domain for psychology (should a study of behavior
include or exclude physiological variables?), the nature of the observation language (should
behaviorists tolerate intensional descriptions or purposive language?), and the types of theo-
retical concepts allowed in the construction of behaviorist theories (are intervening variables
or hypothetical constructs acceptable?).

Historians often distinguish between two types of behaviorism in psychology: meth-
odological and radical behaviorism (Day, 1983; Mills, 2012; Moore, 1981).2 Method-
ological behaviorists view (a)–(c) as a set of methodological prescriptions; they do not
believe that psychologists should say something about the ontological status of mental
states. According to the methodological view, psychologists should aim to scientifically
describe and explain behavior without referring to mental states, images, or processes.
Radical behaviorists, on the other hand, deny that mental entities exist and argue that
private events should be included in the analysis of behavior—that is, that private events
should be analyzed in terms of the same principles that have been used to study overt
behavior (Day, 1983; Ringen, 1999; Skinner, 1974).

Although behaviorism is generally viewed as a distinctively American psychology,
most historians recognize that its roots can be traced back to the work of the Russian
objective psychologists or reflexologists (Boakes, 1984; Fuchs & Milar, 2003; Hergen-
hahn, 2005).3 Ivan Sechenov, often credited as the founder of this school, worked on the
(excitatory and inhibitory) mediational role of the cerebral cortex in reflex actions and
extrapolated his findings to the concepts of psychology. In Reflexes of the Brain, Sechenov
(1863/1965) defended the view that every mental process is reducible to a physiological
reflex: “only physiology holds the key to the scientific analysis of psychical phenomena”
(p. 351). Ivan Pavlov and Vladimir Bekhterev extended Sechenov’s work by indepen-
dently discovering the principles of classical conditioning. Indeed, Sechenov’s call for an
objective psychology seems to have played an important role in Pavlov’s conclusion that

2
We include the qualification “in psychology” because the present overview excludes the role behaviorist

theories played outside psychology (e.g. philosophy, economics, and sociology). See, for example, Pooley and
Solovey (2010) and Hauser (2015).

3
In addition, historians generally recognize that behaviorism also affected the development of psychology

outside the United States. See, for example, Ardilla (2009).

254 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

his experimental results (dogs salivate in response to food stimuli at a distance) could be
explained without positing “psychic secretions” (Pavlov, 1923/1928, p. 39).

Textbooks generally describe the rise of behaviorism in American psychology as a
distinctively Watsonian revolution (Todd, 1994). Indeed, Watson (1913) coined the term
behaviorism and played a substantial role in shaping the debate about objective psychol-
ogy in his manifesto, “Psychology as the Behaviorist Views It.” Historians, however, offer
a more nuanced view, showing that Watson’s manifesto did not receive a lot of attention
until the early 1920s (Coleman, 1988; Leahey, 2001; Samelson, 1981), when psycholo-
gists like W. S. Hunter, J. R. Kantor, Zing-Yan Kuo, Max Meyer, and A. P. Weiss started
to defend distinctively behaviorist conceptions of psychology (Mills, 2012, p. 101).
Historians also note that Watson was by no means the first to practice objective psychol-
ogy in the American psychological community. Psychologists like E. L. Thorndike—who,
despite discovering the law of effect, never identified as a behaviorist nor was viewed as
such by his peers—William James, Jacques Loeb, and Knight Dunlap also used objective
methods prior to 1913 (Burnham, 1968; O’Donnell, 1985; Todd, 1994).

In the late 1920s and early 1930s, the development of behaviorism entered a new phase.
Influenced by P. W. Bridgman’s call for operational definitions of theoretical concepts as
well as the increasing popularity of logical positivism in the United States (Carroll, 2017;
Walter, 1990; Verhaegh, 2019), behaviorists like Hull, Skinner, and Tolman started to
develop more rigidly formulated variants of behaviorism; even though their deductive
(Hull), inductive (Skinner), and purposive (Tolman) approaches strongly differed from
each other.4 As a result, most historians distinguish between classical behaviorism and
neobehaviorism in their views about the development of mid-20th-century American
psychology (DeGrandpré & Buskist, 2000; Parot, 2001; Smith, 1986).

After the Second World War, the popularity of behaviorism gradually declined. The
waning influence of logical positivism, the pioneering work of G. A. Miller, Allen Newell,
and Herbert Simon, developments in artificial intelligence and linguistics, the invention of
the computer (and thereby the growing popularity of a computational perspective on
cognition), as well as doubts about the scope of the behaviorist approach (Breland &
Breland, 1961; Chomsky, 1959; Lashley, 1951), contributed to what has been called the
“cognitive turn” in psychology. Where behaviorists had viewed psychology as a science
of behavior, the cognitivists developed new methods, approaches, and frameworks to
study “mental” processes in a strictly empirical fashion.

The nature of the cognitive turn has been highly disputed by historians of psychology.
Some view the cognitive turn as a paradigmatically Kuhnian revolution (Baars, 1986;
Gardner, 1985); others have suggested that that there is much continuity between cogni-
tive psychology and methodological behaviorism (Leahey, 2001; Mandler, 2002), or even
that the very notion of a “cognitive turn” should be dismissed as an “origin myth”,
invented in the early 1970s by cognitive psychologists (Dember, 1974; Joynson, 1970) to
foster a shared identity (Hobbs & Chiesa, 2011). Still, historians generally agree that fewer
and fewer psychologists identified as behaviorists from the late 1950s onward.

Behaviorism: The Numbers

In this article, we use bibliometric tools to study the development of American
psychology between 1920 and 1970. Bibliometrics is a field of study that is characterized
by the use of statistics for analyses of written publications such as articles or books. It
has proven difficult to pin down exactly when and where the practice of bibliometric

4
It should be noted that although Skinner’s early work was heavily influenced by operationism and logical

positivism, he started to dismiss these views about the nature of science from the mid-1940s onward. See Skinner
(1945a, 1945b), Allen (1980), and Verhaegh (2019).

255RISE AND FALL OF BEHAVIORISM

analysis began (Broadus, 1987; Hood & Wilson, 2001) but historians often cite Eugene
Garfield’s development of the Science Citation Index in 1955 as the birth of modern
bibliometrics (Garfield, 1963). As the available technologies improved during the 1960s
and 1970s, bibliometrics developed from a limited field of study to a practice that uses a
wide variety of data to accomplish a wide variety of goals (Liao, Zhang, Wang, & Wang,
2016; Martínez-Gómez, 2015). Nowadays, there are bibliometric analyses of disciplines
as diverse as history, information science, and pharmacotherapy (Buchanan & Hérubel,
1997; Lee, 2008; Thompson & Walker, 2015).

One of the first bibliometric analyses that aimed to track the development of psycho-
logical schools was published in the late 1990s. Robins, Gosling, and Craik (1999)
investigated the development of psychoanalysis, behaviorism, cognitive psychology, and
neuroscience in the second half of the 20th century to answer the question “[w]hich, if
any, of the schools currently competing for intellectual influence and institutional power
is most prominent” (p. 117, our emphasis). Their assessment of “prominence” consisted
of three indexes. First, the authors selected four journals (i.e., American Psychologist,
Annual Review of Psychology, Psychological Bulletin, and Psychological Review) which
they considered to be representative of the entire field of psychology and assessed its
contents using the PsycINFO database. Second, they examined the subject matter of
dissertations published between 1967 and 1994. Finally, the authors selected four of the
top journals of each school (e.g., Journal of Experimental Analysis of Behavior for
behaviorism) and developed a prominence index by “computing the total number of times
per year the flagship publications cited articles published in each subdisciplinary field”
(Robins et al., 1999, p. 119). Their results suggested that (a) there is little interaction
between psychoanalysis and the rest of psychology, (b) the decreasing popularity of
behaviorism gave rise to cognitive psychology, and (c) cognitive psychology was still the
most prominent school in the 1990s.

The main methodological challenge in studying the development of a discipline is to
develop a reliable way to determine which authors and publications belong to which
school. For most schools and approaches, it is extremely difficult to come up with a
satisfying definition. Most definitions of behaviorism, for example, will be either (a) so
strong that they exclude a whole range of authors that are typically perceived as
representatives of the school or (b) so weak that they fail to exclude authors and
publications from rival schools. This problem seems to be especially pressing for the
classification of behaviorist psychologists and publications because historians have even
debated the question to what extent cognitive psychology should be viewed as a variant
of methodological behaviorism.

Robins et al. (1999) tried to circumvent such classification problems by using keywords. In
determining the number of behaviorist publications, for example, they used the keywords
reinforc# and conditioning to represent behaviorism. They searched for these keywords in the
article titles, abstracts, subject indices, and the keyword phrases listed by the authors.5 This
keyword approach has proven influential. In recent years, many bibliometric studies in the
historiography of psychology collected their data using keyword searches and/or similar
measurement techniques (e.g., Burman, Green, & Shanker, 2015; Guilera, Barrios, & Gómez-
Benito, 2013; Liu & Oakland, 2016; Schui & Krampen, 2010).6

In the present study, however, we develop a different approach. Although we will study the
occurrence of terms like reinforc# and conditioning in article titles (more on this below), we

5
For psychoanalysis, cognitive psychology and neuroscience, they used the keywords psychoanal#, cognit#,

and neurosci# or neuropsy#, respectively. They did not use the keywords behav# or behavior# because they
deemed these words too generic.

6
There has been a number of articles that looked at specific schools of psychology (Bala & Gupta, 2010; Liu

& Oakland, 2016; Schui & Krampen, 2010), but so far there has been no bibliometric study that focuses on the
rise and fall of behaviorism.

256 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

refrain from using keyword analyses to study the development of American psychology
between 1920 and 1970. Our reasons are threefold. First, many articles published between
1920 and 1970 do not have abstracts or author keywords. Our data contain titles and citation
data for each of the 119,278 articles published in this period but this is too limited information
to solely rely on a keyword analysis. After all, it is implausible that most or even a majority
of the behaviorist articles will use terms like reinforc# and conditioning in their titles.7 Second,
we believe that keyword analyses should be generally used with caution because it is unclear
to what extent they measure what they ought to measure. It is unclear, for example, what
proportion of articles containing the term cognit# will actually belong to the cognitivist school
and, conversely, what proportion of cognitive psychology articles will contain the term
cognit#. Indeed, Robins et al. (1999) themselves admit that “keyword analyses do not detect
all of the articles related to each school” and that they do not expect their “keywords to capture
the full range of articles published in the flagship publications” (p. 119n1). Third, we refrain
from using keywords to classify authors and publications because most demarcation criteria
based on keywords are too rigid. Not only do keyword classifications ignore conceptual
changes—for example, the fact that the meaning of concepts like reinforce# and conditioning
changed considerable because of especially Skinner’s theoretical and conceptual innovations
from the mid1930s—most keyword analyses also do not allow for the possibility that (a)
whether or not an author or a publication should be classified as behavioristic is a matter of
degree, and (b) “behaviorism” itself is a tremendously complex, multidimensional concept,
such that it is well possible that authors or publications can be behavioristic in one sense but
not in another.

Rather than employing unnecessarily inflexible, external standards to determine whether or
not a particular author qualifies as a behaviorist, the present study will study the structure and
development of American psychology by mapping the similarities between authors and
concepts. In studying cocitation networks, for example, we do not use a strict demarcation
criterion to determine whether or not a psychologist like J. R. Kantor, who developed an
“interbehaviorist” approach in the early 1920s, should be classified as a “real” behaviorist.
Rather, we use citation data to study the way in which Kantor’s work was perceived to be
similar to the work of other psychologists by mapping the cocitations between Kantor and
every author cited in American psychology journals between 1920 and 1970.8 Moreover, in
studying cocitation networks, we also allow for conceptual changes over time by mapping the
way in which historical actors perceived authors to be similar. It is possible, for example, that
E. G. Boring’s position was perceived to be antithetical to Watson’s approach in the 1910s and
1920s but that the general perception changed when the former started developing operationist
approaches to psychology from the mid-1930s onward. If this is the case, it will be reflected
by the fact that Boring occupies a different position in a network mapping cocitations from the
1920s than in a network mapping cocitations from the 1940s. Cocitation networks allow us to
map these perceived similarities without us having to decide whether or not Boring from the
1930s onward qualified as a (methodological) behaviorist.

In relying on cocitation networks, in sum, we deliberately do not rely on an external,
theoretical definition of behaviorism; nor do we presuppose that whether or not a certain
author or article qualifies as behavioristic cannot be subject to change. Rather, we rely on

7
In addition, the keywords provided by the indexing databases (e.g. PsycINFO; Web of Science) themselves

are notoriously unreliable, especially when used to map developments before the 1960s. Burman (2018), for
example, shows that throughout the development of the PsycINFO database, the meaning, content, and function
of the “metadata” (e.g. keywords) has changed drastically, resulting in different classifications of articles over
different time periods. In the 1960s, for example, the American Psychological Association changed its manner
of archiving and reporting index terms for both new and old articles to such an extent that all data before the
1960s “simply cannot be used to track trends” (p. 313).

8
Co-citation is the frequency with which two documents are cited together. To be strongly co-cited, a lar

number of scholars must cite the two works together. As such, in measuring co-citation strength, one measures
the degree of association as perceived by the population of citing authors (Small, 1976).

257RISE AND FALL OF BEHAVIORISM

a sociological characterization of behaviorism and assess which scholars were considered
to be doing similar work by the psychological community at various points in time. The
more often two authors are cited together in a particular decade, the closer to each other
they will be situated in the cocitation network for that particular decade.

Apart from an analysis on the basis of author cocitation networks, we also employ term
co-occurrence networks to describe the structure and development of American psychol-
ogy. Term co-occurrence networks map the similarities between concepts without pre-
supposing a strict criterion to determine whether or not concepts should be classified as
behavioristic. A recent article by Flis and van Eck (2018) describes a similar analysis of
20th-century psychology on the basis of 673,393 articles taken from 1,269 (psychology)
journals focusing on the time period 1950 to 1999 to test Cronbach’s (1957, 1975)
distinction between correlational and experimental psychology and to see whether there
were significant changes in both content and amount of output. The authors generated six
term maps and identified different clusters of terms representing different fields within
psychology. The six term maps showed significant changes in both content and amount of
input throughout the time period. In our analysis, we will build upon Flis and Van Eck’s
work and apply their method to our dataset. This second analysis will provide us with an
additional method to assess the structure and development of American psychology.
Because our co-occurrence analyses do not depend on citation data, it can be used as an
independent check on the validity of our results in the first analysis. Furthermore, we show
that some of the differences between the author and the term maps give rise to some
interesting hypotheses about the development of 20th-century American psychology.

