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| Riesenhuber, M., Poggio, T. Computational Models of Object Recognition in Cortex: A Review 2000 [pdf] |
| Review des principales tendances dans les théories de reconnaissance d'objet: d'une part les théories centrées sur l'extraction de données structurées indépendantes du point de vue, d'autre part les théories centrées sur l'utilisation en parallèle d'une collection de vues de différents points de vue, dans une approche soit feedforward soit top-down (avec hypothèses, tests, etc). Les études biologiques semblent indiquer que le CNS humain fonctionne avec une collection de vues et en feedforward. |
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| Barsalou, L.W. Perceptual symbols systems Behavioral and Brain Sciences 1999 (22):577-660 [html] |
| Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statistics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable, because they are assumed to implement recording systems, not conceptual systems. A perceptual theory of knowledge is developed here in the contexts of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The storage and reactivation of perceptual symbols operates at the level of perceptual components--not at the level of holistic perceptual experiences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a common frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and for introspection (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and abstract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinatorially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a perceptual theory of knowledge can implement a fully functional conceptual system while avoiding what it is becoming increasingly apparent would be problems for amodal symbol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. |
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| Burgund, E.D., Marsolek, C.J. Viewpoint-invariant and viewpoint-dependent object recognition in dissociable neural subsystems Psychonomics Bulletin & Review 2000 (7)3:480-489 [pdf] |
| Participants viewed objects in the central visual field and then named either same or different depthorientation views of these objects presented briefly in the left or the right visual field. The differentorientation views contained either the same or a different set of parts and relations. Viewpoint-dependent priming was observed when test views were presented directly to the right hemisphere (RH), but not when test views were presented directly to the left hemisphere (LH). Moreover, this pattern of results did not depend on whether the same or a different set of parts and relations could be recovered from the different-orientation views. Results support the theory that a specific subsystem operates more effectively than an abstract subsystem in the RH and stores objects in a manner that produces viewpointdependent effects, whereas an abstract subsystem operates more effectively than a specific subsystem in the LH and does not store objects in a viewpoint-dependent manner. |
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| Palmeri, T.J., Gauthier, I. Visual object understanding Nature reviews Neuroscience 2004 (5):291-304 [pdf] |
| Visual object understanding includes processes at the nexus of visual perception and visual cognition. A traditional approach separates questions that are more associated with perception how are objects represented by high-level vision from questions that are more associated with cognition how are objects identified, categorized and remembered. However, to understand the bridge between perception and cognition, it is fruitful to abandon any sharp distinction between perceptual and cognitive aspects of visual object understanding. We provide a selective review of research from both the Object Recognition and Perceptual Categorization literatures, highlighting relevant behavioural, neuropsychological, neurophysiological and theoretical research into the representations and processes that underlie visual object understanding in humans and primates. |
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| Feldman, J. What is a visual object ? Trends in Cognitive Sciences 2003 (7)6:252-256 [pdf] |
| The concept of an object plays a central role in cognitive science, particularly in vision, reasoning and conceptual development but it has rarely been given a concrete formal definition. Here I argue that visual objects cannot be defined according to simple physical properties but can instead be understood in terms of the hierarchical organization of visual scene interpretations. Within the tree describing such a hierarchical description, certain nodes make natural candidates as the joints between objects, representing division points between parts of the image that cohere internally but do not perceptually group with one another. Thus each subtree hanging from such a node corresponds to a single perceived object . This formal defi- nition accords with several intuitions about the way objects behave. |
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