The long-term objective of this research is the development of a general computational model of perceptual categorization and memory, which interrelates performance across a variety of tasks, including classification, identification, and old-new recognition. The present project is organized around the continued development and testing of Nosofsky and Palmeri's (1997) exemplar-based random walk (EBRW) model. According to the EBRW, people represent categories by storing individual exemplars in memory. The exemplars are retrieved from memory based on how similar they are to presented test objects. These retrieved exemplars drive a random-walk process for making classification decisions. The EBRW goes beyond previous work by providing a detailed processing account of the time course of classification decision making. The first specific aim of the newly proposed research is to develop sharp contrasts between the response-time predictions from the EBRW and those of some competing models of classification, including prototype and decision-boundary models.
A second aim i s to extend the EBRW to account for the time course of old-new recognition decision making.
A third aim i s to develop and test an extended version of the EBRW that will enable the model to account for distinctiveness effects in old-new recognition. The general approach involves modeling of data from a variety of experimental paradigms that collect both response-time and choice-probability data in tasks of classification and recognition. Understanding the fundamental processes of perceptual categorization and recognition is one of the central goals of research in memory and cognition. A direct health-related application of the present work would be to provide information about how radiologists make disease classifications on the basis of imperfect information contained in X-ray displays, with the ultimate goal of developing training techniques and computer technology to assist in radiological decision making.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Biobehavioral and Behavioral Processes 3 (BBBP)
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Kurtzman, Howard S
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Indiana University Bloomington
Schools of Arts and Sciences
United States
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Kurtz, Kenneth J; Levering, Kimery R; Stanton, Roger D et al. (2013) Human learning of elemental category structures: revising the classic result of Shepard, Hovland, and Jenkins (1961). J Exp Psychol Learn Mem Cogn 39:552-72
Nosofsky, Robert M; Little, Daniel R; James, Thomas W (2012) Activation in the neural network responsible for categorization and recognition reflects parameter changes. Proc Natl Acad Sci U S A 109:333-8
Nosofsky, Robert M; Denton, Stephen E; Zaki, Safa R et al. (2012) Studies of implicit prototype extraction in patients with mild cognitive impairment and early Alzheimer's disease. J Exp Psychol Learn Mem Cogn 38:860-80
Nosofsky, Robert M; Little, Daniel R; Donkin, Christopher et al. (2011) Short-term memory scanning viewed as exemplar-based categorization. Psychol Rev 118:280-315
Little, Daniel R; Nosofsky, Robert M; Denton, Stephen E (2011) Response-time tests of logical-rule models of categorization. J Exp Psychol Learn Mem Cogn 37:1-27
Gureckis, Todd M; James, Thomas W; Nosofsky, Robert M (2011) Re-evaluating dissociations between implicit and explicit category learning: an event-related fMRI study. J Cogn Neurosci 23:1697-709
Nosofsky, Robert M; Little, Daniel R (2010) Classification response times in probabilistic rule-based category structures: contrasting exemplar-retrieval and decision-boundary models. Mem Cognit 38:916-27
Fific, Mario; Little, Daniel R; Nosofsky, Robert M (2010) Logical-rule models of classification response times: a synthesis of mental-architecture, random-walk, and decision-bound approaches. Psychol Rev 117:309-48
Zaki, Safa R; Nosofsky, Robeir M (2007) A high-distortion enhancement effect in the prototype-learning paradigm: dramatic effects of category learning during test. Mem Cognit 35:2088-96
Nosofsky, Robert M; Bergert, F Bryabn (2007) Limitations of exemplar models of multi-attribute probabilistic inference. J Exp Psychol Learn Mem Cogn 33:999-1019

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