A rule-plus-exception model for classifying objects in continuous-dimension spaces

Nosofsky, Robert M.; Palmeri, Thomas J.

doi:10.3758/bf03208813

Cited by 125 publications

(105 citation statements)

References 72 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…Nosofsky and colleagues' rule-plus-exception model (RULEX) is an example of a categorization model that has both rule-and exemplar-based mechanisms Nosofsky & Palmeri, 1998). According to RULEX, people first use simple, one-bound, dimensional rules to solve categorization problems.…”

Section: Dual-process/dual-system Categorization Modelsmentioning

confidence: 99%

Comparing categorization models.

Rouder¹,

Ratcliff²

2004

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

Four experiments are presented that competitively test rule-and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which exemplar-and rule-based explanations made qualitatively different predictions.F. G. Ashby and J. T. Townsend's (1986) rule-based general recognition theory provided a better account of the data than R. M. Nosofsky's (1986) exemplar-based generalized context model in conditions in which the to-be-classified stimuli were relatively confusable. However, generalized context model provided a better account when the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts of categorization and demonstrate that stimulus confusion is a determining factor as to which process mediates categorization.In this article we present an empirical paradigm to test different theories of categorization behavior. One theory we test is the exemplar-based theory in which categories are represented by sets of stored exemplars. Category membership of a stimulus is determined by similarity of the stimulus to these exemplars (e.g., Medin & Schaffer, 1978;Nosofsky, 1986Nosofsky, , 1987Nosofsky, , 1991). An exemplar-based process relies on retrieval of specific trace-based information without further abstraction; for example, a person is judged as "tall" if he or she is similar in height to others who are considered "tall." The other theory we test is rule-based or decisionbound theory. Decisions are based on an abstracted rule. The relevant space is segmented into regions by bounds, and each region is assigned to a specific category (e.g., Ashby & Gott, 1988;Ashby & Maddox, 1992Ashby & Perrin, 1988;Ashby & Townsend, 1986;Trabasso & Bower, 1968). For example, a person might be considered tall if he or she is perceived as being over 6 ft (1.83 m). The essence of a rule-based process is that processing is based on greatly simplified abstractions or rules but not on the specific trace-based information itself.Both rule-and exemplar-based theories have gained a large degree of support in the experimental literature. There are both exemplar-based models (e.g., generalized context model; Nosofsky, 1986) and rule-based models (e.g., general recognition theory; Ashby & Gott, 1988;Ashby & Townsend, 1986) that can explain a wide array of behavioral data across several domains. Despite the many attempts to discriminate between these two explanations, there have been few decisive tests. Across several paradigms and domains, rule-and exemplarbased predictions often mimic each other.

show abstract

Section: Dual-process/dual-system Categorization Modelsmentioning

confidence: 99%

Comparing categorization models.

Rouder¹,

Ratcliff²

2004

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

show abstract

“…Some investigators hypothesize that a single system can explain the performance seen in the kind of experiment described above (Nosofsky & Johansen, 2000;Nosofsky & Palmeri, 1998). From this perspective, the rule-based impairment in AD may be due to limitations in dimensional weighting and context sensitivity, which are important properties of the psychological similarity space in which object descriptions are embedded.…”

Section: Sematic Categorization In Alzheimer's Diseasementioning

confidence: 99%

“…An object is categorized with like objects in order to understand its meaning Although some have proposed a singlesystem approach (Nosofsky & Johansen, 2000;Nosofsky & Palmeri, 1998), we tested the hypothesis that at least two kinds of processes contribute to semantic category membership decisions about word meaning. The first kind of process involves a global comparison of a test object with remembered instances of the semantic category (Medin, Goldstone, & Gentner, 1993;Medin & Schaffer, 1978), or, possibly, a comparison with a mental prototype representing category members (Rosch & Mervis, 1975; E. E. Smith & Medin, 1981).…”

mentioning

confidence: 99%

Categorization of object descriptions in Alzheimer's disease and frontotemporal dementia: Limitation in rule-based processing

