Effects of contrasting category,conjoint frequency and typicality on categorization

Two experiments were conducted to investigate whether (a) experience with a contrasting category, (b) conjoint frequency of dimensional values, (c) range of typicality of values, and (d) type of information administered during learning influenced subsequent test performance. Each experiment began with an observational category learning task, employing faces as stimuli. This was followed by a classification test task and by pairwise comparisons of faces. Influence of a contrasting category was studied in experiment 1 by varying frequency of values of the contrasting category, and in experiment 2 by either including or not including a contrasting category in the learning task. Results indicated that (a) categorization is influenced by experience with a contrasting category, (b) conjoint frequency enhances the importance of values to a category, (c) broad typicality range experience reduces typicality differences among exemplars of a category, whereas small range experience diminishes differences in a contrasting category, and (d) information on representativeness of exemplars does not facilitate subsequent test performance. The implications of the results for categorization models are discussed.     

Classification in well-defined and ill-defined categories: evidence for common processing strategies

Early work in perceptual and conceptual categorization assumed that categories had criterial features and that category membership could be determined by logical rules for the combination of features. More recent theories have assumed that categories have an ill-defined structure and have prosposed probabilistic or global similarity models for the verification of category membership. In the experiments reported here, several models of categorization were compared, using one set of categories having criterial features and another set having an ill-defined structure. Schematic faces were used as exemplars in both cases. Because many models depend on distance in a multidimensional space for their predictions, in Experiment 1 a multidimensional scaling study was performed using the faces of both sets as stimuli, In Experiment 2, subjects learned the category membership of faces for the categories having criterial features. After learning, reaction times for category verification and typicality judgments were obtained. Subjects also judged the similarity of pairs of faces. Since these categories had characteristic as well as defining features, it was possible to test the predictions of the feature comparison model (Smith et al.), which asserts that reaction times and typicalities are affected by characteristic features. Only weak support for this model was obtained. Instead, it appeared that subjects developed logical rules for the classification of faces. A characteristic feature affected reaction times only when it was part of the rule system devised by the subject. The procedure for Experiment 3 was like that for Experiment 2, but with ill-defined rather than well-defined categories. The obtained reaction times had high correlations with some of the models for ill-defined categories. However, subjects' performance could best be described as one of feature testing based on a logical rule system for classification. These experiments indicate that whether or not categories have criterial features, subjects attempt to develop a set of feature tests that allow for exemplar classification. Previous evidence supporting probabilistic or similarity models may be interpreted as resulting from subjects' use of the most efficient rules for classification and the averaging of responses for subjects using different sets of rules.     

Associative and similarity-based processes in categorization decisions

Two experiments were directed at distinguishing associative and similarity-based accounts of systematic differences in categorization time for different items in natural categories. Experiment 1 investigated the correlation of categorization time with three measures of instance centrality in a category. Production frequency (PF), rated typicality, and familiarity from category norms for British participants (Hampton & Gardiner, 1983) were used to predict mean categorization times for 531 words in 12 semantic categories. PF and typicality (but not familiarity) were found to make significant and independent contributions to categorization time. Error rates were related only to typicality (apart from errors made to ambiguous or unknown items). Experiment 2 provided a further dissociation of PF and typicality. Manipulating the difficulty of the task through the relatedness of the false items interacted primarily with the effect of typicality on categorization time, whereas, under conditions of easy discrimination, prior exposure to the category exemplars affected only the contribution of PF to the decision time. The dissociation of typicality and PF measures is interpreted as providing evidence that speeded categorization involves both retrieval of associations indexed by PF and a similarity-based decision process indexed by typicality.     

What some effects might not be: The time to verify membership in 'well-defined' categories

Armstrong, Gleitman, and Gleitman (1983) reported shorter categorization times for members of well-defined categories judged more typical. They concluded that these effects could not originate in a graded, similarity-based category representation and consequently that the typicality effects obtained with natural categories might not be indicative of such a structure either. In this article, we re-examine this conclusion, focusing first on the performance obtained with well-defined categories of different sizes. Only the larger categories used showed variations in typicality ratings and produced typicality effects on categorization times. However,multiple regression analyses showed the effects on categorization times to be better explained by a measure of associative strength, called category dominance. The range of various predictor variables was equated in a follow-up experiment involving large, natural, and well-defined categories. Results obtained with well-defined categories showed pronounced dominance effects when typicality was controlled, but no reliable typicality effect when category dominance and instance familiarity were controlled. Results were opposite for natural categories. By showing that well-defined categories fail to produce unbiased typicality effects, our results bring added support to the hypothesis that the effects obtained with natural categories originate in a graded, similarity-based category structure.     

