JMIR FORMATIVE RESEARCH

Beltzer et al

Original Paper

Changes in Learning From Social Feedback After Web-Based
Interpretation Bias Modification: Secondary Analysis of a Digital
Mental Health Intervention Among Individuals With High Social
Anxiety Symptoms
Miranda L Beltzer1,2, PhD; Katharine E Daniel2, MA; Alexander R Daros2, PhD; Bethany A Teachman2, PhD
1Center for Behavioral Intervention Technologies, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

2

Department of Psychology, University of Virginia, Charlottesville, VA, United States

Corresponding Author:
Miranda L Beltzer, PhD
Center for Behavioral Intervention Technologies
Northwestern University Feinberg School of Medicine
750 N. Lake Shore Dr., 10th Floor
Chicago, IL, 60611
United States
Phone: 1 9144622962
Email: miranda.beltzer@northwestern.edu

Abstract
Background: Biases in social reinforcement learning, or the process of learning to predict and optimize behavior based on
rewards and punishments in the social environment, may underlie and maintain some negative cognitive biases that are characteristicof social anxiety. However, little is known about how cognitive and behavioral interventions may change social reinforcement
learning in individuals who are anxious.
Objective: This study assessed whether a scalable, web-based cognitive bias modification for interpretations (CBM-I) intervention
changed social reinforcement learning biases in participants with high social anxiety symptoms. This study focused on 2 typesof social reinforcement learning relevant to social anxiety: learning about other people and learning about one’s own social
performance.
Methods: Participants (N=106) completed 2 laboratory sessions, separated by 5 weeks of ecological momentary assessment
tracking emotion regulation strategy use and affect. Approximately half (n=51, 48.1%) of the participants completed up to 6 brief
daily sessions of CBM-I in week 3. Participants completed a task that assessed social reinforcement learning about other people
in both laboratory sessions and a task that assessed social reinforcement learning about one’s own social performance in thesecond session. Behavioral data from these tasks were computationally modeled using Q-learning and analyzed using mixed
effects models.
Results: After the CBM-I intervention, participants updated their beliefs about others more slowly (P=.04; Cohen d=−0.29) but
used what they learned to make more accurate decisions (P=.005; Cohen d=0.20), choosing rewarding faces more frequently.
These effects were not observed among participants who did not complete the CBM-I intervention. Participants who completed
the CBM-I intervention also showed less-biased updating about their social performance than participants who did not complete
the CBM-I intervention, learning similarly from positive and negative feedback and from feedback on items related to poor versus
good social performance. Regardless of the intervention condition, participants at session 2 versus session 1 updated their
expectancies about others more from rewarding (P=.003; Cohen d=0.43) and less from punishing outcomes (P=.001; Cohen
d=−0.47), and they became more accurate at learning to avoid punishing faces (P=.001; Cohen d=0.20).
Conclusions: Taken together, our results provide initial evidence that there may be some beneficial effects of both the CBM-I
intervention and self-tracking of emotion regulation on social reinforcement learning in individuals who are socially anxious,
although replication will be important.
(JMIR Form Res 2023;7:e44888) doi: 10.2196/44888

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KEYWORDS
social anxiety; reinforcement learning; cognitive bias modification; interpretation bias; reward learning; probabilistic learning;
Q-learning; digital intervention

Introduction
Background
Individuals who are socially anxious tend to see the world
through rejection-colored glasses, perceiving ambiguous social
situations as more negative than they are and expecting toperform poorly in social situations [1,2]. Some of these negative
cognitive biases might be partly maintained by negative learning
biases, wherein individuals who are socially anxious havedifficulty using positive information to update their previously
held negative beliefs [3]. For example, if a person who issocially anxious updated their beliefs about an acquaintancemore from the few times that the acquaintance seemed annoyedwith them than from the many times the acquaintance wasfriendly, they might come to have an overly negative expectancy
of interactions with that acquaintance. Similarly, negative
expectancies about social performance might be partiallyexplained by overweighting instances of negative versus positive
feedback—learning more from criticism than praise. Changing
these learning processes to be less negatively biased may havepositive effects for individuals who are socially anxious, suchas changing rigidly negative beliefs about social interactions
and performance to be more balanced and reducing motivation
to avoid social situations. In this study, we investigated whether
a computerized intervention that trains more positive, flexible
interpretation styles for individuals who are socially anxious
also modifies the learning process by which they update theirsocial expectancies from new experiences (ie, social
reinforcement learning [RL]). We used computational modelingto understand changes in particular parameters of interest,
specifically learning rates, which are the weights given to
positive and negative feedback when updating social
expectancies.

Social RL Biases
RL is the process by which people or other agents learn topredict outcomes and optimize behavior in an environment
where taking actions leads to rewards (positive outcomes) andpunishments (negative outcomes) [4]. RL has been used tounderstand various mental illnesses, including addiction [5],depression [6], and psychosis [7], and has been used to develop
algorithms for diverse applications, such as self-driving cars[8], health care decisions [9], and gaming [10]. Social RL is thesame learning process but specific to the social environment:
updating beliefs about different social actions based on social
rewards (such as acceptance and smiles from others) and social
punishments (such as rejection and scowls). Previous researchhas begun to explore social RL in social anxiety, finding that,
unlike healthy individuals, who update their beliefs about theirsocial performance more from positive versus negative feedback
provided by others, individuals who are socially anxious update
more from negative feedback [11]. When confronted with others
who evaluate them negatively or neutrally, people who aresocially anxious readily learn these evaluations accurately,https://formative.jmir.org/2023/1/e44888

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whereas healthy controls are biased toward believing that others
think more positively of them even when they are receiving
more negative or neutral evaluations [12]. Some research has
found that individuals who are socially anxious tend to learn
more from angry versus happy faces [13], whereas other research
has not found evidence that people who are socially anxious
have biased updating of angry faces [14]. Importantly, several
studies have found that individuals who are socially anxious
have difficulty adapting their learning and behavior to changing
probabilities of social punishment [15-17]. Specifically, thereis accumulating evidence that individuals who are sociallyanxious have difficulty updating negative social expectancies
to become more positive [14,18]. Together, these studies suggestthat individuals who are socially anxious may have negative
biases in social RL that can make it difficult to learn from social
interactions, even when they seemingly go well.
Although there is considerable literature on the neural
underpinnings of RL and how pharmacological interventions
may change RL, less is known about changing RL throughcognitive and behavioral interventions. Several pharmacologicalinterventions have been used to normalize aberrant processingof rewards and punishments in various psychological disorders[19-22], including specifically changing punishment learning
rates (ie, shifting how heavily a person weighs a negative
outcome when updating expectancies) [23,24]. However, very
little research has assessed whether cognitive behavioral
interventions change RL processes, despite several researchers
advocating for computationally modeling learning and cognitivechange in cognitive behavioral therapy [25,26]. Recent studies
have shown that cognitive interventions can change RL
processes in healthy individuals [27] and individuals withschizophrenia [28]. To our knowledge, this study is the first to
behaviorally assess changes in RL through cognitive bias
modification in a population with elevated anxiety symptoms.

