Here we review recent findings that reveal the functional properties of extra-striate regions in the human visual cortex that are involved in the representation and perception of objects. We characterize both the invariant and non-invariant properties of these regions and we discuss the correlation between activation of these regions and recognition. Overall, these results indicate that the lateral occipital complex plays an important role in human object recognition.

Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize OID. (1) Inter-class variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. (2) There may be many classes but few or just one positive “training ” examples per class. Due to (1), a solution must locate possibly subtle objectspecific salient features (a door handle) while avoiding distracting ones (a specular highlight). However, (2) rules out direct techniques of feature selection. We describe an online algorithm that takes one model image from a known category and builds an efficient “same ” vs. “different ” classification cascade by predicting the most discriminative feature set for that object. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered feature sequence unique to a specific model image, maximizing cumulative information content. Learned stopping thresholds make the classifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled

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this article, reporting the results of multiple psychophysical experiments during the acquisition of expertise with novel objects. To leverage these methods, our approach combines psychophysical assessment with neuroimaging techniques in two ways. First, the psychophysical procedure we used to train participants to expertise was the same as that used in a recent neuroimaging study, in which activity for Greebles in the fusiform face area (FFA) increased with expertise (Gauthier, Tarr, Anderson, Skudlarski, & Gore, 1999; half of the 10 participants in the present study also participated in this earlier fMRI study). Second, we correlated behavioral measures of expertise acquisition with concurrent neural changes in these same participants

Faces are among the most important visual stimuli we perceive, informing us not only about a person’s identity, but also about their mood, sex, age and direction of gaze. The ability to extract this information within a fraction of a second of viewing a face is important for normal social interactions and has probably played a critical role in the survival of our primate ancestors. Considerable evidence from behavioural, neuropsychological and neurophysiological investigations supports the hypothesis that humans have specialized cognitive and neural mechanisms dedicated to the perception of faces (the face-specificity hypothesis). Here, we review the literature on a region of the human brain that appears to play a key role in face perception, known as the fusiform face area (FFA). Section 1 outlines the theoretical background for much of this work. The face-specificity hypothesis falls squarely on one side of a longstanding debate in the fields of cognitive science and cognitive neuroscience concerning the extent to which the mind/brain is composed of: (i) special-purpose (‘domain-specific’) mechanisms, each dedicated to processing a specific kind of information (e.g. faces, according to the face-specificity hypothesis), versus (ii) general-purpose (‘domain-general’) mechanisms, each capable of operating on any kind

We address the problem of identifying specific instances of a class (cars) from a set of images all belonging to that class. Although we cannot build a model for any particular instance of the class (as we may be provided with only one “training ” example of it), we can use information extracted from observing other members of the class. We pose this task as a learning problem, in which the learner is given image pairs, labeled as matching or not, and must discover which image features are most consistent for matching instances and discriminative for mismatches. We explore a patch based representation, where we model the distributions of comparison metrics defined on the patches. Finally, we describe an on-line algorithm that selects the most salient patches based on a mutual information criterion that achieves good performance while only matching a few patches. 1

What are the brain and cognitive systems that allow humans to play baseball, compute square roots, cook soufflés, or navigate the Tokyo subways? It may seem that studies of human infants and of non-human animals will tell us little about these abilities, because only educated, enculturated human adults engage in organized games, formal mathematics, gourmet cooking, or map-reading. In this chapter, we argue against this seemingly sensible conclusion. When human adults exhibit complex, uniquely human, culture-specific skills, they draw on a set of psychological and neural mechanisms with two distinctive properties: they evolved before humanity and thus are shared with other animals, and they emerge early in human development and thus are common to infants, children, and adults. These core knowledge systems form the building blocks for uniquely human skills. Without them we wouldn’t be able to learn about different kinds of games, mathematics, cooking, or maps. To understand what is special about human intelligence, therefore, we must study both the core knowledge systems on which it rests and the mechanisms by which these systems are orchestrated to permit new kinds of concepts and cognitive processes. What is core knowledge? A wealth of research on non-human primates and on human

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training ” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same ” versus “different ” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods. 1.

This essay introduces the massive redeployment hypothesis, an account of the functional organization of the brain that centrally features the fact that brain areas are typically employed to support numerous functions. The central contribution of the essay is to outline a middle course between strict localization on the one hand, and holism on the other, in such a way as to account for the supporting data on both sides of the argument. The massive redeployment hypothesis is supported by case studies of redeployment, and compared and contrasted with other theories of the localization of function.

Neuroimaging techniques can be used not only to identify the neural substrates of attention, but also to test cognitive theories of attention. Here we consider four classic questions in the psychology of visual attention: (i) Are some ‘special’ classes of stimuli (e.g. faces) immune to attentional modulation?; (ii) What are the information units on which attention operates?; (iii) How early in stimulus processing are attentional effects observed?; and (iv) Are common mechanisms involved in different modes of attentional selection (e.g. spatial and non-spatial selection)? We describe studies from our laboratory that illustrate the ways in which fMRI and MEG can provide key evidence in answering these questions. A central methodological theme in many of our fMRI studies is the use of analyses in which the activity in certain functionally-defined regions of interest (ROIs) is used to test specific cognitive hypotheses. An analogous sensor-of-interest (SOI) approach is applied to MEG. Our results include: evidence for the modulation of face representations by attention; confirmation of the independent contributions of object-based and location-based selection; evidence for modulation of face representations by non-spatial selection within the first 170 ms of processing; and implication of the intraparietal sulcus in functions general to spatial and non-spatial visual selection.