Auditory category learning is robust across training regimes

Abstract

Multiple lines of research have developed training approaches that foster category learning, with important translational implications for education. Increasing exemplar variability, blocking, or interleaving by category-relevant dimension, and providing explicit instructions about diagnostic dimensions each have been shown to facilitate category learning and/or generalization. However, laboratory research often must distill the character of natural input regularities that define real-world categories. As a result, much of what we know about category learning has come from studies with simplifying assumptions. This entirely reasonable approach nonetheless calls into question the implicit expectation that these studies reflect the process of category learning with real-world input. Here, we put this question to the test by creating an auditory category learning challenge that intentionally violates some common simplifying assumptions of category learning tasks. Across five experiments and nearly 300 adult participants, we used these three training regimes previously shown to facilitate category learning but drawing from a more complex and multidimensional category space with tens of thousands of unique exemplars. Learning was equivalently robust across training regimes with changing exemplar variability, altering the blocking of category exemplars, or providing explicit instructions of the category-diagnostic dimension all driving essentially equivalent generalization of learning over 40 minutes of training.