Cognitive Bias Modification
This study assesses whether several social RL biases, which arebiases in learning from positive and negative social feedback
to update beliefs about others and one’s own social performance,
can be changed through a scalable, computerized intervention
called cognitive bias modification for interpretations (CBM-I).
CBM-I programs aim to train people to make less threatening
and more flexible interpretations of ambiguous stimuli. They
often do this by presenting scenarios that are emotionallyambiguous until the final word. The final word more frequently
resolves the ambiguity in positive or neutral ways than negative
ways, which aims to train participants to interpret ambiguoussituations more benignly. CBM-I is a targeted,mechanism-driven intervention that is easily scalable. Because
individuals who are socially anxious can use this intervention
from the comfort of their own homes or wherever it isconvenient for them, CBM-I holds potential either as an adjunctto other treatments or as a stand-alone intervention. CBM-I caneffectively reduce cognitive biases and, to a lesser extent, thesymptoms of social anxiety disorder [29-32]. However, there
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has been mixed evidence regarding the efficacy of CBM-I,
including small effect sizes [33,34].
CBM-I is a useful intervention to study changes in social RL
because the cognitive mechanism is conceptually related.
Training people to interpret ambiguous situations more benignly
may facilitate changes in social RL by priming awareness ofpotential social rewards and reducing expectations of social
rejection. For example, in the CBM-I intervention used in thisstudy, participants read emotionally ambiguous scenarios inwhich the ambiguity is only resolved in the final word, whichis missing a letter that participants complete (eg, “You arerequired to give a presentation to a group of work colleagues
that you know well. They all are quiet during your presentation.
As you think about the presentation later at home, you thinkthat your colleagues found your presentation...stimul_ting”).
As a participant who is socially anxious reads this scenario,
they might start to think the scenario will end poorly because
they expect punishment in socially threatening situations (eg,that their colleagues hated their presentation), but the actual
resolution of the ambiguity in a rewarding way (ie, that theircolleagues found their presentation stimulating) might train theparticipant to update their future expectancies to be lessthreatening. Notably, previous work has found that RL
parameters predict the response to cognitive bias modification
to shift attention bias (ie, selective focus on threat cues) [35];given their similar focus on shifting threat biases, CBM-I may
also be related to RL.

Evidence for Social RL and CBM-I Change in These
Data
Three previous studies [36,37] (Beltzer, ML, unpublished data,
June 2023) that are relevant to this study have been performed
using data from the same data set, and these studies informed
our hypotheses. Two of these studies [36] (Beltzer, ML,
unpublished data, June 2023) assessed social anxiety–relateddifferences in social RL by comparing participants with highsocial anxiety symptoms (who had not completed CBM-I) withparticipants with low social anxiety symptoms on 2 social RL
tasks. First, the social probabilistic selection task assessedlearning about other people by asking participants to choose
between neutral faces with different probabilities of becoming
happy or angry when chosen. Second, the speech expectancies
task assessed learning about one’s own social performance byasking participants to rate how they expected to perform on a
stressful speech both before the speech and again after the
speech and seeing feedback from the judges on theirperformance (the tasks are fully described in the Methods section
and in their original papers [36] [Beltzer, ML, unpublished data,
June 2023]). For each of these tasks, we used RL to
computationally model how each participant weighted rewarding
and punishing social outcomes when updating their beliefs,
parameterized as the reward and punishment learning rates.
These studies did not find social anxiety–related differences inlearning rates as a function of social anxiety group or whethersocial feedback was rewarding or punishing but did find that,
contrary to hypotheses, participants with high versus low social
anxiety symptoms were less accurate at avoiding punishingfaces once feedback was no longer given [36]. Exploratory
analyses found that participants (regardless of social anxiety
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Beltzer et algroup) updated their expectancies of their social performance
more from positive than negative feedback and that participants
with high versus low social anxiety symptoms used feedback
to update their expectancies more for elements of poor social
performance than good social performance (Beltzer, ML,
unpublished data, June 2023). These results suggest that negativesocial RL biases were not evident in this sample in the ways
we had expected; instead, participants with high social anxiety
showed impaired punishment learning accuracy and greater
updating from feedback about feared, negative aspects of social
performance.
Another study from this data set [37] assessed the effects of
CBM-I on outcomes related to cognitive styles and social
anxiety symptoms. Participants who completed both CBM-I
and ecological momentary assessment (EMA) about theiremotion regulation experienced greater reductions in trait
negative interpretation bias than participants who completed
only EMA. However, CBM-I was not uniquely related tochanges in other trait cognitive styles (ie, cognitive flexibility,
reappraisal ability, social interaction anxiety, and fear ofnegative evaluation). CBM-I also increased the daily
self-reported ability to use cognitive reappraisal but it did notchange other daily cognitive style outcomes more than only
EMA. These results suggest that, in this data set, the CBM-I
intervention effectively changed negative interpretation biases
but did not have effects on many other processes related to social
anxiety that it did not target as directly. Given social RL’s close
conceptual ties to interpretation biases, it is possible that
although null effects were found on many of these peripheral
outcomes, CBM-I might still exert effects on social RL.

Overview and Hypotheses
This study examined whether social RL biases changed as afunction of completing a week of CBM-I in the middle of a
5-week EMA study tracking emotion regulation strategy use
and affect (as compared with the comparison condition that also
completed 5 weeks of EMA but no CBM-I). We assessed
whether CBM-I was associated with differences in the weights
given to socially rewarding versus punishing information when
updating beliefs in 2 domains: learning about other people andlearning about one’s own social performance. Given the slightmethodological differences, these are referred to as “learning
rates” for learning about other people and “update weights” forupdating social performance expectancies based on feedback.
In the domain of learning about other people, we also assessed
whether CBM-I was associated with differences in using what
one has learned about others to guide one’s own behavior toaccurately choose rewarding people and to avoid punishingpeople. To do this, we compared parameters extracted from asocial probabilistic learning task administered at baseline andfollow-up laboratory sessions, and a speech expectancy updatingtask administered at follow-up, as a function of the intervention
group (CBM-I vs only EMA).
To our knowledge, no study to date has assessed changes in RL
as a function of CBM-I. We had competing hypotheses for mostoutcomes, as prior analyses of social RL in this data set foundresults that diverged from hypotheses based on extant literature.

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Plans for analyses and competing hypotheses were preregisteredon the web [38].
On the basis of conceptually related findings of decreased
negative interpretation bias after completing a course of CBM-I(observed both in the literature and in this data set, as describedearlier) [37], we hypothesized that participants who are sociallyanxious would exhibit a decrease in parameters related to social
punishment learning (punishment learning rate and accuracy in
avoiding punishment) and/or an increase in parameters related
to social reward learning (reward learning rate and accuracy inchoosing reward). Modeling these parameters separately allowed
us to pinpoint whether CBM-I led to changes in learning fromsocial reward and/or punishment and whether those changes
were reflected in the weight given to new social information
and/or the accuracy of decisions made based on information
learned about reward and/or punishment. Similarly, wehypothesized that socially anxious participants who completed
CBM-I would update their beliefs about their own social
performance more based on positive feedback and/or less basedon negative feedback, as compared with the EMA-only group.
On the basis of our prior analyses on the social probabilistic
learning task, finding no differences in learning rates as afunction of the social anxiety group, we hypothesized that wewould not find a difference between the intervention groups inlearning rates at either session. On the basis of our surprisingfinding of impaired accuracy in avoiding punishment in thehigh social anxiety group [36], we hypothesized that CBM-I
might mitigate this bias, evidenced as a greater increase inaccuracy in avoiding punishment from baseline to follow-up in
the CBM-I versus EMA-only group. On the basis of our priorhttps://formative.jmir.org/2023/1/e44888

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Beltzer et al
exploratory finding of greater updating on items assessing poorsocial performance in the high versus low social anxiety group(Beltzer, ML, unpublished data, June 2023), we hypothesizedthat a model with 2 update weights (estimated separately foritems assessing good vs poor social performance) would reveal
a CBM-I versus EMA-only intervention–related difference.
Specifically, we hypothesized that CBM-I would mitigate thisbias toward greater updating on items assessing poor social
performance, such that the CBM-I versus EMA-only groupwould update their expectancies less from feedback on items
measuring poor versus good performance.

