Memory storage and retrieval processes in category learning

The detailed course of learning is studied for categorization tasks defined by independent or contingent probability distributions over the features of category exemplars. College-age subjects viewed sequences of bar charts that simulated symptom patterns and responded to each chart with a recognition and a categorization judgment. Fuzzy, probabilistically defined categories were learned relatively rapidly when individual features were correlated with category assignment, more slowly when only patterns carried category information. Limits of performance were suboptimal, evidently because of capacity limitations on judgmental processes as well as limitations on memory. Categorization proved systematically related to feature and exemplar probabilities, under different circumstances, and to similarity among exemplars of categories. Unique retrieval cues for exemplar patterns facilitated recognition but entered into categorization only at retention intervals within the range of short-term memory. The findings are interpreted within the framework of a general array model that yields both exemplar-similarity and feature-frequency models as special cases and provides quantitative accounts of the course of learning in each of the categorization tasks studied.     

Limitations of exemplar models of multi-attribute probabilistic inference

Observers were presented with pairs of objects varying along binary-valued attributes and learned to predict which member of each pair had a greater value on a continuously varying criterion variable. The predictions from exemplar models of categorization were contrasted with classic alternative models, including generalized versions of a "take-the-best" model and a weighted-additive model, by testing structures in which interactions between attributes predicted the magnitude of the criterion variable. Under typical training conditions, observers showed little sensitivity to the attribute interactions, thereby challenging the predictions from the exemplar models. In a condition involving highly extended training, observers eventually learned the relations between the attribute interactions and the criterion variable. However, an analysis of the observers' response times for making their paired-comparison decisions also challenged the exemplar model predictions. Instead, it appeared that most observers recoded the interacting attributes into emergent configural cues. They then applied a set of hierarchically organized rules based on the priority of the cues to make their decisions.     

Comparing Exemplar- and Rule-Based Theories of Categorization

ABSTRACT— We address whether human categorization behavior is based on abstracted rules or stored exemplars. Although predictions of both theories often mimic each other in many designs, they can be differentiated. Experimental data reviewed does not support either theory exclusively. We find participants use rules when the stimuli are confusable and exemplars when they are distinct. By drawing on the distinction between simple stimuli (such as lines of various lengths) and complex ones (such as words and objects), we offer a dynamic view of category learning. Initially, categorization is based on rules. During learning, suitable features for discriminating stimuli may be gradually learned. Then, stimuli can be stored as exemplars and used to categorize novel stimuli without recourse to rules.     

An electrophysiological comparison of visual categorization and recognition memory

Object categorization emphasizes the similarities that bind exemplars into categories, whereas recognition memory emphasizes the specific identification of previously encountered exemplars. Mathematical modeling has highlighted similarities in the computational requirements of these tasks, but neuropsychological research has suggested that categorization and recognition may depend on separate brain systems. Following training with families of novel visual shapes (blobs), event-related brain potentials (ERPs) were recorded during both categorization and recognition tasks. ERPs related to early visual processing (N1, 156–200 msec) were sensitive to category membership. Middle latency ERPs (FN400 effects, 300–500 msec) were sensitive to both category membership and old/new differences. Later ERPs (parietal effects, 400–800 msec) were primarily affected by old/new differences. Thus, there was a temporal transition so that earlier processes were more sensitive to categorical discrimination and later processes were more sensitive to recognition-related discrimination. Aspects of these results are consistent with both mathematical modeling and neuropsychological perspectives.     

Attention, similarity, and the identification-categorization relationship

A unified quantitative approach to modeling subjects' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification data were modeled using Shepard's (1957) multidimensional scaling-choice framework. This framework was then extended to model the subjects' categorization performance. The categorization model, which generalizes the context theory of classification developed by Medin and Schaffer (1978), assumes that subjects store category exemplars in memory. Classification decisions are based on the similarity of stimuli to the stored exemplars. It is assumed that the same multidimensional perceptual representation underlies performance in both the identification and categorization paradigms. However, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained that subjects may have augmented their category representations with inferred exemplars. Implications of the results for theories of multidimensional scaling and categorization are discussed.     

Category induction in autism: Slower,perhaps different,but certainly possible

Available studies on categorization in autism indicate possibly intact category formation, performed through atypical processes. Category learning was investigated in 16 high-functioning autistic and 16 IQ-matched nonautistic participants, using a category structure that could generate a conflict between the application of a rule and exemplar memory. Same–different and matching-to-sample tasks allowed us to verify discrimination abilities for the stimuli to be used in category learning. Participants were then trained to distinguish between two categories of imaginary animals, using categorization tests early in the training and at the end (160 trials). A recognition test followed, in order to evaluate explicit exemplar memory. Similar discrimination performance was found in control tasks for both groups. For the categorization task, autistic participants did not use any identifiable strategy early in the training, but used strategies similar to those of the nonautistic participants by the end, with the same level of accuracy. Memory for the exemplars was poor in both groups. Our findings confirm that categorization may be successfully performed by autistics, but may necessitate longer exposure to material, as the top-down use of rules may be only secondary to a guessing strategy in autistics.     

