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According to predictive processing theories, emotional inference involves simultaneously minimising discrepancies between predictions and sensory evidence relating to both one's own and others' states, achievable by altering either one's own state (empathy) or perception of another's state (egocentric bias) so they are more congruent. We tested a key hypothesis of these accounts, that predictions are weighted in inference according to their precision (inverse variance). If correct, increasingly precise self-related predictions should be associated with increasingly biased perception of another's emotional expression. We manipulated predictions about upcoming own-pain (low or high magnitude) using cues that afforded either precise (a narrow range of possible magnitudes) or imprecise (a wide range) predictions. Participants judged pained facial expressions presented concurrently with own-pain to be more intense when own-pain was greater, and precise cues increased this biasing effect. Implications of conceptualising interpersonal influence in terms of predictive processing are discussed.

More information Original publication

DOI

10.1016/j.cortex.2022.04.021

Type

Journal article

Publication Date

2022-09-01T00:00:00+00:00

Volume

154

Pages

322 - 332

Total pages

10

Keywords

Emotion recognition, Empathy, Generative model, Precision, Predictive coding, Predictive interoceptive coding, Bias, Emotions, Empathy, Facial Expression, Humans, Pain