Representation at different levels in a conceptual hierarchy

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

Effects of spatial frequency content on classification of face gender and expression

The role of different spatial frequency bands on face gender and expression categorization was studied in three experiments. Accuracy and reaction time were measured for unfiltered, low-pass (cut-off frequency of 1 cycle/deg) and high-pass (cutoff frequency of 3 cycles/deg) filtered faces. Filtered and unfiltered faces were equated in root-mean-squared contrast. For low-pass filtered faces reaction times were higher than unfiltered and high-pass filtered faces in both categorization tasks. In the expression task, these results were obtained with expressive faces presented in isolation (Experiment 1) and also with neutral-expressive dynamic sequences where each expressive face was preceded by a briefly presented neutral version of the same face (Experiment 2). For high-pass filtered faces different effects were observed on gender and expression categorization. While both speed and accuracy of gender categorization were reduced comparing to unfiltered faces, the efficiency of expression classification remained similar. Finally, we found no differences between expressive and non expressive faces in the effects of spatial frequency filtering on gender categorization (Experiment 3). These results show a common role of information from the high spatial frequency band in the categorization of face gender and expression.     

Effects of classification context on categorization in natural categories

The patterns of classification of borderline instances of eight common taxonomic categories were examined under three different instructional conditions to test two predictions: first, that lack of a specified context contributes to vagueness in categorization, and second, that altering the purpose of classification can lead to greater or lesser dependence on similarity in classification. The instructional conditions contrasted purely pragmatic with more technical/quasi-legal contexts as purposes for classification, and these were compared with a no-context control. The measures of category vagueness were between-subjects disagreement and within-subjects consistency, and the measures of similarity-based categorization were category breadth and the correlation of instance categorization probability with mean rated typicality, independently measured in a neutral context. Contrary to predictions, none of the measures of vagueness, reliability, category breadth, or correlation with typicality were generally affected by the instructional setting as a function of pragmatic versus technical purposes. Only one subcondition, in which a situational context was implied in addition to a purposive context, produced a significant change in categorization. Further experiments demonstrated that the effect of context was not increased when participants talked their way through the task, and that a technical context did not elicit more all-or-none categorization than did a pragmatic context. These findings place an important boundary condition on the effects of instructional context on conceptual categorization.     

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.     

Recognition memory for typical and unusual faces

In four studies, recognition memory for faces rated as similar to a prototype was found to be inferior to memory for faces rated as unusual in appearance. This result was obtained under both incidental and intentional learning conditions, at presentation rates ranging from 3 to 15 sec, and with retention intervals from 3 to 24 hr. A fifth experiment established interitem similarity as the structural basis of this typicality effect in recognition memory. Difficulties in interpreting the findings in terms of some current models for classification learning, depth of processing models, or present models of the word frequency effect are discussed.     

The role of skin color and facial physiognomy in racial categorization: Moderation by implicit racial attitudes

Previous research has not sufficiently addressed factors that define and moderate racial categorization judgments. This study independently manipulated skin color and facial physiognomy to determine their relative weighting in racial categorization. Participants (N = 250) judged faces varying on 10 levels of facial physiognomy (from Afrocentric to Eurocentric) and 10 levels of skin color (from dark to light) under either no time constraints, a modest time constraint, and under a stringent time constraint. Skin color was a powerful predictor of racial typicality ratings at all levels of facial physiognomy, but participants relied upon facial physiognomy more when rating faces of light than dark skin color. Skin color was a more important cue than facial physiognomy under no time constraints, but as time constraints became more severe, skin color's importance decreased, yet it remained a more important cue at extreme physiognomy levels. The relationship between skin color and racial typicality ratings was stronger for those with more negative implicit racial attitudes. These findings suggest the primary role of skin color in racial categorization and underscore the importance of implicit attitudes in explicit categorization judgments.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity-dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

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.     

Response-time tests of logical-rule models of categorization

A recent resurgence in logical-rule theories of categorization has motivated the development of a class of models that predict not only choice probabilities but also categorization response times (RTs; Fifi?, Little, & Nosofsky, 2010). The new models combine mental-architecture and random-walk approaches within an integrated framework and predict detailed RT-distribution data at the level of individual participants and individual stimuli. To date, however, tests of the models have been limited to validation tests in which participants were provided with explicit instructions to adopt particular processing strategies for implementing the rules. In the present research, we test conditions in which categories are learned via induction over training exemplars and in which participants are free to adopt whatever classification strategy they choose. In addition, we explore how variations in stimulus formats, involving either spatially separated or overlapping dimensions, influence processing modes in rule-based classification tasks. In conditions involving spatially separated dimensions, strong evidence is obtained for application of logical-rule strategies operating in a serial-self-terminating processing mode. In conditions involving spatially overlapping dimensions, preliminary evidence is obtained that a mixture of serial and parallel processing underlies the application of rule-based classification strategies. The logical-rule models fare considerably better than major extant alternative models in accounting for the categorization RTs.     

