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Stephen Grossberg
Boston University
  1.  12
    How Does a Brain Build a Cognitive Code?Stephen Grossberg - 1980 - Psychological Review 87 (1):1-51.
  2.  38
    Competitive Learning: From Interactive Activation to Adaptive Resonance.Stephen Grossberg - 1987 - Cognitive Science 11 (1):23-63.
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  3.  12
    Processing of Expected and Unexpected Events During Conditioning and Attention: A Psychophysiological Theory.Stephen Grossberg - 1982 - Psychological Review 89 (5):529-572.
  4.  4
    Neural Dynamics of Planned Arm Movements: Emergent Invariants and Speed-Accuracy Properties During Trajectory Formation.Daniel Bullock & Stephen Grossberg - 1988 - Psychological Review 95 (1):49-90.
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  5.  5
    Neural Dynamics of Form Perception: Boundary Completion, Illusory Figures, and Neon Color Spreading.Stephen Grossberg & Ennio Mingolla - 1985 - Psychological Review 92 (2):173-211.
  6. The Complementary Brain: Unifying Brain Dynamics and Modularity.Stephen Grossberg - 2000 - Trends in Cognitive Sciences 4 (6):233-246.
  7.  9
    Neural Dynamics of Word Recognition and Recall: Attentional Priming, Learning, and Resonance.Stephen Grossberg & Gregory Stone - 1986 - Psychological Review 93 (1):46-74.
  8. The Link Between Brain Learning, Attention, and Consciousness.Stephen Grossberg - 1999 - Consciousness and Cognition 8 (1):1-44.
    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 is there in the outside world. It (...)
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  9.  11
    Neural Dynamics of Decision Making Under Risk: Affective Balance and Cognitive-Emotional Interactions.Stephen Grossberg & William E. Gutowski - 1987 - Psychological Review 94 (3):300-318.
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  10.  14
    The Quantized Geometry of Visual Space: The Coherent Computation of Depth, Form, and Lightness.Stephen Grossberg - 1983 - Behavioral and Brain Sciences 6 (4):625.
  11.  26
    Four Frames Do Not Suffice.Stephen Grossberg - 1985 - Behavioral and Brain Sciences 8 (2):294-295.
  12. Depth Perception From Pairs of Overlapping Cues in Pictorial Displays.Birgitta Dresp, Severine Durand & Stephen Grossberg - 2002 - Spatial Vision 15:255-276.
    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 factors in the manner predicted by the FACADE model. (...)
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  13.  21
    Cortical Dynamics of Figure-Ground Separation in Response to 2D Pictures and 3D Scenes: How V2 Combines Border Ownership, Stereoscopic Cues, and Gestalt Grouping Rules. [REVIEW]Stephen Grossberg - 2015 - Frontiers in Psychology 6.
  14.  11
    Cortical Dynamics of Three-Dimensional Figure-Ground Perception of Two-Dimensional Pictures.Stephen Grossberg - 1997 - Psychological Review 104 (3):618-658.
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  15.  47
    Neural Computation of Surface Border Ownership and Relative Surface Depth From Ambiguous Contrast Inputs.Birgitta Dresp-Langley & Stephen Grossberg - 2016 - Frontiers in Psychology 7.
    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 or outward relative to the continuous (...)
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  16.  13
    The Resonant Dynamics of Speech Perception: Interword Integration and Duration-Dependent Backward Effects.Stephen Grossberg & Christopher W. Myers - 2000 - Psychological Review 107 (4):735-767.
  17.  5
    A Neural Theory of Attentive Visual Search: Interactions of Boundary, Surface, Spatial, and Object Representations.Stephen Grossberg, Ennio Mingolla & William D. Ross - 1994 - Psychological Review 101 (3):470-489.
  18.  12
    Laminar Cortical Dynamics of Cognitive and Motor Working Memory, Sequence Learning and Performance: Toward a Unified Theory of How the Cerebral Cortex Works.Stephen Grossberg & Lance R. Pearson - 2008 - Psychological Review 115 (3):677-732.
  19.  6
    Neural Dynamics of Autistic Behaviors: Cognitive, Emotional, and Timing Substrates.Stephen Grossberg & Don Seidman - 2006 - Psychological Review 113 (3):483-525.
  20.  10
    Cortical Dynamics of Visual Motion Perception: Short-Range and Long-Range Apparent Motion.Stephen Grossberg & Michael E. Rudd - 1992 - Psychological Review 99 (1):78-121.
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  21.  5
    Binocular Fusion and Invariant Category Learning Due to Predictive Remapping During Scanning of a Depthful Scene with Eye Movements.Stephen Grossberg, Karthik Srinivasan & Arash Yazdanbakhsh - 2014 - Frontiers in Psychology 5.
  22.  5
    Cortical Dynamics of Contextually Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation.Tsung-Ren Huang & Stephen Grossberg - 2010 - Psychological Review 117 (4):1080-1112.
  23.  12
    Direct Perception or Adaptive Resonance?Stephen Grossberg - 1980 - Behavioral and Brain Sciences 3 (3):385-386.
  24. Neural Substrates of Visual Percepts, Imagery, and Hallucinations.Stephen Grossberg - 2002 - Behavioral and Brain Sciences 25 (2):194-195.
