Jackdaws form categorical prototypes based on experience with category exemplars

Categorization represents one cognitive ability fundamental to animal behavior. Grouping of elements based on perceptual or semantic features helps to reduce processing resources and facilitates appropriate behavior. Corvids master complex categorization, yet the detailed categorization learning strategies are less well understood. We trained two jackdaws on a delayed match to category paradigm using a novel, artificial stimulus type, RUBubbles. Both birds learned to differentiate between two session-unique categories following two distinct learning protocols. Categories were either introduced via central category prototypes (low variability approach) or using a subset of diverse category exemplars from which diagnostic features had to be identified (high variability approach). In both versions, the stimulus similarity relative to a central category prototype explained categorization performance best. Jackdaws consistently used a central prototype to judge category membership, regardless of whether this prototype was used to introduce distinct categories or had to be inferred from multiple exemplars. Reliance on a category prototype occurred already after experiencing only a few trials with different category exemplars. High stimulus set variability prolonged initial learning but showed no consistent beneficial effect on later generalization performance. High numbers of stimuli, their perceptual similarity, and coherent category structure resulted in a prototype-based strategy, reflecting the most adaptive, efficient, and parsimonious way to represent RUBubble categories. Thus, our birds represent a valuable comparative animal model that permits further study of category representations throughout learning in different regions of a brain producing highly cognitive behavior.

Distribution-dependent representations in auditory category learning and generalization

A fundamental objective in Auditory Sciences is to understand how people learn to generalize auditory category knowledge in new situations. How we generalize to novel scenarios speaks to the nature of acquired category representations and generalization mechanisms in handling perceptual variabilities and novelty. The dual learning system (DLS) framework proposes that auditory category learning involves an explicit, hypothesis-testing learning system, which is optimal for learning rule-based (RB) categories, and an implicit, procedural-based learning system, which is optimal for learning categories requiring pre-decisional information integration (II) across acoustic dimensions. Although DLS describes distinct mechanisms of two types of category learning, it is yet clear the nature of acquired representations and how we transfer them to new contexts. Here, we conducted three experiments to examine differences between II and RB category representations by examining what acoustic and perceptual novelties and variabilities affect learners' generalization success. Learners can successfully categorize different sets of untrained sounds after only eight blocks of training for both II and RB categories. The category structures and novel contexts differentially modulated the generalization success. The II learners significantly decreased generalization performances when categorizing new items derived from an untrained perceptual area and in a context with more distributed samples. In contrast, RB learners' generalizations are resistant to changes in perceptual regions but are sensitive to changes in sound dispersity. Representational similarity modeling revealed that the generalization in the more dispersed sampling context was accomplished differently by II and RB learners. II learners increased representations of perceptual similarity and decision distance to compensate for the decreased transfer of category representations, whereas the RB learners used a more computational cost strategy by default, computing the decision-bound distance to guide generalization decisions. These results suggest that distinct representations emerged after learning the two types of category structures and using different computations and flexible mechanisms in resolving generalization challenges when facing novel perceptual variability in new contexts. These findings provide new evidence for dissociated representations of auditory categories and reveal novel generalization mechanisms in resolving variabilities to maintain perceptual constancy.

Functional imaging analyses reveal prototype and exemplar representations in a perceptual single-category task

Similarity-based categorization can be performed by memorizing category members as exemplars or by abstracting the central tendency of the category – the prototype. In similarity-based categorization of stimuli with clearly identifiable dimensions from two categories, prototype representations were previously located in the hippocampus and the ventromedial prefrontal cortex (vmPFC) and exemplar representations in areas supporting visual memory. However, the neural implementation of exemplar and prototype representations in perceptual similarity-based categorization of single categories is unclear. To investigate these representations, we applied model-based univariate and multivariate analyses of functional imaging data from a dot-pattern paradigm-based task. Univariate prototype and exemplar representations occurred bilaterally in visual areas. Multivariate analyses additionally identified prototype representations in parietal areas and exemplar representations in the hippocampus. Bayesian analyses supported the non-presence of prototype representations in the hippocampus and the vmPFC. We additionally demonstrate that some individuals form both representation types simultaneously, probably granting flexibility in categorization strategies.

