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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84112

Noppeney,  U
Research Group Cognitive Neuroimaging, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BEBE-A
Zusammenfassung
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.