Method

Data

The publication data were retrieved from Web of Science (WoS). The search was done
using the Science Citation Index Expanded (SCIE) and the Social Science Citation Index
(SSCI) of WoS, the only citation indices that include publications during the time span of
interest. An advanced search query using the term wc�(psychology) OR su�(psychology)
(where “wc” is the WoS category tag and “su” is the research area tag) was run with the
three additional requirements that the results should be (a) articles, (b) published between
1920 and 1970, and (c) written in English. This query resulted in 119,429 articles from
246 different journals. These were downloaded with full record and cited references.

Next, the dataset was cleaned by removing all duplicate articles based on their
following attributes: author, title, publication name, cited references, and International
Standard Serial Number. This led to a total of 119,278 articles in the dataset. Figure 1
shows the 25 journals with the most articles in the dataset, which together contribute 52%
of all data. The titles suggest that the major journals in the dataset were virtually all
psychology journals. Because the data were not equally distributed over all the decades,
the data were analyzed per decade. This ensures that the results are not skewed to the later
decades, for which there are many more publications and citations. The number publica-
tions in the dataset per decade is shown in Figure 2. This figure shows that, except for
1941–1950, the number of publications grows exponentially every decade.9

Coverage of the dataset. Concerns have been raised about the suitability of WoS for
historical research. Its coverage of some areas of psychology has been characterized as
uneven, containing fewer journals in the areas of organizational and clinical psychology

9
Note that the period 1920 –1930 consists of 11 years. This is because the overall dataset actually spans 71

years, not 70. We chose to append this extra year to the earliest period to somewhat offset the sparsity of
publications in that decade.

258 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

than PsycINFO, while having more extensive coverage of neuroscience, methodology,
and statistics journals (García-Pérez, 2010). Approximately 90% of all documents in WoS
originates from English-speaking countries (Orduña-Malea & López-Cózar, 2014). While
this may reflect a geographic or linguistic bias, this is not particularly troubling for our
purposes, as we focus on developments within American psychology. Furthermore, 90%
of all documents in WoS are journal articles, which means that coverage of research
monographs and conference communications is probably limited (López-Cózar, Orduna-

Figure 2. Number of publications in the dataset for each decade during 1920 –1970.

Figure 1. Number of articles per journal for the 25 journals with the most publications in
the dataset.

259RISE AND FALL OF BEHAVIORISM

Malea, & Martín-Martín, 2019). This does not preclude citations of research monographs
in journal articles from the database, but it might result in underrepresentation of fields
where books are a vital aspect of the publication culture (García-Pérez, 2010).

To assess the suitability of the SCIE and SSCI indexes in WoS for our research,
we used a list of 15 journals that were regularly used by psychologists in the 1920s
and 1930s.10 WoS nominally contained full records for 13 of these journals for the
entire period of 1920 –1970 (or from a later starting date if the journal was founded
after 1920); two journals were included for a subset of that period.11 By comparison,
PsycINFO contained records for nine of the 15 journals for the entire period of
1920 –1970; two journals were not included and four journals were included for a
subset of that period.12

Our evaluation of SCIE and SSCI against our own list of sources suggests that
WoS’s coverage of major psychology journals is strong. Still, it is possible that
coverage for other journals is less comprehensive, and that coverage at lower levels
is spotty (e.g., missing issues for included journals, missing articles for included
issues, and missing cited references for included articles). To assess in more detail
whether the dataset provides full and accurate coverage of the literature from 1920 to
1970, we selected two journals for each decade. The sampling procedure for the
journals was as follows. First, for each decade, we ranked the journals in our dataset
according to the ratio of cited references to published articles. The first journal was
randomly sampled from the bottom 5% of this ranking—thus, the set of journals
containing fewer cited references than would be expected based on the average
citation practice for that decade. The rationale was that WoS would be more likely
to contain incomplete records (e.g., missing or incorrect entries in the cited references
column) for these journals. The second journal was randomly sampled from the
remaining 95% of journals for that decade.

For each of the 10 selected journals, we checked the issues included in WoS for that
decade against the issues as listed in the printed journal itself, or, if that was not available,
against the table of contents on the publisher’s website. Next, we randomly sampled one
issue from each of these journals and checked the articles included in WoS against the
articles as listed on the publisher’s website. Finally, for five randomly selected articles
from the issue, we checked the cited references in WoS against the cited references in the
full text. The results of this procedure are displayed in Table 1.

For the journals with a low cited references-to-articles ratio, coverage in WoS varied
somewhat between decades. For the journal selected for 1920 –1930, one issue was not
included, and two out of 20 articles were missing for the selected issue, without any
apparent reason (i.e., the missing issue and articles appeared to be regular research

10
These journals were: Acta Psychologica, American Journal of Psychology, American Psychologist, Con-

temporary Psychology, Journal of Comparative Psychology (and its successor Journal of Comparative and
Physiological Psychology), Journal of Experimental Psychology (and its successors JEP: General, JEP: Human
Perception and Performance, and JEP: Learning, Memory, and Cognition), Journal of General Psychology,
Journal of the Experimental Analysis of Behavior, Proceedings of the American Academy of Arts and Sciences,
Proceedings of the National Academy of Sciences, Psychological Bulletin, Psychological Review, Quarterly
Journal of Experimental Psychology, Science, and The Psychological Record.

11
The two journals that were not completely included are Proceedings of the American Academy of Arts and

Sciences and The Psychological Record.
12

The two journals that were not included are Contemporary Psychology and Proceedings of the American
Academy of Arts and Sciences; the four journals that were not completely included are Acta Psychologica,
Proceedings of the National Academy of Sciences, Quarterly Journal of Experimental Psychology, and Science.

260 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

T
ab

le
1

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o
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ra
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o
f

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eb

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ot

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19

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1
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(6

6/
67

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19

24
–5

,
2

M
is

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ng

(1
8/

20
)

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om

pl
et

19
31

–1
94

0
T

h
e

H
u
m

a
n

F
a
ct

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r
C
om
pl
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(5

5/
55

)a
19

32
–8

,
C

om
pl

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(5
/5

)
C

om
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19
41

–1
95

0
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cc
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p
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o
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Jo
u
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C
om
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(8

1/
80

)b
19

43
–6

,
C
om
pl
et
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(1
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10
)

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19

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)
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61

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8)

c
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20
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(3
5/

35
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19

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19

32
to

19
37

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is

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re
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rr
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em

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(I

ss
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3
in

19
32

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d

19
33

,
Is

su
e

12
in

19
34

–1
93

6)
.

T
he

se
an

nu
al
re
po

rt
s

ar
e

no
t

in
cl

ud
ed

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W

eb
of

S
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ce

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b

W
eb

of
S

ci
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ce
ha

s
re

co
rd

s
fo

r
Is

su
e

21
(8

),
w

hi
ch

w
as

ab
se

nt
fr

om
th

e
pu

bl
is

he
r’

s
w

eb
si

te
.

c
T

he
co

nt
ri

bu
ti

on
s

no
t

pr
es

en
t
in
W
eb
of
S
ci
en
ce

al
l

se
em

to
be

fr
om

a
ca

te
go

ry
ot

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r

th
an

“O
ri

gi
na

l
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rt
ic

le
,”

fo
r

ex
am

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e,

co
m

m
en

ta
ri

es
(s

om
et

im
es

le
ss

th
an

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co

lu
m

n)
,

bo
ok

re
vi

ew
s,

an
d

so
on

.
T
hi
s
do
cu
m
en
t
is
co
py
ri
gh
te
d
by
th
e
A
m
er
ic
an
P
sy
ch
ol
og
ic
al
A
ss
oc
ia
ti
on
or
on
e
of
it
s
al
li
ed
pu
bl
is
he
rs
.
T
hi
s
ar
ti
cl
e
is
in
te
nd
ed
so
le
ly
fo
r
th
e
pe
rs
on
al
us
e
of
th
e
in
di
vi
du
al
us
er
an
d
is
no
t
to
be
di
ss
em
in
at
ed
br
oa
dl
y.

261RISE AND FALL OF BEHAVIORISM

publications). For the journal selected for 1951–1960, only four issues out of many were
available, apparently because the journal is only indexed from 1960 onward. For all other
decades, WoS contained all issues. A number of articles for the selected issue were
missing for 1961–1970. It should be noted, however, that these missing articles were not
standard research reports, but rather a variety of very short (sometimes less than half a
page) contributions. Finally, the cited references in WoS were complete for each article
for each journal. This means that the low ratio of cited references to articles for these
journals was not an artifact, but that these articles actually contained very few cited
references. For the journals sampled from the remaining 95% WoS included all issues, all
articles, and all cited references. Thus, although WoS does not have perfect coverage for
some journals, its overall coverage, especially for major psychology journals, was rather
impressive and at least satisfactory for our purposes.

Citation data. From our data sets, the 200 most-cited authors per decade were
extracted. These were needed for the generation of cocitation networks. The extraction of
the top 200 most-cited authors per decade was done using VOSviewer (Version 1.6.7, van
Eck & Waltman, 2010), which extracts and counts the citations for each author.13 It is
important to note that the cited articles do not have to be from the same decade as the
publication data. Although William James died in 1910, for example, he is still one of the
top 200 most-cited authors in the 1940s. We extracted a number of authors as close to 200
as possible (extracting more than 200 in case authors were tied for the rank of 200). Table
2 shows the number of authors per decade and the minimum required number of citations.
This table shows that the minimum number of citations for an author to belong to the top
200 increases exponentially over the decades.

Author

Cocitation Networks

For each decade we created an author cocitation network using the program
VOSviewer. The association strength (Van Eck & Waltman, 2009) between authors was
used as the measure of similarity. For a given pair of authors, the association strength is
proportional to the ratio between (a) the observed number of cocitations and (b) the
expected number of cocitations if they would be statistically independent. Thus, authors
with a large number of citations require a large number of cocitations to have a high
association strength.

To map the association strengths for all pairs of authors onto a two-dimensional space,
the VOSviewer software uses the VOS (visualization of similarities) method, which is
similar to the more widely known technique of multidimensional scaling. In addition,
VOSviewer uses this method to cluster the authors. In this case, a cluster is a group of
authors with high mutual association strengths. The default clustering resolution of 1.0
was used in every network.14 For a more technical explanation of the clustering technique,
see Waltman, Van Eck, and Noyons (2010).

13
Some authors appear in the dataset under different names. These different names were normalized as much

as possible, but in some cases it was not possible to determine which author a name refers to. For example, the
dataset includes the authors “D. G. Brown,” “J. S. Brown” and “Brown.” It is then not possible to assign the
citations of “Brown” to either D. G. or J. S. Brown. In this case, “Brown” was retained as a separate author.

14
While 1.0 is the default value, this does not in any way represent a “neutral” value. The optimal clustering

resolution may be different for each dataset. A common recommendation is to explore different values for each
map and choose the one that yields the most satisfactory result. We believe this approach entails a risk of
confirmation bias, however, because the result that is deemed most satisfactory is likely to be the result that
matches the researcher’s preconceptions. Still, we did explore different resolution values, but only to see whether
the value of 1.0 represents a stable solution. We did not see appreciable differences for different parameter values
(ranging from 0.8 to 1.2), unless reported otherwise.

262 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

Term Co-Occurrence Networks

To create the term co-occurrence networks, we used the data from all 119,278
publications. Using the VOSviewer software, the titles15 were extracted from the
publication data. The software then identified and extracted the longest possible noun
phrases (or terms) from the titles. Occurrences and co-occurrences of the terms were
counted using a binary counting method, such that multiple occurrences of a term in
a given title were counted as one occurrence. From the list of terms for each decade,
the 100 most frequent terms were selected.16 We used a fixed number of terms to keep
the networks comparable across decades. For the 100 most frequent terms per decade,
association strengths were computed in the same way as for the authors. To create the
term networks, VOSviewer uses the same mapping and clustering algorithm as for the
author cocitation networks.

Results

Cocitation Networks

Cocitation networks per decade. Table 2 provides an overview of the number of
clusters generated by VOSviewer for each decade and the number of authors that are
placed within these clusters. The clusters displayed in boldface are the clusters in which
authors who are typically classified as behaviorists were most strongly represented. For
each decade, we will describe the cocitation network in relation to the information
presented in Table 3.

1920 –1930. For the decade 1920 –1930, VOSviewer generated five clusters, shown
in Figure 3. Cluster 3 (blue) contains most authors who are typically viewed as behav-
iorists by historians (e.g., the above-mentioned Watson, Tolman, Hunter, Kuo, and Weiss;

15
We relied on titles because full texts or even abstracts were available only for a subset of the articles. This

requires that titles be representative of the contents of the articles for our conclusions to be valid. Given the
strongly descriptive and matter-of-fact nature of many of the titles in the dataset, we believe the data go a long
way toward meeting this assumption. Moreover, sporadic instances of titles containing terms that have little
bearing on the contents of the article, or of titles lacking essential information regarding the object or method
of study, only have a minor influence on the overall picture, because each network is based on thousands of titles.
That said, an analysis based on full texts would most likely yield even richer, more nuanced conclusions.

16
VOSviewer also computes relevance scores for terms, such that frequent but uninteresting terms (e.g. study)

may be filtered out. We refrained from using relevance scores as this would intermittently leave out terms like
behavior, thereby possibly distorting the results by removing terms associated with behaviorism. The only term
we excluded from the networks (1931–1940; 1941–1950) was iii, which was erroneously detected as a noun
phrase.

Table 2
Number of Authors Included in the Dataset and Their Publication Threshold per

Decade

Decade Number of authors Minimum number of citations

1920–1930 201 24
1931–1940 200 63
1941–1950 202 74
1951–1960 202 150
1961–1970 200 342

263RISE AND FALL OF BEHAVIORISM

but also H. C Warren, W. T. Heron, and H. Cason).17 The cluster also contains quite a few
biologists and physiologists whose work was often appealed to by behaviorists (e.g., C. P.
Stone, G. E. Coghill, W. T. Preyer, and W. J. Crozier). The most-cited authors in

Cluster

3 are Watson (234 citations) and Hunter (178 citations).

