An exemplar-based random walk model of speeded classification

The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R. M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.     

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)     

Recognition memory for exceptions to the category rule.

Experiments were conducted to demonstrate the utility of a rule-plus-exception model for extending current exemplar-based views of categorization and recognition memory. According to the model, exemplars that are exceptions to category rules have a special status in memory relative to other old items. In each of 4 experiments, participants first learned to categorize items organized into 2 ill-defined categories and then made old–new recognition judgments. Although the categories afforded no perfect rules, a variety of imperfect rules could be formed combined with memorization of exceptions to those rules. In each experiment, superior recognition of exceptions to imperfect logical rules was found. In addition, participants demonstrated better memory for old exemplars than new ones. A mixed model, which assumed a combination of rule-plus-exception processing and residual exemplar storage, provided good quantitative accounts of the data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Similarity, identification, and categorization: Comment on Ashby and Lee (1991).

F. G. Ashby and W. W. Lee (see record 1991-23345-001) tested various models derived from the general recognition theory (GRT; Ashby and N. A. Perrin, 1988; Ashby and J. T. Townsend, 1986) on their ability to predict and interrelate similarity, categorization, and identification performance. This commentary (1) argues that contrary to Ashby and Lee's suggestion, the likelihood-based GRT cannot generally predict categorization from identification without incorporating selective attention, (2) argues that the categorization rule in the likelihood-based GRT is extremely close in spirit to R. M. Nosofsky's (1986) exemplar-based similarity model, (3) reports new model-based analyses that call into question Ashby and Lee's interpretation of their identification-confusion data, (4) raises questions about the identification and similarity models tested by Ashby and Lee, and (5) criticizes Ashby and Lee's methods of fitting and evaluating the various models. (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)     

Exemplar-based accounts of relations between classification, recognition, and typicality.

Previously published sets of classification and old–new recognition memory data are reanalyzed within the framework of an exemplar-based generalization model. The key assumption in the model is that, whereas classification decisions are based on the similarity of a probe to exemplars of a target category relative to exemplars of contrast categories, recognition decisions are based on overall summed similarity of a probe to all exemplars. The summed-similarity decision rule is shown to be consistent with a wide variety of recognition memory data obtained in classification learning situations and may provide a unified approach to understanding relations between categorization and recognition. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Investigations of exemplar and decision bound models in large, ill-defined category structures.

Experiments involving large-size, ill-defined categories were conducted to distinguish between the predictions of an exemplar model and linear and quadratic decision bound models. In conditions in which the optimal classification boundary was of a more complex form than the quadratic model, the exemplar model provided significantly better accounts of study participants' data than did the decision bound models, even in situations in which a linear bound would have yielded nearly optimal performance. The results suggest that participants are not predisposed or constrained to use linear or quadratic decision bounds for classifying multidimensional perceptual stimuli and that exemplar models may provide a parsimonious process-level account of the complex types of decision bounds used by experiment participants. The results also suggest some limitations on the complexity of the decision bounds that can be learned, in contrast to the predictions of the exemplar model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Similarity-scaling studies of dot-pattern classification and recognition.

Classification performance in a dot-pattern, prototype-distortion paradigm (e.g., M. I. Posner and S. W. Keele, 1968) was modeled within a multidimensional scaling (MDS) framework. MDS solutions were derived for sets of dot patterns that were generated from prototypes. These MDS solutions were then used in conjunction with exemplar, prototype, and combined models to predict classification and recognition performance. Across 3 experiments, an MDS-based exemplar model accounted for the effects of several fundamental learning variables, including level of distortion of the patterns, category size, delay of transfer phase, and item frequency. Most important, the model quantitatively predicted classification probabilities for individual dot patterns in the sets, not simply general trends of performance. There was little evidence for the existence of a prototype-abstraction process that operated above and beyond pure exemplar-based generalization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An exemplar-retrieval model of speeded same–different judgments.

R. M. Nosofsky and T. J. Palmeri's (1997) exemplar-based random-walk (EBRW) model of speeded classification is extended to account for speeded same–different judgments among integral-dimension stimuli. According to the model, an important component process of same–different judgments is that people store individual examples of experienced same and different pairs of objects in memory. These exemplar pairs are retrieved from memory on the basis of how similar they are to a currently presented pair of objects. The retrieved pairs drive a random-walk process for making same–different decisions. The EBRW predicts correctly that same responses are faster for objects lying in isolated than in dense regions of similarity space. The model also predicts correctly effects of same-identity versus same-category instructions and is sensitive to observers' past experiences with specific same and different pairs of objects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Seven plus or minus two: A commentary on capacity limitations.

G. A. Miller's classic 1956 article (see PA, Vol 31:2914; see also PA, Vol 81:28291) is best known today for its discussion of capacity limitations in short-term memory, but the bulk of the article dealt with capacity limitations in absolute judgment tasks and the relation of such limitations to information theory. Many of the puzzles of absolute judgment first raised by Miller remain a puzzle today. Some of the literature directed toward this issue is reviewed, and a few models that attempt to elucidate the phenomena are discussed. Since 1956 there has been an enormous research effort aimed at understanding the mechanisms and limitations of short-term memory, resulting in considerable progress. The authors briefly discuss some of these advances. The authors conclude, as did Miller, by noting the probable lack of connection between the limitations observed in these 2 areas of inquiry. (PsycINFO Database Record (c) 2010 APA, all rights reserved)