59 found
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  1. Stephen Grossberg (2000). The Complementary Brain: Unifying Brain Dynamics and Modularity. Trends in Cognitive Sciences 4 (6):233-246.
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  2.  7
    Stephen Grossberg (1983). The Quantized Geometry of Visual Space: The Coherent Computation of Depth, Form, and Lightness. Behavioral and Brain Sciences 6 (4):625.
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  3.  7
    Stephen Grossberg (1987). Competitive Learning: From Interactive Activation to Adaptive Resonance. Cognitive Science 11 (1):23-63.
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  4. Stephen Grossberg, Karthik Srinivasan & Arash Yazdanbakhsh (2015). Binocular Fusion and Invariant Category Learning Due to Predictive Remapping During Scanning of a Depthful Scene with Eye Movements. Frontiers in Psychology 5.
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  5.  91
    Birgitta Dresp, Severine Durand & Stephen Grossberg (2002). Depth Perception From Pairs of Overlapping Cues in Pictorial Displays. Spatial Visions 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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  6.  2
    Stephen Grossberg (1985). Four Frames Do Not Suffice. Behavioral and Brain Sciences 8 (2):294-295.
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  7. Stephen Grossberg (1997). Cortical Dynamics of Three-Dimensional Figure-Ground Perception of Two-Dimensional Pictures. Psychological Review 104 (3):618-658.
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  8. Stephen Grossberg & Ennio Mingolla (1985). Neural Dynamics of Form Perception: Boundary Completion, Illusory Figures, and Neon Color Spreading. Psychological Review 92 (2):173-211.
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  9. Stephen Grossberg (1980). How Does a Brain Build a Cognitive Code? Psychological Review 87 (1):1-51.
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  10.  54
    Stephen Grossberg (2006). The Art of Seeing and Painting. 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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  11.  3
    Aaron R. Seitz, Athanassios Protopapas, Yoshiaki Tsushima, Eleni L. Vlahou, Simone Gori, Stephen Grossberg & Takeo Watanabe (2010). Unattended Exposure to Components of Speech Sounds Yields Same Benefits as Explicit Auditory Training. Cognition 115 (3):435-443.
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  12. Stephen Grossberg, Ennio Mingolla & William D. Ross (1994). A Neural Theory of Attentive Visual Search: Interactions of Boundary, Surface, Spatial, and Object Representations. Psychological Review 101 (3):470-489.
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  13. Tsung-Ren Huang & Stephen Grossberg (2010). Cortical Dynamics of Contextually Cued Attentive Visual Learning and Search: Spatial and Object Evidence Accumulation. Psychological Review 117 (4):1080-1112.
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  14.  1
    Stephen Grossberg & William E. Gutowski (1987). Neural Dynamics of Decision Making Under Risk: Affective Balance and Cognitive-Emotional Interactions. Psychological Review 94 (3):300-318.
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  15.  13
    Stephen Grossberg (1994). Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why so Many Functions? Behavioral and Brain Sciences 17 (3):479-480.
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  16. Stephen Grossberg & Lance R. Pearson (2008). Laminar Cortical Dynamics of Cognitive and Motor Working Memory, Sequence Learning and Performance: Toward a Unified Theory of How the Cerebral Cortex Works. Psychological Review 115 (3):677-732.
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  17. Stephen Grossberg & Christopher W. Myers (2000). The Resonant Dynamics of Speech Perception: Interword Integration and Duration-Dependent Backward Effects. Psychological Review 107 (4):735-767.
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  18.  63
    Stephen Grossberg (2004). The Complementary Brain: From Brain Dynamics to Conscious Experiences. In Christian Kaernbach, Erich Schroger & Hermann Müller (eds.), Psychophysics Beyond Sensation: Laws and Invariants of Human Cognition. Psychology Press 417-449.
  19.  54
    Stephen Grossberg (2002). Neural Substrates of Visual Percepts, Imagery, and Hallucinations. 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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  20.  15
    Alexander Grunewald & Stephen Grossberg (1994). Binding of Object Representations by Synchronous Cortical Dynamics Explains Temporal Order and Spatial Pooling Data. In Ashwin Ram & Kurt Eiselt (eds.), Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society. Erlbaum 387--391.
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  21.  8
    Stephen Grossberg (1990). Neural Facades: Visual Representations of Static and Moving Form-And-Color-And-Depth. Mind and Language 5 (4):411-456.
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  22. Daniel Bullock & Stephen Grossberg (1988). Neural Dynamics of Planned Arm Movements: Emergent Invariants and Speed^Accuracy Properties During Trajectory Formation. Psychological Review 95 (1):49-90.
