TY - JOUR
T1 - Decomposition of a sensory prediction error signal for visuomotor adaptation
AU - Butcher, Peter A.
AU - Taylor, Jordan A.
N1 - Funding Information:
This article was published Online First May 15, 2017. Peter A. Butcher and Jordan A. Taylor, Department of Psychology and Princeton Neuroscience Institute, Princeton University. The authors were supported by Grant R01NS084948 from the National Institute of Neurological Disorders and Stroke and the Princeton Neuroscience Institute’s Innovation Fund. We thank Kristy Snyder and Eugene Poh for helpful feedback on the manuscript. Alyssa Bangel, Krista Bond, and Tyler Osborn helped with data collection. Correspondence concerning this article should be addressed to Peter A. Butcher, Department of Psychology, Princeton University, 428 Peretsman Scully Hall, Princeton, NJ 08544. E-mail: pbutcher@princeton.edu
Publisher Copyright:
© 2017 American Psychological Association.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - To accomplish effective motor control, the brain contains an internal forward model that predicts the expected sensory consequence of a motor command. When this prediction is inaccurate, a sensory prediction error is produced which adapts the forward model to make more accurate predictions of future movements. Other types of errors, such as task performance errors or reward, play less of a role in adapting a forward model. This raises the following question: What unique information is conveyed by the sensory prediction error that results in forward model adaptation? sensory prediction errors typically contain both the magnitude and direction of the error, but it is unclear if both components are necessary for adaptation or a single component is sufficient. In this article, we address this by having participants learn to counter a visuomotor rotation, which induces an angular mismatch between movements of the hand and visual feedback. We manipulated the information content of the visual feedback, in the form of a line, which accurately represented only the magnitude (distance), direction, or both magnitude and direction, of the virtual cursor relative to the target. We demonstrate that sensorimotor adaptation does not occur, or is minimal, when feedback is limited to information about the magnitude of an error. In contrast, sensorimotor adaptation is present when feedback is limited only to the direction of an error or when it contains combined direction and magnitude information. This result stands in contrast to current computational models of cerebellar-based sensorimotor adaptation that use error magnitude to drive adaptation.
AB - To accomplish effective motor control, the brain contains an internal forward model that predicts the expected sensory consequence of a motor command. When this prediction is inaccurate, a sensory prediction error is produced which adapts the forward model to make more accurate predictions of future movements. Other types of errors, such as task performance errors or reward, play less of a role in adapting a forward model. This raises the following question: What unique information is conveyed by the sensory prediction error that results in forward model adaptation? sensory prediction errors typically contain both the magnitude and direction of the error, but it is unclear if both components are necessary for adaptation or a single component is sufficient. In this article, we address this by having participants learn to counter a visuomotor rotation, which induces an angular mismatch between movements of the hand and visual feedback. We manipulated the information content of the visual feedback, in the form of a line, which accurately represented only the magnitude (distance), direction, or both magnitude and direction, of the virtual cursor relative to the target. We demonstrate that sensorimotor adaptation does not occur, or is minimal, when feedback is limited to information about the magnitude of an error. In contrast, sensorimotor adaptation is present when feedback is limited only to the direction of an error or when it contains combined direction and magnitude information. This result stands in contrast to current computational models of cerebellar-based sensorimotor adaptation that use error magnitude to drive adaptation.
KW - Error decomposition
KW - Explicit learning
KW - Motor learning
KW - Sensorimotor adaptation
KW - Sensory prediction error
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U2 - 10.1037/xhp0000440
DO - 10.1037/xhp0000440
M3 - Article
C2 - 28504523
AN - SCOPUS:85042002297
SN - 0096-1523
VL - 44
SP - 176
EP - 194
JO - Journal of Experimental Psychology: Human Perception and Performance
JF - Journal of Experimental Psychology: Human Perception and Performance
IS - 2
ER -