Methods
Participants
Overview
Participants were recruited through the University of Virginia
undergraduate participant pool, advertisements sent to university
email lists for undergraduate and graduate students, and flyers
posted in public areas around the community. Prospective
participants completed a screening survey, and 114 adults (aged18-45 years) with moderate to severe social anxiety symptoms
were invited to participate based on eligibility criteria defineda priori (scoring 29 or greater out of a possible 80 points on theSocial Interaction Anxiety Scale, approximately one-fourth ofan SD below the mean in a sample diagnosed with social phobia[39]; see Table 1 for demographic information about the sample
and Multimedia Appendix 1 for the CONSORT [Consolidated
Standards of Reporting Trials] diagram and details about
dropout).

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Table 1. Demographic characteristics of the final sample of 106 participants analyzed.
CBM-Ia (n=51)

Only EMAb (n=55)

Female38 (74.51)

40 (72.73)

Male

13 (25.49)

15 (27.27)

Nonbinary

0 (0)

0 (0)

Age (years), mean (SD)20.67 (2.92)

20.24 (3.12)

Undergraduates, n (%)

36 (70.59)

46 (83.64)

Latinx or Hispanic

1 (1.96)

2 (3.64)

Not Latinx or Hispanic

50 (98.04)

52 (92.55)

Prefer not to answer

0 (0)1 (3.81)African American or Black

2 (3.92)

6 (10.91)

Asian

11 (21.57)

10 (18.18)

Hawaiian or Pacific Islander

1 (1.96)

2 (3.64)

Middle Eastern

1 (1.96)

2 (3.64)

Native American

0 (0)

0 (0)

White

39 (76.47)

40 (72.73)

Sex, n (%)

Ethnicity, n (%)

Race, n (%)

a

CBM-I: cognitive bias modification for interpretations.

b

EMA: ecological momentary assessment.

Procedure
All participants completed a baseline session in the laboratorythat included informed consent, questionnaires, and social
probabilistic selection and speech expectancy tasks. Participants
then completed 5 weeks of EMA about their emotion regulation(for more information on EMA procedures, see the studies by
Daniel et al [37], Beltzer et al [40], and Daniel et al [41]),
followed by another laboratory session with the same tasks(because the mobile app used for only EMA ran on certain
versions of iOS and Android OS, participants who did not havea compatible smartphone were excluded. More information
about the EMA component of the study, as well as othermeasures administered for the larger parent study but notanalyzed in this study, can be found on the web [42]).
Approximately half (59/114, 51.8%) of the sample was
randomized to complete a week-long CBM-I intervention during
week 3, and this CBM-I condition’s EMA prompts were slightly
reduced during this week to ensure that the time spent on tasks
was similar across conditions.

Cognitive Bias Modification
Over the course of week 3, participants assigned to the CBM-I
condition were asked to complete six 10- to 15-minuteweb-based CBM-I sessions on their PC or smartphone, once
per day. Participants read a series of 30 ambiguous scenariosthat raised the possibility of a threat (18 social, 7 physical, and5 other threat), which only became disambiguated once the final
word of the scenario was read, from which 1 or 2 letters were
missing (eg, “While at the hairdresser’s, you opt for a
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completely different haircut. When you see your friend
afterwards, she gasps. Her gasp probably means that she thinks
the new style makes you look...gre_t”). After filling in thecorrect missing letter or letters to resolve the scenario’s
emotional ambiguity (eg, “great”), the participant was asked ayes-or-no or multiple-choice question about the scenario toensure the participant had read it and understood thedisambiguated ending. The disambiguation resolved theambiguity in a benign, nonthreatening way for 90% of scenarios,
allowing participants to learn through practice that uncertain
situations can end in a variety of ways (including in more
rewarding ways than expected, counteracting their biased
negative expectations). The full list of CBM-I materials(including scenarios, disambiguating words, and comprehension
questions) is available on the web [43]. See Multimedia
Appendix 2 for screenshots of the CBM-I training.

Measures
Social Probabilistic Selection Task
The social probabilistic selection task [13] was used to assess
social RL about other people. This was an adaptation of a widelyused probabilistic category learning paradigm [44] that uses
socially relevant information as stimuli (neutral faces) andsocially evaluative reinforcement as feedback (reward: happy
faces, punishment: angry faces). The task consisted of 2 phases:
training and testing. In the training phase, participants were
presented with 2 neutral faces at a time and were instructed that
one face would become happy if chosen but the other would
become angry. They were instructed to select the face that they
thought was more likely to be happy. The pairs of faces had
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different complementary reward contingencies (ie, in 1 pair,
one face became happy 80% of the time it was chosen and angry
20% and the other face became happy 20% and angry 80%; theother pairs were similar but with 70%/30% and 60%/40%
contingencies) but participants had to learn which faces tochoose through trial and error rather than explicitly being told
the contingencies. In the testing phase, the faces wererecombined into all possible pairs (eg, the 80% rewarding face
was presented in pairs with 70%, 60%, 40%, 30%, and 20%
rewarding faces, rather than just the 20% rewarding face, as in
the training phase). Participants were instructed to select themore rewarding face based on what they had learned during thetraining phase, and no feedback was given (ie, the neutral faces
did not become happy or angry). See Multimedia Appendix 3[11,13,44-49] for more details on the social probabilistic
selection task.

Speech Expectancies Task
The speech expectancies task (modified from Koban et al [11])was used to assess social RL about one’s own social
performance. Modified from the Trier Social Stress Test [50],participants had 2 minutes to mentally prepare to give a stressful,
4-minute speech for a panel of 2 confederate judges, who video
recorded the speech and were instructed to maintain neutral
facial expressions throughout. Before the speech, participants
rated how they expected to perform on 10 items related to goodsocial performance (eg, “I will appear calm”) and 10 related topoor performance (eg, “I will appear to be sweating”) on a scalefrom 0 (“disagree”) to 100 (“agree”; adapted from Cody andTeachman [51]).
After the speech at the second laboratory session, participantsreceived false feedback on their performance, which wasostensibly from the judges but was actually randomly generated
to fall within a range around the participant’s prespeech
self-ratings to be believable [11]. For each social
performance–related item, participants were asked to rate how
they expected to perform on a similar speech in the future.
Before entering their postspeech expectancy rating, they werefirst shown their prespeech rating and then the false feedback.
To generate the false feedback, a random integer between −50
and 50 was added to each participant’s self-rating on each item,
bounded by the 0 to 100 rating scale, to obtain the feedback
rating. Speech ratings and feedback were presented in
PsychoPy2 [52]. Participants only completed the false feedback
and postspeech expectancy ratings in session 2 because we felt
it was not ethical to use deception in session 1 and then notdebrief participants until 5 weeks later at the end of the study.
See Multimedia Appendix 3 for more details about the speech
expectancies task.

