Academic journal article Memory & Cognition

Executive Attention and Task Switching in Category Learning: Evidence for Stimulus-Dependent Representation

Academic journal article Memory & Cognition

Executive Attention and Task Switching in Category Learning: Evidence for Stimulus-Dependent Representation

Article excerpt

One class of multiple-system models of category learning posits that within a single category-learning task people can learn to utilize different systems with different category representations to classify different stimuli. This is referred to as stimulus-dependent representation (SDR). The use of SDR implies that learners switch from subtask to subtask as trials demand. Thus, the use of SDR can be assessed via slowed response times, following a representation switch. Additionally, the use of SDR requires control of executive attention to keep inactive representations from interfering with the current response. Subjects were given a category learning task composed of one- and two-dimensional substructures. Control of executive attention was measured using a working memory capacity (WMC) task. Subjects most likely to be using SDR showed greater slowing of responses following a substructure switch and a greater correlation between learning performance and WMC. These results provide support for the principle of SDR in category learning and the reliance of SDR on executive attention.

As people go through life, they are often called upon to learn new classifications. Some classifications, such as "low carb," tend to be learned rapidly, whereas others, such as "baseball hit so that an outfielder should charge it," tend to be learned more gradually. Category learning researchers have theorized that people utilize multiple systems to learn new classifications, and that these systems are suited to learning different types of classifications at different rates (e.g., Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998,2002; Smith, Patalano, & Jonides, 1998). According to these multiple-system accounts of category learning, when a category can be defined by a small number of criteria that use single psychological dimensions, it is likely to be learned by a system using rule-based category representation; and when a category requires the combination of information from multiple psychological dimensions, it is likely to be learned by a system using similarity-based category representation.

Different theories have been proposed to account for how people select which system to use. One class of theories holds that, over the course of learning, people identify which system is best suited for the current task. Because these theories learn to select a single system with a single category representation for all stimuli, they are referred to in this article as utilizing stimulus-independent representation. Another class of theories holds that people identify which system is best suited for different stimuli within the task. Because these theories can select different classification systems with their own category representation as a function of the current stimulus, they are referred to in this article as utilizing stimulus-dependent representation. This article tests predictions made by this latter class.

Because stimulus-dependent theories of category learning posit that people can learn to use different classification systems to classify the stimuli for which they are best suited, these systems may be thought of as "experts" that learn to classify a subset of the stimuli. A classifier that utilizes stimulus-dependent representation can have a functional advantage, because it can learn to use its representations strategically to partition complex classification tasks into simpler ones that place reduced demands on limited-capacity attention. Evidence of task partitioning has been shown in category learning and in function learning (Erickson, 1999; Erickson & Kruschke, 1998, 2002; Kalish, Lewandowsky, & Kruschke, 2004; Kruschke & Erickson, 1994; Lewandowsky, Kalish, & Ngang, 2002; Lewandowsky & Kirsner, 2000; Lewandowsky, Roberts, & Yang, 2006; Yang & Lewandowsky, 2003, 2004). Task partitioning, however, comes at a cost. The process of dividing a task into subtasks is an additional learning task. …

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