David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Cognitive Science 36 (2):305-332 (2012)
How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory features that optimally explain the unisensory features arising in individual sensory modalities. The model qualitatively accounts for several important aspects of multisensory perception: (a) it integrates information from multiple sensory sources in such a way that it leads to superior performances in, for example, categorization tasks; (b) its performances suggest that multisensory training leads to better learning than unisensory training, even when testing is conducted in unisensory conditions; (c) its multisensory representations are modality invariant; and (d) it predicts ‘‘missing” sensory representations in modalities when the input to those modalities is absent. Our rational analysis indicates that all of these aspects emerge as part of the optimal solution to the problem of learning to represent complex multisensory environments
|Keywords||Bayesian modeling Rational analysis Multisensory perception Learning|
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References found in this work BETA
Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
David Marr (1982). Vision. Freeman.
David Marr (1982). Vison. W. H. Freeman.
Nick Chater & Mike Oaksford (eds.) (2008). The Probabilistic Mind: Prospects for Bayesian Cognitive Science. OUP Oxford.
M. Oaksford & N. Chater (eds.) (1998). Rational Models of Cognition. Oxford University Press Uk.
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