Model-based univariate and multivariate analyses of fMRI data from 62 healthy participants in a dot-pattern paradigm-based task provide further insight into the neural basis of similarity-based categorization.

A Computational Model of Context-Dependent Encodings During Category Learning

Although current exemplar models of category learning are flexible and can capture how different features are emphasized for different categories, they still lack the flexibility to adapt to local changes in category learning, such as the effect of different sequences of study. In this paper, we introduce a new model of category learning, the Sequential Attention Theory Model (SAT‐M), in which the encoding of each presented item is influenced not only by its category assignment (global context) as in other exemplar models, but also by how its properties relate to the properties of temporally neighboring items (local context). By fitting SAT‐M to data from experiments comparing category learning with different sequences of trials (interleaved vs. blocked), we demonstrate that SAT‐M captures the effect of local context and predicts when interleaved or blocked training will result in better testing performance across three different studies. Comparatively, ALCOVE, SUSTAIN, and a version of SAT‐M without locally adaptive encoding provided poor fits to the results. Moreover, we evaluated the direct prediction of the model that different sequences of training change what learners encode and determined that the best‐fit encoding parameter values match learners’ looking times during training.

Prototype-based category learning in autism: A review

Similarity-based categorization, as an important cognitive skill, can be performed by abstracting a categories' central tendency, the so-called prototype, or by memorizing individual exemplars of a category. The flexible selection of an appropriate strategy is crucial for effective cognitive functioning. The detail-focused cognitive style in individuals with autism spectrum disorders (ASD) has been hypothesized to specifically impair prototype-based categorization but to leave exemplar-based categorization unimpaired. We first give an overview of approaches to investigate prototype-based abstraction in the prototype-distortion task, with an emphasis on model-based approaches suitable to discern the two strategies on the individual level. The second part summarizes literature speaking to prototype-based categorization in ASD using that task. Despite considerable inconsistencies, most studies appear to confirm that autistic individuals have more difficulties to perform prototype-distortion tasks than non-autistic individuals. We highlight how inconsistencies in literature can be resolved by taking the differences in task designs into account. The current review illustrates the need for sensitive computational approaches, suitable to detect hidden individual differences and potential compensatory strategies.

Tracking prototype and exemplar representations in the brain across learning

There is a long-standing debate about whether categories are represented by individual category members (exemplars) or by the central tendency abstracted from individual members (prototypes). Neuroimaging studies have shown neural evidence for either exemplar representations or prototype representations, but not both. Presently, we asked whether it is possible for multiple types of category representations to exist within a single task. We designed a categorization task to promote both exemplar and prototype representations and tracked their formation across learning. We found only prototype correlates during the final test. However, interim tests interspersed throughout learning showed prototype and exemplar representations across distinct brain regions that aligned with previous studies: prototypes in ventromedial prefrontal cortex and anterior hippocampus and exemplars in inferior frontal gyrus and lateral parietal cortex. These findings indicate that, under the right circumstances, individuals may form representations at multiple levels of specificity, potentially facilitating a broad range of future decisions.

The Role of Edge-Based and Surface-Based Information in Incidental Category Learning: Evidence From Behavior and Event-Related Potentials

Although it has been demonstrated that edge-based information is more important than surface-based information in incidental category learning, it remains unclear how the two types of information play different roles in incidental category learning. To address this issue, the present study combined behavioral and event-related potential (ERP) techniques in an incidental category learning task in which the categories were defined by either edge- or surface-based features. The results from Experiment 1 showed that participants could simultaneously learn both edge- and surface-based information in incidental category learning, and importantly, there was a larger learning effect for the edge-based category than for the surface-based category. The behavioral results from Experiment 2 replicated those from Experiment 1, and the ERP results further revealed that the stimuli from the edge-based category elicited larger anterior and posterior P2 components than those from the surface-based category, whereas the stimuli from the surface-based category elicited larger anterior N1 and P3 components than those from the edge-based category. Taken together, the results suggest that, although surface-based information might attract more attention during feature detection, edge-based information plays more important roles in evaluating the relevance of information in making a decision in categorization.