Discrimination of multidimensional geometrical figures by chickens: categorization and pattern-learning

Japanese bantam hens were trained to discriminate between geometrical figures varying along four integral dimensions. Only one dimension predicted food: selections of sharp-cornered figures were reinforced, while selections of rounded figures were not. In experiment 1, hens were subsequently trained to discriminate between nine figure pairs in a simultaneous discrimination task. Because single pairs contained multiple redundant cues, whereas the relevant dimension was obvious only across stimulus pairs, the results revealed effects of both generalization and reversal learning. Accordingly, learning speed was enhanced for later discriminations. Experiment 2 tested the hens’ transfer performance to unknown pairs, following experience of 9 or 18 figure pairs. Four of seven hens showed reliable transfer after experience with 9 figures, but only three showed transfer after experience with 18 figures, indicating lower transfer with higher number of stimulus pairs learned. In experiment 3, hens were trained to discriminate 27 figure pairs. Discrimination ratios further decreased and the groups of pairs differed significantly in their ratios of discrimination. Individual hens’ pecking behaviour was analysed in relation to each dimension of single figures and in relation to relative differences in the levels of dimensions between paired figures. Hens were shown to be oriented towards irrelevant information and more towards relational and configurational than elemental and dimensional aspects. The results are discussed in the biological context of individual recognition in chickens’ dominance hierarchies, in which we suppose that chickens identify individual flock mates by representation of their visual pattern rather than by single characteristics. Received: 18 September 1998 / Accepted after revision: 23 December 1998     

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.     

BatMon II: Children’s category norms for 33 categories

Four hundred forty-eight children 3–12 years of age generated category exemplars for 33 distinct categories. The percentage of the participants reporting each exemplar, the percentage of the participants reporting each exemplar first, the percentage of the participants reporting each exemplar across age groups (3–5 years, 6–8 years, and 9–12 years), and the mean rank of each exemplar are presented. A full version of the 29 category norms may be downloaded from www.psychonomic.org/archive.     

Wanted: a new psychology of exemplars.

Present-day exemplar theory faces difficult challenges, and important questions have arisen about the kinds of exemplar effects and processes that are empirically supported, and about the kind of exemplar theory that could still be constructive. One question concerns whether exemplar generalization in memory and categorization is broad and collective--extending to many related exemplars stored in memory--or whether it is focused and singular--extending only to highly similar (nearly identical) exemplars. The present article considers this continuum from broad to narrow generalization. I demonstrate that in prominent memory and category tasks--tasks in which exemplar theory predicts broad generalization--generalization is in psychological reality very tightly focused. These demonstrations could ground a new, productive exemplar theory that is true to psychological process as humans conduct themselves in memory and category tasks. This new psychology may actually reprise the traditional exemplar theory that predated our sophisticated, mathematical exemplar models.     

Prior knowledge and subtyping effects in children's category learning

Two experiments examined how 5- and 10-year-old children revise their category representations when exposed to exemplars that are congruent or incongruent with existing knowledge. During training children were presented with exemplars containing features that were congruent or incongruent with children's social stereotypes together with a stereotype-neutral feature. In the knowledge-subtyping condition this neutral feature predicted the stereotype-congruence of the other features. In the knowledge-standard condition the neutral feature was uncorrelated with stereotype-congruence. At test children made judgements about feature co-occurrence within the learned category. In each experiment these judgements were influenced by both stereotypical beliefs and exemplar observation. Stereotypical beliefs, however, had a greater influence on co-occurrence judgements in the knowledge-subtyping than in the standard conditions. In Experiment 2 these effects were shown to generalize to judgements about features that were not presented during training. These results challenge current models of knowledge-based categorization by showing that exemplar structure determines whether novel exemplar features are incorporated into category representations.     

A probabilistic account of exemplar and category generation

People are capable of imagining and generating new category exemplars and categories. This ability has not been addressed by previous models of categorization, most of which focus on classifying category exemplars rather than generating them. We develop a formal account of exemplar and category generation which proposes that category knowledge is represented by probability distributions over exemplars and categories, and that new exemplars and categories are generated by sampling from these distributions. This sampling account of generation is evaluated in two pairs of behavioral experiments. In the first pair of experiments, participants were asked to generate novel exemplars of a category. In the second pair of experiments, participants were asked to generate a novel category after observing exemplars from several related categories. The results suggest that generation is influenced by both structural and distributional properties of the observed categories, and we argue that our data are better explained by the sampling account than by several alternative approaches.     