If we interpret Cluster 3 as the “behaviorist cluster,” then behaviorist authors make up
19% of the top 201 most-cited authors of 1920 –1930, and also account for 19% of their
citations. As such it is the third largest cluster. Cluster 1 is substantially larger, accounting
for 32% of the most-cited authors and 33% of the citations between 1920 and 1930.
Cluster 1 does not contain any author who is typically classified as a behaviorist18 and
contains mostly psychologists who developed the field of mental testing and the closely
related field of educational psychology (e.g., L. L. Thurstone, L. M. Terman, A. Binet, and
C. E. Spearman, and G. M. Whipple). The most-cited authors of Cluster 1 are Thorndike
(331 citations) and Terman (186 citations).19

Cluster 2 is also substantially larger than the “behaviorist cluster” (25% of the
most-cited authors and 26% of the citations) and contains mostly psychologists connected
to Gestalt psychology (e.g., K. Koffka, M. Wertheimer, and W. Köhler) and the struc-
turalist school (e.g., E. B. Titchener and E. G. Boring). The most-cited authors of Cluster
2 are Titchener (372 citations), C. E. Seashore (141 citations), and Boring (131 citations).
The most-cited authors overall are Titchener and Freud (359 citations). Freud’s position

17
Not all names mentioned are visible in Figure 3, which only displays the most-cited names.

18
The only exception is Clark Hull, who was mostly working on aptitude testing in the 1920s. It was only

in the late 1920s that Hull started to develop a neobehavioristic explanation of higher mental processes.
19

Despite the fact that he discovered the law of effect, Thorndike, as mentioned above, never identified as a
behaviorist nor was viewed as such by his peers. Our analysis of the citation data underlying the co-citation
network in Figure 3 confirms this view as it shows that especially Thorndike’s (1921) Teacher’s Word Book was
frequently cited.

Table 3
The Number of Citations and Authors for Each Cluster per Decade

Decade
Cluster

1 (Red) 2 (Green) 3 (Blue) 4 (Yellow) 5 (Purple) 6 (Aqua)

1920–1930
Citations 3,994 3,149 2,280 2,156 640
Authors 64 51 38 36 12

1931–1940
Citations 10,373 7,562 5,363 3,000 1,931
Authors 75 63 34 16 12

1941–1950
Citations 10,073 9,157 5,892 4,109 2,385 835
Authors 67 56 33 24 15 7

1951–1960
Citations 28,915 18,696 14,807 7,848
Authors 86 55 35 26

1961–1970
Citations 44,779 35,549 28,388 15,656 6,905
Authors 69 55 46 17 13

Note. Boldface indicates the clusters in which authors who are typically classified as behaviorists were most
strongly represented.

264 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

in the cocitation network shows that there was little interaction between psychoanalysis
and the rest of psychology (thereby contextualizing Robins et al.’s (1999) conclusion
about the development of psychoanalysis after the Second World War).

1931–1940. For the decade 1931–1940, VOSviewer generated five clusters, shown in
Figure 4. Cluster 3 (blue) contained the largest number of authors who are typically
classified as behaviorists (e.g., Tolman, Hunter, Watson, Kuo, Spence, and Weiss), yet
Cluster 4 (yellow) also contains a substantial share of authors who are typically viewed as
behaviorists (e.g., Hull, Pavlov, Skinner, Razran, and Guthrie).20 If we view these clusters
together as a “behaviorist supercluster,” then behaviorist authors make up 25% of the top
200 cited authors of 1931–1940, and account for 30% of the citations. The most-cited
authors included in Clusters 3 and 4 are Hull (593 citations), K. S. Lashley (583 citations),
and Tolman (419 citations).

It is tempting to attribute the appearance of two “behaviorist clusters”21 to the
contemporaneous emergence of “neobehaviorism,” a movement generally associated with
Skinner, Hull, and Tolman, and characterized by a stronger focus on formalizing the laws
of behavior. Such an interpretation is not without problems, however; whereas Hull and
Skinner are in the same cluster, Tolman is not. Still, it might be possible that the two
clusters track a distinction between older and younger writers. Tolman (despite the fact
that he would later be classified as a neobehaviorist), Watson, Spence, Kuo, and Muen-

20
Pavlov’s more prominent presence in this network compared to that of 1920 –1930, is because of the

publication, in 1927, of the English translation of Conditioned Reflexes: An Investigation of the Physiological
Activity of the Cerebral Cortex (Pavlov, 1927).

21
To ensure this is not an artefact of the clustering resolution parameter value of 1.0, we tried coarser

parameter values, until the number of clusters was reduced to four. Even in this map, the two “behaviorist
clusters” remained separate (while the green and yellow clusters from Figure 4 had merged). Thus, these two
subclusters most likely capture a real structure in the underlying data.

Figure 3. Cocitation network of the 201 most cited authors from the period 1920 –1930. See
the online article for the color version of this figure.

265RISE AND FALL OF BEHAVIORISM

zinger all started publishing well before 1930 and/or had a background in comparative
psychology, whereas the behaviorists in the yellow cluster started publishing later (Skin-
ner, Razran), were translated into English later (Pavlov), or were doing little experimental
work with animals in the 1920s (Hull, Guthrie).22

All the same, even combined, this “behaviorist supercluster” does not constitute the largest
cluster within psychology. Both Cluster 1 (red) and Cluster 2 (green) individually are larger
than the two “behaviorist clusters” combined. The authors in Cluster 1 account for 37% of the
citations and the cluster contains mostly psychologists connected to the mental testing
literature (Thurstone, Thorndike, and Spearman are the most-cited authors). Cluster 2 contains
32% of the most-cited authors and consists mostly of authors connected to Gestalt psychology
and the structuralist school (the most-cited authors of the cluster are Köhler, L. E. Travis, and
Koffka). Overall, Freud (868 citations) is the most-cited author in the 1930s.

1941–1950. For the decade 1941–1950, VOSviewer generated six clusters, shown in
Figure 5. Virtually all authors who are typically classified as behaviorists (e.g., Hull,
Tolman, Spence, Pavlov, Skinner, Hunter, Guthrie, Razran, Watson) are located in Cluster
2 (green). The most-cited members of this cluster are Hull (829 citations), Thorndike (400
citations), and Tolman (368 citations). If we interpret this as the “behaviorist” cluster, then
behaviorist authors make up 28% of the top 202 most-cited authors of 1931–1940 and
account for 28% of their citations. As such, this cluster is only slightly smaller than
Cluster 1, which contains 33% of the most-cited authors and 31% of the citations. The

22
We thank François Tonneau (personal correspondence) for this suggestion.

Figure 4. Cocitation network of the 200 most cited authors from the period 1931–1940. See
the online article for the color version of this figure.

266 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

most-cited authors in this cluster are Thurstone (696 citations), J. P. Guilford (546
citations), and C. Burt (425 citations), all connected to the study and testing of human
intelligence. Overall, Freud (1,612 citations) is, again, the most-cited author.

To nuance the suggestion that Cluster 2 can be identified as a “behaviorist cluster,” we
should mention that the cluster also contains a large share of authors who are not typically
classified as behaviorists. The cluster contains several Gestalt psychologists (e.g., Köhler
and Koffka) and functionalists (e.g., R. S. Woodworth and W. James).23 A possible
explanation for this composition is that the cluster does not so much represent a coherent
behaviorist movement as a collection of authors who published on a common topic. This
means that the cluster may not only contain proponents of behaviorism, but opponents too.
Publications that are critical about behavioristic assumptions or findings by behaviorist
authors may well end up being cocited with the publications they discuss. Conversely,
some authors may be included in this cluster because they were critically cited in
behavioristic publications.

1951–1960. For the decade 1951–1960, VOSviewer generated four clusters, shown
in Figure 6. Again, virtually all authors who are typically classified as behaviorists (e.g.,
Hull, Spence, Skinner, Tolman, Estes, Pavlov, and Guthrie) are located in Cluster 2
(green). The most-cited authors of Cluster 2 are Hull (1,538 citations) and Spence (1,077
citations). Somewhat surprisingly, Skinner (600 citations) is not even represented in the
top-5 most-cited authors of Cluster 2, despite the received view that Skinner came to
dominate behaviorist research after the Second World War (see footnote 2). If we interpret
Cluster 2 as the “behaviorist” cluster, then behaviorist authors make up 27% of the top 202

23
The cluster also contains operationist psychologists like E. G. Boring and S. S. Stevens, but this only

confirms the above-mentioned hypothesis that operationism was generally viewed as a variant of (methodolog-
ical) behaviorism.

Figure 5. Cocitation network of the 202 most cited authors from the period 1941–1950. See
the online article for the color version of this figure.

267RISE AND FALL OF BEHAVIORISM

most-cited authors and account for 27% of their citations. Cluster 1 is substantially larger,
accounting for 43% of the most-cited authors and 41% of the citations between 1951 and
1960. Cluster 1 does not contain any author who is typically classified as a behaviorist.
The most-cited authors of Cluster 1 are R. B. Cattell (1,264 citations), Guilford (1,191
citations), and H. J. Eysenck (1,167 citations).

The increasing gap between Clusters 1 and 2, compared to the cocitation network for the
period 1941–1950, is explained by the fact that the Gestalt psychologists, the functionalists,
and the operationist psychologists (Koffka, Köhler, Wertheimer, James, Woodworth, Boring,
and Stevens) are now located in a separate cluster (Cluster 4, yellow). In general, the structure
of the map shows a greater dispersion between clusters in comparison to the first two decades.
Most of the “behaviorist” cluster is located at the bottom left corner of the map, more remote
from other clusters than in 1941–1950. The density of connections within this cluster and the
relative sparsity of connections to other clusters suggest that behaviorism, although sustaining
a substantial number of citations and authors, started occupying a slightly more peripheral
position within American psychology.

1961–1970. For the decade 1961–1970, VOSviewer generated five clusters, shown in
Figure 7. Again, virtually all authors typically classified as behaviorists are located in
Cluster 2 (green). The most-cited author is Spence (1,603 citations); Skinner is still not
included in the top five of most-cited authors in his cluster. If we interpret Cluster 2 as the
behaviorist cluster, it remains the second-largest cluster with 28% of the most-cited
authors and 27% of the citations. Cluster 1 is substantially larger, with 35% of the
most-cited authors. Its most-cited authors are Eysenck (2,348 citations), Cattell (2,125
citations), and A. L Edwards (2,048 citations). Freud (6,290 citations) remains the
most-cited author overall.

Notably, Cluster 3 (blue) represents the emerging field of cognitive psychology,
including authors such as G. A. Miller, N. Chomsky, G. H. Bower, and B. J. Underwood.
Although this cluster still appears to be slightly smaller than the “behaviorist” cluster

Figure 6. Cocitation network of the 202 most cited authors from the period 1951–1960. See
the online article for the color version of this figure.

268 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

(23% vs. 28% of the most-cited authors; 22% vs. 27% of the total citations), it should be
noted that Cluster 2 also contains a few cognitive psychologists and developmental
psychologists (e.g., J. S. Bruner and J. Piaget) who are not typically classified as
behaviorists.

Compared to the previous two decades, the “behaviorist cluster” occupies a more
central position on the map. This may partly be explained by the relation between
behaviorism and cognitive psychology: in both subfields, the same phenomena (e.g.,
memory, learning) are studied, albeit from a different set of epistemological and meth-
odological assumptions. One of the catalysts of the “cognitive turn” was a perceived
inadequacy of behaviorism to satisfactorily explain these phenomena. It is therefore not
surprising to find many cocitations between these clusters. The publications concern
similar subject matter and authors affiliated with the cognitive revolution often defined
their positions relative to behaviorism.

Summary of the cocitation networks per decade. In none of the decades that we
analyzed did authors who are typically classified as behaviorists belong to the most
prominent (combination of) clusters. This runs counter to the view that psychology was
dominated by behaviorism in this part of the 20th century. Even on a very catholic
definition of behaviorism— combining the citations of all the clusters that contain authors
who are typically classified as behaviorists—the proportion of behaviorists was never
larger than 28% of the most-cited authors in a particular decade.24 Importantly, in none of
the decades that we studied did the largest cluster contain authors who are typically
classified as behaviorists. Moreover, our analysis also rules out an interpretation in which
behaviorism may have been a small movement in an absolute sense, but larger than any
other cluster in an otherwise disorganized field. In all decades, measurement of personality
and mental abilities (championed by authors such as Cattell, Eysenck, Thurstone, Guil-

24
We call this a very catholic definition of behaviorism because we have shown that these “behaviorist

clusters” regularly also contained authors who are typically classified as Gestalt psychologists, operationists,
functional psychologists, developmental psychologists, and even cognitive psychologists.

Figure 7. Cocitation network of the 200 most cited authors from the period 1961–1970. See
the online article for the color version of this figure.

269RISE AND FALL OF BEHAVIORISM

ford, Burt, and Edwards) seems to be the main preoccupation of the field. Moreover, in the
1920s and 1930s, the clusters that appear to consist predominantly of Gestalt psycholo-
gists (Köhler, Koffka, Lewin) generated more citations than behaviorism.

On the other hand, our cocitation analyses also show that the influence of behaviorism
did not dramatically decline after the Second World War either (at least up until 1970). In
the 1960s, the behaviorist cluster still made up 28% of the field, more or less the same
percentage as in the 1940s and 1950s. Absolutely speaking, the behaviorist cluster(s) even
doubled in size almost every decade (except for the 1940s), growing from 8,363 citations
in the 1930s, to 18,696 citations in the 1950s, to 35,539 citations in the 1960s. These
results suggest not only that the share of citations to behaviorist articles remained stable
between 1940 and 1970, but also that the absolute number of citations to the most
important behaviorists quadrupled in the same period.

Term Co-Occurrence Networks

1920 –1930. For the decade 1920 –1930, VOSviewer generated seven clusters,
which are shown in Figure 8. The aqua cluster contains terms (n � 9) that are typically
associated with behaviorist research, such as behavior, white rat, and maze, whereas
no such words occur in other clusters. The other clusters roughly correspond to areas
such as clinical (yellow), educational (orange), and perceptual psychology (blue). The
largest cluster (red) seems to contain mostly general terms, such as psychology,
theory, and study.

There is no clear spatial separation between the “behaviorist” cluster and some of the
other clusters; in fact, it falls almost completely within the boundaries of the larger green
and purple clusters. Thus, it seems that this clustering is somewhat forced onto the data
and that a given term’s cluster membership does not imply it does not co-occur with terms
from other clusters. Indeed, if we decrease the clustering resolution from 1.0 to 0.8, then

Figure 8. Term co-occurrence network for 1920 –1930. See the online article for the color
version of this figure.

270 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

the “behaviorist” cluster merges with the blue cluster (which still intersects the green
cluster). This new cluster (n � 24) may best be described as “experimental psychology”
(containing terms such as experiment, perception, and memory).

