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19Cortical dynamics of visual motion perception: Short-range and long-range apparent motionPsychological Review 99 (1): 78-121. 1992.
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23Self-organizing features and categories through attentive resonanceBehavioral and Brain Sciences 21 (1): 27-28. 1998.Because “people create features to subserve the representation and categorization of objects” (abstract) Schyns et al. “provide an account of feature learning in which the components of a representation have close ties to the categorization history of the organism” (sect. 1.1). This commentary surveys self-organizing neural models that clarify this process. These models suggest how “top-down information should constrain the search for relevant dimensions/features of categorization” (sect. 3.4.2)…Read more
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48Neural models of development and learningBehavioral and Brain Sciences 20 (4): 566-566. 1997.I agree with Quartz & Sejnowski's points, which are familiar to many scientists. A number of models with the sought-after properties, however, are overlooked, while models without them are highlighted. I will review nonstationary learning, links between development and learning, locality, stability, learning throughout life, hypothesis testing that models the learner's problem domain, and active dendritic processes
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51Adaptive timing, attention, and movement controlBehavioral and Brain Sciences 20 (4): 619-619. 1997.Examples of how LTP and LTD can control adaptively-timed learning that modulates attention and motor control are given. It is also suggested that LTP/LTD can play a role in storing memories. The distinction between match-based and mismatch-based learning may help to clarify the difference.
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9Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formationPsychological Review 95 (1): 49-90. 1988.
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52Unattended exposure to components of speech sounds yields same benefits as explicit auditory trainingCognition 115 (3): 435-443. 2010.
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25Cognitive self-organization and neural modularityBehavioral and Brain Sciences 8 (1): 18-19. 1985.
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68The complementary brain: From brain dynamics to conscious experiencesIn Christian Kaernbach, Erich Schröger & Hermann Müller (eds.), Psychophysics Beyond Sensation: Laws and Invariants of Human Cognition, Psychology Press. pp. 417-449. 2004.
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19Realistic constraints on brain color perception and category learningBehavioral and Brain Sciences 28 (4): 495-496. 2005.Steels & Belpaeme (S&B) ask how autonomous agents can derive perceptually grounded categories for successful communication, using color categorization as an example. Their comparison of nativism, empiricism, and culturalism, although interesting, does not include key biological and technological constraints for seeing color or learning color categories in realistic environments. Other neural models have successfully included these constraints.
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10Neural dynamics of autistic behaviors: Cognitive, emotional, and timing substratesPsychological Review 113 (3): 483-525. 2006.
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How do representations of visual form organize our percepts of visual motion?In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society: August 13 to 16, 1994, Georgia Institute of Technology, Erlbaum. pp. 16--330. 1994.
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39Linking brain to mind in normal behavior and schizophreniaBehavioral and Brain Sciences 26 (1): 90-90. 2003.To understand schizophrenia, a linking hypothesis is needed that shows how brain mechanisms lead to behavioral functions in normals, and also how breakdowns in these mechanisms lead to behavioral symptoms of schizophrenia. Such a linking hypothesis is now available that complements the discussion offered by Phillips & Silverstein (P&S).
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32Filling-in the formsBehavioral and Brain Sciences 21 (6): 758-759. 1998.Boundary completion and surface filling-in are computationally complementary processes whose multiple processing stages form processing streams that realize a hierarchical resolution of uncertainty. Such complementarity and uncertainty principles provide a new foundation for philosophical discussions about visual perception, and lead to neural explanations of difficult perceptual data.
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16The role of learning in sensory-motor controlBehavioral and Brain Sciences 8 (1): 155-157. 1985.
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14Cortical dynamics of three-dimensional figure–ground perception of two-dimensional picturesPsychological Review 104 (3): 618-658. 1997.
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32Self-organizing neural models of categorization, inference and synchronyBehavioral and Brain Sciences 16 (3): 460-461. 1993.
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129Neural substrates of visual percepts, imagery, and hallucinationsBehavioral and Brain Sciences 25 (2): 194-195. 2002.Recent neural models clarify many properties of mental imagery as part of the process whereby bottom-up visual information is influenced by top-down expectations, and how these expectations control visual attention. Volitional signals can transform modulatory top-down signals into supra-threshold imagery. Visual hallucinations can occur when the normal control of these volitional signals is lost.
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15Bring ART into the ACTBehavioral and Brain Sciences 26 (5): 610-611. 2003.ACT is compared with a particular type of connectionist model that cannot handle symbols and use nonbiological operations which do not learn in real time. This focus continues an unfortunate trend of straw man debates in cognitive science. Adaptive Resonance Theory, or ART-neural models of cognition can handle both symbols and subsymbolic representations, and meet the Newell criteria at least as well as connectionist models.
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11Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreadingPsychological Review 92 (2): 173-211. 1985.
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14How the venetian blind percept emerges from the laminar cortical dynamics of 3D visionFrontiers in Psychology 5. 2014.
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37Hippocampal modulation of recognition, conditioning, timing, and space: Why so many functions?Behavioral and Brain Sciences 17 (3): 479-480. 1994.
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4Do all neural models really look alike? A comment on Anderson, Silverstein, Ritz, and JonesPsychological Review 85 (6): 592-596. 1978.
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17The microscopic analysis of behavior: Toward a synthesis of instrumental, perceptual, and cognitive ideasBehavioral and Brain Sciences 7 (4): 594-595. 1984.
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19Classical conditioning: The role of interdisciplinary theoryBehavioral and Brain Sciences 12 (1): 144-145. 1989.
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49Representations need self-organizing top-down expectations to fit a changing worldBehavioral and Brain Sciences 21 (4): 473-474. 1998.“Chorus embodies an attempt to find out how far a mostly bottom-up approach to representation can be taken.” Models that embody both bottom-up and top-down learning have stronger computational properties and explain more data about representation than feedforward models do.
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6Neural Facades: Visual Representations of Static and Moving Form‐And‐Color‐And‐DepthMind and Language 5 (4): 411-456. 1990.
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25Attention and recognition learning by adaptive resonanceBehavioral and Brain Sciences 13 (2): 241-242. 1990.
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Boston UniversityRegular Faculty
Boston, Massachusetts, United States of America
Areas of Interest
Philosophy of Mind |
Philosophy of Cognitive Science |