Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998, 2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al's experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exemplars, Prototypes, and the Flexibility of Classification Models.

J. P. Minda and J. D. Smith (2001) showed that a prototype model outperforms an exemplar model, especially in larger categories or categories that contained more complex stimuli. R. M. Nosofsky and S. R. Zaki (2002) showed that an exemplar model with a response-scaling mechanism outperforms a prototype model. The authors of the current study investigated whether excessive model flexibility could explain these results. Using cross-validation, the authors demonstrated that both the prototype model and the exemplar model with a response-scaling mechanism suffered from overfilling in the linearly separable category structure. The results illustrate the need to make sure that the best-fitting model is not chasing error variance instead of variance attributed to the cognitive process it is supposed to model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Exemplar representation without generalization? Comment on Smith and Minda's (2000) "Thirty categorization results in search of a model."

J. D. Smith and J. P. Minda (see record 1999-15928-001) conducted a meta-analysis of 30 data sets reported in the classification literature that involved use of the "5–4" category structure introduced by D. L. Medin and M. M. Schaffer (1978). The meta-analysis was aimed at investigating exemplar and elaborated prototype models of categorization. In this commentary, the author argues that the meta-analysis is misleading because it includes many data sets from experimental designs that are inappropriate for distinguishing the models. Often, the designs involved manipulations in which the actual 5–4 structure was not, in reality, tested, voiding the predictions of the models. The commentary also clarifies various aspects of the workings of the exemplar-based context model. Finally, concerns are raised that the all-or-none exemplar processes that form part of Smith and Minda's (see record 1999-15928-001) elaborated prototype models are implausible and lacking in generality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Thirty-Something Categorization Results Explained: Selective Attention, Eyetracking, and Models of Category Learning.

An eyetracking study testing D. L. Medin and M. M. Schaffer's (1978) 5-4 category structure was conducted. Over 30 studies have shown that the exemplar-based generalized context model (GCM) usually provides a better quantitative account of 5-4 learning data as compared with the prototype model. However, J. D. Smith and J. P. Minda (2000) argued that the GCM is a psychologically implausible account of 5-4 learning because it implies suboptimal attention weights. To test this claim, the authors recorded undergraduates' eye movements while the students learned the 5-4 category structure. Eye fixations matched the attention weights estimated by the GCM but not those of the prototype model. This result confirms that the GCM is a realistic model of the processes involved in learning the 5-4 structure and that learners do not always optimize attention, as commonly supposed. The conditions under which learners are likely to optimize attention during category learning are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

"Prototypes in the mist: The early epochs of category learning": Correction to Smith and Minda (1998).

Reports an error in "Prototypes in the mist: The early epochs of category learning" by J. David Smith and John Paul Minda (Journal of Experimental Psychology: Learning, Memory, and Cognition, 1998[Nov], Vol 24[6], 1411-1436). As a result of errors made in production, two equations in the article were printed incorrectly. The corrected equations are included in the erratum. (The following abstract of the original article appeared in record 1998-12790-005.) Recent ideas about category learning have favored exemplar processes over prototype processes. However, research has focused on small, poorly differentiated categories and on task-final performances--both may highlight exemplar strategies. Thus, we evaluated participants' categorization strategies and standard categorization models at successive stages in the learning of smaller, less differentiated categories and larger, more differentiated categories. In the former case, the exemplar model dominated even early in learning. In the latter case, the prototype model had a strong early advantage that gave way slowly. Alternative models, and even the behavior of individual parameters within models, suggest a psychological transition from prototype-based to exemplar-based processing during category learning and show that different category structures produce different trajectories of learning through the larger space of strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Cognitive Substrate of Subjective Probability.

The prominent cognitive theories of probability judgment were primarily developed to explain cognitive biases rather than to account for the cognitive processes in probability judgment. In this article the authors compare 3 major theories of the processes and representations in probability judgment: the representativeness heuristic, implemented as prototype similarity, relative likelihood, or evidential support accumulation (ESAM; D. J. Koehler, C. M. White, & R. Grondin, 2003); cue-based relative frequency; and exemplar memory, implemented by probabilities from exemplars (PROBEX; P. Juslin & M. Persson, 2002). Three experiments with different task structures consistently demonstrate that exemplar memory is the best account of the data whereas the results are inconsistent with extant formulations of the representativeness heuristic and cue-based relative frequency. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Exemplar models and prototype effects in similarity-based categorization.

I. P. L. McLaren, C. H. Bennett, T. Guttman-Nahir, K. Kim, and N. J. MacKintosh (1995) have argued that exemplar models have problems explaining their finding that people often respond more accurately to the prototypes from certain categories than to other exemplars. The author reviews McLaren et al's arguments and shows that two exemplar theories, the generalized context model with Euclidean distances and the weighted ratio model, can account for the prototype-superiority effects in their experiments. The author concludes that McLaren et al's results support, rather than challenge, exemplar models of categorization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

"Selective attention and the formation of linear decision boundaries": Erratum.

