Affective valence does not reflect progress prediction errors in perceptual decisions.

Affective valence and intensity form the core of our 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, whether the same principle applies to progress prediction errors remains unclear. 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 an expected drift-rate parameter. 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 sequential sampling models provide a promising framework to model 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.