The Role of Alexithymia in Social Learning and Feedback-Driven Social Inferences

TL;DR

This study explores how alexithymia affects social learning and decision-making when faced with conflicting emotional cues.

Contribution

The paper introduces computational models to quantify how alexithymia alters social learning and decision-making processes.

Findings

High alexithymia is linked to lower accuracy in learning from social feedback.

Individuals with high alexithymia show a perceptual bias toward visual cues in emotional inference.

Greater evidence accumulation is needed for emotion inference in those with high alexithymia.

Abstract

Dynamic social interactions and feedback are crucial for understanding others’ emotions, particularly when confronted with contradictory emotional cues. Alexithymia, a condition that co-occurs with many psychiatric disorders, is characterized by impairment in emotional processing. However, computational mechanisms by which it alters social inferences based on feedback cues remain unexplored. To examine this, 60 participants with low and high levels of alexithymia completed an emotional learning task involving contradictory social (verbal and visual) cues to infer targets’ emotions. Computational analyses, including bin-based, reinforcement learning, and drift-diffusion modeling, revealed how alexithymia alters latent parameters that govern value updating and choice. Individuals with high alexithymia demonstrated lower accuracy in learning from social feedback, and learning rate for verbal cues was negatively associated with difficulties in identifying and describing feelings. Drift diffusion analysis revealed a perceptual bias toward the visual cue, with higher drift rates and bias in the visual-correct condition, and a general requirement for greater evidence accumulation to infer others’ emotions. These findings suggest that individuals with high alexithymia exhibit impaired social learning and difficulty with decision-making in situations with conflicting social information, with computational modeling quantifying the latent processes involved and advancing mechanistic targets for computational psychiatry.

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