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.     

Prototype and exemplar accounts of category learning and attentional allocation: a reassessment

In a recent article. J. P. Minda and J. D. Smith (2002; see record 2002-00620-002) argued that an exemplar model provided worse quantitative fits than an alternative prototype model to individual subject data from the classic D. L. Medin and M. M. Schaffer (1978) 5/4 categorization paradigm. In addition, they argued that the exemplar model achieved its fits by making untenable assumptions regarding how observers distribute their attention. In this article, we demonstrate that when the models are equated in terms of their response-rule flexibility, the exemplar model provides a substantially better account of the categorization data than does a prototype or mixed model. In addition, we point to shortcomings in the attention-allocation analyses conducted by J. P. Minda and J. D. Smith (2002). When these shortcomings are corrected, we find no evidence that challenges the attention-allocation assumptions of the exemplar model.     

Information-accumulation theory of speeded categorization

A process model of perceptual categorization is presented, in which it is assumed that the earliest stages of categorization involve gradual accumulation of information about object features. The model provides a joint account of categorization choice proportions and response times by assuming that the probability that the information-accumulation process stops at a given time after stimulus presentation is a function of the stimulus information that has been acquired. The model provides an accurate account of categorization response times for integral-dimension stimuli and for separable-dimension stimuli, and it also explains effects of response deadlines and exemplar frequency.     

Rule-based extrapolation in perceptual categorization

Erickson and Kruschke (1998) provided a demonstration that in certain situations people will classify novel stimuli according to an extrapolated rule, even when the most similar training exemplar is an exception to the rule. This result challenged exemplar models. Nosofsky and Johansen (2000) have called this finding into question by offering an exemplar-based explanation for those data based on the perceptual features of the stimuli. Here, we describe the results of a new experiment that yields results similar to those found previouslywithout the questionable perceptual features:Participantswho learn to classify all the training stimuli have patterns of generalization that indicate a combination of rule and exemplar representation. ATRIUM, a hybrid rule and exemplar model (Erickson & Kruschke, 1998), is shown to account for these data much better than ALCOVE, an exemplar model (Kruschke, 1992).Moreover, four alternate exemplar explanations, including one suggested by Nosofsky and Johansen, cannot account for our new findings.     

Grammaticality is inferred from global similarity: A reply to Kinder (2010)

Jamieson and Mewhort (2009b) proposed an account of performance in the artificial-grammar judgement-of-grammaticality task based on Hintzman's (1986) model of retrieval, Minerva 2. In the account, each letter is represented by a unique vector of random elements, and each exemplar is represented by concatenating its constituent letter vectors. Although successful in simulating several experiments, Kinder (2010) showed that the model fails for three selected experiments. We track the model's failure to a constraint introduced by concatenating letter vectors to construct the exemplar representation. To fix the problem, we use a holographic representation. Holographic representation not only provides the flexibility missing with the concatenation scheme but also acknowledges variability in what subjects notice when they inspect training exemplars. Armed with holographic representations, we show that the model successfully captures the three problematic data sets. We argue for retrospective accounts, like the present one, that acknowledge subjects' skill in drawing unexpected inferences based on memory of studied items against prospective accounts that require subjects to learn statistical regularities in the training set in anticipation of an undefined classification test.     

Representation at different levels in a conceptual hierarchy

The present study examines the influence of hierarchical level on category representation. Three computational models of representation – an exemplar model, a prototype model and an ideal representation model – were evaluated in their ability to account for the typicality gradient of categories at two hierarchical levels in the conceptual domain of clothes. The domain contains 20 subordinate categories (e.g., trousers, stockings and underwear) and an encompassing superordinate category (CLOTHES). The models were evaluated both in terms of their ability to fit the empirical data and their generalizability through marginal likelihood. The hierarchical level was found to clearly influence the type of representation: For concepts at the subordinate level, exemplar representations were supported. At the superordinate level, however, an ideal representation was overwhelmingly preferred over exemplar and prototype representations. This finding contributes to the increasingly dominant view that the human conceptual apparatus adopts both exemplar representations and more abstract representations, contradicting unitary approaches to categorization.     

Similarity and dissimilarity as evidence in perceptual categorization

In exemplar models the similarities between a new stimulus and each category exemplar constitute positive evidence for category membership. In contrast, other models assume that, if the new stimulus is sufficiently dissimilar to a category member, then that dissimilarity constitutes evidence against category membership. We propose a new similarity-dissimilarity exemplar model that provides a framework for integrating these two types of accounts. The evidence for a category is assumed to be the summed similarity to members of that category plus the summed dissimilarity to members of competing categories. The similarity-dissimilarity exemplar model is shown to mimic the standard exemplar model very closely in the unidimensional domain.     