Ethics Approval
This study was approved by the University of Virginia
Institutional Review Board (UVA IRB 2270). and was
conducted in accordance with the ethical standards of the
responsible committee on human experimentation (institutionaland national) and the Helsinki Declaration of 1975, as revisedin 2000.

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Informed Consent and Participation
All the participants provided informed consent. Participants
were compensated US $25 for each laboratory session they
completed and up to US $70 for completing EMA surveys and
CBM-I sessions, with more compensation for higher completion,
as detailed in the informed consent form, for a totalcompensation of up to US $120 for the completion of all study
components. Participants who were undergraduate students at
University of Virginia could alternatively claim up to 4 hours
of research credits for participation. Data were deidentified foranalysis. Patients were fully debriefed at the end of the study.
As described earlier, the feedback and expectancy updating
portion of the speech expectancies task was administered onlyat each participant’s final session to be able to debrief about
false feedback on the same day they received feedback.

Plan for Analyses
Overview
This study assessed whether individuals who are sociallyanxious who completed CBM-I plus EMA versus only EMA
learned differently from positive and negative social feedback
when updating their expectancies about other people and theirown social performance. To do this, we applied versions of a
Q-learning model to the social probabilistic selection task andthe speech expectancies task. We then compared parameters
related to learning (ie, learning rates and update weights for
positive and negative feedback) between groups. In the domainof learning about other people, we also assessed whether CBM-I
changed the participants’ accuracy in choosing sociallyrewarding and avoiding socially punishing faces during thetesting phase of the social probabilistic selection task. Rewardand punishment learning accuracy were defined as the
proportion of times the participant accurately selected the more
rewarding face when the most rewarding (80% happy) and mostpunishing (80% angry) faces, respectively, were paired with allintermediate faces (70%, 60%, 40%, and 30%) during the testing
phase.

Computational Modeling of the Social Probabilistic
Selection Task
The social probabilistic selection task was computationallymodeled using the hBayesDM R package [53], which includes
hierarchical Bayesian modeling of the Q-learning estimation
procedure [54]. Q-learning models the process of updating the
expected values of each neutral face by learning from eachexperience of a rewarding (eg, happy) or punishing (eg, angry)
outcome. We compared 2 candidate models. One model fitted
the participants’ choice data from the training phase of the taskwith separate reward and punishment learning rate parameters,
which, respectively, represent the weights given to positive andnegative prediction errors (the value difference between theobserved and expected outcomes) when updating these expectedvalues. The other model included a single learning rate
parameter for all trials (and so was nested in the more complex
model). We compared the prediction accuracy for each conditionat each session using the leave-one-out information criterion(LOOIC), given our relatively small sample size [55]. Because
the more complex model had a lower LOOIC for one group and
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the models provided a relatively similar fit for the other groups,
the more complex model was chosen (see Multimedia Appendix
3 for LOOIC values and for more detail about computationalmodeling of this task).

Computational Modeling of the Speech Expectancies
Task
For the speech expectancies task, we used similar models toestimate how participants learned from feedback when updating
their postspeech expectancies of how they would perform on afuture speech. Given concerns about having too few items percell to reliably model separate update weights for positive andnegative feedback, we modeled update weights in 2 ways:
separately for feedback that was more positive than participants’prespeech self-ratings (ie, positive prediction errors) andfeedback that was more negative than participants’ prespeech
self-ratings (ie, negative prediction errors) and separately foritems measuring good versus poor speech performance (to bethorough, we also ran models with 1 update weight estimatedacross all 20 items and with 4 update weights estimated [onefor each combination of prediction error and item valence]; see
Multimedia Appendix 3 for more details). For items measuring
good speech performance, a positive prediction error occurred
when the feedback rating was higher than the participant’s
prespeech self-rating; for items measuring poor speech
performance, this was reversed, so a positive prediction error
occurred when the feedback rating was lower than theparticipant’s prespeech self-rating. A similar classification
procedure was used to identify negative prediction errors, withthe scoring reversed for items measuring poor speech
performance. Note that the update weights were similar to thelearning rates modeled for the social probabilistic selection task
but were estimated over only 1 instance of updating per item.
See the Multimedia Appendix 3 for more details on thecomputational modeling of this task.

Results
Analyses of the Social Probabilistic Selection Task (RL
About Other People)
Participants’ behavior on the social probabilistic selection task
was analyzed to better understand how CBM-I affected social
RL about other people. Specifically, we assessed reward andhttps://formative.jmir.org/2023/1/e44888

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Beltzer et alpunishment learning rates, which described the weights given
to happy and angry faces, respectively, in updating expectancies
of other people during the training phase and accuracy inchoosing rewarding and avoiding punishing faces in the testing
phase.

Learning Rates Estimated Over the Training Phase
To assess learning rate differences as a function of CBM-I
condition, a mixed effects model was used to predict learning
rate from fixed effects of CBM-I condition, session, prediction
error valence, and all 2- and 3-way interactions, with a random
intercept for participant. There were significant
session-by-condition and session-by-prediction error valenceinteractions. Post hoc pairwise comparisons of estimatedmarginal means (with a Tukey adjustment; for all post hoc tests,
note that estimated marginal means and odds ratios are presented
on the logit scale) revealed that, in the CBM-I condition,
learning rates decreased from session 1 (mean 0.23, SE 0.01)
to session 2 (mean 0.20, SE 0.01; t303=−2.04; P=.04; Cohen
d=−0.29) but did not significantly change in the EMA-only
condition (session 1: mean 0.22, SE 0.01; session 2: mean 0.25,
SE 0.01; t308=1.79; P=.07; Cohen d=0.25; Figure 1). In otherwords, after completing CBM-I, participants updated their
expectancies about other people less based on feedback,regardless of whether that feedback was a happy or angry face.
Punishment learning rates decreased from session 1 (mean 0.25,
SE 0.01) to session 2 (mean 0.20, SE 0.01; t299=−3.34; P=.001;
Cohen d=−0.47), whereas reward learning rates increased fromsession 1 (mean 0.21, SE 0.01) to session 2 (mean 0.26, SE
0.01; t299=3.04; P=.003; Cohen d=0.43; Figure 2). This meansthat by the end of the study, participants (regardless of theintervention condition) updated their expectancies about others
less from angry faces and more from happy faces (Table 2).
These findings are partially in line with hypotheses based onprior literature. We hypothesized that punishment learning rates
would decrease and/or reward learning rates would increase in
the CBM-I condition, whereas the results suggested that bothof these changes were observed regardless of the intervention
condition (rather than being specific to CBM-I). Furthermore,learning rates decreased in the CBM-I condition, regardless ofthe prediction error valence (rather than being specific tonegative prediction errors).

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Figure 1. Learning rates in the social probabilistic selection task decreased from session 1 to session 2 in the cognitive bias modification for interpretations(CBM-I) condition but did not significantly change in the ecological momentary assessment (EMA)–only condition. Condition=intervention condition(CBM-I vs only EMA).

