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Lack of optimistic bias during social evaluation learning reflects reduced positive self-beliefs in depression and social anxiety, but via distinct mechanisms

  • University College London

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Abstract

Processing social feedback optimistically may maintain positive self-beliefs and stable social relationships. Conversely, a lack of this optimistic bias in depression and social anxiety may perpetuate negative self-beliefs and maintain symptoms. Research investigating this mechanism is scarce, however, and the mechanisms by which depressed and socially anxious individuals respond to social evaluation may also differ. Using a range of computational approaches in two large datasets (mega-analysis of previous studies, n = 450; pre-registered replication study, n = 807), we investigated how depression (PHQ-9) and social anxiety (BFNE) symptoms related to social evaluation learning in a computerized task. Optimistic bias (better learning of positive relative to negative evaluations) was found to be negatively associated with depression and social anxiety. Structural equation models suggested this reflected a heightened sensitivity to negative social feedback in social anxiety, whereas in depression it co-existed with a blunted response to positive social feedback. Computational belief-based learning models further suggested that reduced optimism was driven by less positive trait-like self-beliefs in both depression and social anxiety, with some evidence for a general blunting in belief updating in depression. Recognizing such transdiagnostic similarities and differences in social evaluation learning across disorders may inform approaches to personalizing treatment.

Original languageEnglish
Article number22471
Number of pages14
JournalScientific Reports
Volume14
Issue number1
Early online date28 Sept 2024
DOIs
Publication statusPublished - 31 Dec 2024

Data Availability Statement

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. This study was pre-registered on Open Science Framework (https://osf.io/ke3d5), where study materials, data for the Preregistered dataset and code are also made publicly available (https://osf.io/utyw5/). Data for the Mega-analysis dataset is available upon request as participants did not provide informed consent to publish data as open access.

ASJC Scopus subject areas

  • General

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