All in all, the term co-occurrence map for this decade shows that a behaviorist
vocabulary can already be distinguished in the publications, but also that the different
clusters tend to be quite similar to one another, at least when compared to later
decades.

1931–1940. For the decade 1931–1940, VOSviewer generated six clusters, which are
shown in Figure 9. Terms typically associated with behaviorist research (behavior,
learning, discrimination) are included in the red cluster (n � 27), which may best be
described as representing experimental psychology. If we set the clustering parameter to
a minimally finer value (1.0 to 1.01), this cluster breaks down into several subclusters, of
which one remaining subcluster (n � 12) consists exclusively of terms one would expect
in behaviorist research, and a few typical behaviorist terms (conditioning, reflex) located
in a larger adjacent cluster.

Compared to the network for 1920 –1930, the cluster with behaviorist terms is larger
and more clearly separated from the other clusters. Looking ahead at the maps that will
follow, we might see here the start of a bifurcation that will be most apparent from the
1950s onward: correlational psychology on the left and experimental psychology on the
right (for a discussion on how to characterize these two superclusters, see Flis & van Eck,
2018).

1941–1950. For the decade 1941–1950, VOSviewer generated six clusters, which are
shown in Figure 10. The yellow cluster (n � 15) contains terms typically associated with
behaviorist research, (behavior, discrimination, rat), but also terms that have a wider
usage in experimental psychology (effect, influence, degree). Most of this cluster is clearly
separated from the other clusters in the map, although one term (study) intersects with

Figure 9. Term co-occurrence network for 1931–1940. See the online article for the color
version of this figure.

271RISE AND FALL OF BEHAVIORISM

other clusters. Compared to the network of 1931–1940, all terms that typically associated
with behaviorist research are located within the same cluster. The “behaviorist” cluster
contains more terms, including some novel terms (motivation, function and response).
This reflects the growing popularity of terms typically associated with behaviorism. At the
same time, a handful of its terms are located at the edge of the map, indicating that these
terms have a relatively weak association with other terms in the map.

The other clusters invite straightforward interpretations too. The purple cluster reflects
clinical psychology (therapy, case, treatment). The blue and green clusters contain terms
relating to mental testing and the measurement of personality (personality, test, predic-
tion) and aptitude (child, ability, comparison), respectively. The red cluster seems to have
a mixed composition, consisting chiefly of general psychological terms (psychology,
theory, research), but also psychoanalytical (dream) and organizational terms (work,
vocational guidance).25

1951–1960. For the decade 1951–1960, VOSviewer generated four clusters, which
are shown in Figure 11. Typical behaviorist terms (reinforcement, learning, extinction)
appear in the blue cluster (n � 23). The blue cluster also contains terms that can take on
specific behaviorist meanings, but have a more general usage too (effect, function,
response). These general terms are located at the left side of the cluster, near the center
of the map.

Compared to the network of 1941–1950, the “behaviorist” cluster has again increased in
size. This is in line with a more general trend toward fewer clusters, which, because of the
fixed number of terms in each network, must also be larger. Clinical psychology no longer has
its own cluster (clinical terms are now subsumed under the red cluster) and the clusters for
personality and aptitude testing have merged into one large cluster (the green cluster).

25
Not all these terms are visible in Figure 10 because of its resolution.

Figure 10. Term co-occurrence network for 1941–1950. See the online article for the color
version of this figure.

272 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

1961–1970. For the decade 1961–1970, VOSviewer generated four clusters,
which are shown in Figure 12. Typical behaviorist terms (n � 21) appear in the blue
cluster. As in the previous decade, this cluster contains both terms that are highly
specific to behaviorism (reinforcement, extinction, reward, resistance) and terms that
are also used outside of behaviorist contexts (effect, function, response). The latter set
of terms is again located near the center of the map. Compared to 1951–1960, the
number of behaviorist terms remains more or less constant. The yellow cluster
contains several novel terms that reflect the emergence of cognitive psychology
(short-term memory, recall, information).

Figure 12. Term co-occurrence network for 1961–1970. See the online article for the color
version of this figure.

Figure 11. Term co-occurrence network for 1951–1960. See the online article for the color
version of this figure.

273RISE AND FALL OF BEHAVIORISM

Summary of the term co-occurrence networks per decade. When we track the
development of the term co-occurrence networks across the time period of 1920 –1970, we can
draw a number of conclusions. First, the networks appear to develop a more orderly structure
over time, with smaller numbers of clusters (from seven to four) and less spatial overlap
between clusters. This might well reflect the development of the overall field of psychology,
in which several subdisciplines became integrated throughout the 20th century. Second, the
share of terms typically associated with behaviorist research in the top 100 title words for each
decade increases until at least the 1950s. This might be because the behaviorist vocabulary
grew over these decades, such that new terms were coined, or rather, that existing terms were
increasingly used in a specific behaviorist sense. Alternatively, certain terms, which were
already in use earlier, could have become more frequent in titles and entered the top 100 at the
cost of other terms. In both scenarios, a plausible explanation is that more publications
appeared that can be characterized as behavioristic.

There are some interesting commonalities and differences between the author and term
networks. Both series of maps are similar in that they display growing popularity of authors
and concepts typically associated with behaviorism after the 1920s, but whereas the size of the
behaviorist clusters remained stable in the author networks, the popularity of the behaviorist
vocabulary continues to grow (and plateaus in the 1960s). We see two potential explanations.
First, it is possible that a lot of behaviorist work was published after the 1940s, but that this
was work generated relatively few citations in the field of psychology as a whole. Second,
some terms in the “behaviorist clusters” might have been used in work that scholars would not
commonly classify as behaviorist, but that did build on methods developed by paradigmatic
behaviorists, or was concerned with phenomena (e.g., behavior, learning) that were originally
put on the agenda by paradigmatic behaviorists. In that scenario, what these networks might
reflect is the success of behaviorists in setting the direction of at least a part of psychological
inquiry (cf. Roediger, 2004).

The combination of the term co-occurrence networks and author cocitation networks could
also shed light on another discussion. From the term co-occurrence networks alone, one might
get the impression that psychology, at least from the 1950s onward, consists of two subdis-
ciplines, characterized as experimental psychology and correlational psychology (Flis & van
Eck, 2018), that coexist with very little interaction. The author cocitation analyses, however,
suggest a different interpretation. If both subdisciplines were truly separated, one would expect
to see this reflected in the author cocitation networks, such that authors should mostly cite
work from other authors in their own area. This was not the case. The different overall
structure of the author cocitation networks, then, might reflect the simple fact that authors do
regularly cite work from those working in different disciplines. For example, it is common for
experimental studies on the influence of one variable on another to refer to studies showing
correlational evidence for a link between these variables. The cocitation map is also consistent
with a more radical reinterpretation, however; the term co-occurrence maps might not relate
to any underlying division in the field, but rather to the fact that different types of studies use
their own vocabulary. As such, any given author might publish both correlational and
experimental studies, but use distinct terms, at least in the title, to describe these studies.

Discussion and Conclusion

This study aimed to critically evaluate the received view about the development of
20th-century American psychology. Using advanced text analysis tools, we quantitatively
analyzed the metadata of 119,278 articles published in American psychology journals
between 1920 and 1970. We extracted and analyzed the most-cited authors and the most

274 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

frequently occurring title words in each of the five consecutive decades. Furthermore, we
generated cocitation and co-occurrence networks to visually map the similarity relations
between psychologists and title words as well as to analyze the development of American
psychology over time.

On the basis of these analyses, we submit that there are good reasons to suppose that
the standard view needs reappraising: (a) In none of the decades that we analyzed did
authors who are typically classified as behaviorists belong to the most prominent (com-
bination of) clusters. Even on a very liberal definition of behaviorism— combining the
citations of all the clusters that contain authors who are commonly classified as behav-
iorists (clusters which often also contained Gestalt psychologists, functional psycholo-
gists, developmental psychologists, and even cognitive psychologists)—the proportion of
behaviorists was never larger than a third of the most-cited authors in a particular decade.
Moreover, in all decades analyzed, measurement of personality and mental abilities seems
to be have been the main preoccupation of the field. (b) Our cocitation analyses show that
the influence of behaviorism did not decline dramatically after the Second World War
either (at least until 1970). Not only did the absolute number of citations to the most
important behaviorists quadruple between 1940 and 1970, the share of citations to
behaviorist articles also remained stable between 1940 and 1970 (approximately 28% of
the field). (c) Finally, our analyses also challenge certain presuppositions about the
development of behaviorism itself, suggesting, for example, that Skinner’s work was less
influential than the received view suggests.

Still, we would like to be cautious in our conclusions for both methodological and
historiographical reasons. For one thing, there are several methodological limitations to
the present study. First, we only analyzed two types of data: citation data and title words.
There are many alternative data sources that might be useful to analyze the structure and
the development of American psychology. We would be curious to see, for example, to
what extent American graduate students were influenced by behaviorist approaches to
psychology (e.g., by analyzing dissertations published between 1920 and 1970), to what
extent behaviorism can be said to have been ideologically dominant (e.g., by studying
textbook introductions to psychology between 1920 and 1970), and to what extent
behaviorism played a role in American culture (e.g., by analyzing discussions about
psychology outside academia).

A second methodological reason to be cautious is that our analysis is based exclusively
on WoS data. Although our analysis of the coverage of WoS shows that its overall
coverage is rather impressive, especially for major psychology journals, WoS only
provides citation data for scientific articles. Because research monographs also played an
important role in the development of American psychology, especially before the Second
World War, our analyses do not take into account all academic output produced by
American psychologists between 1920 and 1970. We do not have reason to presume that
including data from scientific monographs would significantly affect our results, however,
as our dataset does include citations to research monographs.26 The only data that the
present study does not analyze are the citations from research monographs. We do not
have reason to believe that cocitation patterns in research monographs will paint a
significantly different picture of the development of American psychology.

Second, our dataset only allowed us to construct cocitation networks on the basis of the
first author of each publication. Taking coauthorship into account would have three
interrelated consequences: (a) authors with a relatively large share of nonfirst authored
publications would become more prominent in the networks, (b) the prominence of
authors with relatively few nonfirst authored publications would be attenuated, and (c) the

26
Indeed, research monographs such as Freud’s (1913) The Interpretation of Dreams and Thorndike’s

Teacher’s Word Book belong to the most-cited documents in our dataset.

275RISE AND FALL OF BEHAVIORISM

distances between authors would change in ways that are difficult to predict. These are
probably minor concerns, however, as single-authored articles were the norm for each of
the decades under consideration.27

Third, our analysis is based on a dataset comprising all English-language psychology
articles published between 1920 and 1970, whereas our primary aim is to draw conclu-
sions about the structure and development of American psychology. It is likely that our
analyses somewhat misrepresent the structure of American psychology if there were
significant differences between American psychology and psychology in non-American-
English-speaking countries. It should be noted, however, that out of the top 25 journals
with the most articles in our dataset (which together deliver 51% of the 119,278 articles
in our catalogue), 24 are American journals.28

Most importantly, however, we would like to be cautious in drawing any major
conclusions about the rise and fall of behaviorism for historiographical reasons. Rather
than claiming that our analyses refute the received view that behaviorism was the
dominant approach in mid-20th-century psychology, we would prefer to use the results of
our analyses to offer a broader perspective on the development of American psychology.
For one thing, “dominance” is a notoriously vague concept. Indeed, the two main
measures of influence examined in the present study— citations and title terms—paint
subtly different pictures about the development of American psychology. Whereas our
cocitation networks suggest that authors who are typically identified as behaviorists were
less influential than the received view suggests, our term co-occurrence analyses reveal
that behaviorist terms gradually became more prominent during the decades, thereby
suggesting that behaviorist research might have played an important role in setting the
direction of at least a part of psychological inquiry. And these are just two measures of
“influence.” We can only imagine what still other (conceptual, sociological, educational,
therapeutical, societal, or political) measures would show us about the way in which
behaviorist research influenced the development of American psychology, American
academia, and maybe even American society. Although Skinner’s influence appears to
have been limited if one solely focuses on citations, for example, it is well possible that
his work (a) influenced the conceptual development of American psychology (e.g., by
developing the concept of operant conditioning), (b) contributed to the technological
development of American psychology (e.g., by inventing the Skinner box), and (c)
impacted American society because of his frequent media interviews, his contributions to
debates about the ideal society (e.g., Walden Two), and his technical inventions (e.g., the
air crib and the teaching machine; Rutherford, 2003). The story about the development of
American psychology is a complex one and cannot be reduced to a single question about
“dominance.”

Second, the term behaviorism itself is not a rigid, one-dimensional concept that can be
defined using a fixed set of strictly delineated criteria. If anything, behaviorism is a broad
collection of loosely associated psychologists and small research groups who share similar
philosophical and empirical assumptions—assumptions, moreover, that changed signifi-
cantly over time. For historians, it makes more sense to speak about similarities between
authors, texts, and concepts, than to try to classify them using strict ahistorical categories.

27
The percentages of publications by one author in the WoS dataset were 86%, 79%, 76%, 66%, and 55%

for each of the consecutive decades. The co-citation networks were based on authors cited in these publications,
but the numbers above provide a good estimate. If anything, because the co-citation analyses always “look back,”
the percentage of single-authored articles in each of the networks is probably even higher.

28
See Figure 1. The exception is Journal of Mental Science, which is British. We decided not to exclude

articles from British journals because we have insufficient information to determine the origin of all the 246
journals in our dataset. Furthermore, it should be noted that American psychologists also often published in
British psychology journals (and vice versa), such that selection at the journal level will never deliver a clean-cut
distinction between American and non-American Anglophone psychology.

276 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

Indeed, the present study shows that it is perfectly possible to study the development of
behaviorist research without relying on a fixed, ahistorical definition of behaviorism. In
fact, our study shows that relying on similarities between authors and concepts helps us
to draw richer and subtler conclusions about the development of American psychology.

Finally, and most importantly, a too narrow focus on the rise and fall of behaviorism
diverts the attention away from a broader perspective on the development of American
psychology. Not only do our analyses show that American psychology was more diverse
than the received view suggests, they also show that the story about the development of
American psychology is mostly a story about a rapidly expanding field of study (growing
from about 6,000 publications in the 1920s to more than 60,000 publications in the
1960s)—a story about the rise of a new and increasingly important discipline with ever
broadening theoretical and applied concerns, characterized by a diversity of approaches.
Our quantitative analyses, in sum, not only help us to reappraise the standard view about
the development of behaviorism, they also show us that historians need to tell both a more
diverse and a subtler story about the development of American psychology.

