Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex.
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Abstract
How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal 'hub' in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.
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Funder: European Research Council; Grant(s): GAP: 502670428 - BRAIN2MIND_NEUROCOMP
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2050-084X
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Medical Research Council (MC_UU_00005/18)
European Research Council (670428)