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)     

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)     

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)     

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. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Phonetic category structure of [I]: extent, best exemplars, and organization

The current investigation examined the structure of the phonetic category [I] for 13 listeners. Experiments reported are results from identification, "best exemplar," and discrimination tasks using 105 [I] stimuli. The tokens were synthesized long a mel-spaced vowel continuum that differed in first and second formants. All stimuli ended in a 30 ms [b] sound. Results showed that 10 of 13 listeners demonstrated differing choices of the best exemplars, although most were within 37.5 mels of the central best exemplar chosen in the first experiment. Seven of the participants demonstrated "circular" patterns in identification of the [I] category that appeared to be organized around a central "best exemplar." Six participants showed other identification patterns: "downward," "upward," and "left-extending," with "best exemplars" on an edge or border of the phonetic categories. Graded category structure from a central "best exemplar" was apparent only in the averaged identification results, and not for individual participants. The size of the [I] category was significantly smaller than that surrounding the [i] best exemplar reported in a prior study by Sussman and Lauckner-Morano (1995). Finally, listeners had equivalent or better discrimination sensitivity with the best exemplar as the fixed standard compared to that for a "poor" exemplar token 45 mels away from the best exemplar. Results showed that phonetic category structure for the lax vowel [I] was different from the similar, but tense vowel [i]. The findings question whether prototype theory is generalizable to vowel categories other than [i].     

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)     

Distinguishing prototype-based and exemplar-based processes in dot-pattern category learning.

The authors contrast exemplar-based and prototype-based processes in dot-pattern categorization. In Experiments 1A and 1B, participants provided similarity ratings of dot-distortion pairs that were distortions of the same originating prototype. The results show that comparisons to training exemplars surrounding the prototype create flat typicality gradients within a category and small prototype-enhancement effects, whereas comparisons to a prototype center create steep typicality gradients within a category and large prototype-enhancement effects. Thus, prototype and exemplar theories make different predictions regarding common versions of the dot-distortion task. Experiment 2 tested these different predictions by having participants learn dot-pattern categories. The steep typicality gradients, the large prototype effects, and the superior fit of prototype models suggest that participants refer to-be-categorized items to a representation near the category's center (the prototype), and not to the training exemplars that surround the prototype. (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)     

Category Representation for Classification and Feature Inference.

This research's purpose was to contrast the representations resulting from learning of the same categories by either classifying instances or inferring instance features. Prior inference learning research, particularly T. Yamauchi and A. B. Markman (1998), has suggested that feature inference learning fosters prototype representation, whereas classification learning encourages exemplar representation. Experiment 1 supported this hypothesis. Averaged and individual participant data from transfer after inference training were better fit by a prototype than by an exemplar model. However, Experiment 2, with contrasting inference learning conditions, indicated that the prototype model was mimicking a set of label-based bidirectional rules, as determined by the inference learning task demands in Experiment 1. Only the set of rules model accounted for all the inference learning conditions in these experiments. (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)     

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)     

The Origin of Exemplar Effects in Rule-Driven Categorization.

S. W. Allen and L. R. Brooks (1991) have shown that exemplar memory can affect categorization even when participants are provided with a classification rule. G. Regehr and L. R. Brooks (1993) argued that stimuli must be individuated for such effects to occur. In this study, the authors further analyze the conditions that yield exemplar effects in this rule application paradigm. The results of Experiments 1-3 show that interchangeable attributes, which are not part of the rule, influence categorization only when attention is explicitly drawn on them. Experiment 4 shows that exemplar effects can occur in an incidental learning condition, whether stimulus individuation is preserved or not. The authors conclude that the influence of exemplar learning in rule-driven categorization stems from the attributes specified in the rule or in the instructions, not from the stimulus gestalts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparing exemplar and prototype models of categorization.

Five experiments contrasted the prototype and exemplar theories of categorization. The stimuli for the experiments were simple perceptual figures that varied along two multi-valued dimensions. During the learning phase of each experiment, participants assigned each of a series of stimuli to one of two categories and received feedback concerning their assignments. During the generalization phase, participants made category judgments with respect to novel stimuli, including some crucial test stimuli that were (a) more similar to the prototype of one of the two categories, and at the same time, (b) more similar, on average, to the exemplars of the other category. Participants assigned the test stimuli to the latter category. This was taken as evidence favoring the exemplar over the prototype theory. (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)     

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)     

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)     

Speeded Classification in a Probabilistic Category Structure: Contrasting Exemplar-Retrieval, Decision-Boundary, and Prototype Models.

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models. (PsycINFO Database Record (c) 2010 APA, all rights reserved)