When nothing is "off the record": exploring the theoretical implications of the continuous recording of cognitive process in memory

This theoretical paper explores the idea of acropetal memory; arguing that representations emanating from all stages of processing an individual stimulus can be registered in memory and potentially available for recall. I describe Lansdale's (1998) HELM model of location memory as a suitable candidate theory within which to implement and test this idea. An exemplar model, HELM-2, is presented and examined against available data. This model provides a satisfactory account of the data and an elegant solution to some interesting puzzles. It also suggests that some retrieval failure can be attributed to interference from representations of a stimulus derived from early stages of processing and that this has much broader implications for modelling encoding, forgetting, and false memory.     

Rule-based extrapolation: A continuing challenge for exemplar models

Erickson and Kruschke (1998, 2002) demonstrated that in rule-plus-exception categorization, people generalize category knowledge by extrapolating in a rule-like fashion, even when they are presented with a novel stimulus that is most similar to a known exception. Although exemplar models have been found to be deficient in explaining rule-based extrapolation, Rodrigues and Murre (2007) offered a variation of an exemplar model that was better able to account for such performance. Here, we present the results of a new rule-plus-exception experiment that yields rule-like extrapolation similar to that of previous experiments, and yet the data are not accounted for by Rodrigues and Murre's augmented exemplar model. Further, a hybrid rule-and-exemplar model is shown to better describe the data. Thus, we maintain that rule-plus-exception categorization continues to be a challenge for exemplar-only models.     

When nothing is “off the record”: Exploring the theoretical implications of the continuous recording of cognitive process in memory

This theoretical paper explores the idea of acropetal memory; arguing that representations emanating from all stages of processing an individual stimulus can be registered in memory and potentially available for recall. I describe Lansdale (1998) HELM model of location memory as a suitable candidate theory within which to implement and test this idea. An exemplar model, HELM-2, is presented and examined against available data. This model provides a satisfactory account of the data and an elegant solution to some interesting puzzles. It also suggests that some retrieval failure can be attributed to interference from representations of a stimulus derived from early stages of processing and that this has much broader implications for modelling encoding, forgetting, and false memory.     

Comments on “Getting scarred and winning lotteries: Effects of exemplar cuing and statistical format on imagining low‐probability events,” By Newell,Mitchell, and Hayes (2008)

Newell, Mitchell, and Hayes (NMH) conduct three experiments designed to test whether exemplar cuing (EC) theory or a statistical format theory provides a more accurate account for how people make judgments about low‐probability events. They report finding support for the statistical format theory and little or no support for EC. However, NMH misstate the requirements for the production of exemplars in EC theory. As a result, they confuse non‐exemplar conditions with exemplar conditions in their experiments, and find results that are virtually irrelevant to EC theory. Copyright © 2009 John Wiley & Sons, Ltd.     

The effect of category variability in perceptual categorization

Exemplar and distributional accounts of categorization make differing predictions for the classification of a critical exemplar precisely halfway between the nearest exemplars of 2 categories differing in variability. Under standard conditions of sequential presentation, the critical exemplar was classified into the most similar, least variable category, consistent with an exemplar account. However, if the difference in variability is made more salient, then the same exemplar is classified into the more variable, most likely category, consistent with a distributional account. This suggests that participants may be strategic in their use of either strategy. However, when the relative variability of 2 categories was manipulated, participants showed changes in the classification of intermediate exemplars that neither approach could account for.     

A formal ideal-based account of typicality

Inspired by Barsalou’s (Journal of Experimental Psychology: Learning, Memory, and Cognition, 11, 629–654, 1985) proposal that categories can be represented by ideals, we develop and test a computational model, the ideal dimension model (IDM). The IDM is tested in its account of the typicality gradient for 11 superordinate natural language concepts and, using Bayesian model evaluation, contrasted with a standard exemplar model and a central prototype model. The IDM is found to capture typicality better than do the exemplar model and the central tendency prototype model, in terms of both goodness of fit and generalizability. The present findings challenge the dominant view that exemplar representations are most successful and present compelling evidence that superordinate natural language categories can be represented using an abstract summary, in the form of ideal representations. Supplemental appendices for this article can be downloaded from .     

