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Category Learning                                                                                        [back]

Human and non-human animals are capable of grouping visual objects into novel categories. A growing body of evidence suggests that at least two category learning mechanisms may be available to people: a procedural learning system, implemented by the circuitry of the basal ganglia, and a rule-based learning system, implemented in the prefrontal cortex.

Learning of representations that support rule learning

rule learningFast, accurate, and generalizable categorization of novel objects depends on the ability to extract some information from the objects that is relevant for a task, while completely ignoring other information irrelevant for the task. Such performance is only possible when the known representation of relevant information (the “relevant dimension”) is independent from the known representation of irrelevant information (the “irrelevant dimension”), so that processing of irrelevant information does not interfere with processing of relevant information. This means that the key to understand fast, accurate and generalizable object category learning is a better understanding of how independent object dimensions are learned from experience and used in categorization tasks. However, very little is known about such processes.

Using multidimensional signal detection theory to measure perceptual independence, we have found that categorization training produces learning of novel separable dimensions. We recently asked whether newly-learned dimensions support the kind of rule-based category learning observed with traditional separable dimensions (Soto & Ashby, 2018). Surprisingly, we found that extensive categorization training is not necessary for rule-based category learning. Instead, representations that support the use of rule-based learning seem to be acquired on-the-fly during categorization training with stimuli that lack a previous dimensional structure. It seems as if people have a predisposition to learn categorization tasks using rule-based strategies, even when stimuli are not represented in a way that would facilitate such learning.