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Received August 22, 2019
Revision received December 23, 2019

Accepted January 24, 2020 �

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280 BRAAT, ENGELEN, VAN GEMERT, AND VERHAEGH

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http://dx.doi.org/10.1016/j.joi.2010.07.002

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http://dx.doi.org/10.7312/zuri90466

The Rise and Fall of Behaviorism: The Narrative and the Numbers

Behaviorism: The Narrative
Behaviorism: The Numbers
Method
Data
Coverage of the dataset
Citation data
Author Cocitation Networks
Term Co-Occurrence Networks
Results
Cocitation Networks
Cocitation networks per decade
1920–1930
1931–1940
1941–1950
1951–1960
1961–1970
Summary of the cocitation networks per decade

Term Co-Occurrence Networks
1920–1930
1931–1940
1941–1950
1951–1960
1961–1970
Summary of the term co-occurrence networks per decade

Discussion and Conclusion
References

ORIGINAL ARTICLE

If Everyone Is Doing It, It Must Be Safe: College Students’ Development of
Attitudes toward Poly-Substance Use

Erin Willisa , Robyn Adamsb, and Justin Keeneb

aAdvertising, Public Relations & Media Design, University of Colorado Boulder, Boulder, Colorado, USA; bCreative Media Industries,
College of Media and Communication, Texas Tech University, Lubbock, Texas, USA

ABSTRACT
Background: While binge drinking on college campuses has been a topic of concern for dec-
ades, especially among fraternity and sorority members, recreational drug use is on the rise
and mixing alcohol and drugs is now more of a concern than ever. Objective: Social learning
theory was used as a framework for understanding how students develop attitudes regard-
ing the possible risks and rewards of various behaviors such as binge drinking and drug
use. Method: This research reports the results of 13 focus group discussions with 63 college
students. A thematic approach was used and revealed several themes: participating in col-
lege culture, experimenting is expected, ignoring risk-taking, and resisting peer pressure.
Findings: Participants felt as if it was expected that college students would experiment with
alcohol and drugs, and that it was just “part of going away to college.” Students reported
ignoring the known risks of mixing alcohol and drugs use despite prior education efforts.
Conclusions: The findings of this study suggest that alcohol and drug use on college cam-
puses is, at least in part, driven by a perception of college culture and a poor balancing of
the risks and rewards associated with these behaviors.

KEYWORDS
Binge drinking; social
learning theory; focus
groups; non-medical use of
prescription medication;
risky behavior

Over one-third of full-time college students (18–22)
engaged in binge drinking in the past month; about
one in five used an illicit drug in the past month
(Center for Behavioral Health Statistics & Quality,
2015). The Centers for Disease Control and
Prevention (2017) characterize substance use as the
foremost public health hazard facing college students.
Substance use creates negative health, social, and eco-
nomic consequences for students, their families, and
their communities (National Institute on Drug Abuse,
2017; Substance Abuse and Mental Health Services,
2006). While binge drinking on college campuses has
been a topic of concern for decades (Hahm, Kolaczyk,
Jang, Swenson, & Bhindarwala, 2012; Wechsler, Lee,
Kuo, & Lee, 2000; White & Hingson, 2013), recre-
ational drug use is on the rise and poly-substance use
(mixing alcohol and drugs) is now more of a concern
than ever (CDC, 2017). In addition, much of this
poly-substance use involves prescription medication in
lieu of more illicit drugs (e.g., cocaine, marijuana).
Young people who engage in the non-medical use of
prescription medications have an increased risk of
using other drugs (e.g., alcohol, marijuana), suffering
from health issues (e.g., weight gain, mental health

problems), and engaging in risky behaviors (e.g.,
unprotected sex, criminal activity) (Ford &
Arrastia, 2008).

Exposure and access to prescription medication is
high on college campuses with 61% of college students
being offered these medications at least once, and 31%
using them non-medically (Garnier-Dykstra, Caldeira,
Vincent, O’Grady, & Arria, 2012). In addition, stu-
dents often overestimate the risky behaviors of their
peers; they overestimate stimulant use by 12.2% and
pain medication use by 8.8%, whereas marijuana use
by only 2.9% (McCabe, 2008). Such an overestimation
of risky behavior likely influences students’ percep-
tions of the risks associated with particular behaviors
and shifts their likelihood of partaking in such behav-
ior in the future.

Many universities and colleges now require stu-
dents to complete alcohol education programs prior
to arriving on campus (Croom et al., 2009). Previous
research notes that these brief alcohol interventions
yield only modest results, and that education alone is
not effective (Carey, Scott-Sheldon, Garey, Elliott, &
Carey, 2016; Tanner-Smith & Lipsey, 2015). Thus, this
study has two goals: (1) to better understand the role

CONTACT Erin Willis Erin.Willis@Colorado.edu University of Colorado Boulder, 1511 University Ave, Boulder, CO, 478 UCB, 80309, USA.
� 2019 Taylor & Francis Group, LLC

SUBSTANCE USE & MISUSE
2019, VOL. 54, NO. 11, 1886–1893
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of social influence and peer behavior on the creation
and maintenance of attitudes toward various sub-
stance use behaviors, like mixing alcohol and drugs;
(2) to better understand how these attitudes interact
with perceptions of rewarding outcomes and risky
consequences to influence planned behavior. The goals
of this study add to our understanding of college stu-
dents’ decision-making processes, and better inform
health education and promotion targeted at new and
incoming students. We employed qualitative method-
ology to gain insight into college students’ perspec-
tives specific to the social learning theory. The results
are discussed within the context of this theory with
particular emphasis on the practical outcomes associ-
ated with cessation efforts on college campuses related
to education and intervention.

Social learning theory

Social learning theory provides a theoretical frame-
work for understanding risk-taking behaviors among
college students. The theory posits that people can
learn by observing and modeling others’ behaviors
(Bandura, 1977). Deviant behavior is learned and pri-
mary groups, such as peer groups, play a central role
in this learning. One place where the influence of
peers is prevalent is college campuses. Indeed, collegi-
ate peer use of alcohol often determines individual
use, and peer norms predict binge drinking (Bandura,
1977; Crawford & Novak, 2010; Read, Wood, Kahler,
Maddock, & Palfai, 2003; Sher, Bartholow, & Nanda,
2001; Tyler, Schmitz, Ray, Adams, & Gordon Simons,
2017). Previous research also notes that many college
students have positive attitudes toward alcohol use
(Peralta & Steele, 2010; Schultz, Nolan, Cialdini,
Goldstein, & Griskevicius, 2007; Wechsler et al., 2003),
have well-defined reasons for drinking, for instance,
mood enhancement or reducing stress (O’Connor &
Colder, 2005; O’Hara, Armeli, & Tennen, 2015).

In addition, the perceived benefits (e.g., social
interaction, fun/enjoyment) of drinking are significant
predictors of alcohol use (Brooks-Russel, Simons-
Morton, Haynie, Farhat, & Wang, 2014). Prior work
has demonstrated that first-year students are highly
susceptible to modeling the behavior of their older
peers, and are at the highest risk for the negative con-
sequences of alcohol use (Armeli, Conner, Cullum, &
Tennen, 2010; Maggs, Williams, & Lee, 2011).
Students with the highest likelihood of engaging in
multiple health-risk behaviors reported poorer mental
health, particularly related to stress and anxiety
(Martinez, Klanecky, & McChargue, 2018). Perceived

norms influence college students’ level of drinking
through the observation and comparison of their
peers’ drinking levels (Fournier, Hall, Ricke, & Storey,
2013; Stappenbeck, Quinn, Wetherill, & Fromme,
2010). The prediction here is that risky substance use
on college campuses is, at least in part, a product of
social learning processes that lead to attitudes regard-
ing specific substances and situations.

Differential association and reinforcement

There are several key elements to the learning process,
including differential association and differential
reinforcement (Akers, 2011). Differential association is
the association with individuals who engage in certain
types of conduct, as well as the exposure to different
sets of values and norms as a consequence of such
associations (Akers, 2011). For example, over 70% of
students nationwide overestimated the quantity of
alcohol consumed by their peers; further, the percep-
tion of campus drinking norms was by far the stron-
gest predictor of personal consumption, stronger even
than the actual campus drinking norm (Wesley
Perkins, Haines, & Rice, 2005).

Differential reinforcement is “the balance of antici-
pated and actual rewards and punishments that follow
or are consequences of behavior” (Akers, 2000, p. 78).
Within the context of alcohol use, this could take the
form of several different outcomes (e.g., hangovers,
Driving While Impaired (DWIs), alcohol poisoning).
However, as Durkin, Wolfe, and Clark (2005) demon-
strated, college-aged binge drinkers reported that alco-
hol consumption has more rewarding outcomes than
negative consequences.

In addition to the prediction related to the role of
social learning processes in risky behaviors, the cur-
rent study also seeks to understand how these social
learning processes interact with prior knowledge
regarding the risky nature of certain behaviors. By
concentrating on the differential associations and rein-
forcements regarding binge drinking and drug use
among college students, this study explores how atti-
tudes are formed and how behaviors are reinforced by
perceptions of normative behavior within peer groups.
This research fills a gap in the literature related to the
qualitative exploration of college students’ perceptions
of poly-substance use and risk-taking behaviors.

Method

This study used focus groups drawn from a larger stu-
dent population at a southwestern university. Focus

SUBSTANCE USE & MISUSE 1887

groups provide insights into a target audience’s per-
ceptions and motivations (Krueger & Casey, 2015),
and can capture the complexities of attitude and
behavioral intentions (Kitzinger, 1994). Enrollment at
the southwestern university was approximately 37,000
students. The college students were recruited from
general media and communication studies courses via
an online recruitment system, and they received extra
course credit for their participation. This research
study was approved by the southwestern university’s
institutional review board. The key ethical considera-
tions reviewed for this study relate to informed con-
sent, confidentiality, and the right to withdraw.
Participants had to be at least 18 years old to register
for the study and be enrolled at the southwestern uni-
versity. The recruitment procedures, discussion guide,
transcription process, and data analysis were approved
by the IRB. Age and university enrollment were the
only exclusion criterion; participants were not
excluded based on substance use history.

Thirteen group discussions were held in October
2017 with a total of 63 college students (27 men, 36
women; 3–8 per group) who were between 18 and
25 years old. Prior to the focus group discussions, a
trained moderator reviewed the goals of the study,
consent forms, and the right to withdraw with the
participants. One author, who had received training in
conducting focus group discussions, moderated each
semi-structured focus group discussion. The duration
of the focus groups ranged from 45 to 80 min. A dis-
cussion guide was developed to probe participants’ per-
ceptions of the college “party scene” and substance use
on campus. Open-ended questions helped minimize
researchers’ bias and allow participants to respond.
Following open-ended questions, probing questions
focused on participants’ feedback. Sample questions
included: What role does alcohol play in college for
you and your friends? When drinking alcohol, are there
times when drugs are around? In your experience, is it
common for your peers to do drugs? What social pres-
sures do you feel regarding drinking/drug use?

The focus groups were audio-recorded, transcribed
verbatim by an online transcription service, and then
analyzed using a thematic approach (Miles &
Huberman, 1994). Thematic analysis identifies themes
that emerge as being important to the description of a
phenomenon; it is a form of pattern recognition
within the data, while emerging themes become cate-
gories for analysis (Miles & Huberman, 1994). The
theoretical proposition that led this study – social
learning theory, specifically differential associations
and reinforcements – was used to assist in the

development of themes. This research used Miles and
Huberman’s data-analysis procedures. The first author
reviewed all transcripts and derived a set of themes
from the discussions, and the second and third authors
independently reviewed all transcripts and the appro-
priateness of the themes derived by the first author.
Disagreements on themes were resolved through dis-
cussion. The first author then coded all of the tran-
scripts by categorizing relevant statements in the
transcripts under themes (Stappenbeck et al., 2010).
The second and third authors then reviewed the coded
statements, and disagreements on the codes were
resolved. This rigorous analysis by multiple researchers
enhanced the reliability of the themes (Miles &
Huberman, 1994). The number of focus groups needed
was determined by saturation, or the point where no
new themes emerged (Krueger & Casey, 2015).

Findings

The current study used the social learning theory as
the analytical framework for identifying themes.
Focusing on differential associations and differential
reinforcements related to “partying” and substance
use, the following themes emerged: participating in
college culture, experimenting is expected, ignoring
risk-taking, and resisting peer pressure, and are
described below.

Participating in college culture

The majority of participants felt that alcohol and drug
use is part of attending college, and that most college
students engage in this type of behavior as part of
socializing. Five participants reported not drinking
due to various factors including religion, addiction, or
hiatus. Several participants mentioned that there was
“nothing else to do” or that drinking “is just – you
don’t think about it, it’s what you do.” The partici-
pants felt as if “partying” was expected of them since
they were in college, “that’s what college students do.”

I mean in general I feel like when you want to do
something, it’s always like centered around drinking.
Like it’s kind of like the social thing to do. I think it’s
just sort of the college thing. (Female, 19, Group #5)

You feel more inclined to drink since everybody, or a
lot of people, are doing it in this demographic area.
(Male, 21, Group #1)

I just can’t imagine being here without alcohol.
(Female, 22, Group #2)

Some participants even chose to attend this particu-
lar university because of its “party school” reputation.

1888 E. WILLIS ET AL.

Many participants reported binge drinking more while
underage than after they turned age 21 due to several
factors, including being new to alcohol and not know-
ing their limits or lack of access. However, many par-
ticipants reported “having a drink at dinner” and “just
staying home on the weekends and drinking” rather
than frequenting bars after they were legally allowed
to drink.

I mean, I’m not 21 anymore. I don’t go out and get
lit every weekend. Yeah, we might drink at home, but
it’s not like going to the bars. (Female, 22, Group #6)

We will just kick back and chill sometimes at the
house … might have a few people stop by, but it’s
very chill. (Male, 22, Group #10)

All of the participants mentioned football games
and tailgating specifically as times where “drinking is
taken to the next level,” meaning that people are
drinking heavily (some reported “up to 22 drinks on
game day”). “Pre-gaming,” by student definition is
where five to eight drinks are consumed before going
out, was commonly reported. While out at the bar,
participants reported drinking more alcohol and
sometimes using drugs such as cocaine, Molly, or
Xanax. Participants reported keeping a running list of
what types of alcohol mixed best with specific types of
available drugs. While many reported experimenting
with alcohol and drugs prior to college, that experi-
mentation drastically increased while in college.

