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Larry Maloney

 

Tuesday 25th November 2014

Time: 4pm

 

B10 Basement Floor Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

The neural representation of visual and motor uncertainty is quantized

 

In many visuo-motor decision tasks subjects compensate for their own visuo-motor uncertainty, earning close to the maximum reward possible. To do so they must combine information about the distribution of visuo-motor error with rewards and penalties associated with different movement outcomes. The optimal Bayesian movement planner requires knowledge of the probability density function (pdf) of motor error associated with each possible planned movement. It is far from clear how the brain represents such pdfs or computes with them.

In two experiments, we
used a novel forced-choice method to reveal subjects’ internal representation of their spatial error distribution in a speeded reaching movement. We demonstrate by three different analytic methods that human representation of motor error is quantized: while their objective error distribution is close to Gaussian, the representation used in choosing among alternative motor tasks is coarse: it is well approximated by step functions with 2 to 4 non-zero steps.

Joint work with Hang Zhang and Nathaniel D Daw. Supported by NIH NEI 019889 and the Alexander v. Humboldt Foundation.