Experiments were conducted in which observers learned to classify simple perceptual stimuli into low-variability and high-variability categories. Similarities between objects were measured in independent psychological-scaling tasks. The results showed that observers classified transfer stimuli into the high-variability categories with greater probability than was predicted by a baseline version of an exemplar-similarity model. Qualitative evidence for the role of category variability on perceptual classification, which could not be explained in terms of the baseline exemplar-similarity model, was obtained as well. Possible accounts of the effects of category variability are considered in the General Discussion section. According to exemplar models of perceptual classification, people represent categories by storing individual exemplars in memory and classify objects on the basis of their similarity to these stored exemplars (Hintzman, 1986; Medin & Schaffer, 1978; Nosofsky, 1986). Exemplar models have been successful at predicting a wide variety of perceptual classification phenomena, including details of classification learning, patterns of generalization to new transfer stimuli, and the time course of classification decision making. In the present research, however, we pursued an avenue that may demonstrate a fundamental limitation of these models. The key previous study that motivated the present work was the classic set of experiments reported by Rips (1989) on the role of category variability in classification judgment. An example of one of these experiments is as follows. Participants were asked to imagine a circular object with a 3-in. diameter. One group of participants was asked whether

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In addition to having communicative functions, verbal labels may play a role in shaping concepts. Two experiments assessed whether the presence of labels affected category formation. Subjects learned to categorize ‘‘aliens’’ as those to be approached or those to be avoided. After accuracy feedback on each response was provided, a nonsense label was either presented or not. Providing

Artificial grammar learning (AGL) is one of the most commonly used paradigms for the study of implicit learning and the contrast between rules, similarity, and associative learning. Despite five decades of extensive research, however, a satisfactory theoretical consensus has not been forthcoming. Theoretical accounts of AGL are reviewed, together with relevant human experimental and neuroscience data. The author concludes that satisfactory understanding of AGL requires (a) an understanding of implicit knowledge as knowledge that is not consciously activated at the time of a cognitive operation; this could be because the corresponding representations are impoverished or they cannot be concurrently supported in working memory with other representations or operations, and (b) adopting a frequency-independent view of rule knowledge and contrasting rule knowledge with specific similarity and associative learning (co-occurrence) knowledge.

Consider two individuals, John and Mary, who each possess a number of concepts. How can we determine that John and Mary both have a concept of, say, Horse? John and Mary may not have exactly the same knowledge of horses, but it is important to be able to place their horse concepts into correspondence with one another, if only so that we can say things like, “Mary’s concept of horse is much more sophisticated than John’s. ” Concepts should be public in the sense that they can be possessed by more than one person (Fodor, 1998; Fodor & Lepore, 1992), and for this to be the possible, we must be able to determine correspondences, or translations, between two individuals ’ concepts. There have been two major approaches in cognitive science to conceptual meaning that could potentially provide a solution to finding translations between conceptual systems. According to an “external grounding” account, concepts ’ meanings depend on their connection to the external world (this account is more thoroughly defined in the next section). By this account, the concept Horse means what it does because our perceptual apparatus can identify features that characterize horses. According to what we will call a “Conceptual web ” account, concepts ’ meanings depend on their connections to each other. By this account, Horse’s meaning depends on Gallop, Domesticated, and Quadruped, and in turn, these concepts depend on other concepts, including Horse (Quine & Ullian, 1970). In this chapter, we will first present a brief tour of some of the main proponents of conceptual web and external grounding accounts of conceptual meaning. Then, we will describe a computer algorithm that translates between conceptual systems. The initial goal of this computational work is to show how translating across systems is possible using only withinsystem relations, as is predicted by a conceptual web account. However, the subsequent goal is to show how the synthesis of external and internal information can dramatically improve translation. This work suggests that the external grounding and conceptual web accounts should not be

Classification “rules” in expert and everyday discourse are usually deficient by formal standards, lacking explicit decision procedures and precise terms. The authors argue that a central function of such weak rules is to focus on perceptual learning rather than to provide definitions. In 5 experiments, transfer following learning of family resemblance categories was influenced more by familiar-appearing features than by novel-appearing features equally acceptable under the rule. This occurred both when rules were induced and when rules were given at the beginning of instruction. To model this and other phenomena in categorization, features must be represented on 2 levels: informational and instantiated. These 2 feature levels are crucial to provide broad generalization while reflecting the known peculiarities of a complex world.

Traditional models of perceptual learning usually assume that learning occurs through changes of weights to fixed primitive features or dimensions. A new model for perceptual learning is presented which relies on simple and physiologically plausible mechanisms. The model suggests how flexible features can be dynamically derived from input characteristics in the course of learning and how diagnostic shape representations could be formed due to conceptual influences.

Disentangling bottom-up and top-down processing in adult category learning is notoriously difficult. Studying category learning in infancy provides a simple way of exploring category learning while minimizing the contribution of top-down information. Three- to 4-month-old infants presented with cat or dog images will form a perceptual category representation for cat that excludes dogs and for dog that includes cats. The authors argue that an inclusion relationship in the distribution of features in the images explains the asymmetry. Using computational modeling and behavioral testing, the authors show that the asymmetry can be reversed or removed by using stimulus images that reverse or remove the inclusion relationship. The findings suggest that categorization of nonhuman animal images by young infants is essentially a bottom-up process. Few in cognitive science would dispute the argument that both bottom-up (i.e., perceptually driven) and top-down (i.e., conceptually driven) processes are involved in adult categorization. Numerous studies have discussed the relationship between these two mechanisms of categorization (e.g., French, 1995; Murphy & Kaplan, 2000; Schyns, Goldstone, & Thibaut, 1998). However, in adults, perceptual and conceptual processes are deeply intertwined, making them difficult to isolate and study independently (Goldstone & Barsalou, 1998).

A familiar adage in the philosophy of science is that general hypotheses are better supported by varied evidence than by uniform evidence. Several studies suggest that young children do not respect this principle, and thus su#er from a defect in their inductive methodology. We argue that the diversity principle does not have the normative status that psychologists attribute to it, and should be replaced by a simple rule of probability. We then report an experiment designed to detect conformity to the latter rule in children's inductive judgment. Evidential Diversity 1 Introduction A central issue in cognitive development is whether children's scientific reasoning is methodologically sound (simply short on facts), or else neglectful of fundamental principles of inductive reasoning (Carey, 1985; Markman, 1989; Keil, 1989; Kuhn, 1996; Gopnik and Meltzo#, 1996; Koslowski, 1996). To address the issue, normative standards of inductive reasoning must be formulated, and children's thinking e...

This study was undertaken to examine relationships between the similarity structures of optical phonetic measures and visual phonetic perception. For this study, four talkers who varied in visual intelligibility were recorded simultaneously with a 3-dimensional optical recording system and a video camera. Subjects perceptually identified the talkers' consonant-vowel nonsense syllable utterances in a forced-choice identification task. Then, perceptual confusion matrices were analyzed using multidimensional scaling, and Euclidean distances among stimulus phonemes were obtained. Physical Euclidean distances between phonemes were computed on the raw 3-dimensional optical recordings for the phonemes used in the perceptual testing. Multilinear regression was used to generate a transformation vector between physical and perceptual distances. Then, correlations were computed between transformed physical and perceptual distances. These correlations ranged between .77 and .81 (59% and 66% variance accounted for), depending on the vowel context. This study showed that the relatively raw representations of the physical stimuli were effective in accounting for visual speech perception, a result consistent with the hypothesis that perceptual representations and similarity structures for visual speech are modality-specific.