Hebbian Learning is about contingency, not contiguity, and explains the emergence of predictive mirror neurons

Abstract

Hebbian Learning should not be reduced to contiguity, as it detects contingency and causality. Hebbian Learning accounts of mirror neurons make predictions that differ from associative learning: Through Hebbian Learning, mirror neurons become dynamic networks that calculate predictions and prediction errors and relate to ideomotor theories. The social force of imitation is important for mirror neuron emergence and suggests canalization.

Figure 1

(a) Temporal asymmetry in Hebbian Learning. (b) ASL predicts associations between corresponding phases of an action sequence. Hebbian Learning predicts associations between subsequent phases, i.e. predictions (c); utilizes inhibitory feedback (d) for prediction errors (e).