A Reduced Self-Positive Belief Underpins Greater Sensitivity to Negative Evaluation in Socially Anxious Individuals

Abstract

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.

Author summary: Understanding how we form and maintain positive self-beliefs is crucial to understanding how things go awry in disorders such as social anxiety. The loss of positive self-belief in social anxiety, especially in inter-personal contexts, is thought to be related to how we integrate evaluative information that we receive from others. We frame this social information integration as a learning problem and ask how people learn whether someone approves of them or not. We thus elucidate why the decrease in positive evaluations manifests only for the self, but not for an unknown other, given the same information. We investigated the mechanics of this learning using a novel computational modelling approach, comparing models that treat the learning process as series of stimulusresponse associations with models that treat learning as updating of beliefs about the self (or another). We show that both models characterise the process well and that individuals higher in symptoms of social anxiety learn more from negative information specifically about the self. Crucially, we provide evidence that this originates from a reduction in the amount of positive attributes that are activated when the individual is placed in a social evaluative context.

Keywords: associative learning; belief update; computational psychiatry; social anxiety.

Figure 1

Each task block consisted of 32 trials. Participants had to choose between positive and negative words. There were 6 blocks in total, corresponding to 6 evaluative conditions, termed personas – Self-like, self-neutral, self-dislike, other-like, other-neutral, other-dislike. Self/other refers to who is being evaluated, like/neutral/dislike refers to the probability of a positive word being correct (0.8, 0.5, 0.2 for the like/neutral/dislike rules respectively).

Figure 2

Individual log likelihoods for associative learning vs belief-update model. Positive values indicates greater evidence for the associative learning model. The horizontal bars indicate log likelihood differences of +/–3 and +/–6, conventionally mild and strong evidence in favour of one model over the other.

Figure 3

Generative performance for the Associative Learning S/O asymmetric model; mean cumulative positive words chosen for actual data (in black) vs. data generated from ‘clinically informed fitting’ (cyan). Data is visualised using median-split FNE scores (lighter=lower BFNE) and shaded zones represent +/– SEM. The generated data captures the asymmetries in positive vs. negative word selection and the group differences between high and low FNE for the self-referential condition well. There is slower initial learning, especially in the like condition and this model chooses over-optimistically, especially in ‘dislike’ conditions.

Figure 4

Generative performance for the Self/Other Belief-Update model; mean cumulative positive words chosen for actual data (in grey) vs. model (mauve). Again data is visualised using median-split FNE scores, with shaded zones representing +/– SEM for high (darker shade) vs. low (lighter shade) BFNE scores. The generated data captures well the asymmetries in positive vs. negative word selection and the group differences between high and low FNE for the crucial self-referential dislike condition.