Breaking the perceptual-conceptual barrier: Relational matching and working memory

Cognitive, comparative, and developmental psychologists have long been interested in humans' and animals' ability to respond to abstract relations, as this ability may underlie important capacities like analogical reasoning. Cross-species research has used relational matching-to-sample (RMTS) tasks in which participants try to find stimulus pairs that "match" because they both express the same abstract relation (same or different). Researchers seek to understand the cognitive processes that underlie successful matching performance. In the present RMTS paradigm, the abstract-relational cue was made redundant with a first-order perceptual cue. Then the perceptual cue faded, requiring participants to transition from a perceptual to a conceptual approach by realizing the task's abstract-relational affordance. We studied participants' ability to make this transition with and without a working-memory load. The concurrent load caused participants to fail to break the perceptual-conceptual barrier unless the load was abandoned. We conclude that finding the conceptual solution depends on reconstruing the task using cognitive processes that are especially reliant on working memory. Our data provide the closest existing look at this cognitive reorganization. They raise important theoretical issues for cross-species comparisons of relational cognition, especially regarding animals' limitations in this domain.

Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features

Although many researches have shown that edge-based information is more important than surface-based information in object recognition, it remains unclear whether edge-based features play a more crucial role than surface-based features in category learning. To address this issue, a modified prototype distortion task was adopted in the present study, in which each category was defined by a rule or a similarity about either the edge-based features (i.e., contours or shapes) or the corresponding surface-based features (i.e., color and textures). The results of Experiments 1 and 2 showed that when the category was defined by a rule, the performance was significantly better in the edge-based condition than in the surface-based condition in the testing phase, and increasing the defined dimensions enhanced rather than reduced performance in the edge-based condition but not in the surface-based condition. The results of Experiment 3 showed that when each category was defined by a similarity, there was also a larger learning effect when the category was defined by edge-based dimensions than by surface-based dimensions in the testing phase. The current study is the first to provide convergent evidence that the edge-based information matters more than surface-based information in incidental category learning.

A dual-process account of digit invariance learning

Performance in the McGeorge and Burton (1990) digit invariance task was originally thought to be mediated by unconscious abstraction of a “rule” that identified the invariant feature across all study items. Subsequent explanations have suggested explicit strategy use or similarity-to-exemplar matching rather than abstraction. This paper presents data that suggest that both similarity and abstraction can be used under different task demands. Delay between study and test afforded abstraction of the invariant knowledge whereas reducing the pool of study exemplars enhanced responding based on specific similarity. These results parallel effects found in the categorization literature. Rule abstraction in this sense may be due to statistical learning of feature frequency rather than abstraction of a central tendency or a complex/conceptual rule. Categorizing responses into subjective memory states (remember/know/guess) demonstrates that neither the similarity matching nor the abstraction mechanism uses information from episodic memory. Confidence measures show that participants are more confident of responses when the prototypical representation is used but not specific similarity. Taken together, these data suggest that abstracted knowledge is not held consciously but that participants have meta-awareness of when they are using the abstracted representation.

Formal models in animal-metacognition research: the problem of interpreting animals' behavior

Ongoing research explores whether animals have precursors to metacognition-that is, the capacity to monitor mental states or cognitive processes. Comparative psychologists have tested apes, monkeys, rats, pigeons, and a dolphin using perceptual, memory, foraging, and information-seeking paradigms. The consensus is that some species have a functional analog to human metacognition. Recently, though, associative modelers have used formal-mathematical models hoping to describe animals' "metacognitive" performances in associative-behaviorist ways. We evaluate these attempts to reify formal models as proof of particular explanations of animal cognition. These attempts misunderstand the content and proper application of models. They embody mistakes of scientific reasoning. They blur fundamental distinctions in understanding animal cognition. They impede theoretical development. In contrast, an energetic empirical enterprise is achieving strong success in describing the psychology underlying animals' metacognitive performances. We argue that this careful empirical work is the clear path to useful theoretical development. The issues raised here about formal modeling-in the domain of animal metacognition-potentially extend to biobehavioral research more broadly.

Knowledge

We investigate the basic principles of structural knowledge. Structural knowledge underlies cognition, and it organizes, selects and assigns meaning to information. It is the result of evolutionary, cultural and developmental processes. Because of its own constraints, it needs to discover and exploit regularities and thereby achieve a complexity reduction.

