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Computational Modelling of Embodied Semantic Cognition: A Deep LearningApproach

Abstract

perceptual symbol systems hypothesis describes how semantic knowledge is grounded insensorimotor experience. According to the theory, knowledge is acquired through sensorimotor simulations. This challengesthe classical view supported by the disembodied cognition hypothesis, which generally favours an abstract and symbolic sys-tem. We propose a unified perspective, in which, the embodied cognition hypothesis, with a particular focus on the semanticdomain, is provided with a mechanistically tractable computational framework based on the parallel distributed processing(PDP) paradigm. A critical difference between the current approach and previous mechanistic accounts of embodied cognitionis that the current approach avoids using hand-coded representations and instead, relies on an agent-based simulation withenvironmental interaction for the creation of situated inputs and outputs, supplemented with supervised and unsupervised deeplearning mechanisms, from which semantic cognition emerges.

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