Figure 2. Punishment learning rates decreased from session 1 to session 2, whereas reward learning rates increased from session 1 to session 2 in thesocial probabilistic selection task. CBM-I: cognitive bias modification for interpretations; EMA: ecological momentary assessment.

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Table 2. Model estimates predicting learning rate in the training phase of the social probabilistic selection task (n=106 participants)a.
Predictors

Learning rate
Estimates (95% CI)

t test (df)

P value

Intercept

0.23 (0.21 to 0.24)

32.10 (100.88)<.001 bSession−0.00 (−0.01 to 0.01)−0.21 (305.78).83

Conditionc−0.01(−0.02 to 0.00)−1.39 (100.88).16

PE valenced

0.00 (−0.01 to 0.01)

0.63 (291.80).53

Session × condition−0.02 (−0.03 to −0.00)−2.71 (305.78).007

Session × PE valence

0.03 (0.01 to 0.04)

4.53 (291.80)<.001

Condition × PE valence

0.01 (−0.00 to 0.02)

1.13 (291.80).26

Session × condition × PE valence

0.01 (−0.00 to 0.02)

1.16 (291.80).25

Fixed effectsaRandom effects: σ2=0.01, τ00 (participant)=0.00, intraclass correlation coefficient=0.13, observations=404, marginal R2=0.067, conditional R2=0.185.

b

Italicization indicates P<.05.

c

Condition: intervention condition (cognitive bias modification for interpretations vs only ecological momentary assessment).

d

PE valence: prediction error valence (positive vs negative).

Accuracy Measured During the Testing Phase
To assess differences in accuracy at choosing the most rewarding
face and avoiding the most punishing face in the testing phaseas a function of the CBM-I condition, a generalized linear mixed
model was used to predict the proportion of times the participant
accurately chose the most rewarding face and accurately did
not choose the most punishing face when each of them waspaired with all other faces in the testing phase. This model

included fixed effects of the CBM-I condition, session,
prediction error valence, and all 2- and 3-way interactions, witha random intercept for participant. There was a significant main
effect of trial type (choose reward vs avoid punishment) thatwas not interpreted because it was subsumed within a significant
interaction (Table 3), which more thoroughly and accurately
describes the effects [56]. There were statistically significant
session-by-condition,
session–by–trial
type,
and
condition–by–trial type interactions.

Table 3. Model estimates predicting accuracy in the testing phase of the social probabilistic selection task (n=106 participants)a.
Predictors

Probability of selecting the more rewarding face
Odds ratio (95% CI)

z scoreP value

4.55 (3.85-5.38)

17.71<.001 bFixed effects
Intercept
Session1.04 (0.99-1.09)

1.67

.09

c

1.05 (0.89-1.25)

0.62

.54

Trial typed

1.33 (1.27-1.39)

12.25

<.001

Session × condition

1.06 (1.01-1.11)

2.44

.02

Session × trial type0.94 (0.90-0.98)

−2.67

.008

Condition × trial type1.09 (1.04-1.14)

3.54

<.001

Session × condition × trial type1.02 (0.98-1.07)

0.99

.32

ConditionaRandom effects: σ2=3.29, τ00 (participant)=0.70, intraclass correlation coefficient=0.18, observations=12,863, marginal R2=0.024, conditional R2=0.195.

b

Italicization indicates P<.05.

c

Condition: intervention condition (cognitive bias modification for interpretations vs only ecological momentary assessment).

d

Trial type: choose reward versus avoid punishment.

To better understand these statistically significant interactions,
we performed post hoc pairwise comparisons of the estimatedmarginal means (with a Tukey adjustment). We report those
relevant to our hypotheses (those that include session) here andhttps://formative.jmir.org/2023/1/e44888

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the less-relevant interactions in Multimedia Appendix 3.
Following up on the session-by-condition interaction, we foundthat testing phase accuracy increased from session 1 (mean 1.47,
SD 0.13) to session 2 (mean 1.67, SD 0.13; z score 2.84; P=.005;
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Cohen d=0.20) in the CBM-I condition but did not change inthe EMA-only condition (session 1: mean 1.48, SD 0.12; session
2: mean 1.44, SD 0.12; z score 0.56; P=.58; Cohen d=−0.04;
Figure 3). In other words, after completing CBM-I, participants

Beltzer et alused the probabilities they had learned to make better decisions
about other people in the testing phase, regardless of whether
that meant choosing rewarding faces or avoiding punishingfaces.

Figure 3. Decision accuracy in the testing phase of the probabilistic selection task (percentage of times the more rewarding face was chosen from pairsthat included intermediate faces paired with either the most rewarding face or the most punishing face) increased from session 1 to session 2 in the
cognitive bias modification for interpretations (CBM-I) condition, but did not significantly change in the ecological momentary assessment (EMA)–only
condition.

Following up on the session–by–trial type interaction, we foundthat accuracy in avoiding punishment increased from session 1(mean 1.13, SD 0.09) to session 2 (mean 1.33, SD 0.09; z score
3.28; P=.001; Cohen d=0.20) but accuracy in choosing rewards
did not (session 1: mean 1.82, SD 0.10; session 2: mean 1.78,
SD 0.10; z score −0.62; P=.54; Cohen d=−0.04; Figure 4). Thissuggests that by the end of the study, participants (regardlessof the CBM-I condition) became better at learning to avoid themost punishing face. These results are partially in line with the
hypotheses based on prior studies using this data set. We
hypothesized that accuracy in avoiding punishment would
increase for the CBM-I condition, whereas the results suggested
that accuracy (not specific to punishment) increased for the
CBM-I condition, and accuracy in avoiding punishment
increased but not specifically for the CBM-I condition.

Figure 4. Accuracy in avoiding the most punishing face in the testing phase of the probabilistic selection task increased from session 1 to session 2,
but accuracy in choosing the most rewarding face did not significantly change across sessions.

Analyses of the Speech Expectancies Task (RL About
One’s Own Social Performance)
Overview
Self-ratings before and after feedback on the speech expectancies
task were analyzed to better understand how CBM-I affectedsocial RL about one’s own social performance. Specifically,
we assessed update weights describing the extent to whichhttps://formative.jmir.org/2023/1/e44888

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feedback from the judges was weighted when participants
updated their expectancies of their own public-speaking
performance. These update weights were conceptually similar
to the learning rates on the social probabilistic selection task
but described learning in a different domain (about one’s own
social performance, rather than about other people). Because
feedback was only given during the speech expectancies task
in session 2, we did not have a session-1 measure of social RL
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Beltzer et al

about one’s own social performance. However, we were still
able to analyze the differences at session 2 between the CBM-I
and EMA-only groups.