Ecology, Fitness, Evolution: New Perspectives on Categorization

Categorization's great debate has weighed single-system exemplar theory against the possibility of alternative processing systems. We take an evolutionary perspective toward this debate to illuminate it in a new way. Animals are crucial behavioral ambassadors to this area. They reveal the roots of human categorization, the basic assumptions of vertebrates entering category tasks, and the surprising weakness of exemplar memory as a category-learning strategy. These results have joined neuroscience results to prompt important changes in categorization theory. Categorization's great debate is ending. Categorization is served by multiple systems of process and representation.

Likelihood and its use in Parameter Estimation and Model Comparison

Parameter estimation and model fitting underlie many statistical procedures. Whether the objective is to examine central tendency or the slope of a regression line, an estimation method must be used. Likelihood is the basis for parameter estimation, for determining the best relative fit among several statistical models, and for significance testing. In this review, the concept of Likelihood is explained and applied computation examples are given. The examples provided serve to illustrate how likelihood is relevant, and related to, the most frequently applied test statistics (Student’s t-test, ANOVA). Additional examples illustrate the computation of Likelihood(s) using common population model assumptions (e.g., normality) and alternative assumptions for cases where data are non-normal. To further describe the interconnectedness of Likelihood and the Likelihood Ratio with modern test statistics, the relationship between Likelihood, Least Squares Modeling, and Bayesian Inference are discussed. Finally, the advantages and limitations of Likelihood methods are listed, alternatives to Likelihood are briefly reviewed, and R code to compute each of the examples in the text is provided.

Evidence of the preferential use of disease prototypes over case exemplars among early year one medical students prior to and following diagnostic training

Two core dual processing theory (DPT) System I constructs (Exemplars and Prototypes) were used to: 1) formulate a training exercise designed to improve diagnostic performance in year one medical students, and 2) explore whether any observed performance improvements were associated with preferential use of exemplars or prototypes.

With IRB approval, 117 year one medical students participated in an acute chest pain diagnostic training exercise. A pre- and post-training test containing the same 27 case vignettes was used to determine if the subjects’ diagnostic performance improved via training in both exemplars and prototypes. Exemplar and Prototype theory was also used to generate a unique typicality estimate for each case vignette. Because these estimates produce different performance predictions, differences in the subjects’ observed performance would make it possible to infer whether subjects were preferentially using Exemplars or Prototypes.

Pre- vs. post-training comparison revealed a significant performance improvement; t=14.04, p<0.001, Cohen’s d=1.32. Pre-training, paired t-testing demonstrated that performance against the most typical vignettes>mid typical vignettes: t=4.94, p<0.001; and mid typical>least typical: t=5.16, p<0.001. Post-training, paired t-testing again demonstrated that performance against the most typical vignettes>mid typical: t=2.94, p<0.01; and mid typical>least typical: t=6.64, p<0.001. These findings are more consistent with the performance predictions generated via Prototype theory than Exemplar theory.

DPT is useful in designing and evaluating the utility of new approaches to diagnostic training, and, investigating the cognitive factors driving diagnostic capabilities among early medical students.

Time- but not sleep-dependent consolidation promotes the emergence of cross-modal conceptual representations

Conceptual knowledge about objects comprises a diverse set of multi-modal and generalisable information, which allows us to bring meaning to the stimuli in our environment. The formation of conceptual representations requires two key computational challenges: integrating information from different sensory modalities and abstracting statistical regularities across exemplars. Although these processes are thought to be facilitated by offline memory consolidation, investigations into how cross-modal concepts evolve offline, over time, rather than with continuous category exposure are still missing. Here, we aimed to mimic the formation of new conceptual representations by reducing this process to its two key computational challenges and exploring its evolution over an offline retention period. Participants learned to distinguish between members of two abstract categories based on a simple one-dimensional visual rule. Underlying the task was a more complex hidden indicator of category structure, which required the integration of information across two sensory modalities. In two experiments we investigated the impact of time- and sleep-dependent consolidation on category learning. Our results show that offline memory consolidation facilitated cross-modal category learning. Surprisingly, consolidation across wake, but not across sleep showed this beneficial effect. By demonstrating the importance of offline consolidation the current study provided further insights into the processes that underlie the formation of conceptual representations.

