Academic journal article Memory & Cognition

Cognitive Complexity Effects in Perceptual Classification Are Dissociable

Academic journal article Memory & Cognition

Cognitive Complexity Effects in Perceptual Classification Are Dissociable

Article excerpt

It has been proposed that a procedural-based classification system mediates the learning of information-integration categories, whereas a hypothesis-testing system mediates the learning of rule-based categories. Ashby, Ell and Waldron (2003) provided support for this claim by showing that a button switch introduced during classification transfer adversely affected information-integration but not rule-based performance. Nosofsky, Stanton and Zaki (2005) showed that increasing "cognitive complexity" can lead to button switch costs on rule-based performance. They argue that "cognitive complexity," and not the existence of separable classification systems, accounts for Ashby et al.'s empirical dissociation. The present study shows that experimental manipulations that increase "cognitive complexity" often have dissociable effects on information-integration and rule-based classification that are predicted a priori from the processing characteristics associated with the procedural-based and hypothesis-testing systems. These results suggest that manipulations of "cognitive complexity" can be dissociated, suggesting that "cognitive complexity" in not a unitary construct that affects a single psychological process.

Ashby, Alfonso-Reese, Türken & Waldron (1998) proposed a Competition between Verbal and Implicit Systems (COVIS) model of classification learning that assumes mat a procedural-based classification system mediates the learning of information-integration categories, whereas a hypothesis-testing system mediates the learning of rule-based categories.1 Information-integration category learning tasks are those in which accuracy is maximized only if information from two or more stimulus dimensions is integrated at some pre-decisional stage (Ashby & Gott, 1988). For example, Figure 1 presents a scatter-plot of stimuli from a diagonal information-integration condition with two categories. Each point in the plot denotes the length and orientation of a single line that is displayed centered on the computer screen. Different symbols denote different categories. The diagonal broken line denotes the optimal decision bound. It has no verbal or rule-based analog because length and orientation are measured in different units. Although one can certainly state the rule as, "respond A if the orientation is greater than the length; otherwise respond B," it is unclear how to interpret the term "greater than" since the dimensional values are measured in different units, so this type of decision rule makes no sense to naive participants. The hypothesistesting system is assumed to mediate the learning of rule-based categories. Rule-based category learning tasks are those in which the category structures can be learned via some explicit reasoning process. Figure 1 also displays a scatter-plot of stimuli from a unidimensional rule-based condition with two categories. Frequently, the rule that maximizes accuracy (i.e., the optimal rule) is easy to describe verbally. In this example, the rule is unidimensional and requires the participant to respond A to "short" lines and B to "long" lines.

One source of evidence in support of the existence of the procedural-based learning system comes from a study by Ashby, Ell, and Waldron (2003) who incorporated into a category learning task a procedure originally developed by Willingham, Wells, Farrell, and Stemwedel (2000) to study serial reaction time. Willingham et al. (2000) showed that changing the location of the response keys interferes with serial reaction time learning, even when the sequence of stimulus positions is unchanged. Since the serial reaction time task is a classic measure of procedural learning, Ashby et al. reasoned that changing the location of the response keys during classification should adversely affect processing in the procedural-based classification system.

Ashby et al. (2003; Experiment 1) trained participants on a diagonal (information-integration) category structure or a unidimensional (rule-based) category structure (similar to those in Figure 1). …

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