On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Abstract

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature.

Developments in associative theory: A tribute to the contributions of Robert A. Rescorla.

“…Wasserman and Castro’s contribution to this special issue further shows how this model relates to David Hume’s theory of causation (Wasserman & Castro, 2022). The article by Marchant and Chaigneau in this issue (Marchant & Chaigneau, 2022) applies an adaptive filter model inspired by Rescorla-Wagner thinking to problems of human categorization learning. While aspects of the model are surely incomplete (e.g., Dickinson et al, 1976; Miller et al, 1995; Pearce, 1994, 2002), one reason why the model has enjoyed an enduring success is that it receives support from underlying neurobiological investigations of basic learning processes.…”