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A depictive neural model for the representation of motion verbs

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Abstract

In this paper, we present a depictive neural model for the representation of motion verb semantics in neural models of visual awareness. The problem of modelling motion verb representation is shown to be one of function application, mapping a set of given input variables defining the moving object and the path of motion to a defined output outcome in the motion recognition context. The particular function-applicative implementation and consequent recognition model design presented are seen as arising from a noun-adjective recognition model enabling the recognition of colour adjectives as applied to a set of shapes representing objects to be recognised. The presence of such a function application scheme and a separately implemented position identification and path labelling scheme are accordingly shown to be the primitives required to enable the design and construction of a composite depictive motion verb recognition scheme. Extensions to the presented design to enable the representation of transitive verbs are also discussed.

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Acknowledgments

The authors wish to gratefully acknowledge that this work was funded by the Engineering and Physical Sciences Research Council, UK (doctoral studentship for SR), and the Leverhulme Trust (Emeritus Fellowship for IA).

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Authors and Affiliations

  1. Imperial College London, South Kensington Campus, London, SW7 2AZ, England, UK

    Sunil Rao & Igor Aleksander

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  1. Sunil Rao
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  2. Igor Aleksander
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Correspondence to Sunil Rao.

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Rao, S., Aleksander, I. A depictive neural model for the representation of motion verbs. Cogn Process 12, 395–405 (2011). https://doi.org/10.1007/s10339-011-0400-5

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