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      Conceptual grounding of language in action and perception: a neurocomputational model of the emergence of category specificity and semantic hubs

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          Current neurobiological accounts of language and cognition offer diverging views on the questions of ‘where’ and ‘how’ semantic information is stored and processed in the human brain. Neuroimaging data showing consistent activation of different multi‐modal areas during word and sentence comprehension suggest that all meanings are processed indistinctively, by a set of general semantic centres or ‘hubs’. However, words belonging to specific semantic categories selectively activate modality‐preferential areas; for example, action‐related words spark activity in dorsal motor cortex, whereas object‐related ones activate ventral visual areas. The evidence for category‐specific and category‐general semantic areas begs for a unifying explanation, able to integrate the emergence of both. Here, a neurobiological model offering such an explanation is described. Using a neural architecture replicating anatomical and neurophysiological features of frontal, occipital and temporal cortices, basic aspects of word learning and semantic grounding in action and perception were simulated. As the network underwent training, distributed lexico‐semantic circuits spontaneously emerged. These circuits exhibited different cortical distributions that reached into dorsal‐motor or ventral‐visual areas, reflecting the correlated category‐specific sensorimotor patterns that co‐occurred during action‐ or object‐related semantic grounding, respectively. Crucially, substantial numbers of neurons of both types of distributed circuits emerged in areas interfacing between modality‐preferential regions, i.e. in multimodal connection hubs, which therefore became loci of general semantic binding. By relating neuroanatomical structure and cellular‐level learning mechanisms with system‐level cognitive function, this model offers a neurobiological account of category‐general and category‐specific semantic areas based on the different cortical distributions of the underlying semantic circuits.

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          Most cited references 62

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          Grounded cognition.

          Grounded cognition rejects traditional views that cognition is computation on amodal symbols in a modular system, independent of the brain's modal systems for perception, action, and introspection. Instead, grounded cognition proposes that modal simulations, bodily states, and situated action underlie cognition. Accumulating behavioral and neural evidence supporting this view is reviewed from research on perception, memory, knowledge, language, thought, social cognition, and development. Theories of grounded cognition are also reviewed, as are origins of the area and common misperceptions of it. Theoretical, empirical, and methodological issues are raised whose future treatment is likely to affect the growth and impact of grounded cognition.
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            Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statistics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement recording systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The storage and reactivation of perceptual symbols operates at the level of perceptual components--not at the level of holistic perceptual experiences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a common frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspection (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and abstract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinatorially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a perceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal symbol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored.
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              We explore the extent to which neocortical circuits generalize, i.e., to what extent can neocortical neurons and the circuits they form be considered as canonical? We find that, as has long been suspected by cortical neuroanatomists, the same basic laminar and tangential organization of the excitatory neurons of the neocortex is evident wherever it has been sought. Similarly, the inhibitory neurons show characteristic morphology and patterns of connections throughout the neocortex. We offer a simple model of cortical processing that is consistent with the major features of cortical circuits: The superficial layer neurons within local patches of cortex, and within areas, cooperate to explore all possible interpretations of different cortical input and cooperatively select an interpretation consistent with their various cortical and subcortical inputs.

                Author and article information

                Eur J Neurosci
                Eur. J. Neurosci
                The European Journal of Neuroscience
                John Wiley and Sons Inc. (Hoboken )
                09 February 2016
                March 2016
                : 43
                : 6 ( doiID: 10.1111/ejn.2016.43.issue-6 )
                : 721-737
                [ 1 ] Brain Language Laboratory Department of Philosophy and HumanitiesFreie Universität Berlin Habelschwerdter Allee 45 14195 BerlinGermany
                [ 2 ] Centre for Robotics and Neural Systems (CRNS)University of Plymouth Plymouth DevonUK
                Author notes
                [* ] Correspondence: Dr M. Garagnani, as above.

                E‐mail: M.Garagnani@

                © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

                This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Pages: 17
                Funded by: UK EPSRC/BBSRC
                Award ID: EP/J004561/1
                Funded by: Freie Universität Berlin
                Funded by: Deutsche Forschungsgemeinschaft
                Award ID: Pu 97/15‐1
                Award ID: 16‐1
                Computational Neuroscience
                Computational Neuroscience
                Custom metadata
                March 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.4 mode:remove_FC converted:16.08.2016


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