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 language | English |
|---|---|
| Article number | 22471 |
| Number of pages | 14 |
| Journal | Scientific Reports |
| Volume | 14 |
| Issue number | 1 |
| Early online date | 28 Sept 2024 |
| DOIs | |
| Publication status | Published - 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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