•  150
    The link between brain learning, attention, and consciousness
    Consciousness and Cognition 8 (1): 1-44. 1999.
    The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what i…Read more
  •  10
    How does your mind work? How does your brain give rise to your mind? These are questions that all of us have wondered about at some point in our lives, if only because everything that we know is experienced in our minds. They are also very hard questions to answer. After all, how can a mind understand itself? How can you understand something as complex as the tool that is being used to understand it? This book provides an introductory and self-contained description of some of the exciting answer…Read more
  •  157
    The thresholds of human observers detecting line targets improve significantly when the targets are presented in a spatial context of collinear inducing stimuli. This phenomenon is referred to as spatial facilitation, and may reflect the output of long-range interactions between cortical feature detectors. Spatial facilitation has thus far been observed with luminance-defined, achromatic stimuli on achromatic backgrounds. This study compares spatial facilitation with line targets and collinear, …Read more
  •  165
    This article introduces an experimental paradigm to selectively probe the multiple levels of visual processing that influence the formation of object contours, perceptual boundaries, and illusory contours. The experiments test the assumption that, to integrate contour information across space and contrast sign, a spatially short-range filtering process that is sensitive to contrast polarity inputs to a spatially long-range grouping process that pools signals from opposite contrast polarities. Th…Read more
  •  175
    The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward o…Read more
  •  50
    Unattended exposure to components of speech sounds yields same benefits as explicit auditory training
    with Aaron R. Seitz, Athanassios Protopapas, Yoshiaki Tsushima, Eleni L. Vlahou, Simone Gori, and Takeo Watanabe
    Cognition 115 (3): 435-443. 2010.
  •  24
  •  15
    Brain metaphors, theories, and facts
    Behavioral and Brain Sciences 9 (1): 97-98. 1986.
  •  17
    Realistic constraints on brain color perception and category learning
    Behavioral 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.
  •  8
    Neural dynamics of autistic behaviors: Cognitive, emotional, and timing substrates
    with Don Seidman
    Psychological Review 113 (3): 483-525. 2006.
  •  38
    Linking brain to mind in normal behavior and schizophrenia
    Behavioral 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).
  •  31
    Filling-in the forms
    Behavioral 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.
  •  13
    Bring ART into the ACT
    Behavioral 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.
  •  128
    Neural substrates of visual percepts, imagery, and hallucinations
    Behavioral 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.
  •  35
  •  48
    Representations need self-organizing top-down expectations to fit a changing world
    Behavioral 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.