Figure 1. (A) The Predicted Response Outcome (PRO) model.

In an idealized experiment, a task-related stimulus (S) signaling the onset of a trial is presented. Over the course of a task, the model learns a timed prediction (V) of possible responses and outcomes (r). The temporal difference learning signal (δ) is decomposed into its positive and negative components (ω^P and ω^N, respectively), indicating unpredicted occurrences and unpredicted non-occurrences, respectively. (B) ω^N accounts for typical effects observed in mPFC from human imaging studies. Conflict and error likelihood panels show activity magnitude aligned on trial onset; error and error unexpectedness panels show activity magnitude aligned on feedback. Model activity is in arbitrary units. EL is error likelihood (HEL=High EL; LEL=Low EL). Error bars indicate standard error of the mean (C) Typical time courses for components of the PRO model.