Yeah, you get here and you see everyone drinking.
Eventually, you start drinking as much as everyone
else. (Female, 21, Group #9)

Participants reported having more opportunities to
binge drink in college, and because alcohol seemed to
be at most social functions, drinking (and often, binge
drinking) was expected behavior.

Experimenting is expected

Participants felt experimentation with combining
drugs and alcohol was normal, and most agreed that
marijuana use was very common among college stu-
dents. All of the focus group participants reported
seeing or using marijuana while at college. Six partici-
pants reported being regular users of marijuana.

I smoke weed every single day. I do everything high
and I have great grades. (Female, 19, Group #8)

Other drugs were reported to be popular among
some crowds or in “bathrooms everywhere in the
bars.” For example, half of participants reported being
offered cocaine at a party.

I was at a party. We’ve had [fraternity] events where
a couple of my friends, they’d just be like, “hey, you
want a bump or something? I’d be like, ‘yeah, why
not’, it’s free, it’s nothing I would pay for because it’s
not worth it to me. (Male, 21, Group #7)

It was just at a gathering and some girl just had some
cocaine and she was like, ‘who wants a free line?’
And I was like, ‘why not?’. (Female, 20, Group #10)

Being in a social environment and having the
opportunity to try the drug facilitated participants’
willingness to act. “It’s like since so many others are
doing it, I just can’t see the harm, I guess.” The
majority of participants reported seeing others mix
drugs and alcohol. The participants who reported
common drug use also reported having prior desires
and positive attitudes toward such risky behavior.

Ten of the participants reported actively using
drugs recreationally, sometimes in combination with
alcohol. For example, many participants reported
using Xanax prior to drinking or cocaine after a long
night “to sober you up.” Half of the participants
reported experimenting with drugs once or twice, but
then after the experience, refraining from future drug
use because “it just isn’t my thing.” The remaining
participants had never used drugs, but felt like it was
okay for others who wanted to experiment.

Ignoring risk-taking

All of the participants discussed “blacking out from
alcohol,” either having personal experience or hearing
first-hand from friends. Participants reported
“blacking out” because they either did not yet know
their limits or as a planned behavior.

Usually I feel like alcohol plays the biggest part in
blacking out. I think people do plan to black out,
because I have so many friends that are like, ‘Oh, my
God, I had four tests this week. I just want to go out
tonight and not remember anything’. They completely
black out. I’ve blacked out before. Personally, I don’t
really like it because I don’t like not remembering
anything, because I feel like I did something stupid
and you just don’t remember it … (Female, 21,
Group #5)

In terms of drug use, one participant noted that
‘everyone is medicated’ and that ‘prescription pills are
everywhere’. Many reported the use of prescription
pills specifically. For example, some reported friends
‘stockpiling’ prescription Xanax to use recreationally,
or purchasing Adderall from friends with prescrip-
tions to use while studying or ‘partying’. Participants
reported prescription drugs as being ‘very accessible
around here’.

SUBSTANCE USE & MISUSE 1889

Most of the participants reported not associating
marijuana with any risk, ‘probably because it’s legal in
most states’. Other drugs were thought of as being
dangerous, including cocaine and prescription medica-
tions such as Adderall, Ambien, and Xanax, when
taken with alcohol. Additionally, when asked to rank
substances by most dangerous to least dangerous,
most participants agreed that alcohol was the most
dangerous drug, followed by prescription pills
and cocaine.

I probably would just say that alcohol is one of the
worst because you don’t see it as a drug, but it really
is because people get addicted to it every day.
(Female, 24, Group #11)

I’d say I think alcohol is worse [than prescription
drugs]. So I would really say … of course the
cocaine, most definitely, and then alcohol after that,
you know what I’m saying? Not because of the long-
term effects or whatever. You got to drink a whole lot
to actually mess your kidneys, but just the fact that
people get messed up on it, drive and stuff like that.
(Male, 19, Group #2)

The majority of participants reported engaging in
binge drinking and/or drug use, but did not discuss
the risks with friends due to several factors men-
tioned, including “we learned about that in high
school” and “that’s her life, whatever.” Participants
reported drug users as not wanting to think about the
risks, or “they’re not going to be proactive with it.
You’ve got to ask them.” Five participants reported
knowing friends who passed away from drug overdose
but still reported engaging in drinking and drug use,
just “in moderation.” In addition, most agreed that
they would not warn strangers of the risks of sub-
stance use. Participants felt as if strangers would not
heed their warning, and, therefore, agreed that they
would not try to dissuade a stranger from using drugs
in combination with alcohol, despite the obvious risks.

Resisting peer pressure

The participants agreed that they felt social pressure
to drink but not to do drugs.

I’ve never been pressured to take any drugs. I’ve been
asked, but they weren’t really as persistent as with
alcohol, because I think they know that drugs is a
little more than just alcohol. (Female, 20, Group #12)

No one’s like, ‘Shove this [drink] down or you’re not
part of the [name of school] family!’ I don’t think it’s
like that, it’s just we all do it [binge drink]. (Female,
19, Group #4)

There’s no pressure to do drugs … it’s just, it’s just
if you want it. (Male, 18, Group #10)

If participants were offered drugs and they did not
want to partake, many felt as if it would be easy to
decline the offer. Additionally, participants noted that
drugs were common among ‘certain crowds’ and that
peer pressure was about choice.

I think there’s always going to be that social pressure.
The kind of friend they’re going to be like, ‘Oh,
smoke this, smoke this, smoke this’. But it’s really like
at the end of the day, it’s your own decision to make.
(Male, 23, Group #7)

I feel that it’s like a lot who you surround yourself
with and like who your friends are, and like who you
choose to be around and stuff. (Female, 20,
Group #11)

Although it would seem drugs are commonly
offered with no pressure, alcohol carries a greater
social influence, “I mean, it’s hard because everyone is
doing it.” The majority of participants reported drink-
ing frequently, and when opting not to drink – did
not mention being pressured by their peers. Most
reported that their friends did not pressure them to
do anything they didn’t already want to do.

Well, I guess you are on Snapchat and you see all
your friends out, you’re like, oh, crap. I want to go
out drinking with them. I don’t want to be stuck at
home. (Female, 19, Group #8)

Participants reported fearing missing out on the
social scene if they were not out drinking with their
friends. Specifically, seeing friends having fun on
social media platforms created a desire for participants
to engage in the same activities. However, educational
duties often dictated when participants engaged in
heavy drinking, and most agreed that “drinking
shouldn’t get in the way of why I’m here.”

For those participants who did not drink at the
time of the focus groups, all reported not having any
difficulty resisting peer pressure in social settings
whether related to drugs and alcohol. After “pre-
gaming,” many participants reported drinking two to
four drinks while out at the bar unless “someone buys
me a drink, and yeah, I’ll drink it.” One participant
said: “I’ve never seen anyone be pressured to start
drinking, but I have seen people pressured to
keep drinking.”

Discussion

This study had two goals: (1) to understand how both
social influence and peer behavior influences attitudes
toward various substance use behaviors, and (2) to
understand how perceptions of rewarding outcomes
and risky consequences interact with these attitudes to

1890 E. WILLIS ET AL.

influence planned behavior. Focus groups were used
to discuss binge drinking and substance use with col-
lege students to understand their perceptions of these
risky behaviors; specifically, the differential associa-
tions and differential reinforcements that students
perceive of these behaviors was probed with direct
questioning. Although previous research has demon-
strated that experimentation with drugs and alcohol is
common and increasing among college students
(Behavioral Health Coordinating Committee:
Prescription Drug Abuse Subcommittee, Department
of Health & Human Services, 2013), this study
revealed how perceptions of peer norms and the bal-
ancing of risk with reward influence behavior.

Four themes were identified within the data: partic-
ipating in college culture, experimenting is expected,
ignoring risk-taking, and resisting peer pressure.
Students believed they were expected to experiment
with alcohol and drugs, and that sometimes included
use. In every focus group, it was said at least once,
“everybody’s doing it” in regard to drinking. Students
also noted that their university had a “party school”
reputation and that was attractive during recruitment.

Marijuana was considered “normal” amongst stu-
dents because it was often reported as “always being
around.” Interestingly, however, students reported
feeling pressure to partake in alcohol but not drugs.
Both illicit and prescription drugs were common and
available to students, and some reported accepting
while others declined. Regardless of the type, drug use
was highest in conjunction with alcohol. Although
students reported knowing the risks of combining
alcohol with prescription medications and illicit drugs,
these risks did not outweigh the perceived rewards.
Few students reported avoiding discussing the risks
with their friends because “it’s their life, you know?.”

Theoretical implications

Theoretically, the findings from this study offer
insight into the differential associations and reinforce-
ments among college students related to alcohol and
drug use. About half of the participants reported
abstaining from drug use, while only five reported not
drinking alcohol. The reported variance in drinking
behavior was evidence of different peer groups at this
university and how these associations influence the
perception of norms. Several students reported seeing
“hard drugs” at parties and making the decision to
leave because they did not feel comfortable around
illicit drug use. However, when it came to drinking,
excessive consumption was permitted because “that’s

what everyone else is doing.” Students reported “pre-
gaming” as a common behavior prior to drinking
more while out at a bar where drug use might also
occur. Many students perceive alcohol and drug use
as part of the college experience and thus, they per-
ceive this to be the norm. Because of their desire to
be part of the “in-group,” they report more reward
than risk due to their perception that “everybody’s
doing it.” Additionally, participants also mentioned
seeing “partying” on social media applications –
sometimes from peers at their same university, some-
times from peers at different universities – contribu-
ting to students’ perceptions that indeed, “everybody’s
doing it.”

In terms of differential reinforcement, students
reported learning their limits “the hard way,” for
example, drinking until sick, “blacking out,” and then
adjusting their behavior as to avoid negative conse-
quences in the future. Most of the students reported
socialization and “having fun” as benefits to engaging
in risk-taking behaviors, but they also acknowledge
the negative consequences, for example, hangovers,
sexual assault. Akers notes that differential reinforce-
ment is the “balance” of rewards and consequences of
engaging in a particular behavior (Akers, 2000; Maggs
et al., 2011). It is clear that over time, students find a
balance in the rewards and consequences of particu-
larly risky behavior that is common to their perceived
college experience. Interestingly, students’ evaluations
of their risk-taking behaviors are determined by their
performance in classes. If drinking or drug use was
interfering with attendance or their personal (or par-
ental) definition of success, then it was perceived as a
problem. However, health issues were not cited as a
reason for the cessation of the risky behavior.

Public health implications

The findings from this study inform public health
interventions targeted at high school and college stu-
dents specifically. Although the current study focused
on college students, many said they used drugs and/or
alcohol prior to attending college. Therefore, predis-
positions to alcohol and drugs start prior to college,
as does students’ perception of college culture; thus
attempts to prevent or cessate must start earlier and
grow as college progresses. More health education
should be targeted to this age group to help them
form an understanding of the risks of substance use,
especially mixing alcohol and drugs. Realistic, targeted
health messaging could help set this population’s
expectations of college “partying” and demonstrate the

SUBSTANCE USE & MISUSE 1891

consequences of risky behaviors. Findings from this
study might be used by college university’s health cen-
ters to design messages that highlight the consequen-
ces of substance abuse. Additionally, many universities
require students to complete alcohol education prior
to their first year on campus. Often, university educa-
tion includes bystander training which focuses mainly
on sexual misconduct, but perhaps should also include
lessons for students on substance use and discourag-
ing peers (not just friends!) from mixing drugs and
alcohol. Our research offers insight into substance
abuse topics that is informed by students and should
be considered when designing health messages to this
audience. This research demonstrates that students are
likely ignoring the negative consequences to risky
behaviors in order to be accepted by their peer group
and to achieve the perceived benefits of alcohol and
drug use. Future public health messaging related to
the use of drugs and alcohol concurrently should con-
centrate on accentuating the rewards of avoiding such
behavior rather than simply focusing on the risks as
these risks seem to be easily dismissed by this
age group.

Limitations

This study used a qualitative approach; focus groups
cannot be generalized to a general population of col-
lege students. The lack of independent coding by
more than one author is a limitation. In addition, the
students in the sample are from one university and,
thus, future work should include other campuses
across the country. Additionally, the university’s IRB
limited the demographic and behavioral information
that could be collected from students. Future work
should attempt to probe the relationships between
these differential associations and reinforcements
through quantitative measures such as large-scale sur-
veys or experimental designs intended to present mes-
sages that might appeal to one or both of the social
learning processes.

Taken together, the findings of this study suggest
that poly-substance use on college campuses is, at
least in part, driven by a perception of college culture
and a poor balancing of the risks and rewards associ-
ated with these behaviors.

Declaration of interest

The authors declare that they have no conflict of interest.
The authors alone are responsible for the content and writ-
ing of the article.

ORCID

Erin Willis http://orcid.org/0000-0002-1582-0867
Justin Keene http://orcid.org/0000-0002-1404-0025

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Abstract

Social learning theory
Differential association and reinforcement
Method
Findings
Participating in college culture
Experimenting is expected
Ignoring risk-taking
Resisting peer pressure
Discussion
Theoretical implications
Public health implications
Limitations
Declaration of interest
References

172 asrt.org/publications

Editorial

Learning Theories: Behaviorism
Kevin R Clark, EdD, R.T.(R)(QM)

I
n its simplest form, learning is defined as gaining
knowledge through study, teaching, instruction, or
experience.1 Interestingly, learning is described and
viewed differently by theorists, researchers, and

practitioners who have spent time investigating and
experimenting in the educational psychology field.1,2
The differences in how educational theorists believe
individuals acquire, retain, and recall knowledge result-
ed in the development of multiple learning theories.1-3
Based on the context of the theorists’ work and other
factors at the time of investigation, these theories
explain how learning occurs, what internal or external
factors inf luence learning, how memory affects learn-
ing, and how transfer of knowledge occurs.1-3 In addi-
tion, the roles of the instructors and learners are
described according to each theory of learning. A basic
understanding of the various learning theories is essen-
tial for educators who strive to lead a classroom that is
conducive to learning and success.