Update Weights Estimated Separately by Prediction Error
ValenceFor update weights estimated separately by prediction error
valence, a mixed effects model was used to predict updateweights from fixed effects of the CBM-I condition, prediction
error valence, and their interaction, with a random intercept forparticipant. Nine influential outliers were removed. A significant
main effect of prediction error valence was observed but was
not interpreted because it was subsumed within a significant
interaction with the CBM-I condition. Post hoc pairwise

comparisons of the estimated marginal means (with a Tukey
adjustment) revealed that, contrary to hypotheses based on priorliterature, participants in the EMA-only condition updated their
expectancies about their social performance more from positive(mean 0.62, SE 0.05) than negative feedback (mean 0.42, SE
0.05; t68.9=3.43; P=.001; Cohen d=−0.78), whereas participantsin the CBM-I condition updated similarly from positive (mean0.55, SE 0.05) and negative feedback (mean 0.54, SE 0.05;
t68.4=0.20; P=.85; Cohen d=−0.05; Figure 5). This suggests that
EMA-only participants showed a bias toward learning frompositive feedback about their social performance, whereas
CBM-I participants did not learn significantly differently frompositive versus negative feedback (Table 4).

Figure 5. Update weights in the 2 weights (prediction error valence) model for the speech expectancies task. Condition=intervention condition (cognitive
bias modification for interpretations [CBM-I] vs only ecological momentary assessment [EMA]). In the 2 weights (prediction error valence) model,
update weights were estimated separately for items on which feedback was more negative versus positive than participants’ prespeech expectancies(prediction error valence), without regard for item valence (items measuring good vs poor social performance). PE: prediction error.

Table 4. Model estimates predicting update weights estimated separately by prediction error valence in the speech expectancies task (n=78 participants)a.
Predictors

Update weight
Estimates (95% CI)

t test (df)

P value

Intercept

0.53 (0.48 to 0.58)

20.15 (70.93)<.001 b

PE valence

0.05 (0.01 to 0.09)

2.48 (68.64).01

Conditionc

0.01 (−0.04 to 0.07)

0.56 (70.93).58

PE valenced × condition−0.05 (−0.09 to −0.00)−2.19 (68.64).03

Fixed effectsaRandom effects: σ2=0.06, τ00 (participant)=0.02, intraclass correlation coefficient=0.24, observations=147, marginal R2=0.061, conditional R2=0.286.

bItalicization indicates P<.05.

c

Condition: intervention condition (cognitive bias modification for interpretations vs only ecological momentary assessment).

d

PE valence: prediction error valence (positive vs negative).

Update Weights Estimated Separately by Item Valence
For update weights estimated separately by item valence, amixed effects model was used to predict update weights fromfixed effects of the CBM-I condition, item valence, and theirinteraction, with a random intercept for participant. Ten
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influential outliers were removed. A significant main effect ofitem valence was observed but was not interpreted because it
was subsumed within a significant interaction with the CBM-I
condition. Post hoc pairwise comparisons of the estimatedmarginal means (with a Tukey adjustment) revealed that, in linewith the hypotheses, participants in the EMA-only condition
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Beltzer et al

updated their expectancies more from feedback on items
measuring poor (mean 0.52, SE 0.04) versus good social
performance (mean 0.41, SE 0.04; t70.2=−4.60; P<.001; Cohen
d=1.04), whereas participants in the CBM-I condition updated
similarly from feedback on both types of items (poor items:
mean 0.51, SE 0.04; good items: mean 0.51, SE 0.04;

t69.6=−0.12; P=.91; Cohen d=0.03; Figure 6). This suggests that
EMA-only participants showed a bias toward learning fromfeedback about poor aspects of social performance, such as
speaking too quickly, versus good aspects, such as seeming
confident, whereas CBM-I participants did not learn
significantly differently from feedback on poor versus good
items (Table 5).

Figure 6. Update weights in the 2 weights (item valence) model for the speech expectancies task. Condition=intervention condition (cognitive bias
modification for interpretations [CBM-I] vs only ecological momentary assessment [EMA]). In the 2 weights (item valence) model, update weights
were estimated separately for items measuring good versus poor social performance, without regard for prediction error valence (whether feedback was
more negative vs positive than participants’ prespeech expectancies).

Table 5. Model estimates predicting update weights estimated separately by item valence in the speech expectancies task (n=75 participants)a.
Predictors

Update weight
Estimates (95% CI)

t test (df)

P value

Intercept

0.49 (0.43 to 0.54)

17.81 (73.06)<.001 bItem valence−0.03 (−0.05 to −0.01)−3.17 (69.83).002

Conditionc

0.02 (−0.03 to 0.08)

0.79 (73.06).43

Item valence × conditiond

0.03 (0.01 to 0.05)

3.00 (69.83).003

Fixed effectsaRandom effects: σ2=0.01, τ00 (participant)=0.05, intraclass correlation coefficient=0.80, observations=146, marginal R2=0.036, conditional R2=0.810.

bItalicization indicates P<.05.

c

Condition: intervention condition (cognitive bias modification for interpretations vs only ecological momentary assessment).

d

Item valence: items measuring good versus poor social performance.

Discussion
Overview
This study assessed the effects of CBM-I on social RL in
individuals who are socially anxious, focusing on 2 important
domains: learning about other people and learning about one’s
own social performance. For the most part, we did not findevidence supporting the 3-way interactions that we had
hypothesized (ie, interactions among session, intervention
condition, and prediction error valence), but we did findevidence of several related 2-way interactions. After versus
before CBM-I, participants updated their expectancies of otherhttps://formative.jmir.org/2023/1/e44888

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people on the social probabilistic selection task more slowly
from new social information and used what they learned tomake more accurate decisions. These changes were not observedin the EMA-only condition, suggesting that they were likely
related to the CBM-I intervention. Furthermore, participants
who completed CBM-I used social feedback to update their
expectancies of their own performance on the speech
expectancies task in less-biased ways than EMA-only
participants, in that they learned similarly from positive andnegative feedback and learned similarly from feedback on items
related to poor and good social performance. We also observed
several effects as a function of session (ie, change over time),
regardless of the intervention condition, which might be a result
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JMIR FORMATIVE RESEARCH
of either practice with the social probabilistic selection task orfrom the 5 weeks of EMA tracking affect and emotion regulationthat all participants completed. Specifically, at the end of thestudy, participants updated their expectancies about others morefrom reward and less from punishment, and they became moreaccurate at avoiding punishment, as compared to at the start ofthe study. Although the results were mixed, these findings point
to several intriguing changes in social RL associated with
CBM-I.

Updating Expectancies Based on New Social
Information
We assessed how individuals who are socially anxious update
their beliefs based on new social information by analyzing
learning rates on the social probabilistic selection task (tomeasure updating expectancies about other people) and updateweights on the speech expectancies task (to measure updating
expectancies about one’s own social performance). There were
3 models evaluating updating: 1 for learning about other people
and 2 for learning about one’s own performance (because updateweights were estimated in 2 ways).

