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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.
Beltzer, Miranda L; Daniel, Katharine E; Daros, Alexander R; Teachman, Bethany A.
Afiliação
  • Beltzer ML; Center for Behavioral Intervention Technologies, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
  • Daniel KE; Department of Psychology, University of Virginia, Charlottesville, VA, United States.
  • Daros AR; Department of Psychology, University of Virginia, Charlottesville, VA, United States.
  • Teachman BA; Department of Psychology, University of Virginia, Charlottesville, VA, United States.
JMIR Form Res ; 7: e44888, 2023 Aug 09.
Article em En | MEDLINE | ID: mdl-37556186
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 characteristic of 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 types of 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 the second 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: JMIR Form Res Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: JMIR Form Res Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos
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