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. One paradigm often used to study error-driven learning is blocking. In forward blocking, participants are first presented with stimulus A, followed by outcome X (A[->]X). In the second phase, A and B are presented together, followed by X (AB[->]X). Here, A[->]X blocks the formation of B[->]X, given that X is already fully predicted by A. In backward blocking, the order of phases is reversed. Here, the association between B and X that is formed during the first learning phase of AB[->]X is weakened when participants learn exclusively A[->]X in the second phase. The present study asked the question whether forward and backward blocking occur during visual statistical learning, the largely automatic and incidental learning of the statistical structure of the environment. In a series of studies, using both forward and backward blocking, we observed robust statistical learning of temporal associations among pairs of images. While we found no compelling evidence for forward blocking, we observed reliable backward blocking in visual statistical learning.