Statistical learning is not error-driven

Abstract

Prediction errors have a prominent role in many forms of learning. For example, in reinforcement learning agents learn by updating the association between states and outcomes as a function of the prediction error elicited by the event. An empirical hallmark of such error-driven learning is Kamin blocking, whereby the association between a stimulus and outcome is only learnt when the outcome is not already fully predicted by another stimulus. It remains debated however to which extent error-driven computations underlie learning of automatically formed associations as in statistical learning. Here we asked whether the automatic and incidental learning of the statistical structure of the environment is error-driven, like reinforcement learning, or instead does not rely on prediction errors for learning associations. We addressed this issue in a series of Kamin blocking studies. In three consecutive experiments, we observed robust incidental statistical learning of temporal associations among pairs of images, but no evidence of blocking. Our results suggest that statistical learning is not error-driven but may rather follow the principles of basic Hebbian associative learning.