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  Mind Reading by Machine Learning: A doubly Bayesian Method for Inferring Mental Representations

Huszar, F., Noppeney, U., & Lengyel, M. (2010). Mind Reading by Machine Learning: A doubly Bayesian Method for Inferring Mental Representations. In R. Ohlsson, & S. Catrambone (Eds.), Cognition in Flux (pp. 2810-2815). Austin, TX, USA: Cognitive Science Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BEBE-A Version Permalink: http://hdl.handle.net/21.11116/0000-0002-8125-2
Genre: Conference Paper

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 Creators:
Huszar, F, Author
Noppeney, U1, 2, Author              
Lengyel, M, Author
Affiliations:
1Research Group Cognitive Neuroimaging, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497804              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: A central challenge in cognitive science is to measure and quantify the mental representations humans develop in other words, to `read' subject's minds. In order to elimi- nate potential biases in reporting mental contents due to verbal elaboration, subjects' responses in experiments are often limited to binary decisions or discrete choices that do not require conscious re ection upon their mental contents. However, it is unclear what such impoverished data can tell us about the potential richness and dy- namics of subjects' mental representations. To address this problem, we used ideal observer models that for- malise choice behaviour as (quasi-)Bayes-optimal, given subjects' representations in long-term memory, acquired through prior learning, and the stimuli currently avail- able to them. Bayesian inversion of such ideal observer models allowed us to infer subjects' mental representation from their choice behaviour in a variety of psychophysical tasks. The inferred mental representations also allowed us to predict future choices of subjects with reasonable accuracy, even in tasks that were dierent from those in which the representations were estimated. These results demonstrate a signicant potential in standard binary decision tasks to recover detailed information about sub- jects' mental representations.

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 Dates: 2010-08
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: HuszarNL2010
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Title: 32nd Annual Conference of the Cognitive Science Society (COGSCI 2010)
Place of Event: Portland, OR, USA
Start-/End Date: 2010-08-11 - 2010-08-14

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Title: Cognition in Flux
Source Genre: Proceedings
 Creator(s):
Ohlsson, R, Editor
Catrambone, S, Editor
Affiliations:
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Publ. Info: Austin, TX, USA : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 2810 - 2815 Identifier: -