Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance

van Dam L, Ernst MO (2013)
PLOS ONE 8(10): e78757.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
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.
Erscheinungsjahr
2013
Zeitschriftentitel
PLOS ONE
Band
8
Ausgabe
10
Art.-Nr.
e78757
ISSN
1932-6203
eISSN
1932-6203
Page URI
https://pub.uni-bielefeld.de/record/2624805

Zitieren

van Dam L, Ernst MO. Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance. PLOS ONE. 2013;8(10): e78757.
van Dam, L., & Ernst, M. O. (2013). Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance. PLOS ONE, 8(10), e78757. doi:10.1371/journal.pone.0078757
van Dam, L., and Ernst, M. O. (2013). Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance. PLOS ONE 8:e78757.
van Dam, L., & Ernst, M.O., 2013. Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance. PLOS ONE, 8(10): e78757.
L. van Dam and M.O. Ernst, “Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance”, PLOS ONE, vol. 8, 2013, : e78757.
van Dam, L., Ernst, M.O.: Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance. PLOS ONE. 8, : e78757 (2013).
van Dam, Loes, and Ernst, Marc O. “Knowing Each Random Error of Our Ways, but Hardly Correcting for It: an Instance of Optimal Performance”. PLOS ONE 8.10 (2013): e78757.
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5 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Did I do that? Detecting a perturbation to visual feedback in a reaching task.
Gaffin-Cahn E, Hudson TE, Landy MS., J Vis 19(1), 2019
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