Academic journal article Attention, Perception and Psychophysics

The Time Course of Explicit and Implicit Categorization

Academic journal article Attention, Perception and Psychophysics

The Time Course of Explicit and Implicit Categorization

Article excerpt

Published online: 30 May 2015

# The Psychonomic Society, Inc. 2015

Abstract Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

Keywords Category learning . Explicit cognition . Implicit cognition . Response deadlines . Cognitive neuroscience

Categorization is an essential cognitive ability and an important topic of cognitive and neuroscience research (e.g., Ashby &Maddox,2011;Brooks, 1978; Knowlton & Squire, 1993; Medin & Schaffer, 1978; Murphy, 2003; Nosofsky, 1987; Smith & Minda, 1998). The contemporary categorization literature contains an influential multiple-systems perspective (Ashby, Alfonso-Reese, Turken, & Waldron, 1998;Ashby& Ell, 2001;Cook&Smith,2006; Erickson & Kruschke, 1998; Homa, Sterling, & Trepel, 1981; Rosseel, 2002)thatproposes that multiple categorization utilities in cognition use different information-processing principles to serve different ecological needs.

In particular, among the processes and utilities that serve categorization, cognitive and neuroscience researchers have distinguished between explicit and implicit categorization. The explicit system learns using focused attentional processes that target individual stimulus features. It learns through hypothesis testing and something like logical reasoning. It depends on working memory and executive attention. It produces category knowledge that is declaratively conscious. The implicit system learns using multidimensional processes that can integrate across stimulus features. It depends on associative-learning processes to link stimulus to adaptive responses. It produces category knowledge that is opaque to declarative consciousness. These processing attributes are documented in many studies (e.g., Ashby et al., 1998; Ashby & Maddox, 2011; Ashby & Valentin, 2005;Maddox & Ashby, 2004; Maddox, Ashby, & Bohil, 2003;Maddox& Ing, 2005).

Explicit and implicit categorization have been differentiated using rule-based (RB) and information-integration (II) category tasks. In Fig. 1, each Category A and B instance (gray and black symbols, respectively) is a conjoint stimulus presenting values from perceptual dimensions X and Y.TheRB task (Fig. 1A) fosters explicit category learning. Category A and B instances are contrasted only by their Y-axis position. A horizontal category boundary-that is, a dimensional rule with a central criterion along the Y-axis-partitions the categories. The mean and variation along the X dimension are identical for both categories, providing no useful information for category decisions. Participants are not presented with the whole stimulus space. They see individual instances with feedback following each response, and they must discover category rules within this trial-by-trial framework. Many researchers have granted explicit rules an important role in categorization (e.g., Ahn & Medin, 1992; Erickson & Kruschke, 1998;Feldman,2000; Medin, Wattenmaker, & Hampson, 1987; Nosofsky, Palmeri, & McKinley, 1994; Regehr & Brooks, 1995; Shepard, Hovland, & Jenkins, 1961), and thus fully understanding rule-based categorization remains an important empirical goal. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed


An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.