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Mind Reading by Machine Learning: Optimal Experimental Design

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If you have a question about this talk, please contact Zoubin Ghahramani.

Short talk

The aim of the project is to infer a subject’s mental representation of a set of objects. In order to avoid verbal elaboration, subjects respond only to simple tasks in which they are presented with stimuli about which they make a binary choice. Bayesian inference is used infer their mental representation of the objects from this impoverished data. Some task types are much more informative than others, and the desire for shorter, cheaper experiments motivates the investigation of optimal experimental design; how can we select the stimulus that will provide us with the most information about the subject’s mental contents?

This talk is part of the Machine Learning @ CUED series.

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