AffectDDM – a computational perspective to affect generation in perceptual decisions

Decisions are often accompanied by feelings of positive or negative valence with some intensity, also called affect. It has been proposed that affect functions as a monitoring signal, recruiting subsequent regulatory control processes. However, it's unclear what are the mechanisms that generate affect in decision-making. Inspired by control process theory (Carver, 2015), we model affect as the difference between expected and actual progress in an evidence accumulation framework. 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 evidence in a trial. We then test predictions of this model in a perceptual decision-making experiment, where expected and actual progress are experimentally manipulated. We find that affect reflects the sum of actual and expected progress, but not their discrepancy as predicted by control process theory. Comparing the empirical data with model predictions, we find that the model is able to simultaneously account for choice, reaction times, and affect in perceptual decisions.