David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Topics in Cognitive Science 3 (2):273-302 (2011)
Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to a specific statistical process or to activation of embodied representations should be attributed to language itself. Specifically, the performance of LSA can be attributed to the linguistic surface structure, more than special characteristics of the algorithm, and embodiment findings attributed to perceptual simulations can be explained by distributional linguistic information.
|Keywords||LSA Semantic knowledge Symbolic Perceptual simulations Modal Amodal Symbol interdependency Embodied|
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Citations of this work BETA
Max Louwerse & Louise Connell (2011). A Taste of Words: Linguistic Context and Perceptual Simulation Predict the Modality of Words. Cognitive Science 35 (2):381-398.
Brendan T. Johns & Michael N. Jones (2012). Perceptual Inference Through Global Lexical Similarity. Topics in Cognitive Science 4 (1):103-120.
Mark Andrews, Stefan Frank & Gabriella Vigliocco (2014). Reconciling Embodied and Distributional Accounts of Meaning in Language. Topics in Cognitive Science 6 (3):359-370.
Ian S. Hargreaves & Penny M. Pexman (2014). Get Rich Quick: The Signal to Respond Procedure Reveals the Time Course of Semantic Richness Effects During Visual Word Recognition. Cognition 131 (2):216-242.
Guy Dove (2014). Thinking in Words: Language as an Embodied Medium of Thought. Topics in Cognitive Science 6 (3):371-389.
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