Visual category learning

Visual categories group together different objects as the same kinds of thing. We review a selection of research on how visual categories are learned. We begin with a guide to visual category learning experiments, describing a space of common manipulations of objects, categories, and methods used in the category learning literature. We open with a guide to these details in part because throughout our review we highlight how methodological details can sometimes loom large in theoretical discussions of visual category learning, how variations in methodological details can significantly affect our understanding of visual category learning, and how manipulations of methodological details can affect how visual categories are learned. We review a number of core theories of visual category learning, specifically those theories instantiated as computational models, highlighting just some of the experimental results that help distinguish between competing models. We examine behavioral and neural evidence for single versus multiple representational systems for visual category learning. We briefly discuss how visual category learning influences visual perception, describing empirical and brain imaging results that show how learning to categorize objects can influence how those objects are represented and perceived. We close with work that can potentially impact translation, describing recent experiments that explicitly manipulate key methodological details of category learning procedures with the goal of optimizing visual category learning. WIREs Cogn Sci 2014, 5:75-94. doi: 10.1002/wcs.1268 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

Human semi-supervised learning

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization.

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.

Array training in a categorization task

Two components of categorization, within-category commonalities and between-category distinctiveness, were investigated in a categorization task. Subjects learned three prototype categories composed of moderately high distortions, by observing arrays containing patterns that belonged either to a common prototype category or to three different categories; a third group learned patterns presented one at a time, mirroring the standard paradigm. Following 6 learning blocks, subjects transferred to old patterns and new patterns at low-, medium-, and high-level distortions of the category prototype. The results showed that array training facilitated learning, especially when patterns in the array belonged to the same category. Transfer results showed a strong gradient effect across pattern distortion level for all conditions, with the highest performance obtained following array training on different category patterns and worst in the control condition. Interestingly, the old training patterns were classified worse than new low and no better than medium distortions. Neither this ordering nor the steepness of the gradient across prototype similarity for each condition could be predicted by the generalized context model. A prototype model better captured the steep gradient and ordinal pattern of results, although the overall fits were only slightly better than the exemplar model. The crucial role played by category commonalities and distinctiveness on categorical representations is addressed.

Perceptual fluency can be used as a cue for categorization decisions

Learning in the prototype distortion task is thought to involve perceptual learning in which category members experience an enhanced visual response (Ashby & Maddox. Annual Review of Psychology, 56, 149-178, 2005). This response likely leads to more-efficient processing, which in turn may result in a feeling of perceptual fluency for category members. We examined the perceptual-fluency hypothesis by manipulating fluency independently from category membership. We predicted that when perceptual fluency was induced using subliminal priming, this fluency would be misattributed to category membership and would affect categorization decisions. In a prototype distortion task, the participants were more likely to judge stimuli that were not members of the category as category members when the nonmembers were made perceptually fluent with a matching subliminal prime. This result suggests that perceptual fluency can be used as a cue during some categorization decisions. In addition, the results provided converging evidence that some types of categorization are based on perceptual learning.

Intact Prototype Formation but Impaired Generalization in Autism

Cognitive processing in autism has been characterized by a difficulty with the abstraction of information across multiple stimuli or situations and subsequent generalization to new stimuli or situations. This apparent difficulty leads to the suggestion that prototype formation, a process of creating a mental summary representation of multiple experienced stimuli that go together in a category, may be impaired in autism. Adults with high functioning autism and a typically developing comparison group matched on age and IQ completed a random dot pattern categorization task. Participants with autism demonstrated intact prototype formation in all four ways it was operationally defined, and this performance was not significantly different from that of control participants. However, participants with autism categorized dot patterns that were more highly distorted from the category prototypes less accurately than did control participants. These findings suggest, at least within the constraints of the random dot pattern task, that although prototype formation may not be impaired in autism, difficulties may exist with the generalization of what has been learned about a category to novel stimuli, particularly as they become less similar to the category's prototype.

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.

