Knowing Each Random Error of Our Ways, but Hardly Correcting for It: An Instance of Optimal Performance
Identifieur interne : 002309 ( Pmc/Corpus ); précédent : 002308; suivant : 002310Knowing Each Random Error of Our Ways, but Hardly Correcting for It: An Instance of Optimal Performance
Auteurs : Loes C. J. Van Dam ; Marc O. ErnstSource :
- PLoS ONE [ 1932-6203 ] ; 2013.
Abstract
Random errors are omnipresent in sensorimotor tasks due to perceptual and motor noise. The question is, are humans aware of their random errors on an instance-by-instance basis? The appealing answer would be ‘no’ because it seems intuitive that humans would otherwise immediately correct for the errors online, thereby increasing sensorimotor precision. However, here we show the opposite. Participants pointed to visual targets with varying degree of feedback. After movement completion participants indicated whether they believed they landed left or right of target. Surprisingly, participants' left/right-discriminability was well above chance, even without visual feedback. Only when forced to correct for the error after movement completion did participants loose knowledge about the remaining error, indicating that random errors can only be accessed offline. When correcting, participants applied the optimal correction gain, a weighting factor between perceptual and motor noise, minimizing end-point variance. Together these results show that humans optimally combine direct information about sensorimotor noise in the system (the current random error), with indirect knowledge about the variance of the perceptual and motor noise distributions. Yet, they only appear to do so offline after movement completion, not while the movement is still in progress, suggesting that during movement proprioceptive information is less precise.
Url:
DOI: 10.1371/journal.pone.0078757
PubMed: 24205308
PubMed Central: 3813602
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