The ideas of behaviorism date back to the late
19th and early 20th centuries when John Watson, an
American psychologist, believed the general public
would accept and recognize the new philosophy of psy-
chology as a true science only if it involved processes
of objective observation and scientific measurement.1
This notion of detailed observation and measurement
became central to the work of behaviorists.1

Behaviorism emphasizes that learning occurs when
an individual responds favorably to some type of

external stimuli.1-4 Behaviorism sometimes is referred
to as the stimulus-response theory.1 For example, when
presented with a math f lashcard showing the equation
6 3 8, the learner responds with the answer 48. The
equation is the stimulus, and the answer is the associ-
ated response.2 Essential elements with behaviorism
include the stimulus, the response, and the association
between these 2 elements.2 Of particular importance
is how the association between the stimulus and the
response is made, strengthened, and maintained.2

Behaviorists define learning as nothing more than
the acquisition of new behaviors. Behaviorists do not
emphasize thinking or other mental activities as a part
of the learning process because such variables are not
observable behaviors.1-4 Although the behaviorism
theory discounts any mental activity, other educational
theorists considered these processes to be a vital part of
learning and cognition, which resulted in the develop-
ment of other theories of learning.1,4 Behaviorists do not
address memory and how new behaviors or changes
in behaviors are stored or recalled for future use.2
Behaviorists refer to this type of learning, where a reac-
tion is made to a particular stimulus, as conditioning.1
Two main types of conditioning include Pavlov’s classi-
cal conditioning and Skinner’s operant conditioning.

Classical Conditioning
Ivan Pavlov, a Russian physiologist, noticed that dogs

salivated every time they ate or saw food and believed

173RADIOLOGIC TECHNOLOGY, November/December 2018, Volume 90, Number 2

Editorial
Clark

bowling alley.1 Skinner made generalizations about his
findings with rats and pigeons to humans.1 In addition,
he noted that operant conditioning also worked in a
negative way: stopping a behavior from occurring by
punishing it.1,3

Reinforcement and Punishment
Key aspects of operant conditioning include rein-

forcement and punishment, both of which can be
positive or negative. Reinforcement refers to anything
that has the effect of strengthening a particular behav-
ior for it to occur again.1,3 Positive reinforcement is the
addition of a rewarding stimulus to get the behavior to
happen again (eg, rewarding learners for making a high
grade on an exam in hopes they study harder for future
assessments and score high again). Negative reinforce-
ment is the removal of an unpleasant stimulus to get the
behavior to continue (eg, students learning the rules to
solve a particular problem so their instructor quits nag-
ging them about the importance of it). The unpleasant
behavior of the instructor’s nagging is removed when
students learn the rules, solve the problem correctly,
and continue the action so the nagging does not return.

Conversely, punishment refers to anything that
has an effect of lessening or discouraging a particular
behavior so that it does not occur again.1,3 Positive pun-
ishment is the addition of an unpleasant stimulus to get
the behavior to stop; any type of disciplinary action is
considered positive punishment. Negative punishment
is the removal of a rewarding stimulus to get the behav-
ior to stop (eg, not offering extra credit opportunities in
hopes the behavior stops so that the learners can receive
these beneficial opportunities in the future). Skinner
maintained that rewards and punishments control most
human behaviors.1-3

In addition to Watson, Pavlov, and Skinner, other
theorists were associated with the behaviorist move-
ment. The Table summarizes their contributions to the
theory of behaviorism.

Implications in Teaching and Learning
Behaviorists believe learning begins when a cue

or stimulus from the environment is presented, and
the learner reacts to the stimulus with some type of
response.1-3 Those responses are reinforced or punished,

he could condition the dogs to salivate at the sound of
a bell.1 Initially, Pavlov sounded a bell at the time food
was presented to the dogs and repeated this process
frequently.1 Eventually, the sound of the bell became
an indication to the dogs that food was about to be pre-
sented, and they responded by salivating at the sound
of the bell regardless of whether food was presented.1
This type of reinforcement of a natural ref lex or some
involuntary behavior that occurs as a response to a par-
ticular stimulus is called classical conditioning.1 Pavlov
was able to condition the dogs to salivate in response to
the sound of the bell.

Pavlov identified 4 stages of classical conditioning:
acquisition, extinction, generalization, and discrimina-
tion.1 The acquisition stage is the initial learning of
the conditioned response (the dogs salivating at the
sound of the bell).1 Pavlov believed the conditioned
response would not remain indefinitely, so he used the
term extinction to describe the disappearance of a con-
ditioned response.1 Pavlov demonstrated extinction by
repeatedly sounding the bell without presenting food
to the dogs.1 The final 2 stages, generalization and dis-
crimination, are opposites and explain how behaviorists
believe knowledge is transferred within learners.2 The
generalization stage implies that a conditioned response
might occur with similar stimuli without further train-
ing (the dogs salivating at the sound of something
similar to a bell).1 In contrast, the discrimination stage
indicates that a conditioned response might occur with
1 stimulus but not with another (the dogs not salivating
at the sound of something similar to a bell).1

Operant Conditioning
BF Skinner, a psychologist working in the United

States in the 1930s, established the theory of oper-
ant conditioning: a process of reinforcing a voluntary
behavior by rewarding it.1,3 Studying the behaviors of
rats, Skinner used a device (now called a Skinner box)
that contained a lever.1 W henever the rats pressed the
lever (an action Skinner considered normal, random,
and voluntary), a pellet of food was presented.1 As the
food rewards continued during the repetition of the
action, the rats learned that they had to press the lever
to be fed.1 Skinner also used reinforcement techniques
to teach pigeons to dance and to roll a ball down a mini

174 asrt.org/publications

Editorial
Learning Theories: Behaviorism

the reinforcement of appropriate classroom behaviors,
which can create a more orderly classroom environment
that is conducive to learning and success for all.1

Learning Activities
Classroom learning activities connected to the

behaviorism theory include1-3:
� lecturing
� recalling facts
� defining and illustrating concepts
� applying explanations
� participating in rote learning (ie, memorization

based on repetition)
� completing drill and practice exercises
� establishing classroom management policies
� using rewards and punishments

Implications in Medical Imaging Education
In medical imaging education, lecturing is a domi-

nant approach to presenting information because of
the complexity of the content. Considering time man-
agement issues and restrictions in higher education,
lecturing affords instructors an opportunity to pres-
ent a large amount of information to a large audience.
Often, medical imaging students memorize some of the
content presented and recall that knowledge during an
exam. The role of repetition aids in the learning of new
and challenging content. Medical imaging students
benefit from drill and practice exercises when working
with formulas, including the Inverse-Square Law, the
milliampere-seconds–distance compensation formula,

and this process is repeated so that the responses
become automatic.3 Ultimately, the change in behav-
ior indicates learning has occurred.3 As revealed,
behaviorism has little regard for mental processes or
understanding and, therefore, does not prepare learners
for problem-solving or critical-thinking skills.1-3

The instructor plays a dominant role in behaviorism
by leading the learning environment, using positive and
negative reinforcement to shape learners’ behaviors,
and presenting the content.1 With behaviorism, learn-
ers are described as passive individuals who voluntarily
respond to external stimuli.1 Other behaviorist implica-
tions in teaching and learning include1:

� creating procedures and expectations to manage
the classroom

� using rewards as incentives for learners to work
hard and behave

� using punishments (eg, loss of privileges or with-
holding of rewards) effectively and sparingly to
change learners’ behaviors

Critics of behaviorism argue that rewards can belittle
or demean a learning experience and, therefore, should
be used with caution.1 Often, rewards can evoke feel-
ings of unfairness or competition, and some learners
might become distracted from the real issue involved
in completing a task or learning new material.1 Using a
rewards system or giving 1 learner increased attention
might have a detrimental effect on others in the class or
cause them to feel isolated.1 Not surprisingly, rewards
do not always lead to higher-quality work; however,
using a behaviorist approach, rewards can result in

Table

Key Theorists and Their Contributions to Behaviorism1

Theorists Contribution

Ivan Pavlov Classical conditioning

Edward Thorndike Connectionism (emphasized the role of experience in the strengthening and weakening of stimulus-response
connections)

John Watson Scientific objectivity; Law of frequency (the more frequent a stimulus and response occur in association with
each other, the stronger the habit will become)

Edwin Guthrie Contiguity (the same response to a stimulus most likely will occur over and over again during repeated expo-
sures)

BF Skinner Operant conditioning

175RADIOLOGIC TECHNOLOGY, November/December 2018, Volume 90, Number 2

Editorial
Clark

and the grid conversion formula, as well as calculations
involving skin dose. Medical imaging instructors ben-
efit from using a behaviorist approach by implementing
a classroom management plan to lead a classroom con-
ducive to learning and success.

Conclusion
The theory of behaviorism can be illustrated by

the adage, “practice makes perfect.” Behaviorists see
learning as an observable change in behavior as a result
of experience and repetition. This stimulus-response
theory makes no attempt to assess the mental processes
necessary for learners to acquire, retain, and recall
information. The change in behavior is simply achieved
through a conditioning process using reinforcement
and punishment. Even though little importance is
placed on mental activity, concept formation, or under-
standing, there is a place for behaviorism in today’s
classrooms, especially in medical imaging education, in
the areas of rote learning and classroom management.

Kevin R Clark, EdD, R.T.(R)(QM), is assistant
professor and graduate coordinator for the School of Health
Professions at The University of Texas MD Anderson
Cancer Center in Houston. He serves on the Radiologic
Technology Editorial Review Board and can be reached at
krclark@mdanderson.org.

References
1. Pritchard A. Behaviourism and the beginnings of theory. In:

Ways of Learning – Learning Theories and Learning Styles in
the Classroom. 3rd ed. New York, NY: Routledge; 2014:6-17.

2. Ertmer PA, Newby TJ. Behaviorism, cognitivism, construc-
tivism: comparing critical features from an instructional
design perspective. Perform Improv Q. 2013;26(2):43-71.
doi:10.1002/piq.21143.

3. Kelly J. Learning theories. The Peak Performance Center
website. http://thepeakperformancecenter.com/education
al-learning/learning/theories/. Published September 2012.
Accessed June 10, 2017.

4. David L. Behaviorism. Learning Theories website. https://
www.learning-theories.com/behaviorism.html. Published
January 31, 2007. Accessed June 10, 2017.

https://doi.org/10.1002/piq.21143

Copyright of Radiologic Technology is the property of American Society of Radiologic
Technologists and its content may not be copied or emailed to multiple sites or posted to a
listserv without the copyright holder’s express written permission. However, users may print,
download, or email articles for individual use.

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Encyclopedia, 2019

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Operant conditioning

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Operant conditioning, a term coined by B. F. Skinner, American psychologist and radical
behaviorist, is the idea that behavior is the learned result of consequences. Skinner, who
introduced the concept in his 1938 book The Behavior of Organisms: An Experimental Analysis,
theorized that operant conditioning in the form of reinforcements and punishments leads to an
association between a behavior and its consequence. Positive reinforcement increases a
desirable behavior by following it with a favorable stimulus. Negative reinforcement increases a
desirable behavior by removing an unfavorable stimulus after the behavior is performed. Both
positive and negative reinforcement seek to increase a desirable behavior. Punishment, like
reinforcement, also has positive and negative varieties. Positive punishment is adding an
unfavorable stimulus in an effort to eradicate an undesirable behavior. Negative punishment is
removing an unpleasant stimulus in order to decrease undesirable behavior. Both positive and
negative punishment seek to decrease an undesirable behavior.

Overview

Skinner designed an operant conditioning chamber, which
came to be known as the Skinner box, to test his theory of
operant conditioning on animals. The Skinner box
prevented human interruption of the experimental session
and allowed the experimenter to study the behavior of an
animal as a continuous process. The box includes at least
one lever or key that the animal can manipulate to release
food, water, or some other reward or to avoid punishment
such as an electric shock. Skinner’s experiments with rats
and pigeons showed that the animals first hit the lever and
released food accidentally; after a few accidental releases,
the reinforcement of manipulating the lever ensured that the
behavior would be repeated. Skinner believed that operant
conditioning could be used in similar ways with human
beings.

Modifying behavior through operant conditioning has been used in the treatment of phobias,
obsessive-compulsive disorders, substance-abuse problems, and some sexual disorders, but
the impact of Skinner’s theories about operant conditioning has proved to be immense, reaching
far beyond the field of psychology. Zoos and other animal facilities routinely use food as a
positive reinforcement to train animals to move within enclosed areas and to increase safety
during veterinary examinations. With human subjects, operant conditioning has been used to

control absenteeism in the workplace (such as when employers offer staff members with no
absences a chance to win cash rewards), to increase sales (coupons), and to manage agitation
in older adults with dementia. Perhaps no field has been more influenced by operant
conditioning than education. Skinner’s assertion that positive reinforcement is more effective
than punishment at changing and establishing desirable behavior led to the discrediting of
punitive punishment in schools and the common application of timeouts (negative
reinforcement) and a token economy (i.e., rewarding good behavior with gold stars that can be
accumulated for prizes) instead.

Critics of operant conditioning have been vehement in pointing out its detriments. As early as
1959, American linguist and cognitive scientist Noam Chomsky argued that what worked in
Skinner’s laboratory could be applied to complex human behavior only in a superficial way. In
1960 progressive educator A. S. Neil insisted that rewarding good behavior taught that the
behavior was not worth doing for reasons other than the reward. Other critics were even more
severe, charging that operant conditioning was dangerous and inhumane. Gradually, the
influence of Skinner’s ideas declined, and by the twenty-first century, some declared that
operant conditioning had become peripheral in psychology and related fields. However, in 2002
a list of ninety-nine top psychologists was published in the Review of General Psychology and
B. F. Skinner topped the list.

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Rapanelli, Maximiliano, Luciana Romina Frick, and Bonifacio Silvano Zanutto. “Learning an
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Title:

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Pavlovian conditioning. By: Sparzo, Frank J., Salem Press
Encyclopedia of Health, 2019

Research Starters

Pavlovian conditioning

Date: 1890s forward

Type of psychology: Learning

Pavlovian conditioning is a basic process of learning that relates especially to reflexes and
emotional behavior. Interest in this form of learning has been long-standing and continues to the
present day. Pavlovian principles apply to a very wide range of organisms, situations, and
events.

Introduction

Pavlovian conditioning, also known as respondent conditioning and classical conditioning (to
distinguish it from instrumental or operant conditioning), is an elementary learning process and
has been of major interest to psychologists ever since the Russian physiologist Ivan Petrovich
Pavlov discovered that a dog could learn to salivate to a neutral stimulus after the stimulus was
paired repeatedly with food.