Refining the visual-cortical hypothesis in category learning

Participants produce steep typicality gradients and large prototype-enhancement effects in dot-distortion category tasks, showing that in these tasks to-be-categorized items are compared to a prototypical representation that is the central tendency of the participant's exemplar experience. These prototype-abstraction processes have been ascribed to low-level mechanisms in primary visual cortex. Here we asked whether higher-level mechanisms in visual cortex can also sometimes support prototype abstraction. To do so, we compared dot-distortion performance when the stimuli were size constant (allowing some low-level repetition-familiarity to develop for similar shapes) or size variable (defeating repetition-familiarity effects). If prototype formation is only mediated by low-level mechanisms, stimulus-size variability should lessen prototype effects and flatten typicality gradients. Yet prototype effects and typicality gradients were the same under both conditions, whether participants learned the categories explicitly or implicitly and whether they received trial-by-trial reinforcement during transfer tests. These results broaden out the visual-cortical hypothesis because low-level visual areas, featuring retinotopic perceptual representations, would not support robust category learning or prototype-enhancement effects in an environment of pronounced variability in stimulus size. Therefore, higher-level cortical mechanisms evidently can also support prototype formation during categorization.

How is the individuality of a face recognized?

A familiar face is instantly recognized in a crowd. This cannot be achieved through a feature by feature comparison of the observed face with either an average face (norm-based model of face recognition) or with a set of similarly constructed faces stored in memory (exemplar-based model of face recognition). A modified norm-based model is thus proposed. Instead of memorizing an average face, the normal variations for each facial feature are used to construct a multidimensional volume of face-space devoid of unusual features, here defined as features whose metrics lie below the 5th or above the 95th percentiles for that feature. A face consisting of 100 independently variable features will thus have, on average, 10 unusual features. Face identification then becomes exception-reporting. It requires only 10 such rare features to render a given face a one in 10(13) faces (P=0.05(10)=9.8 x 10(-14)). In a world containing 6.7 x 10(9) people, such a face would be unique. Faces remembered in this way can have their unusual features exaggerated or attenuated without loss of identity. This is the basis of caricatures and anti-caricatures. It also means that individuals belonging to a foreign race, possessing several features with modes beyond the "usual range" of the own-race population, will all look alike. Features that render a face unique in the own-race population are now shared by everyone in the foreign race. Average faces are more beautiful than the faces used in the averaging process. This makes evolutionary sense. Natural selection increases the frequency of fit features at the expense of maladaptive features. "Usual features" are therefore fitter than "unusual features", and play an important role in mate selection. Such an existing fundamental sexual attribute could easily have been harnessed for the fast and efficient recognition of individuals in the community.

The comparative psychophysics of complex shape perception

The authors compared the complex shape perception of humans and monkeys. Members of both species participated in a Same-Different paradigm in which they judged the similarity of shape pairs that could be variations of the same underlying prototype. For both species, similarity gradients were found to be steep going out from the transformational center of psychological space. In contrast, similarity gradients were found to be flat going from the periphery in toward the center of psychological space. These results show that there are important common principles in the shape-perception and shape-comparison processes of humans and monkeys. The same general organization of psychological space is obtained. The same quantifiable metric of psychological distance is applied. Established methods for creating controlled shape variation have the same effect on both species' similarity judgments. The member of the to-be-judged pair of shapes that is peripheral in psychological space controls the strength of the perceived similarity of the pair. The results have broader implications for the comparative study of perception and categorization.

Graded Structure in Odour Categories: A Cross-Cultural Case Study

The existence of graded structure in fruit and flower odour categories and its stability in different cultures is examined. Groups of students from France, the United States, and Vietnam performed a typicality rating task, a similarity judgment task, a membership verification task, a recognition memory task, a familiarity rating task, and a free identification task using a set of 40 odorants (20 fruit odorants and 20 flower odorants). Overall, our results demonstrate that fruit and flower odour categories possess graded structure. Moreover, principal component analyses of the data revealed the implication of typicality in a variety of cognitive tasks where typical odours receive a preferential processing compared to atypical ones. Finally, our results suggest that typicality can be predicted to a certain extent by experiential knowledge but that other determinants play a role in odour category structure. Altogether, this study confirms that graded structure is a universal property of categories and suggests that universals and cultural specifics can both constrain the emergence of odour category structures.