Same items, different order: effects of temporal variability on infant categorization

How does variability between members of a category influence infants’ category learning? We explore the impact of the order in which different items are sampled on category formation. Two groups of 10-months-olds were presented with a series of exemplars to be organized into a single category. In a low distance group, the order of presentation minimized the perceptual distance between consecutive exemplars. In a high distance group, the order of presentation maximized the distance between successive exemplars. At test, only infants in the High Distance condition reliably discriminated between the category prototype and an atypical exemplar. Hence, the order in which infants learnt about the exemplars impacted their categorization performance. Our findings demonstrate the importance of moment-to-moment variations in similarity during infants’ category learning.     

Instance memorization and category influence: Challenging the evidence for multiple systems in category learning

A class of dual-system theories of categorization assumes a categorization system based on actively formed prototypes in addition to a separate instance memory system. It has been suggested that, because they have used poorly differentiated category structures (such as the influential “5-4” structure), studies supporting the alternative exemplar theory reveal little about the properties of the categorization system. Dual-system theories assume that the instance memory system only influences categorization behaviour via similarity to single isolated instances, without generalization across instances. However, we present the results of two experiments employing the 5-4 structure to argue against this. Experiment 1 contrasted learning in the standard 5-4 structure with learning in an even more poorly differentiated 5-4 structure. In Experiment 2, participants memorized the 5-4 structure based on a five minute simultaneous presentation of all nine category instances. Both experiments revealed category influences as reflected by differences in instance learnability and generalization, at variance with the dual-system prediction. These results have implications for the exemplars versus prototypes debate and the nature of human categorization mechanisms.     

知道与不知道要学多少：类别学习中样例量的预期作用

采用“5/4模型”类别结构探讨了类别学习中样例量的预期作用。设置了两种学习条件(“知道样例量”和“不知道样例量”), 分别探讨两种学习条件下的学习效率、学习策略以及所形成的类别表征。106名大学生参加了实验, 结果表明：在类别学习中, 样例量的预期作用显著, 知道样例量组的学习效率高于不知道样例量组; 样例量的预期作用对类别学习效率的影响是通过影响学习过程中使用的策略来实现的; 样例量的预期作用不影响两种学习条件的学习后形成的类别表征, 且两种学习条件的被试自始至终表现出样例学习的表征模式。     

Feature frequency in concept learning: What is counted?

Frequency theories of concept learning assume that people count how often features occur among instances of a concept, but different versions make various assumptions about what features they count. According to the basic feature model, only basic features are counted, whereas according to the configural model, basic features and configural features (all combinations of basic features) are counted. Two experiments assessed the predictions of both versions of frequency theory. Subjects viewed schematic human faces, which included both positive and negative instances of the concept to be learned, and then provided typicality ratings, classification responses, and frequency estimates of configural features, basic features, and whole exemplars. Because both models assume that basic features are counted, they make the same predictions in many situations. Here, the basic feature estimation and whole exemplar tests were designed such that both models make the same predictions, whereas the typicality rating, classification, and confignral feature estimation tests were designed to distinguish between the models. The pattern of results clearly supported the basic feature version of frequency theory.     

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.     

Are there representational shifts during category learning?

Early theories of categorization assumed that either rules, or prototypes, or exemplars were exclusively used to mentally represent categories of objects. More recently, hybrid theories of categorization have been proposed that variously combine these different forms of category representation. Our research addressed the question of whether there are representational shifts during category learning. We report a series of experiments that tracked how individual subjects generalized their acquired category knowledge to classifying new critical transfer items as a function of learning. Individual differences were observed in the generalization patterns exhibited by subjects, and those generalizations changed systematically with experience. Early in learning, subjects generalized on the basis of single diagnostic dimensions, consistent with the use of simple categorization rules. Later in learning, subjects generalized in a manner consistent with the use of similarity-based exemplar retrieval, attending to multiple stimulus dimensions. Theoretical modeling was used to formally corroborate these empirical observations by comparing fits of rule, prototype, and exemplar models to the observed categorization data. Although we provide strong evidence for shifts in the kind of information used to classify objects as a function of categorization experience, interpreting these results in terms of shifts in representational systems underlying perceptual categorization is a far thornier issue. We provide a discussion of the challenges of making claims about category representation, making reference to a wide body of literature suggesting different kinds of representational systems in perceptual categorization and related domains of human cognition.