    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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  25.  11
    Contour Integration Across Gaps: From Local Contrast To Grouping.Birgitta Dresp & Stephen Grossberg - 1997 - Vision Research 7 (37):913-924.
    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. The stimuli consisted of thin subthreshold lines, flashed upon gaps between collinear inducers which (...)
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  26.  8
    How the Venetian Blind Percept Emerges From the Laminar Cortical Dynamics of 3D Vision.Yongqiang Cao & Stephen Grossberg - 2014 - Frontiers in Psychology 5.
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  27.  15
    Neural Facades: Visual Representations of Static and Moving Form‐And‐Color‐And‐Depth.Stephen Grossberg - 1990 - Mind and Language 5 (4):411-456.
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  28.  38
    Unattended Exposure to Components of Speech Sounds Yields Same Benefits as Explicit Auditory Training.Aaron R. Seitz, Athanassios Protopapas, Yoshiaki Tsushima, Eleni L. Vlahou, Simone Gori, Stephen Grossberg & Takeo Watanabe - 2010 - Cognition 115 (3):435-443.
  29.  13
    Spatial Facilitation by Color and Luminance Edges: Boundary, Surface, and Attentional Factors.Birgitta Dresp & Stephen Grossberg - 1995 - Vision Research 39 (20):3431-3443.
    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, edge-like inducers defined by luminance contrast to spatial facilitation with targets and inducers defined (...)
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  30. STaRT: A Bridge Between Emotion Theory and Neurobiology Through Dynamic System Modeling.Stephen Grossberg - 2005 - Behavioral and Brain Sciences 28 (2):207-208.
    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.
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  31. The Complementary Brain: From Brain Dynamics to Conscious Experiences.Stephen Grossberg - 2004 - In Christian Kaernbach, Erich Schroger & Hermann Müller (eds.), Psychophysics Beyond Sensation: Laws and Invariants of Human Cognition. Psychology Press. pp. 417-449.
  32. The Art of Seeing and Painting.Stephen Grossberg - 2006 - Technical Report.
    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 illustrate how various artists have intuitively understand paradoxical properties about (...)
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  33. How Do Representations of Visual Form Organize Our Percepts of Visual Motion?Gregory Francis & Stephen Grossberg - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 16--330.
     
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  34. Conscious Experiences.Stephen Grossberg - 2004 - In Christian Kaernbach, Erich Schroger & Hermann Müller (eds.), Psychophysics Beyond Sensation: Laws and Invariants of Human Cognition. Psychology Press. pp. 417.
     
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  35. Binding of Object Representations by Synchronous Cortical Dynamics Explains Temporal Order and Spatial Pooling Data.Alexander Grunewald & Stephen Grossberg - 1994 - In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum. pp. 387--391.
  36.  54
    Linking Visual Cortex to Visual Perception: An Alternative to the Gestalt Bubble.Stephen Grossberg - 2003 - Behavioral and Brain Sciences 26 (4):412-413.
    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.
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  37.  43
    Adaptive Timing, Attention, and Movement Control.Stephen Grossberg - 1997 - Behavioral and Brain Sciences 20 (4):619-619.
    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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  38.  27
    Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why so Many Functions?Stephen Grossberg - 1994 - Behavioral and Brain Sciences 17 (3):479-480.
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  39.  41
    Representations Need Self-Organizing Top-Down Expectations to Fit a Changing World.Stephen Grossberg - 1998 - Behavioral and Brain Sciences 21 (4):473-474.
    “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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  40.  14
    Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning.Stephen Grossberg & Devika Kishnan - 2018 - Frontiers in Psychology 9.
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  41.  37
    Neural Models of Development and Learning.Stephen Grossberg - 1997 - Behavioral and Brain Sciences 20 (4):566-566.
    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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  42.  37
    Localist but Distributed Representations.Stephen Grossberg - 2000 - Behavioral and Brain Sciences 23 (4):478-479.
    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.
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  43.  37
    From Working Memory to Long-Term Memory and Back: Linked but Distinct.Stephen Grossberg - 2003 - Behavioral and Brain Sciences 26 (6):737-738.
    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.
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  44.  20
    Self-Organizing Neural Models of Categorization, Inference and Synchrony.Stephen Grossberg - 1993 - Behavioral and Brain Sciences 16 (3):460-461.
  45.  18
    Neural Models of Reaching.Stephen Grossberg - 1997 - Behavioral and Brain Sciences 20 (2):310-310.
    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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  46.  16
    Cognitive Self-Organization and Neural Modularity.Stephen Grossberg - 1985 - Behavioral and Brain Sciences 8 (1):18-19.
  47.  25
    Filling-in the Forms.Stephen Grossberg - 1998 - Behavioral and Brain Sciences 21 (6):758-759.
    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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  48.  13
    Attention and Recognition Learning by Adaptive Resonance.Stephen Grossberg - 1990 - Behavioral and Brain Sciences 13 (2):241-242.
  49.  11
    Brain Metaphors, Theories, and Facts.Stephen Grossberg - 1986 - Behavioral and Brain Sciences 9 (1):97-98.
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  50.  11
    Interdisciplinary Aspects of Perceptual Dynamics.Stephen Grossberg - 1983 - Behavioral and Brain Sciences 6 (4):676.
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