Does response scaling cause the generalized context model to mimic a prototype model?

Smith and Minda (1998, 2002) argued that the response scaling parameter y in the exemplar-based generalized context model (GCM) makes the model unnecessarily complex and allows it to mimic the behavior of a prototype model. We evaluated this criticism in two ways. First, we estimated the complexity of the GCM with and without the yparameter and also compared its complexity to that of a prototype model. Next, we assessed the extent to which the models mimic each other, using two experimental designs (Nosofsky & Zaki, 2002, Experiment 3; Smith & Minda, 1998, Experiment 2), chosen because these designs are thought to differ in the degree to which they can discriminate the models. The results show that y can increase the complexity of the GCM, but this complexity does not necessarily allow mimicry. Furthermore, if statistical model selection methods such as minimum description length are adopted as the measure of model performance, the models will be highly discriminable, irrespective of design.

The Modulating Influence of Category Size on the Classification of Exception Patterns

Generalization gradients to exception patterns and the category prototype were investigated in two experiments. In Experiment 1, participants first learned categories of large size that contained a single exception pattern, followed by a transfer test containing new instances that had a manipulated similarity relationship to the exception or a nonexception training pattern as well as distortions of the prototype. The results demonstrated transfer gradients tracked the prototype category rather than the feedback category of the exception category. In Experiment 2, transfer performance was investigated for categories varying in size (5, 10, 20), partially crossed with the number of exception patterns (1, 2, 4). Here, the generalization gradients tracked the feedback category of the training instance when category size was small but tracked the prototype category when category size was large. The benefits of increased category size still emerged, even with proportionality of exception patterns held constant. These, and other outcomes, were consistent with a mixed model of classification, in which exemplar influences were dominant with small-sized categories and/or high error rates, and prototype influences were dominant with larger sized categories.

Category contingent aftereffects for faces of different races, ages and species

Exposure to faces biases perceptions of subsequently viewed faces such that normality judgments of similar faces are increased. Simultaneously inducing such an aftereffect in opposite directions for two groups of faces might indicate discrete responding of the neural populations coding for those groups. Here we show such "category contingent" aftereffects following exposure to faces differing in eye-spacing (wide versus narrow) for European versus African faces, adult versus infant faces, and human versus monkey faces. As aftereffects reflect changes in responses of neural populations that code faces, our results may then suggest that functionally distinct neural populations code faces of different ages, races and species and that the human brain potentially contains discrete representations of these categories.

When parameters collide: a warning about categorization models

Similarity-choice (S-C) models of categorization contain two principal mathematical transformations: an exponential-decay similarity function and a choice rule. However, there is a tension between the psychological processes that models emulate and the mathematics they use to do so. To illustrate this, I show that in these models an unappreciated interaction occurs between the mathematical transformations so that the stages of the model essentially cancel each other out. The result is that the model's output reflects its input linearly. This cancellation phenomenon has potentially serious implications regarding the interpretation and use of S-C models. The phenomenon also raises questions about the simplification and psychological grounding of categorization models. Modelers broadly might benefit from an internal analysis of their models, such as that described here.

Effects of category diversity on learning, memory, and generalization

In this study, we examined the effect of within-category diversity on people's ability to learn perceptual categories, their inclination to generalize categories to novel items, and their ability to distinguish new items from old. After learning to distinguish a control category from an experimental category that was either clustered or diverse, participants performed a test of category generalization or old-new recognition. Diversity made learning more difficult, increased generalization to novel items outside the range of training items, and made it difficult to distinguish such novel items from familiar ones. Regression analyses using the generalized context model suggested that the results could be explained in terms of similarities between old and new items combined with a rescaling of the similarity space that varied according to the diversity of the training items. Participants who learned the diverse category were less sensitive to psychological distance than were the participants who learned a more clustered category.

