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Category: Project

Description: Affective valence is the positive or negative dimension of feelings that, combined with the intensity of affect, forms the core of emotional experiences. It has been proposed that affect reflects the prediction error between expected and actual states such that better/worse-than-expected discrepancies result in positive/negative affect. However, it remains unclear if the same principle applies for progress prediction errors. Here we empirically and computationally evaluate the hypothesis that affect reflects the difference between expected and actual progress in forming a perceptual decision. We model affect within an evidence accumulation framework where actual progress is mapped onto the drift-rate parameter and expected progress onto a novel expected drift-rate parameter during a perceptual decision. Affect is computed as the difference between the expected and actual amount of accumulated evidence. We find that expected and actual progress both influence affect, but in an additive manner that does not align with a prediction error account. Our computational model reproduces both task behavior and affective ratings suggesting that evidence accumulation is a promising framework for modelling progress appraisals. These results show that although affect is sensitive to both expected and actual progress, it does not reflect the computation of a progress prediction error.