Timing matters: the impact of immediate and delayed feedback on artificial language learning

In the present experiment, we used event-related potentials (ERP) to investigate the role of immediate and delayed feedback in an artificial grammar learning (AGL) task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of 1 s. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP) elicited by positive feedback only. The feedback-related negativity (FRN), however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for AGL as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.