•  860
    Depth perception from pairs of overlapping cues in pictorial displays
    with Birgitta Dresp and Severine Durand
    Spatial Vision 15 255-276. 2002.
    The experiments reported herein probe the visual cortical mechanisms that control near–far percepts in response to two-dimensional stimuli. Figural contrast is found to be a principal factor for the emergence of percepts of near versus far in pictorial stimuli, especially when stimulus duration is brief. Pictorial factors such as interposition (Experiment 1) and partial occlusion Experiments 2 and 3) may cooperate, as generally predicted by cue combination models, or compete with contrast factor…Read more
  •  174
    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
  •  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
  •  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
  •  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
  •  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.
  •  107
    STaRT: A bridge between emotion theory and neurobiology through dynamic system modeling
    Behavioral and Brain Sciences 28 (2): 207-208. 2005.
    Lewis proposes a “reconceptualization” of how to link the psychology and neurobiology of emotion and cognitive-emotional interactions. His main proposed themes have actually been actively and quantitatively developed in the neural modeling literature for more than 30 years. This commentary summarizes some of these themes and points to areas of particularly active research in this area.
  •  64
    The Art of Seeing and Painting
    Technical Report. 2006.
    The human urge to represent the three-dimensional world using two-dimensional pictorial representations dates back at least to Paleolithic times. Artists from ancient to modern times have struggled to understand how a few contours or color patches on a flat surface can induce mental representations of a three-dimensional scene. This article summarizes some of the recent breakthroughs in scientifically understanding how the brain sees that shed light on these struggles. These breakthroughs i…Read more
  •  61
    Linking visual cortex to visual perception: An alternative to the gestalt bubble
    Behavioral and Brain Sciences 26 (4): 412-413. 2003.
    Lehar's lively discussion builds on a critique of neural models of vision that is incorrect in its general and specific claims. He espouses a Gestalt perceptual approach rather than one consistent with the “objective neurophysiological state of the visual system” (target article, Abstract). Contemporary vision models realize his perceptual goals and also quantitatively explain neurophysiological and anatomical data.
  •  58
    Localist but distributed representations
    Behavioral and Brain Sciences 23 (4): 478-479. 2000.
    A number of examples are given of how localist models may incorporate distributed representations, without the types of nonlocal interactions that often render distributed models implausible. The need to analyze the information that is encoded by these representations is also emphasized as a metatheoretical constraint on model plausibility.
  •  51
  •  50
    Adaptive timing, attention, and movement control
    Behavioral 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.
  •  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.
  •  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.
  •  46
    Neural models of development and learning
    Behavioral 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
  •  44
    From working memory to long-term memory and back: Linked but distinct
    Behavioral and Brain Sciences 26 (6): 737-738. 2003.
    Neural models have proposed how short-term memory (STM) storage in working memory and long-term memory (LTM) storage and recall are linked and interact, but are realized by different mechanisms that obey different laws. The authors' data can be understood in the light of these models, which suggest that the authors may have gone too far in obscuring the differences between these processes.
  •  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).
  •  35
  •  35
    Principles of cortical synchronization
    Behavioral and Brain Sciences 20 (4): 689-690. 1997.
    Functional roles for cortical synchronization in self-organizing neural networks are described. These properties are best understood by models that link brain to behavior. Synchrony can express itself differently in cortical circuits that perform different behavioral tasks. Cortical temporal properties that seem inexplicable by synchrony are also mentioned.
  •  35
    Four frames do not suffice
    Behavioral and Brain Sciences 8 (2): 294-295. 1985.
  •  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.
  •  26
    Neural models of reaching
    Behavioral and Brain Sciences 20 (2): 310-310. 1997.
    Plamondon & Alimi (P&A) have unified much data on speed/accuracy trade-offs during reaching movements using a delta-lognormal form factor that describes notably neuromuscular systems. Their approach raises questions about whether a large number of systems is needed, whether they are linear, and whether the results disclose the neural design principles that control reaching behaviors. The authors admit that (sect. 6, para. 4)
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  •  23
    Self-organizing features and categories through attentive resonance
    Behavioral 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