Human Category Learning

Much recent evidence suggests some dramatic differences in the way people learn perceptual categories, depending on exactly how the categories were constructed. Four different kinds of category-learning tasks are currently popular-rule-based tasks, information-integration tasks, prototype distortion tasks, and the weather prediction task. The cognitive, neuropsychological, and neuroimaging results obtained using these four tasks are qualitatively different. Success in rule-based (explicit reasoning) tasks depends on frontal-striatal circuits and requires working memory and executive attention. Success in information-integration tasks requires a form of procedural learning and is sensitive to the nature and timing of feedback. Prototype distortion tasks induce perceptual (visual cortical) learning. A variety of different strategies can lead to success in the weather prediction task. Collectively, results from these four tasks provide strong evidence that human category learning is mediated by multiple, qualitatively distinct systems.

Visual Search and the Collapse of Categorization

Categorization researchers typically present single objects to be categorized. But real-world categorization often involves object recognition within complex scenes. It is unknown how the processes of categorization stand up to visual complexity or why they fail facing it. The authors filled this research gap by blending the categorization and visual-search paradigms into a visual-search and categorization task in which participants searched for members of target categories in complex displays. Participants have enormous difficulty in this task. Despite intensive and ongoing category training, they detect targets at near-chance levels unless displays are extremely simple or target categories extremely focused. These results, discussed from the perspectives of categorization and visual search, might illuminate societally important instances of visual search (e.g., diagnostic medical screening).

Specific-Token Effects in Screening Tasks: Possible Implications for Aviation Security

Screeners at airport security checkpoints perform an important categorization task in which they search for threat items in complex x-ray images. But little is known about how the processes of categorization stand up to visual complexity. The authors filled this research gap with screening tasks in which participants searched for members of target categories in visual displays. The authors found that when targets were sampled with replacement and repetition, participant screeners relied on recognizing familiar targets and had great difficulty using category-general knowledge. The authors observed a "heartbeat" in detection performance--it improved while test images repeated but dropped sharply when unfamiliar targets from the same categories appeared. This reliance on familiarity illuminates the processes of categorization under conditions of visual complexity and suggests limits on those processes. This reliance also has implications for the training and evaluation of screeners in the field.

False prototype enhancement effects in dot pattern categorization

Results from the classic dot pattern distortion paradigm have sometimes yielded prototype enhancement effects that could not be accounted for by exemplar models of categorization. However, in these experiments the status of the prototype was confounded with certain stimulus-specific properties as well as with the frequency of presentation of the prototype during testing. In two mock-subliminal experiments, participants made categorization judgments to patterns that were generated as prototypes, low-level distortions, or high-level distortions. The participants rated the prototypes as being more likely to be members of a category, although no patterns were presented during training, and there was no objective category structure. In two other experiments, greater prototype enhancement effects were observed when the prototype and low-level distortions were presented with greater frequency during transfer. These results suggest that classic prototype enhancement effects may not be due to the abstraction of a prototype at time of original learning, but rather to other factors not formalized in extant models.

The cognitive neuroscience of category learning

Recently, a multidisciplinary approach has provided new insights into the mechanisms of category learning. In this article, results from theoretical modeling, experimental psychology, clinical neuropsychology, functional neuroimaging, and single-cell studies are reviewed. Although the results are not conclusive, some general principles have emerged. Areas localized in the sensory neocortex are responsible for the perceptual representation of category exemplars, whereas lateral and anterior prefrontal structures are necessary for the encoding of category boundaries and abstract rules. The prefrontal cortex may influence categorical representation in the sensory neocortex via top-down control. The neostriatum is important in stimulus-response mapping, and the orbitofrontal cortex/ventral striatum are related to stimulus-reward associations accompanying category learning. Many category learning tasks can be performed implicitly. In conclusion, category learning paradigms provide a unique opportunity to investigate cognitive processes such as perception, memory, and attention in a systematic and interactive manner. Category learning tasks are suitable for mapping damaged brain systems in clinical populations.

Exemplar theory's predicted typicality gradient can be tested and disconfirmed

One of exemplar theory's central predictions concerns the shape of typicality gradients. The typicality gradient it predicts is a consequence of its exemplar-based comparisons and appears no matter how the theory is evaluated. However, this predicted typicality gradient does not fit the empirical typicality gradients obtained in an influential version of the dot-distortion category task, and this is true even when the exemplar model is made more flexible and mathematically powerful. Thus, exemplar theory is disconfirmed in this domain of categorization. In contrast, prototype theories are consistent with the empirically obtained typicality gradients.