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  23.  8
    Stephen Grossberg (1997). Neural Models of Reaching. 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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  24. Stephen Grossberg & Gregory Stone (1986). Neural Dynamics of Word Recognition and Recall: Attentional Priming, Learning, and Resonance. Psychological Review 93 (1):46-74.
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  25.  1
    Stephen Grossberg (2016). 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] Frontiers in Psychology 6.
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  26. Stephen Grossberg (1982). Processing of Expected and Unexpected Events During Conditioning and Attention: A Psychophysiological Theory. Psychological Review 89 (5):529-572.
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  27.  21
    Stephen Grossberg (2005). STaRT: A Bridge Between Emotion Theory and Neurobiology Through Dynamic System Modeling. 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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  28.  22
    Stephen Grossberg (2003). From Working Memory to Long-Term Memory and Back: Linked but Distinct. 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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  29.  12
    Stephen Grossberg (1997). Adaptive Timing, Attention, and Movement Control. 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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  30.  5
    Stephen Grossberg (1993). Self-Organizing Neural Models of Categorization, Inference and Synchrony. Behavioral and Brain Sciences 16 (3):460.
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  31.  15
    Stephen Grossberg (2003). Linking Visual Cortex to Visual Perception: An Alternative to the Gestalt Bubble. 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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  32.  14
    Stephen Grossberg (2003). Linking Brain to Mind in Normal Behavior and Schizophrenia. Behavioral and Brain Sciences 26 (1):90-90.
    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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  33.  11
    Stephen Grossberg (2000). Localist but Distributed Representations. 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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  34.  2
    Stephen Grossberg (1985). Cognitive Self-Organization and Neural Modularity. Behavioral and Brain Sciences 8 (1):18-19.
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  35.  2
    Stephen Grossberg (1983). Interdisciplinary Aspects of Perceptual Dynamics. Behavioral and Brain Sciences 6 (4):676.
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  36.  10
    Stephen Grossberg (1997). Neural Models of Development and Learning. 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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  37.  9
    Stephen Grossberg (2005). Realistic Constraints on Brain Color Perception and Category Learning. Behavioral and Brain Sciences 28 (4):495-496.
    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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  38.  3
    Stephen Grossberg (1990). Attention and Recognition Learning by Adaptive Resonance. Behavioral and Brain Sciences 13 (2):241-242.
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  39.  3
    Stephen Grossberg (1987). Stable Self-Organization of Sensory Recognition Codes: Is Chaos Necessary? Behavioral and Brain Sciences 10 (2):179.
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  40.  9
    Stephen Grossberg (1998). Self-Organizing Features and Categories Through Attentive Resonance. Behavioral and Brain Sciences 21 (1):27-28.
    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).
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  41.  2
    Stephen Grossberg (1989). Classical Conditioning: The Role of Interdisciplinary Theory. Behavioral and Brain Sciences 12 (1):144.
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  42.  1
    Stephen Grossberg (1980). Direct Perception or Adaptive Resonance? Behavioral and Brain Sciences 3 (3):385.
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  43.  1
    Stephen Grossberg (1984). The Microscopic Analysis of Behavior: Toward a Synthesis of Instrumental, Perceptual, and Cognitive Ideas. Behavioral and Brain Sciences 7 (4):594.
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  44.  3
    Stephen Grossberg (1998). Filling-in the Forms. 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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  45.  3
    Stephen Grossberg (1998). Representations Need Self-Organizing Top-Down Expectations to Fit a Changing World. 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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  46.  1
    Stephen Grossberg (1997). Principles of Cortical Synchronization. Behavioral and Brain Sciences 20 (4):689-690.
    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.
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  47.  1
    Stephen Grossberg (1985). The Role of Learning in Sensory-Motor Control. Behavioral and Brain Sciences 8 (1):155-157.
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  48.  3
    Stephen Grossberg (2003). Bring ART Into the ACT. Behavioral and Brain Sciences 26 (5):610-611.
    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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  49.  3
    Stephen Grossberg (2000). Brain Feedback and Adaptive Resonance in Speech Perception. Behavioral and Brain Sciences 23 (3):332-333.
    The brain contains ubiquitous reciprocal bottom-up and top-down intercortical and thalamocortical pathways. These resonating feedback pathways may be essential for stable learning of speech and language codes and for context-sensitive selection and completion of noisy speech sounds and word groupings. Context-sensitive speech data, notably interword backward effects in time, have been quantitatively modeled using these concepts but not with purely feedforward models.
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  50. Yongqiang Cao & Stephen Grossberg (2014). How the Venetian Blind Percept Emerges From the Laminar Cortical Dynamics of 3D Vision. Frontiers in Psychology 5.
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