Face encoding is not categorical: Consistent evidence across multiple types of contingent aftereffects

ABSTRACT

Race-contingent aftereffects indicate that faces of different races are encoded via dissociable visual channels. Race-contingent aftereffects reflect perceptions of typicality, indicating a gradual transition of activity across channels as faces change from looking more typical of one race to the other We examine whether faces forming more discrete categories (sex: male/female; species: human/monkey) are encoded in a similar fashion, or whether they are instead encoded via more dichotomous categorical judgements. Curves representing the rate of change of aftereffect magnitude as stimuli changed from male to female produced shallow slopes, closely resembling ratings of typicality, but differing significantly from slopes for categorical judgements. For species, aftereffect slopes were significantly shallower than both ratings of typicality and categorical judgements. Overall, these results suggest that the encoding of facial properties such as these is not categorical, but instead involves a graded response as stimulus typicality varies. Aftereffect slopes were similar across the two experiments, raising the possibility of a common system that is recruited during contingent adaptation, regardless of the specific dimension involved or the categories used by the experimenter.     

Distinctiveness,typicality, and recollective experience in face recognition: A principal components analysis

In this study, participants rated previously unseen faces on six dimensions: familiarity, distinctiveness, attractiveness, memorability, typicality, and resemblance to a familiar person. The faces were then presented again in a recognition test in which participants assigned their positive recognition decisions to either remember (R), know (K), or guess categories. On all dimensions except typicality, faces that were categorized as R responses were associated with significantly higher ratings than were faces categorized as K responses. Study ratings for R and K responses were then subjected to a principal components analysis. The factor loadings suggested that R responses were influenced primarily by the distinctiveness of faces, but K responses were influenced by moderate ratings on all six dimensions. These findings indicate that the structural features of a face influence the subjective experience of recognition.     

Category norm data and relationships with lexical frequency and typicality within verb semantic categories

The aim of the present study was to expand the scope of category norm and typicality data to include verbs for use when investigating semantic memory in fields such as linguistics, psychology, and aphasiology. Two experiments were conducted. In the first, participants were asked to list verbs within 10 semantic categories (e.g. breaking, cleaning, cooking, etc.) and 10 noun categories (e.g. animals, fruit, tools, etc.). In the second experiment, participants were asked to rate the typicality of verbs within 8 of the previously investigated verb categories. Although participants listed fewer verbs in verb categories than nouns in noun categories, the overall patterns with regard to correlation analyses between production frequency, mean rank of responses, lexical frequency, and typicality were consistent with those observed in noun categories. These patterns are also consistent with those observed in previous research. Potential similarities and differences between nouns and verbs, as well as future applications of such data, are discussed.     

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.     

Naming very familiar people: When retrieving names is faster than retrieving semantic biographical information

One of the most reliable findings in the literature on person indentification is that semantic categorization of a face occurs more quickly than naming a face. Here we present two experiments in which participants are shown the faces of their colleagues, i.e., personally familiar people, encountered with high frequency. In each experiment, naming was faster than making a semantic classification, despite the fact that the semantic classifications were highly salient to the participants (Experiment 1: highest degree obtained; Experiment 2: nationality). The finding is consistent with models that allow or parallel access from faces to semantic information and to names, and demonstrates the need for the frequency of exposure to names to be taken into account in models of proper name processing e.g. Burke, Mackay, Worthley and Wade (1991) .     

Natural categorization through multiple feature learning in pigeons

Recently (Troje, Huber, Loidolt, Aust, & Fieder 1999), we found that pigeons discriminated between large sets of photorealistic frontal images of human faces on the basis of sex. This ability was predominantly based on information contained in the visual texture of those images rather than in their configural properties. The pigeons could learn the distinction even when differences of shape and average intensity were completely removed. Here, we proved more specifically the pigeons' flexibility and efficiency to utilize the class-distinguishing information contained in complex natural classes. First, we used principal component as well as discriminant function analysis in order to determine which aspects of the male and female images could support successful categorization. We then conducted various tests involving systematic transformations and reduction of the feature content to examine whether or not the pigeons' categorization behaviour comes under the control of categorylevel feature dimensions-that is, those stimulus aspects that most accurately divide the stimulus classes into the experimenter-defined categories of 'Male' and 'Female'. Enhanced classification ability in the presence of impoverished test faces that varied only along one of the first three principal components provided evidence that the pigeons used these class-distinguishing stimulus aspects as a basis for generalization to new instances.