Comparing decision bound and exemplar models of categorization

The performance of a decision bound model of categorization (Ashby, J992a; Ashby & Maddox, in press) is compared with the performance of two exemplar models. The first is the generalized context model (e.g., Nosofsky, 1986, 1992) and the second is a recently proposed deterministic exemplar model (Ashby & Maddox, in press), which contains the generalized context model as a special case. When the exemplars from each category were normally distributed and the optimal decision bound was linear, the deterministic exemplar model and the decision bound model provided roughly equivalent accounts of the data. When the optimal decision bound was nonlinear, the decision bound model provided a more accurate account of the data than did either exemplar model. When applied to categorization data collected by Nosofsky (1986, 1989), in which the category exemplars are not normally distributed, the decision bound model provided excellent accounts of the data, in many cases significantly outperforming the exemplar models. The decision bound model was found to be especially successful when(1) single subject analyses were performed, (2) each subject was given relatively extensive training, and (3) the subject's performance was characterized by complex suboptimalities. These results support the hypothesis that the decision bound is of fundamental importance in predicting asymptotic categorization performance and that the decision bound models provide a viable alternative to the currently popular exemplar models of categorization.     

An interactive race model of divided attention.

Two classes of models have been proposed to explain how redundant information extracted from separate sources comes to activate a single response. Each provides a fundamentally different account of why responses to redundant signals are typically faster than those to either signal alone (the redundant-signals effect). Independent race models assume that a race occurs between perceptual codes on independent channels and that only the winner activates the response. Coactivation models assume that there is some form of energy or activation-strength summation, with information being pooled across channels prior to decision. An intermediate class of models is introduced and a specific exemplar, the interactive race model, is tested in a series of redundant-target detection experiments. In particular, we examine the effects on performance of two types of contingency that have previously been overlooked as sources of task-relevant information. The results reveal that response times are significantly influenced by both interstimulus and stimulus-response contingencies. The interactive race model provides a natural account of these findings as well as several otherwise puzzling results in the divided-attention literature.     

Idealness and similarity in goal-derived categories: A computational examination

The finding that the typicality gradient in goal-derived categories is mainly driven by ideals rather than by exemplar similarity has stood uncontested for nearly three decades. Due to the rather rigid earlier implementations of similarity, a key question has remained—that is, whether a more flexible approach to similarity would alter the conclusions. In the present study, we evaluated whether a similarity-based approach that allows for dimensional weighting could account for findings in goal-derived categories. To this end, we compared a computational model of exemplar similarity (the generalized context model; Nosofsky, Journal of Experimental Psychology. General 115:39–57, 1986) and a computational model of ideal representation (the ideal-dimension model; Voorspoels, Vanpaemel, & Storms, Psychonomic Bulletin & Review 18:1006-114, 2011) in their accounts of exemplar typicality in ten goal-derived categories. In terms of both goodness-of-fit and generalizability, we found strong evidence for an ideal approach in nearly all categories. We conclude that focusing on a limited set of features is necessary but not sufficient to account for the observed typicality gradient. A second aspect of ideal representations—that is, that extreme rather than common, central-tendency values drive typicality—seems to be crucial.     

Towards a General Model of Applying Science

How is scientific knowledge used, adapted, and extended in deriving phenomena and real‐world systems? This paper aims at developing a general account of ‘applying science’ within the exemplar‐based framework of Data‐Oriented Processing (DOP), which is also known as Exemplar‐Based Explanation (EBE). According to the exemplar‐based paradigm, phenomena are explained not by deriving them all the way down from theoretical laws and boundary conditions but by modelling them on previously derived phenomena that function as exemplars. To accomplish this, DOP proposes to maintain a corpus of derivation trees of previous phenomena together with a matching algorithm that combines subtrees from the corpus to derive new phenomena. By using a notion of derivational similarity, a new phenomenon can be modelled as closely as possible on previously explained phenomena. I will propose an instantiation of DOP which integrates theoretical and phenomenological modelling and which generalises over various disciplines, from fluid mechanics to language technology. I argue that DOP provides a solution for what I call Kuhn’s problem and that it redresses Kitcher’s account of explanation.     

Dissociations Between Categorization and Recognition in Amnesic and Normal Individuals: An Exemplar-Based Interpretation

In recent work, the finding of dissociations between categorization and recognition in amnesic and normal individuals has been taken as evidence of multiple memory systems mediating these tasks. The present research provides support for the alternative idea that these dissociations can be interpreted in terms of a single-system exemplar-memory model that makes allowance for parameter differences across groups. In one experiment, a parameter change in memory sensitivity was induced by testing classification and recognition at varying delays; the results closely matched the ones observed by Knowlton and Squire (1993) for normal and amnesic participants. The exemplar model also yielded good quantitative predictions of the categorization-recognition dissociation. A second analysis demonstrated that dissociations between early versus late probabilistic classification learning and memory sensitivity were also well predicted by the single-system exemplar model. Limitations of the exemplar interpretation and future research directions are also discussed.