Grossman

Smith

Koenig

et al. 2003

Cognitive, Affective, & Behavioral Neuroscience

View full text Add to dashboard Cite

Studies of semantic memory in probable Alzheimer's disease (AD) have focused on the degradation of semantic knowledge, but other work in AD suggests an impairment in the semantic categorization processes that operate on this knowledge. We examined the categorization of object descriptions, where semantic category membership judgments were based on rule-based or similarity-based categorization processes. We found that AD patients were selectively limited in their semantic categorization under conditions requiring a rule-based approach. However, AD patients did not differ from healthy seniors under conditions based on judgments of overall similarity. We showed that this was not due to nonspecific or overall task-related difficulty associatedwith the rule condition by asking the subjects to use similarity-based judgments of perceptually degraded versions of the stimuli. The results of this condition did not differ from other similarity-based judgments but did differ from the rule-based condition in AD. Rule-based judgments of semantic category membership correlated with executive measures of inhibitory control and mental search, but not with measures of episodic memory or overall dementia severity, suggesting a contribution of executive resources to rule-based semantic categorization. Moreover, the pattern of limited rule-based categorization in AD closely resembled the performance profile of patients with frontotemporal dementia, further implying that executive resource limitations underlie AD patients' limited rule-based semantic categorization. These findings suggest that semantic memory difficulty in AD is due in part to a deficit in executive processes that are central to rule-based categorization in semantic memory.

show abstract

“…In fact, in part due to the perceived lack of constraints in neural network models that learn concepts by gradually building up associations, the rule-based approach experienced a rekindling of interest in the 1990s after its low point in the 1970s and 1980s (Marcus, 1998). Nosofsky and Palmeri (1998;Nosofsky et al, 1994;Palmeri & Nosofsky, 1995) have proposed a quantitative model of human concept learning that learns to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. This work is reminiscent of earlier computational models of human learning that created rules such as if white and square, then Category 1 from experience with specific examples (Anderson, Kline, & Beasley, 1979;Medin, Wattenmaker, & Michalski, 1987).…”

Section: Rulesmentioning

confidence: 99%

“…Evidence for prototypes tends to be found for categories made up of members that are distortions around single prototypes (Posner & Keele, 1968). Evidence for exemplar models is particular strong when categories include exceptional instances that must be individually memorized (Nosofsky & Palmeri, 1998;Nosofsky et al, 1994). Evidence for theories is found when categories are created that subjects already know something about (Murphy & Kaplan, 2000).…”

Section: Summary To Representation Approachesmentioning

confidence: 99%

Concepts and Categorization

Goldstone

Kersten

Carvalho

2003

Handbook of Psychology

102

View full text Add to dashboard Cite

Whenever people think of an object as something (e.g. that Fido is a dog, a pet, or loyal), they are categorizing it using their internal concepts. A concept is a mental representation that allows different things to be treated equivalently for some purpose. People learn concepts to facilitate communication, to make useful predictions about their world, to create mental building blocks for expressing more sophisticated thoughts, and to form efficient representations for objects and situations. We discuss five approaches to how concepts are learned and represented: rules, prototypes, exemplars, category boundaries, and theories. Whereas some of these approaches leave the impression that concepts are isolated mental structures, connections between concepts are also critically important. We discuss connections between concepts and perception that link categorization to object recognition and serve to ground concepts in the world. Consistent with this connection, concepts and perceptual processes mutually influence one another. We also describe connections between concepts and language that allow concepts to subserve abstract communication, and for language needs to reciprocally affect concepts. Finally, we predict future directions for concept learning research, including formal computational modeling and educational applications.

show abstract

A rule-plus-exception model for classifying objects in continuous-dimension spaces

Cited by 125 publications

References 72 publications

Comparing categorization models.

Comparing categorization models.

Categorization of object descriptions in Alzheimer's disease and frontotemporal dementia: Limitation in rule-based processing

Concepts and Categorization

Contact Info

Product

Resources

About