CBM-I’s Effects on Expectancy Updating
Overview
We found slightly different effects of CBM-I on expectancy
updating in the 2 domains assessed. Participants in the CBM-I
but not the EMA-only condition updated their expectancies
about other people on the social probabilistic selection task
more slowly at session 2 versus session 1. We did not find any
similar main effects of CBM-I on updating expectancies about
one’s social performance on the speech expectancies task.
However, we found evidence of biased updating about one’ssocial performance in the EMA-only group on the speech
expectancies task (learning more from positive than negative
feedback and learning more from feedback on items related topoor vs good social performance) but did not observe thesebiases in the CBM-I group. This suggests that CBM-I might beassociated with different changes in social RL in these 2
domains relevant to social anxiety: slower updating about otherpeople and less-biased updating about one’s social performance(the results could also be due to other differences in the 2 tasks
used besides the focus on updating about others vs oneself).
Slower Updating About Other People
Learning rates on the social probabilistic selection task
decreased from session 1 to session 2 for participants whocompleted the CBM-I intervention but not for participants whocompleted only the EMA intervention. This suggests that CBM-I
led individuals who are socially anxious to update their beliefs
about other people less based on each new happy or angry face.
This unexpected finding may be due to the format of the CBM-I
training, in which participants read through each emotionallyambiguous scenario sentence by sentence, accumulating
information slowly, and they cannot come to an emotional
resolution until the final word. This intervention may work to
slow learning rates by training participants to rely less on any
one piece of information when figuring out what to expect ofa situation or a person. Slower updating about other people islikely clinically useful in situations where the probabilities of
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Beltzer et al
reward and punishment are stable; another person mightsometimes respond positively and sometimes negatively todifferent things you do, but because these probabilities would
be relatively stable, it would be adaptive not to change yourexpectancies too much based on each new reaction from them.
Individuals who are socially anxious tend to revise theirimpressions of others more quickly than individuals who areless anxious [57], so decreasing these learning rates, as appears
to happen through CBM-I, might be beneficial. However, it isalso worth considering the potential downsides of slower
updating about others. If an individual who is socially anxious
already has negatively biased expectancies about others (eg,“Other people will reject me.”), these biases may be corrected
more slowly if the person updates their prior beliefs more slowly
from new information that is more positive than expected.
Although these beliefs would eventually move toward
less-biased expectancies through unbiased updating from both
positive and negative outcomes (ie, similar learning rates forreward and punishment, as we observed), this change would
occur more slowly for people who update their beliefs aboutothers. However, other work in this data set [37] found that
CBM-I was associated with a decrease in negative interpretation
bias (the target of CBM-I), suggesting that the slower updating
elicited by CBM-I does not preclude a shift toward more positive
beliefs in uncertain situations.
Less-Biased Updating About One’s Own Performance
Note that because speech expectancy updating was only
measured at session 2, we cannot draw conclusions about achange in these biases as a function of CBM-I but we canconsider what a difference in updating between people whocompleted the CBM-I intervention versus only the EMA
intervention might mean. When updating expectancies of one’s
own social performance based on feedback on the speech
expectancies task, 2 different modeling approaches (ie, updateweights estimated separately by item valence and estimatedseparately by prediction error valence) found that 2 different
updating biases (learning more from feedback on aspects ofpoor vs good social performance and learning more frompositive than negative feedback) occurred in the EMA-only
condition but not in the CBM-I condition.
However, it is not clear that the unbiased updating observed in
the CBM-I condition is uniformly beneficial, given the natureof the biases observed in the EMA-only condition, that is, thebiases observed in the EMA-only condition were towardlearning more from feedback on items related to poor versus
good social performance and toward learning more from positive
than negative feedback. The bias toward learning more fromfeedback on aspects of poor versus good social performance
was previously found in this data set to characterize participants
with high, but not low, social anxiety symptoms. This bias may
be related to individuals who are socially anxious being more
motivated to avoid embarrassment than to aim for excellent
performance. As such, it may be good that participants whocompleted the CBM-I intervention did not show this bias.
However, participants in the EMA-only condition also learned
more from positive than negative feedback, which is likely
adaptive, as it would lead to updating expectancies of one’s
own social performance to be more positive, and these types of
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JMIR FORMATIVE RESEARCH
positive biases are usually associated with psychologicalwell-being [58,59]. So, it is likely maladaptive that participants
who completed CBM-I did not show this positive bias, but future
work will need to determine what outcomes this and the otherobserved biases actually predict.

More Positively Biased Expectancy Updating Over Time
Because the social probabilistic selection task was administeredat both sessions but the speech expectancies task was only
administered at session 2, we were able to analyze changes in
expectancy updating about other people over time but only had
a snapshot of expectancy updating about one’s own performanceat the end of the study. We found that at the end of the study,
participants (regardless of intervention condition) showed morepositively biased updating about other people on the social
probabilistic selection task, updating their beliefs more fromreward and less from punishment at session 2 than at session 1.
This change over time is consistent with the session 2 snapshotof updating expectancies about one’s social performance on the
speech expectancies task in the EMA-only group, who learned
more from positive than negative feedback from others. Thesefindings suggest that the EMA portion of the study, completedby all participants, might have had positive effects on social RL
in both the self and other domains. Tracking one’s emotions
multiple times each day for several weeks and bringing attention
to one’s use of emotion regulation strategies and their associatedeffectiveness might have increased participants’ motivation toregulate their emotions, which might be accomplished bylearning in positively biased ways. We know from prior workin this data set that trait and state social anxiety symptoms andfear of negative evaluation decreased over the course of thisstudy, regardless of intervention condition [37], further
supporting the beneficial effects of EMA about emotion
regulation. However, we did not find evidence of social
anxiety–linked biases in expectancy updating in prior studies
using this data set (Beltzer, ML, unpublished data, June 2023)[36]. Thus, although participants with high social anxiety came
to update their expectancies in more positively biased ways
over the course of the study, their expectancy updating was not
significantly different from that of participants with low social
anxiety.

Learning to Choose Social Reward and Avoid Social
Punishment
In addition to assessing the effects of CBM-I on how individuals
who are socially anxious use different types of social
information to update their expectancies, we also analyzed how
individuals who are socially anxious use what they have learned
about other people to make decisions. This process was only
measured in the domain of learning about other people and notlearning about one’s own social performance. Although we did
not find evidence of the hypothesized 3-way interaction, we
found evidence of all the composite 2-way interactions. First,
for the CBM-I condition, but not the only EMA condition,
participants’ decision accuracy improved over the course of thestudy, such that they were able to more frequently select themore rewarding face during testing phase trials that included
the most rewarding and most punishing faces. This suggeststhat CBM-I may improve social decision-making in individuals
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who are socially anxious, which may manifest in choosing tointeract with people who are more likely to respond positively
to them or in choosing actions that are more likely to elicit apositive response. Second, participants became better at avoiding
social punishment but not at choosing social reward over thecourse of the study. This effect was not specific to CBM-I andsuggests that either EMA about emotion regulation or practicewith the task might improve accuracy in learning to avoid social
punishment. This finding is particularly noteworthy because
previous research, both in this data set [36] and elsewhere [17],has found that individuals who are anxious may show
impairments in learning to avoid people who respond negatively
to or take advantage of them. Frequent monitoring of emotions(as occurred in the EMA portion of this study) might help
participants with social anxiety become more aware of whencertain people make them feel bad, which might help them learn
to avoid these types of punishing people.

