Thirty categorization results in search of a model.

One category structure dominated in the shift toward exemplar-based theories of categorization. Given the theoretical burden on this category structure, the authors reanalyzed 30 of its uses over 20 years in 8 articles. The authors suggest 4 conclusions. (1) This category structure may encourage exemplar-memorization processes because of its poor structure, the learning difficulties it causes, and its small, memorizable exemplar sets. Its results may only generalize narrowly. (2) Exemplar models have an advantage in fitting these 30 data sets only because they reproduce a performance advantage for training items. Other models fit equally well if granted this capacity. (3) A simpler exemplar process than assumed by exemplar models suffices to explain these data sets. (4) An important qualitative result predicted by exemplar theory is not found overall and possibly should not even be expected. The authors conclude that the data produced by this category structure do not clearly support exemplar theory. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Rules and exemplars in category learning

Psychological theories of categorization generally focus on either rule- or exemplar-based explanations. We present 2 experiments that show evidence of both rule induction and exemplar encoding as well as a connectionist model, ATRIUM, that specifies a mechanism for combining rule- and exemplar-based representation. In 2 experiments participants learned to classify items, most of which followed a simple rule, although there were a few frequently occurring exceptions. Experiment 1 examined how people extrapolate beyond the range of training. Experiment 2 examined the effect of instance frequency on generalization. Categorization behavior was well described by the model, in which exemplar representation is used for both rule and exception processing. A key element in correctly modeling these results was capturing the interaction between the rule- and exemplar-based representations by using shifts of attention between rules and exemplars.     

Knowledge Partitioning in Categorization: Constraints on Exemplar Models.

The authors present 2 experiments that establish the presence of knowledge partitioning in perceptual categorization. Many participants learned to rely on a context cue, which did not predict category membership but identified partial boundaries, to gate independent partial categorization strategies. When participants partitioned their knowledge, a strategy used in 1 context was unaffected by knowledge demonstrably present in other contexts. An exemplar model, attentional learning covering map, was shown to be unable to accommodate knowledge partitioning. Instead, a mixture-of-experts model, attention to rules and instances in a unified model (ATRIUM), could handle the results. The success of ATRIUM resulted from its assumption that people memorize not only exemplars but also the way in which they are to be classified. (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)     

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)     

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)     

Prototype abstraction by monkeys (Macaca mulatta).

The authors analyze the shape categorization of rhesus monkeys (Macaca mulatta) and the role of prototype- and exemplar-based comparison processes in monkeys' category learning. Prototype and exemplar theories make contrasting predictions regarding performance on the Posner-Homa dot-distortion categorization task. Prototype theory--which presumes that participants refer to-be-categorized items to a representation near the category's center (the prototype)--predicts steep typicality gradients and large prototype-enhancement effects. Exemplar theory--which presumes that participants refer to-be-categorized items to memorized training exemplars-predicts flat typicality gradients and small prototype-enhancement effects. Across many categorization tasks that, for the first time, assayed monkeys' dot-distortion categorization, monkeys showed steep typicality gradients and large prototype-enhancement effects. These results suggest that monkeys--like humans--refer to-be-categorized items to a prototype-like representation near the category's center rather than to a set of memorized training exemplars. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cue abstraction and exemplar memory in categorization.

In this article, the authors compare 3 generic models of the cognitive processes in a categorization task. The cue abstraction model implies abstraction in training of explicit cue-criterion relations that are mentally integrated to form a judgment, the lexicographic heuristic uses only the most valid cue, and the exemplar-based model relies on retrieval of exemplars. The results from 2 experiments showed that, in lieu of the lexicographic heuristic, most participants spontaneously integrate cues. In contrast to single-system views, exemplar memory appeared to dominate when the feedback was poor, but when the feedback was rich enough to allow the participants to discern the task structure, it was exploited for abstraction of explicit cue-criterion relations. (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)     

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)     

Beyond nonutilization: Irrelevant cues can gate learning in probabilistic categorization.

