& There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of ‘‘categorical perception.’ ’ In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, ‘‘surprise’ ’ expressions lie between ‘‘happiness’ ’ and ‘‘fear’’ expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks’ implementations in the brain. &

Language is about symbols, and those symbols must be grounded in the physical world. Children learn to associate language with sensorimotor experiences during their development. In light of this, we first provide a computational account of how words are mapped to their perceptually grounded meanings. Moreover, the main part of this work proposes and implements a computational model of how word learning influences the formation of object categories to which those words refer. This model simulates the bi-directional relationship between word and object category learning: (1) object categorization provides mental representations of meanings that are mapped to words to form lexical items; (2) linguistic labels help object categorization by providing additional teaching signals; and (3) these two learning processes interplay with each other and form a developmental feedback loop. Compared with the method that performs these two tasks separately, our model shows promising improvements in both word-to-world mapping and perceptual categorization, suggesting a unified view of lexical and category learning in an integrative framework. Most importantly, this work provides a cognitively plausible explanation of the mechanistic nature of early word learning and object learning from co-occurring multisensory data.

Humans have a massive capacity to store detailed information in visual long-term memory. The present studies explored the fidelity of these visual long-term memory representations and examined how conceptual and perceptual features of object categories support this capacity. Observers viewed 2,800 object images with a different number of exemplars presented from each category. At test, observers indicated which of 2 exemplars they had previously studied. Memory performance was high and remained quite high (82 % accuracy) with 16 exemplars from a category in memory, demonstrating a large memory capacity for object exemplars. However, memory performance decreased as more exemplars were held in memory, implying systematic categorical interference. Object categories with conceptually distinctive exemplars showed less interference in memory as the number of exemplars increased. Interference in memory was not predicted by the perceptual distinctiveness of exemplars from an object category, though these perceptual measures predicted visual search rates for an object target among exemplars. These data provide evidence that observers ’ capacity to remember visual information in long-term memory depends more on conceptual structure than perceptual distinctiveness.

The perceptual magnet effect involves reduced discriminability near prototypical vowel sounds in the native language. We present a Bayesian model to explain why this reduced discriminability might occur: it arises as a consequence of optimally solving the statistical problem of perceiving speech sounds in the presence of noise. In the optimal solution to this problem, listeners ’ perception of speech sounds is biased toward the means of phonetic categories because they use knowledge of these categories to guide their inferences about speakers’ target productions. Simulations show that the predictions of the model closely correspond to human data.

A variety of studies have demonstrated that organizing stimuli into categories can affect the way the stimuli are perceived. We explore the influence of categories on perception through one such phenomenon, the perceptual magnet effect, in which discriminability between vowels is reduced near prototypical vowel sounds. We present a Bayesian model to explain why this reduced discriminability might occur: It arises as a consequence of optimally solving the statistical problem of perception in noise. In the optimal solution to this problem, listeners’ perception is biased toward phonetic category means because they use knowledge of these categories to guide their inferences about speakers ’ target productions. Simulations show that model predictions closely correspond to previously published human data, and novel experimental results provide evidence for the predicted link between perceptual warping and noise. The model unifies several previous accounts of the perceptual magnet effect and provides a framework for exploring categorical effects in other domains.

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

This paper provides experimental and computational support for the hypothesis that categorization is accomplished by modifying the similarity space underlying perception by investigating the change in the initial structure of perceived similarity of objects after perceptual learning and categorization. Data from two human experiments on discrimination and categorization of multi-dimensional stimuli were modeled with a connectionist model, in order to determine how the initial structure of perceived similarity between stimuli influences the final structure of the similarity space after categorization. An interesting finding was that, on the one hand, categorization based on a salient dimension may improve discrimination along dimensions that are not salient for the task, and, on the other hand, categorization based on a non-salient dimension may still reveal separation along salient dimensions. The results of the reported study may provide additional insights into a possible mechanism of categorization. 1 Theoretical overview 1.1

We present the results of a study designed to show that dissociations between lexical and similaritybased boundary partitions for a set of items can be produced in the laboratory. This is achieved by an incremental process of learning to assign a category label to items increasingly far removed (in similarity space) from the center of that category and

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

The difference between integral and separable interaction of dimensions is a classic problem in cognitive psychology (Garner, 1970; Shepard, 1964) and remains an essential component of most current experimental and theoretical analyses of category learning (e.g. Ashby & Maddox, 1994; Goldstone, 1994; Kruschke, 1993; Melara, Marks & Potts, 1993; Nosofsky, 1992). So far the problem has been addressed through post-hoc analysis in which empirical evidence of integral and separable processing is used to fit human data, showing how the impact of a pair of dimensions interacting in an integral or a separable manner enters into later learning processes. In this paper, we argue that a mechanistic connectionist explanation for variations in dimensional interactions can provide a new perspective through exploration of how similarities between stimuli are transformed from physical to psychological space when learning to identify, discriminate, and categorize them. We substantiate this claim by demonstrating how even a standard backpropagation network combined with a simple image-processing Gabor filter component provides limited but clear potential to process monochromatic stimuli that are composed of integral pairs of dimensions differently from monochromatic stimuli that are composed of separable pairs of dimensions. Interestingly, the responses from Gabor filters are shown to already capture most of the dimensional interaction, which in turn can be operated upon by the neural network during a given learning task. In addition, we introduce a basic attention mechanism to backpropagation that gives it the ability to selectively attend to relevant dimensions and illustrate how this serves the model in solving a filtration vs. condensation task (Kruschke, 1993). The model may serve a...