Mark Andrews [9]Mark W. Andrews [1]
  1.  24
    Integrating experiential and distributional data to learn semantic representations.Mark Andrews, Gabriella Vigliocco & David Vinson - 2009 - Psychological Review 116 (3):463-498.
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    Reconciling Embodied and Distributional Accounts of Meaning in Language.Mark Andrews, Stefan Frank & Gabriella Vigliocco - 2014 - Topics in Cognitive Science 6 (3):359-370.
    Over the past 15 years, there have been two increasingly popular approaches to the study of meaning in cognitive science. One, based on theories of embodied cognition, treats meaning as a simulation of perceptual and motor states. An alternative approach treats meaning as a consequence of the statistical distribution of words across spoken and written language. On the surface, these appear to be opposing scientific paradigms. In this review, we aim to show how recent cross-disciplinary developments have done much to (...)
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    The Hidden Markov Topic Model: A Probabilistic Model of Semantic Representation.Mark Andrews & Gabriella Vigliocco - 2010 - Topics in Cognitive Science 2 (1):101-113.
  4.  15
    Law, History... and Desertion.Ilya Prigogine & Mark Andrews - 1983 - Substance 12 (3):4.
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  5. Inferring a probabilistic model of semantic memory from word association norms.Mark Andrews, David Vinson & Gabriella Vigliocco - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 1941--1946.
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    On computational and behavioral evidence regarding Hebbian transcortical cell assemblies.Michael Spivey, Mark Andrews & Daniel Richardson - 1999 - Behavioral and Brain Sciences 22 (2):302-302.
    Pulvermüller restricts himself to an unnecessarily narrow range of evidence to support his claims. Evidence from neural modeling and behavioral experiments provides further support for an account of words encoded as transcortical cell assemblies. A cognitive neuroscience of language must include a range of methodologies (e.g., neural, computational, and behavioral) and will need to focus on the on-line processes of real-time language processing in more natural contexts.
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