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

Balasubramaniam, Ramesh ; Dam, Loes C. J. van ; Ernst, Marc O. (2013)
Knowing Each Random Error of Our Ways, but Hardly Correcting for It: An Instance of Optimal Performance.
In: PLoS ONE, 8 (10)
doi: 10.1371/journal.pone.0078757
Artikel, Bibliographie

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Kurzbeschreibung (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.

Typ des Eintrags: Artikel
Erschienen: 2013
Autor(en): Balasubramaniam, Ramesh ; Dam, Loes C. J. van ; Ernst, Marc O.
Art des Eintrags: Bibliographie
Titel: Knowing Each Random Error of Our Ways, but Hardly Correcting for It: An Instance of Optimal Performance
Sprache: Englisch
Publikationsjahr: 2013
Ort: San Francisco
Verlag: PLOS
Titel der Zeitschrift, Zeitung oder Schriftenreihe: PLoS ONE
Jahrgang/Volume einer Zeitschrift: 8
(Heft-)Nummer: 10
Kollation: 9 Seiten
DOI: 10.1371/journal.pone.0078757
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Kurzbeschreibung (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.

ID-Nummer: Artikel-ID: e78757
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 100 Philosophie und Psychologie > 150 Psychologie
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin, Gesundheit
Hinterlegungsdatum: 31 Jul 2024 08:18
Letzte Änderung: 31 Jul 2024 08:18
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