In probabilistic categorization, also known as multiple cue probability learning (MCPL), people learn to predict a discrete outcome on the basis of imperfectly valid cues. In MCPL, normatively irrelevant cues are usually ignored, which stands in apparent conflict with recent research in deterministic categorization that has shown that people sometimes use irrelevant cues to gate access to partial knowledge encapsulated in independent partitions. The authors report 2 experiments that sought support for the existence of such knowledge partitioning in probabilistic categorization. The results indicate that, as in other areas of concept acquisition (such as function learning and deterministic categorization), a significant proportion of participants partitioned their knowledge on the basis of an irrelevant cue. The authors show by computational modeling that knowledge partitioning cannot be accommodated by 2 exemplar models (Generalized Context Model and Rapid Attention Shifts 'N Learning), whereas a rule-based model (General Recognition Theory) can capture partitioned performance. The authors conclude by pointing to the necessity of a mixture-of-experts approach to capture performance in MCPL and by identifying reduction of complexity as a possible explanation for partitioning. (PsycINFO Database Record (c) 2010 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)     

The role of similarity in artificial grammar learning.

The authors examine the role of similarity in artificial grammar learning (AGL; A. S. Reber, 1989). A standard finite-state language was used to create stimuli that were arrangements of embedded geometric shapes (Experiment 1), connected lines (Experiment 2), and sequences of shapes (Experiment 3). Main effects for well-known predictors from the literature (grammaticality, associative global and anchor chunk strength, novel global and anchor chunk strength, length of items, and edit distance) were observed, thus replicating previous work. However, the authors extend previous research by using a widely known similarity-based exemplar model of categorization (the generalized context model; R. M. Nosofsky, 1989) to fit grammaticality judgments, by nested regression analyses. The results suggest that any explanation of AGL that is based on the existing theories is incomplete without a similarity process as well. Also, the results provide a foundation for further interpreting AGL in the wider context of categorization research. (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)     

Category Learning in Rhesus Monkeys: A Study of the Shepard, Hovland, and Jenkins (1961) Tasks.

In influential research, R. N. Shepard, C. I. Hovland, and H. M. Jenkins (1961) surveyed humans' categorization abilities using tasks based in rules, exclusive-or (XOR) relations, and exemplar memorization. Humans' performance was poorly predicted by cue-conditioning or stimulus-generalization theories, causing Shepard et al. to describe it in terms of hypothesis selection and rule application that were possibly supported by verbal mediation. The authors of the current article surveyed monkeys' categorization abilities similarly. Monkeys, like humans, found category tasks with a single relevant dimension the easiest and perceptually chaotic tasks requiring exemplar memorization the most difficult. Monkeys, unlike humans, found tasks based in XOR relations very difficult. The authors discuss the character and basis of the species difference in categorization and consider whether monkeys are the generalization-based cognitive system that humans are not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Categorization of sounds.

The authors conducted 4 experiments to test the decision-bound, prototype, and distribution theories for the categorization of sounds. They used as stimuli sounds varying in either resonance frequency or duration. They created different experimental conditions by varying the variance and overlap of 2 stimulus distributions used in a training phase and varying the size of the stimulus continuum used in the subsequent test phase. When resonance frequency was the stimulus dimension, the pattern of categorization-function slopes was in accordance with the decision-bound theory. When duration was the stimulus dimension, however, the slope pattern gave partial support for the decision-bound and distribution theories. The authors introduce a new categorization model combining aspects of decision-bound and distribution theories that gives a superior account of the slope patterns across the 2 stimulus dimensions. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Predicting reasoning from memory.

In an effort to assess the relations between reasoning and memory, in 8 experiments, the authors examined how well responses on an inductive reasoning task are predicted from responses on a recognition memory task for the same picture stimuli. Across several experimental manipulations, such as varying study time, presentation frequency, and the presence of stimuli from other categories, there was a high correlation between reasoning and memory responses (average r = .87), and these manipulations showed similar effects on the 2 tasks. The results point to common mechanisms underlying inductive reasoning and recognition memory abilities. A mathematical model, GEN-EX (generalization from examples), derived from exemplar models of categorization, is presented, which predicts both reasoning and memory responses from pairwise similarities among the stimuli, allowing for additional influences of subtyping and deterministic responding. (PsycINFO Database Record (c) 2010 APA, all rights reserved)