
A Reduced Self-Positive Belief Underpins Greater Sensitivity to Negative Evaluation in Socially Anxious Individuals
Author(s) -
Alexandra Hopkins,
Raymond J. Dolan,
Katherine S. Button,
Michael Moutoussis
Publication year - 2021
Publication title -
computational psychiatry
Language(s) - English
Resource type - Journals
ISSN - 2379-6227
DOI - 10.5334/cpsy.57
Subject(s) - psychology , associative property , anxiety , class (philosophy) , vulnerability (computing) , social psychology , cognitive psychology , population , developmental psychology , computer science , artificial intelligence , medicine , mathematics , computer security , environmental health , psychiatry , pure mathematics
Positive self-beliefs are important for well-being, and are influenced by how others evaluate us during social interactions. Mechanistic accounts of self-beliefs have mostly relied on associative learning models. These account for choice behaviour but not for the explicit beliefs that trouble socially anxious patients. Neither do they speak to self-schemas, which underpin vulnerability according to psychological research. Here, we compared belief-based and associative computational models of social-evaluation, in individuals that varied in fear of negative evaluation (FNE), a core symptom of social anxiety. We used a novel analytic approach, 'clinically informed model-fitting', to determine the influence of FNE symptom scores on model parameters. We found that high-FNE participants learn more easily from negative feedback about themselves, manifesting in greater self-negative learning rates. Crucially, we provide evidence that this bias is underpinned by an overall reduced belief about self-positive attributes. The study population could be characterized equally well by belief-based or associative models, however large individual differences in model likelihood indicated that some individuals relied more on an associative (model-free), while others more on a belief-guided strategy. Our findings have therapeutic importance, as positive belief activation may be used to specifically modulate learning.