Temporal response modelling uncovers electrophysiological correlates of trial-by-trial error-driven learning

Humans learn from statistical regularities in the environment. We tested if prediction and prediction error may play a role in such learning in the brain. We used Error-Driven Learning (EDL) to simulate participants’ trial-by-trial learning during exposure to a bimodal distribution of non-native lexical tones. We simulated incremental trial-by-trial learning to get estimates of the degree of expectation of upcoming stimuli over the course of the experiment. The expectation estimates were combined with Temporal Response Function fitting to generate a prediction of the trial-by-trial ERP waveform. EDL simulations captured the data significantly better than chance and better than models based on either stimulus characteristics or statistical distributions. The results provide tentative evidence that trial-by-trial learning as measured in neural activity is error-driven.