Linked Life Data

9723616

Source:http://linkedlifedata.com/resource/pubmed/id/9723616

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6695
pubmed:dateCreated
1998-9-17
pubmed:abstractText
When we make saccadic eye movements or goal-directed arm movements, there is an infinite number of possible trajectories that the eye or arm could take to reach the target. However, humans show highly stereotyped trajectories in which velocity profiles of both the eye and hand are smooth and symmetric for brief movements. Here we present a unifying theory of eye and arm movements based on the single physiological assumption that the neural control signals are corrupted by noise whose variance increases with the size of the control signal. We propose that in the presence of such signal-dependent noise, the shape of a trajectory is selected to minimize the variance of the final eye or arm position. This minimum-variance theory accurately predicts the trajectories of both saccades and arm movements and the speed-accuracy trade-off described by Fitt's law. These profiles are robust to changes in the dynamics of the eye or arm, as found empirically. Moreover, the relation between path curvature and hand velocity during drawing movements reproduces the empirical 'two-thirds power law. This theory provides a simple and powerful unifying perspective for both eye and arm movement control.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0028-0836
pubmed:author
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
394
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
780-4
pubmed:dateRevised
2009-9-29
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Signal-dependent noise determines motor planning.
pubmed:affiliation
Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Trust, and Institute of Child Health, University College London, UK.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't