Feedback discounting in probabilistic categorization: Converging evidence from EEG and cognitive modelling

Abstract

In simple probabilistic learning environments, the informational value of corrective feedback gradually declines over time. This is because prediction errors persist despite learners acquiring the contingencies between stimuli and outcomes. An adaptive solution to the problem of unavoidable prediction error is to discount feedback from the learning environment. We provide novel neural evidence of feedback discounting using a combination of behavioral modeling and electroencephalography (EEG). Participants completed a probabilistic categorization task while EEG activity was recorded. We used a model-based analysis of choice behavior to identify individuals that did and did not discount feedba..