Reports an error in the original article by S. C. McKinley and R. M. Nosofsky (Journal of Experimental Psychology: Human Perception & Performance, 1996[Apr], Vol 22[2], 294–317). In each row of Table 2, the Akaike's information criterion (AIC) fits for the models are in error by a roughly constant amount. (When calculating the fits, the constant portion of the log-likelihood function that enters into the AIC computation was inadvertently deleted.) The relative AIC fits of the models, the proportion of variance accounted for, as well as all conclusions based on these fits, remains the same. The corrected table appears here. (The following abstract of this article originally appeared in record 1996-03036-003.) Classification experiments were designed to compare the predictions of a linear decision bound model with those of an exemplar-similarity model incorporating an explicit selective attention mechanism. Linear boundaries could account for the data only in tasks involving separable dimension stimuli and where the boundary separating the categories was orthogonal to the psychological dimensions. Linear boundaries provided poor fits to the classification data in situations involving integral dimensions or when the boundary needed to be oriented in oblique directions in the space. The results were consistent with the selection-attention assumptions embodied in the exemplar model. It was argued that similar assumptions about selective attention need to be incorporated within decision bound models. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prototypes in the mist: The early epochs of category learning.

[Correction Notice: An erratum for this article was reported in Vol 25(1) of Journal of Experimental Psychology: Learning, Memory, and Cognition (see record 2008-09597-001). As a result of errors made in production, two equations in the article were printed incorrectly. The corrected equations are included in the erratum.] Recent ideas about category learning have favored exemplar processes over prototype processes. However, research has focused on small, poorly differentiated categories and on task-final performances—both may highlight exemplar strategies. Thus, we evaluated participants' categorization strategies and standard categorization models at successive stages in the learning of smaller, less differentiated categories and larger, more differentiated categories. In the former case, the exemplar model dominated even early in learning. In the latter case, the prototype model had a strong early advantage that gave way slowly. Alternative models, and even the behavior of individual parameters within models, suggest a psychological transition from prototype-based to exemplar-based processing during category learning and show that different category structures produce different trajectories of learning through the larger space of strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the relationship among identification, similarity, and categorization: Reply to Nosofsky and Smith (1992).

R. M. Nosofsky and J. K. Smith (See PA, Vol 79:33888) challenged the theoretical, methodological, and empirical results of F. G. Ashby and W. W. Lee (1991). This reply (1) shows that models derived from general recognition theory (GRT) can predict categorization from identification without incorporating selective attention, at least in the data sets suggested by Nosofsky and Smith; (2) argues that the categorization processes postulated by GRT are extremely dissimilar to the exemplar-based similarity model proposed by Nosofsky (1986); (3) criticizes Nosofsky and Smith's post hoc analysis of Ashby and Lee's identification-confusion data; (4) answers questions raised by Nosofsky and Smith about the identification and similarity models tested by Ashby and Lee; and (5) argues that with the excellent fits reported by Ashby and Lee (i.e., 99.7% of variance accounted for), least squares and maximum likelihood model fitting procedures lead to similar conditions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Stages of category learning in monkeys (Macaca mulatta) and humans (Homo sapiens).

Smith and Minda (1998) and Blair and Homa (2001) studied the time course of category learning in humans. They distinguished an early, abstraction-based stage of category learning from a later stage that incorporated a capacity for categorizing exceptional category members. The present authors asked whether similar processing stages characterize the category learning of nonhuman primates. Humans (Homo sapiens) and monkeys (Macaca mulatta) participated in category-learning tasks that extended Blair and Homa’s paradigm comparatively. Early in learning, both species improved on typical items more than on exception items, indicating an initial mastery of the categories’ general structure. Later in learning, both species selectively improved their exception-item performance, indicating exception-item resolution or exemplar memorization. An initial stage of abstraction-based category learning may characterize categorization across a substantial range of the order Primates. This default strategy may have an adaptive resonance with the family resemblance organization of many natural-kind categories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Selective attention and the formation of linear decision boundaries: Comment on McKinley and Nosofsky (1996).

S. C. McKinley and R. M. Nosofsky (see record 83-21756) compared a linear decision-bound model with the generalized context model (GCM) in their ability to account for categorization data from experiments that used integral- or separable-dimension stimuli and required selective attention or attention to both dimensions. McKinley and Nosofsky found support for the GCM and concluded that decision-bound theory needs to incorporate assumptions about selective attention. In this commentary it is argued that (a) unlike the GCM, decision-bound theory provides a framework for independently investigating perceptual and decisional forms of selective attention; (b) the effect of stimulus integrality on the form of the optimal decision bound is misinterpreted; (c) averaged data is biased against decision-bound theory and toward the GCM; (d) many a priori predictions of the GCM are violated empirically; and (e) exemplar theory has lost much of its initial theoretical structure. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effect of memory decay on predictions from changing categories.

