Literature DB >> 17898140

Movement planning with probabilistic target information.

Todd E Hudson1, Laurence T Maloney, Michael S Landy.   

Abstract

We examined how subjects plan speeded reaching movements when the precise target of the movement is not known at movement onset. Before each reach, subjects were given only a probability distribution on possible target positions. Only after completing part of the movement did the actual target appear. In separate experiments we varied the location of the mode and the scale of the prior distribution for possible targets. In both cases we found that subjects made use of prior probability information when planning reaches. We also devised two tests (Composite Benefit and Row Dominance tests) to determine whether subjects' performance met necessary conditions for optimality (defined as maximizing expected gain). We could not reject the hypothesis of optimality in the experiment where we varied the mode of the prior, but departures from optimality were found in response to changes in the scale of prior distributions.

Mesh:

Year:  2007        PMID: 17898140      PMCID: PMC2638584          DOI: 10.1152/jn.00858.2007

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  45 in total

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8.  Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements.

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9.  The Psychophysics Toolbox.

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10.  Interaction of visual and proprioceptive feedback during adaptation of human reaching movements.

Authors:  Robert A Scheidt; Michael A Conditt; Emanuele L Secco; Ferdinando A Mussa-Ivaldi
Journal:  J Neurophysiol       Date:  2005-01-19       Impact factor: 2.714

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  43 in total

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2.  Rapid Automatic Motor Encoding of Competing Reach Options.

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3.  Parallel specification of competing sensorimotor control policies for alternative action options.

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5.  The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory.

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6.  Young children combine sensory cues with learned information in a statistically efficient manner: But task complexity matters.

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