Clinical Implications
To the best of our knowledge, this study is the first to examine
the effects of CBM-I and EMA about emotion regulation on
RL. Both CBM-I and EMA are digital interventions that can bescaled up at low cost to increase access to treatment, which may
help bridge the treatment gap for individuals who are sociallyanxious. We found that CBM-I was associated with several
changes in social RL that might be helpful for individuals withsocial anxiety. Participants who completed CBM-I were slower
to change their beliefs about others based on each instance offacial feedback. In real life, this might mean that CBM-I could
help people with social anxiety resist the urge to jump to aconclusion about a person based on a single bad or goodinteraction. CBM-I also improved the accuracy of social
decision-making about other people (as evidenced by their more
frequent selection of more rewarding faces in the social
probabilistic selection task at follow-up vs baseline). This higher
accuracy might help individuals with social anxiety navigatesocial situations more successfully, resulting in more positive
interactions with others and better relationships. Participants
who completed the CBM-I intervention also used feedback to
update their beliefs about their own social performance inless-biased ways than participants who completed only the EMA
intervention, but this unbiased updating seems to be a mixed
blessing. CBM-I seems to have mitigated the bias (which wasobserved in the EMA-only group) toward learning more fromfeedback on poor rather than good aspects of speech
performance and having this type of unbiased updating is likely
healthier for the CBM-I group (than the bias observed in the
EMA-only group). However, CBM-I also seems to havemitigated the bias (that was observed in the EMA-only group)
toward learning more from positive feedback than negative
feedback. The EMA-only group’s bias toward learning frompositive feedback would likely lead to more positive
expectancies of one’s own social performance over time, which
may be healthier than the CBM-I group’s unbiased learningfrom positive and negative feedback, although this remains to
be more directly tested.
The effects observed over time regardless of the intervention
condition suggest that tracking emotions and emotion regulation
may also be helpful for social RL, possibly by increasing
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JMIR FORMATIVE RESEARCH
learning from positive feedback and decreasing learning fromnegative feedback. Given that all participants completed EMA,
it is difficult to disentangle the mechanisms that might be atwork. Emotion tracking might make individuals who are sociallyanxious more aware of the outcomes of their emotion regulation
attempts, or it might encourage them to regulate in ways that
promote positive affect, increasing their motivation to learn
from positive social outcomes. Alternatively, they may pay
more attention to when people treat them poorly and learn tomake better decisions accordingly to avoid that treatment.
Determining the specific mechanisms of change in social RL
will be helpful in future studies.
Other studies in this data set have found that CBM-I decreased
participants’ trait negative interpretation bias and increased theirability to use reappraisal day-to-day, but CBM-I was not
associated with changes in cognitive flexibility and decreases
in social anxiety symptoms occurred regardless of theintervention condition [37]. The fact that we foundintervention-related changes in social RL and interpretation bias
but nonspecific changes in symptoms raises interesting questions
regarding the relationships among these cognitive, emotional,
and behavioral processes. The current research design does notallow us to disentangle the temporal relations between changes
in social RL and changes in interpretation bias and symptoms.
However, it seems possible (and worth testing empirically infuture research) that changes in social RL precede changes insocial anxiety symptoms. For example, improvements in social
decision-making accuracy may lead to more rewarding social
interactions, which may decrease social avoidance and fear ofnegative evaluation.

Limitations
There are several limitations worth noting in this study’s
methodology. First, although the CBM-I intervention included
mostly social scenarios, it also included several nonsocial
scenarios. Its effects on social RL might have been stronger if
the intervention specifically targeted negative interpretation
bias in social situations. Second, this study might have been
underpowered to detect the hypothesized 3-way interactions.

Beltzer et al
Unfortunately, we did not conduct an a priori power analysis
specific to these questions (as these data were part of a larger
parent study). Relevant effect sizes in other studies with fewer
participants were medium-large (eg, Cohen d=0.54 forparticipants with high social anxiety avoiding punishing faces
more frequently than those with low social anxiety [13]). On
the basis of a post hoc power analysis [60] using summary
statistics from the study by Abraham and Hermann [13], oursample size of 106 was close to the recommended sample sizeof 111. However, sample sizes need to be larger to detect 3-way
interactions versus the 2-way interactions tested in past studies[61]. As such, our sample may have been appropriately powered
to detect 2-way interactions but may have been underpowered
to detect higher-degree interactions. Third, some characteristics
of our sample, including their relative homogeneity in age, race,and ethnicity and that they were not clinically diagnosed withsocial anxiety disorder, limit generalizability to clinicalpopulations more broadly. Fourth, because we were only able
to ethically collect data on social RL about one’s own social
performance at the end of the study, we do not know if there
were baseline differences in performance on this task between
the CBM-I and EMA-only groups.

Future Directions
Given our initial findings of several positive changes in social
RL associated with CBM-I, future research may help illuminate
the mechanisms of change as well as how changes in social RL
relate to changes in other important constructs, such as reduced
anxious avoidance. Furthermore, studies may look at how social
RL changes over the course of interventions other than CBM-I,
such as cognitive behavioral or interpersonal therapy, may
predict response to treatment. Future studies may also
incorporate passive sensing to determine the optimal time andcontext to deliver CBM-I as a just-in-time adaptive intervention,
reaching people when it can help them the most [62]. Updating
rigidly negative expectancies and beliefs to effectively consider
a range of feedback and information is key to healthyinterpersonal and emotional functioning; continued research onsocial RL in social anxiety may help us understand why certain
biases persist and how we might work to change them.

Acknowledgments
The authors would like to thank Laura Barnes, Mehdi Boukhechba, and their laboratory for their work on the parent study that
supported this data collection; the Program for Anxiety, Cognition, and Treatment Laboratory research assistants for their assistancein data collection; and the participants for contributing their time and responses to our study. This research was supported by a
University of Virginia Hobby Postdoctoral and Predoctoral Fellowship Grant and the R01MH113752 grant awarded to BAT;
grant T32 MH115882; and a Jefferson Scholars Foundation Fellowship, a PEO Scholar Award, and a University of Virginia
Graduate School of Arts and Sciences Dean’s Dissertation Completion Fellowship awarded to MLB. The funding sources had
no involvement in the study design; data collection, analysis, or interpretation; report writing; or the decision to submit themanuscript for publication.

Data Availability
The data sets analyzed during this study are available on the web [38].

Conflicts of Interest
None declared.

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Beltzer et alMultimedia Appendix 1
CONSORT (Consolidated Standards of Reporting Trials) diagram.
[DOCX File , 37 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Screenshots of cognitive bias modification for interpretations training.
[DOCX File , 944 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Supplementary methods, results, tables, and figures.
[DOCX File , 2092 KB-Multimedia Appendix 3]

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Abbreviations
CBM-I: cognitive bias modification for interpretations
CONSORT: Consolidated Standards of Reporting Trials
EMA: ecological momentary assessment
LOOIC: leave-one-out information criterion
RL: reinforcement learning

Edited by A Mavragani; submitted 08.12.22; peer-reviewed by U Masud, J Wang, R Zabag; comments to author 12.04.23; revised
version received 26.05.23; accepted 20.06.23; published 09.08.23
Please cite as:
Beltzer ML, Daniel KE, Daros AR, Teachman BA
Changes in Learning From Social Feedback After Web-Based Interpretation Bias Modification: Secondary Analysis of a Digital
Mental Health Intervention Among Individuals With High Social Anxiety Symptoms
JMIR Form Res 2023;7:e44888
URL: https://formative.jmir.org/2023/1/e44888
doi: 10.2196/44888
PMID: 37556186©Miranda L Beltzer, Katharine E Daniel, Alexander R Daros, Bethany A Teachman. Originally published in JMIR Formative
Research (https://formative.jmir.org), 09.08.2023. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The
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