Pavlov’s early career focused on the study of heart circulation and digestion in animals (usually
dogs), for which he received the Nobel Prize in Physiology or Medicine in 1904. However, by

UAGC Library-test

Listen American Accent

One of the many
dogs Pavlov used in his
experiments (possibly

Baikal[1]), Pavlov
Museum Ryazan, Russia. Note
the saliva catch container and
tube surgically implanted in the

dog’s muzzle. By Rklawton
(English Wikipedia, see below)

[GFDL
(http://www.gnu.org/copyleft/fdl.html)

or CC-BY-SA-3.0
(http://creativecommons.org/licenses/by-

sa/3.0/)], via Wikimedia
Commons

The Pavlov memorial museum,
Ryazan; former home of the

physiologist I. P. Pavlov (built in
the early 19th century) By Ceroi

(Own work) [CC-BY-SA-3.0
(http://creativecommons.org/licenses/by-

sa/3.0)], via Wikimedia
Commons

that time Pavlov had already turned his attention to
experiments on conditioned reflexes, from which flowed a
new psychological nomenclature.

Conditioning

The core of Pavlovian conditioning is the pairing
(association) of stimuli to elicit responses. Food (meat
powder) placed in a dog’s mouth naturally produces
salivation. Pavlov called the food an unconditioned stimulus
(US) and salivation, elicited by the food, the unconditioned
response (UR). When a neutral stimulus—for example, a
tone that does not naturally elicit salivation—is repeatedly
followed by food, the tone alone eventually evokes
salivation. Pavlov labeled the tone a conditioned stimulus
(CS) and the response (salivation) elicited by it the
conditioned response (CR).

Pavlov’s formulation can be summarized as follows:

Before conditioning:
Food (US) elicits Salivation (UR)
Conditioning procedure:
Neutral Stimulus (Tone) plus Food (US) elicits Salivation (UR)
After conditioning:
Tone (CS) elicits Salivation (CR)

Pavlov believed that conditioned responses were identical
to unconditioned responses. That is usually not the case.
For example, conditioned responses may be less
pronounced (weaker) or a bit more lethargic than
unconditioned responses.

Several phenomena turn up in studies of Pavlovian
conditioning. Extinction, generalization, and discrimination
are among the most important. Extinction refers to the
procedure as well as to the elimination of a CR. If the CS is

repeatedly presented without the US, extinction occurs: The dog stops salivating to the tone.
During the course of extinction, the CR may return from time to time until it is finally
extinguished. Pavlov called the occasional return of the CR “spontaneous recovery.”

Stimulus generalization refers to responding not only to a particular CS but also to similar but
different stimuli. Further, the magnitude (amount of salivation) of a generalized response tends
to decline as stimuli become less and less like the CS. For example, a dog trained to salivate to
a 5,000-cycle-per-second (cps) tone is likely to salivate also to 5,300 cps and 4,700 cps tones
without specific training to do so (stimulus generalization). Responses tend to weaken in an
orderly way as tones become more and more unlike the CS—that is, as the tones move away
from the CS in both directions, say, to 4,400 cps from 4,100 cps, and 5,600 cps to 5,900 cps, the
flow of salivation becomes less and less.

Stimulus generalization in effect extends the number of stimuli that elicit a conditioned response.
Discrimination procedures restrict that number by conditioning a subject not to generalize across
stimuli. The procedure involves two processes: acquisition and extinction. The CS is paired
repeatedly with the US (acquisition) while the US is withheld as generalized stimuli are
presented repeatedly (extinction). If the dog now salivates to the CS and not to the generalized
stimuli, the dog has learned to discriminate or to act discriminatively. Pavlov reported that some
dogs displayed a general breakdown in behavior patterns (experimental neurosis) when called
on to make discriminations that were too difficult for them.

Pavlov’s work on what he called the second-signal system implies that conditioning principles
are relevant to human as well as to animal learning. Once, say, a tone is established as a CS in
first-order conditioning, the tone can be paired with a neutral stimulus to establish a second-
order CS. Thus, in the absence of food, a light might precede the tone (CS) several times until
the light itself begins to function as a CS. Second-order conditioning appears to follow many of
the same rules as first-order conditioning.

Pavlov’s work has clearly provided one way to study the learning process in great detail. It has
also provided the kind of data and theory that have affected research in other areas of learning,
such as instrumental conditioning and, subsequently, cognitive science and neuroscience.

In late 2015, neuroscientists at Johns Hopkins University conducted an experiment in the hope
of finally determining how this learning process occurs, or exactly how Pavlov’s dogs were
conditioned to drool. For the first time, the scientists were able to prove in a lab the link between
neurotransmitters and the conditioned response by studying brain cells of mice. After stimulating

the cells with neurotransmitters, the scientists analyzed the significance of the brain’s chemical
reward system in terms of conditioning. This research prompted discussion about whether this
knowledge could be used to enhance learning processes or possibly treat cognitive issues.

Range of Pavlovian Conditioning

Pavlovian phenomena have been demonstrated with different kinds of organisms and a wide
variety of stimuli and responses far beyond those studied by Pavlov. Stimuli that precede such
unconditioned stimuli as sudden loud noises (leading to rapid heart rate), a puff of air delivered
to the eye (evoking blinking), or a large temperature increase (eliciting sweating) may become
conditioned stimuli capable of eliciting conditioned responses on their own. The idea of second-
order (higher-order) conditioning is profoundly important because it suggests how rewards such
as words of praise and money are established apart from primary (biologically necessary)
rewards, such as food and water. It also may in part explain the power of films, plays, novels,
and advertisements to evoke strong emotion in the absence of direct experience with primary
(unconditioned) stimuli. Studies concerned with conditioned emotional reactions (CER),
especially fear and anxiety in people—a subject much more complex than simple reflexes—
have been of special interest to researchers and therapists for many years.

Additional Research Findings

Studies of conditioning essentially look at how various unconditioned and conditioned stimuli
influence responses under different arrangements of time and space. Following are a few
general findings.

Pavlovian conditioning tends to be readily established when stimuli or responses or both are
strong rather than weak. For example, in response to a near-drowning experience, some people
promptly learn to fear such conditioned stimuli as the sight of water, boats, palm trees, bathing
suits, and so on. In such cases, relevant stimuli and responses (panic) are presumably quite
strong.

Conditioned stimuli are most likely to elicit conditioned responses when unconditioned and
conditioned stimuli are paired consistently. If a mother always hums when she rocks her infant
daughter to sleep, humming is likely to become a potent and reliable CS, which soothes and
comforts her daughter. This outcome is less likely if the mother hums only occasionally.

When several stimuli precede a US, the one most often paired with the US will likely emerge as

the strongest CS. If, for example, both parents threaten to punish their young son, but only
father always carries out the threats, father’s threats are more likely than mother’s to evoke
apprehension in the child.

For some responses, such as eye blinking, conditioned stimuli tend to be strongest when they
precede the US by about one-half second. The optimal interval for other responses varies from
seconds to fractions of seconds: A neighbor’s dog barks immediately before little Sophie falls
from her swing, bumping her nose very hard. She cries. If the dog’s bark subsequently makes
Sophie feel uneasy, the bark is functioning as a CS. This outcome becomes less and less likely
as the bark and fall increasingly separate in time.

Conditioned responses are usually not established if a US and CS occur together (simultaneous
conditioning)—the potency of the UC overshadows the potential CS—or when a neutral stimulus
follows the US (backward conditioning).

Some Practical Applications

In a widely cited study reported in 1920, American researchers John B. Watson and Rosalie
Rayner conditioned a phobic reaction in an eleven-month-old infant named Albert. The
researchers discovered that Albert feared loud noises but seemed unafraid of a number of other
things, including small animals.

Watson and Rayner subsequently placed a white rat in Albert’s crib. When Albert reached for it,
the researchers struck a piece of resonant metal with a hammer, making a “loud sound.” After a
few such presentations, presenting the rat alone elicited crying and various avoidance reactions.
Albert also showed signs of fear to similar things, such as a rabbit, a furry object, and fluffy
clumps of cotton (stimulus generalization). Thus, Watson and Rayner provided early
experimental evidence that Pavlovian principles are involved in the acquisition of human
emotional reactions.

While this study induced a phobic reaction in the subject, systematic desensitization is a
procedure designed to eliminate phobias and anxieties. The procedure was largely developed
and named by South African-born therapist Joseph Wolpe. Noting that it is very difficult to have
pleasant and anxious feelings simultaneously, Wolpe fashioned a systematic technique to teach
clients to engage in behavior (relaxation) that competes with anxiety.

Therapy typically begins with an interview designed to identify specific sources of the client’s

fears. The therapist helps the client assemble a list of items that elicit fear. Items associated with
the least amount of fear are positioned at the bottom of the list; most feared items are placed
near the top. For example, if a client has a strong fear of dogs, the therapist and client would
develop a list of scenes that make the client fearful. Situations may vary from hearing the word
“dog” to seeing pictures of dogs, being in the vicinity of a dog, hearing a dog bark, being close to
dogs, and patting a dog.

The client is next taught to relax by tensing and releasing various groups of muscles—
shoulders, face, arms, neck, and so on. This phase of treatment ends when the client has
learned to fully relax on his or her own in a matter of minutes.

The client and therapist now move on to the next phase of therapy. While remaining fully
relaxed, the client is asked to imagine being in the first situation at the bottom of the list. The
image is held for several seconds. The client then relaxes for about twenty seconds before
imagining the same situation again for several seconds. When the client is able to imagine an
item and remain fully relaxed, the therapist presents a slightly more fearful situation to imagine.
This procedure continues until an image causes distress, at which time the session ends. The
next session begins with relaxation, followed by the client slowly moving up the list. As before,
the client stops at the point of distress. Therapy is successful when the client can imagine all the
items on the list while remaining fully relaxed. The technique is less helpful when clients have
difficulty identifying fearful situations or calling up vivid images.

In the hands of a skillful therapist, systematic desensitization is an effective technique for
reducing a wide variety of fears. Its Pavlovian features involve pairing imagined fearful scenes
with relaxation. When relaxation successfully competes with fear, it becomes a new CR to the
imagined scenes. As relaxation becomes sufficiently strong as a CR, anxiety is replaced by
calmness in the face of earlier aversive stimuli.

Extinction offers a more direct route to the reduction of fear than systematic desensitization. The
technique called flooding makes use of extinction. Flooding exposes the client to fear-arousing
stimuli for a prolonged period of time. Suppose a child is afraid of snakes. Although fear is likely
to increase initially, flooding would require the child to confront the snake directly and
continuously—to be “flooded” by various stimuli associated with the snake—until the conditioned
stimuli lose their power to elicit fear. Some therapists think that the application of this technique
is probably best left to professionals.

Some Everyday Examples

Pavlovian principles may be plausibly applied to daily life, as the following examples illustrate.

Couples sometimes refer to a certain tune as “our song.” A plausible interpretation is that
Pavlovian conditioning has been at work. The favored tune may have been popular and
repeated often at the time of the couple’s courtship and marriage. The tune has since become a
CS that evokes a variety of pleasant feelings associated with initial love.

A babysitter notes that giving a young child a blue blanket in the absence of his mother markedly
reduces his irritability. Most likely the blanket has been sufficiently associated with the soothing
actions of his mother (US) and now functions as a calming stimulus (CS).

An adolescent steadfastly avoids the location where he was seriously injured in an automobile
accident. He says that just thinking about the highway makes him nervous. The location
doubtless contains a number of conditioned aversive stimuli that now trigger unpleasant feelings
(CR) and avoidance.

After a bitter divorce, a woman finds that the sight of household items (CS) associated with her
former husband is terribly upsetting (CR). She has reduced her resentment by getting rid of the
offending items.

A wife often places flower arrangements in her husband’s den. The flowers (CS) now bring him
a measure of comfort (CR) when she is away on trips.

Respondent Conditioning and Reinforcement

Pavlovian behaviors are principally elicited by antecedent events (just as low temperatures elicit
shivering), while many behaviors are strengthened (in reinforcement) or weakened (in
punishment) by what follows behavior. In Pavlovian conditioning, two stimuli are presented, one
following another, regardless of what a subject does. What follows behavior is usually not
important in this form of conditioning. In studying the role of reinforcement on behavior
(instrumental or operant conditioning), the consequences that follow a person’s actions often
determine what the person is likely to do under similar circumstances in the future. What follows
behavior is important in this type of conditioning.

The topic of reinforcement is introduced here because Pavlovian conditioning and reinforcement
are intricately related in that any Pavlovian conditioning is likely to contain elements of

instrumental conditioning, and vice versa. For example, if someone has a near-drowning
experience and now avoids bodies of water, it is plausible to say that conditioned stimuli
associated with the experience evoke unsettling feelings. The person reduces the unpleasant
feelings by avoiding bodies of water. In this example, negative feelings are conditioned
according to Pavlovian principles. The avoidance reaction is maintained by (negative)
reinforcement and involves instrumental learning. Virtually all the previous examples can be
analyzed similarly.

Bibliography

Baldwin, John D., and Janice I. Baldwin. Behavior Principles in Everyday Life. 4th ed. Upper
Saddle River: Prentice Hall, 2001. Print.

Dance, Scott. “Johns Hopkins Neuroscientists Trace What Made Pavlov’s Dog Salivate.”
Baltimore Sun. Tribune, 6 Dec. 2015. Web. 23 Feb. 2016.

Hergenhahn, B. R. An Introduction to the History of Psychology. 6th ed. Belmont: Wadsworth,
2009. Print.

Levis, Donald J. Foundations of Behavioral Therapy. New Brunswick: Transaction, 2010. Print.

“Pavlovian Test Finds Sleeping Consciousness.” New Scientist 26 Sept. 2009: 18. Print.

Ramnerö, Jonas, and Niklas Törneke. ABCs of Human Behavior: Behavioral Principles for the
Practicing Clinician. Oakland: New Harbinger, 2008. Print.

Redish, A. David. The Mind Within the Brain. Oxford: Oxford UP, 2013. Print.

Rescorla, Robert A. “Pavlovian Conditioning: It’s Not What You Think It Is.” American
Psychologist 43.3 (1988): 151–60. Print

Watson, J. B., and R. Rayner. “Conditioned Emotional Reactions.” Journal of Experimental
Psychology 3 (1920): 1–14. Print.

Wolpe, Joseph. The Practice of Behavior Therapy. 4th ed. Boston: Allyn, 2008. Print.

Copyright of Salem Press Encyclopedia of Health is the property of Salem Press. The
copyright in an individual article may be maintained by the author in certain cases. Content may
not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
express written permission. However, users may print, download, or email articles for individual
use. Source: Salem Press Encyclopedia of Health, 2019, 5p
Item: 93872139

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