Scalp event-related potentials (ERPs) in humans indicate that face and object processing differ approximately 170 ms following stimulus presentation, at the point of the N170 occipitotemporal component. The N170 is delayed and enhanced to inverted faces but not to inverted objects. We tested whether this inversion effect reflects early mechanisms exclusive to faces or whether it generalizes to other stimuli as a function of visual expertise. ERPs to upright and inverted faces and novel objects (Greebles) were recorded in 10 participants before and after 2 weeks of expertise training with Greebles. The N170 component was observed for both faces and Greebles. The results are consistent with previous reports in that the N170 was delayed and enhanced for inverted faces at recording sites in both hemispheres. For Greebles, the same effect of inversion was observed only for experts, primarily in the left hemisphere. These results suggest that the mechanisms underlying the electrophysiological face-inversion effect extend to visually homogeneous nonface object categories, at least in the left hemisphere, but only when such mechanisms are recruited by expertise. It is often claimed that face recognition is realized by distinct processes within dedicated brain areas (e.g., Kanwisher, 2000). Countering this claim, both behavioral (Diamond & Carey, 1986; Gauthier & Tarr, 1997; Gauthier, Williams, Tarr, & Tanaka, 1998) and neuroimaging (Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999; Gauthier, Anderson, Skudlarski, & Gore, 2000) studies, as well as a recent event-related potential (ERP) study (Tanaka & Curran, 2001), reveal that perceptual expertise with nonface objects can recruit the same cognitive mechanisms and brain areas that are implicated in face recognition (for a revi...

This thesis aims to develop a psychologically plausible account of concepts by integrating key insights from philosophy (on the metaphysical basis for concept possession) and psychology (on the mechanisms underlying concept acquisition). I adopt an approach known as informational atomism, developed by Jerry Fodor. Informational atomism is the conjunction of two theses: (i) informational semantics, according to which conceptual content is constituted exhaustively by nomological mind–world relations; and (ii) conceptual atomism, according to which (lexical) concepts have no internal structure. I argue that informational semantics needs to be supplemented by allowing content-constitutive rules of inference (“meaning postulates”). This is because the content of one important class of concepts, the logical terms, is not plausibly informational. And since, it is argued, no principled distinction can be drawn between logical concepts and the rest, the problem that this raises is a general one.

Investigations of face processing in persons with an autism spectrum disorder (ASD) inform upon theories of the development of "normal" face processing, and the story that emerges challenges some models of the nature and origin of cortical face specialization. Individuals with an ASD possess deficits in face processing and a lack of a fusiform face area (FFA). Evidence from studies of ASD can best be conceptualized using an expertise framework of face processing rather than models that postulate a face module in the fusiform gyrus. Because persons with an ASD have reduced social interest, they may fail to develop cortical face specialization. Face specialization may develop in normal individuals because they are socially motivated to regard the face, and such motivation promotes expertise for faces. The amygdala is likely the key node in the system that marks objects as emotionally salient and could be crucial to the development of cortical face specialization.

A recent paper in this journal reports two event-related potential (ERP) experiments interpreted as supporting the domain specificity of the visual mechanisms implicated in processing faces (Cogni- tion 83 (2002) 1). The authors argue that because a large neurophysiological response to faces (N170) is less influenced by the task than the response to objects, and because the response for human faces extends to ape faces (for which we are not expert), we should reject the hypothesis that the face-sensitivity reflected by the N170 can be accounted for by the subordinate-level expertise model of object recognition (Nature Neuroscience 3 (2000) 764). In this commentary, we question this conclusion based on some of our own ERP work on expert object recognition as well as the work of others. q 2002 Elsevier Science B.V. All rights reserved.

Human concept learning depends upon perception. Our concept of “car ” is built out of perceptual features such as “engine, ” “tire, ” and “bumper. ” However, recent research indicates that the dependency works both ways. We see bumpers and engines in part because we have acquired “car ” concepts and detected examples of them. Perception both influences and is influenced by the concepts that we learn. We have been exploring the psychological mechanisms by which concepts and perception mutually influence one another, and building computational models to show that the circle of influences is benign rather than vicious. Perceptual Learning Is “Early ” Neurologically, Functionally, and Developmentally An initial suggestion that concept learning influences perception comes from a consideration of the differences between novices and experts. Experts in many domains, including radiologists, wine tasters, and Olympic judges, develop specialized perceptual tools for analyzing the objects in their domains of expertise. Much of training and expertise involves not only developing a database of cases or explicit strategies for dealing with the world but also tailoring perceptual processes to more efficiently represent the world (Gibson 1991). Tuning one’s perceptual representation to the environment is a risky proposition. Once a perceptual representation has been altered, it affects all “downstream ” processes that act as consumers of this altered representation. It makes sense to adapt perceptual systems slowly and conservatively. However, the payoffs for perceptual flexibility are also too enticing to forego. They allow an organism to respond quickly, efficiently, and effectively to stimuli without dedicating on-line attentional resources. Instead of strategically determining how to use an unbiased perceptual representation to fit one’s needs, it is often easier to rig up a perceptual system to give task-relevant representations, and then simply leave this rigging in place without strategic control. Perceptual learning is early in several senses: neurological, functional, and developmental.

Abstract not found

This paper presents a four-stage functional model of the expert object recognition pathway, where each stage models one area of anatomic activation. It implements this model in an end-to-end computer vision system, and tests it on real images to provide feedback for the cognitive science and computer vision communities

ovides us with the fundamental representations that we subsequently combine and tweak. In assessing the contribution of developmental research on concepts and categories to our general understanding of human concepts, we will ask four questions. What are concepts? What is the relation between perception and concepts? What are the constraints on concept learning? What are promising future directions for research on concepts? What Are Concepts? A good starting place is Edward Smith's (1989) characterization of a concept as "a mental representation of a class or individual and deals with what is being represented and how that information is typically used during the categorization" (p. 502). It is common to distinguish between a concept and a category (e.g., Hampton & Dubois, 1993). A concept refers to a mentally possessed idea or notion, whereas a category refers to a set of entities that are grouped together. The concept dog is whatever psychological state signifies thoughts of dogs.

The paper describes a computational model of human expert object recognition in terms of pattern recognition algorithms. In particular, we model the process by which people quickly recognize familiar objects seen from familiar viewpoints at both the instance and category level. We propose a sequence of unsupervised pattern recognition algorithms that is consistent with all known biological data. It combines the standard Gabor-filter model of early vision with a novel cluster-based local linear projection model of expert object recognition in the ventral visual stream. This model is shown to be better than standard algorithms at distinguishing between cats and dogs.

Are the mechanisms for face perception selectively involved in processing faces per se, or do they also participate in the processing of any class of visual stimuli that share the same basic configuration and for which the observer has gained substantial visual expertise? Here we tested the effects of visual expertise on the face-selective “M170”, a magnetoencephalography (MEG) response component that occurs 170 ms after stimulus onset and is involved in the identification of individual faces. In Experiment 1, cars did not elicit a higher M170 response (relative to control objects) in car experts compared to controls subjects. In Experiment 2, the M170 amplitude was correlated with successful face identification, but not with successful car identification in car experts. These results indicate that the early face processing mechanisms marked by the M170 are involved in the identification of faces in particular, not in the identification of any objects of expertise. © 2004 Elsevier Ltd. All rights reserved.