In contrast to the static categories assumed in most categorization experiments, many real-world categories undergo gradual and systematic change in their definitions over time. Four experiments were carried out to study such category change. In these studies, participants successfully adjusted as category change occurred, but also showed a lingering and cumulative effect of past observations. The participants' performance was closely modeled by incorporating memory decay for past observations into J. R. Anderson's (1990, 1991) rational categorization algorithm and into a version of R. M. Nosofsky's (1986) exemplar categorization model. The resulting models suggest that the decay function is closer to a power law than to an exponential and that decay occurs both by item and by time, with the item decay being stronger than the time decay. The finding of power law decay gives additional support to claims that exemplar memories are used in categorization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prototypes in category learning: The effects of category size, category structure, and stimulus complexity.

Although research in categorization has sometimes been motivated by prototype theory, recent studies have favored exemplar theory. However, some of these studies focused on small, poorly differentiated categories composed of simple, 4-dimensional stimuli. Some analyzed the aggregate data of entire groups. Some compared powerful multiplicative exemplar models to less powerful additive prototype models. Here, comparable prototype and exemplar models were fit to individual-participant data in 4 experiments that sampled category sets varying in size, level of category structure, and stimulus complexity (dimensionality). The prototype model always fit the observed data better than the exemplar model did. Prototype-based processes seemed especially relevant when participants learned categories that were larger or contained more complex stimuli. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Selective attention and the formation of linear decision boundaries.

Classification experiments were designed to compare the predictions of a linear decision bound model with those of an exemplar-similarity model incorporating an explicit selective attention mechanism. Linear boundaries could account for the data only in tasks involving separable dimension stimuli and where the boundary separating the categories was orthogonal to the psychological dimensions. Linear boundaries provided poor fits to the classification data in situations involving integral dimensions or when the boundary needed to be oriented in oblique directions in the space. The results were consistent with the selection-attention assumptions embodied in the exemplar model. It was argued that similar assumptions about selective attention need to be incorporated within decision bound models. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The IQ paradox is still resolved: Reply to Loehlin (2002) and Rowe and Rodgers (2002).

In their original article (W. T. Dickens & J. R. Flynn, 2001), the authors formalized the consensus model of reciprocal effects between IQ and environment and showed that it can resolve the apparent paradox between high heritability and large environmental effects. Commentators suggested that the model has undesirable properties that call its usefulness into question. J. L. Loehlin (2002) argued that incorporating persistence of IQ into the model causes problematic behavior. D. C. Rowe and J. L. Rodgers (2002) argued that an increasing correlation of IQ and environment should have caused growing variance of IQ. Empirical evidence suggests that IQ is not sufficiently persistent to cause the problems Loehlin found and that the correlation of IQ and environment has not grown much over time so that the reciprocal effects model need not imply increasing variance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

How to hit scylla without avoiding charybdis: Comment on Perruchet, Tyler, Galland, and Peereman (2004).

M. Pe?a, L. L. Bonatti, M. Nespor, and J. Mehler (see record 2002-06215-001) argued that humans compute nonadjacent statistical relations among syllables in a continuous artificial speech stream to extract words, but they use other computations to determine the structural properties of words. Instead, when participants are familiarized with a segmented stream, structural generalizations about words are quickly established. P. Perruchet, M. D. Tyler, N. Galland, and R. Peereman (see record 2004-21166-008) criticized M. Pe?a et al.'s work and dismissed their results. In this article, the authors show that P. Perruchet et al.'s criticisms are groundless. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Journey to the center of the category: The dissociation in amnesia between categorization and recognition.

The authors' theoretical analysis of the dissociation in amnesia between categorization and recognition suggests these conclusions: (a) Comparing to-be-categorized items to a category center or prototype produces strong prototype advantages and steep typicality gradients, whereas comparing to-be, categorized items to the training exemplars that surround the prototype produces weak prototype advantages and flat typicality gradients; (b) participants often show the former pattern, suggesting their use of prototypes; (c) exemplar models account poorly for these categorization data, but prototype models account well for them; and (d) the recognition data suggest that controls use a single-comparison exemplar-memorization process more powerfully than amnesics. By pairing categorization based in prototypes with recognition based in exemplar memorization, the authors support and extend other recent accounts of cognitive performance that intermix prototypes and exemplars, and the authors reinforce traditional interpretations of the categorization-recognition dissociation in amnesia. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Base-rate neglect in ALCOVE: A critical reevaluation.

A recent hybrid model of categorization (Attention Learning Covering Map [ALCOVE]; J. K. Kruschke, 1992) has combined the most desirable properties of exemplar models with a connectionist architecture and learning rule. A critically important property of ALCOVE is its apparent ability to account for base-rate neglect, a phenomenon beyond the purview of previous exemplar models. This article reexamines ALCOVE's base-rate neglect predictions and shows that they are confined to a very limited set of circumstances. In most cases, ALCOVE is unable to produce base-rate neglect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)