Academic journal article Psychonomic Bulletin & Review

Multiple Stages of Learning in Perceptual Categorization: Evidence and Neurocomputational Theory

Academic journal article Psychonomic Bulletin & Review

Multiple Stages of Learning in Perceptual Categorization: Evidence and Neurocomputational Theory

Article excerpt

Published online: 28 April 2015

© Psychonomic Society, Inc. 2015

Abstract Virtually all current theories of category learning assume that humans learn new categories by gradually forming associations directly between stimuli and responses. In information-integration category-learning tasks, this purported process is thought to depend on procedural learning implemented via dopamine-dependent cortical-striatal synaptic plasticity. This article proposes a new, neurobiologically detailed model of procedural category learning that, unlike previous models, does not assume associations are made directly from stimulus to response. Rather, the traditional stimulus-response (S-R) models are replaced with a two-stage learning process. Multiple streams of evidence (behavioral, as well as anatomical and fMRI) are used as inspiration for the new model, which synthesizes evidence of multiple distinct cortical-striatal loops into a neurocomputational theory. An experiment is reported to test a priori predictions of the new model that: (1) recovery from a full reversal should be easier than learning new categories equated for difficulty, and (2) reversal learning in procedural tasks is mediated within the striatum via dopamine-dependent synaptic plasticity. The results confirm the predictions of the new two-stage model and are incompatible with existing S-R models.

Keywords Categorization · Procedural learning · Striatum


Categorization is the process of assigning unique responses to different groups of stimuli. A variety of different category-learning theories have been proposed, yet virtually all assume that category learning is a process via which a single association is formed between each stimulus and every possible response (henceforth referred to as S-R models). However, recent empirical investigations have challenged this seemingly simple assumption, suggesting that a fundamental revision of current theories may be in order (Maddox et al., 2010;Kruschke,1996; Wills et al., 2006). This article proposes a new biologically detailed model of procedural learning that successfully addresses these challenges. New behavioral data are presented to further support the general necessity of model revision and to justify the specific details of the proposed new model.

A strong prediction of any theory that assumes category learning is mediated by S-R associations is that reversing the correct responses for all stimuli should cause catastrophic interference because recovery from a full reversal would require unlearning all prior S-R associations, followed by new learning of the reversed associations. In contrast, creating new categories from the same stimuli in any other way should be less disruptive, because only some of the associations would have to be relearned, but not all. Existing empirical data, however, indicate that reversal learning is easier than learning novel categories (Kruschke, 1996; Maddox et al., 2010; Sanders, 1971; Wills et al., 2006). These results challenge the validity of S-R learning assumptions that underlie the existing category-learning theories.

Although virtually all existing category-learning models are of the S-R type, a number assume that the S-R learning process is mediated via the interaction of multiple systems (Ashby et al., 1998; Erickson & Kruschke, 1998), or that SR R are formed via context-dependent Bayesian inference (Anderson, 1991;Gershmanetal.,2010;Redish et al., 2007). Thus, the basic intuition about full reversals in S-R models may not be directly applicable to these models.

The multiple memory systems framework assumes that S-R associations are formed via procedural learning, whereas declarative memory is used to formulate and test explicit strategies (Ashby & O'Brien, 2005). Such a framework may, in principle, be able to account for the observation that recovery from full reversal is easier than new learning. However, since these models assume that procedural category learning is an S-R process, the only way this seems possible is if the reversal is dominated by declarative mechanisms. …

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