Decreased Temporal Sensorimotor Adaptation Due to Perturbation-Induced Measurement Noise

dc.contributor.author
Knelange, Elisabeth B.
dc.contributor.author
López-Moliner, Joan
dc.date.issued
2019-12-03T16:49:00Z
dc.date.issued
2019-12-03T16:49:00Z
dc.date.issued
2019-02-14
dc.date.issued
2019-12-03T16:49:01Z
dc.identifier
1662-5161
dc.identifier
https://hdl.handle.net/2445/146017
dc.identifier
688820
dc.identifier
30837854
dc.description.abstract
In daily life, we often need to make accurate and precise movements. However, our movements do not always end up as intended. When we are consistently too late to catch a ball for example, we need to update the predictions of the temporal consequences of our motor commands. These predictions can be improved when the brain evaluates sensory error signals. This is thought to be an optimal process, in which the relative reliabilities of the error signal and the prediction determine how much of an error is updated. Perturbation paradigms are used to identify how the brain learns from errors. Temporal perturbations (delays) between sensory signals impede the multisensory integration of these signals. Adaptation to these perturbations is often incomplete. We propose that the lack of adaptation is caused by an increased measurement noise that accompanies the temporal perturbation. We use a modification of the standard Kalman filter that allows for increases in measurement uncertainty with larger delays, and verify this model with a timing task on a screen. Participants were instructed to press a button when a ball reached a vertical line. Temporal feedback was given visually (unisensory consequence) or visually and auditory (multisensory consequence). The consequence of their button press was delayed incrementally with one ms per trial. Participants learned from their errors and started pressing the button earlier, but did not adapt fully. We found that our model, a Kalman filter with non-stationary measurement variance, could account for this pattern. Measurement variance increased less for the multisensory than the unisensory condition. In addition, we simulated our model's output for other perturbation paradigms and found that it could also account for fast de-adaptation. Our paper highlights the importance of evaluating changes in measurement noise when interpreting the results motor learning tasks that include perturbation paradigms.
dc.format
11 p.
dc.format
application/pdf
dc.format
application/pdf
dc.language
eng
dc.publisher
Frontiers Media
dc.relation
Reproducció del document publicat a: https://doi.org/10.3389/fnhum.2019.00046
dc.relation
Frontiers in Human Neuroscience, 2019, vol. 13, p. 46
dc.relation
https://doi.org/10.3389/fnhum.2019.00046
dc.relation
info:eu-repo/grantAgreement/EC/H2020/642961/EU//PACE
dc.rights
cc-by (c) Knelange, Elisabeth B. et al., 2019
dc.rights
http://creativecommons.org/licenses/by/3.0/es
dc.rights
info:eu-repo/semantics/openAccess
dc.source
Articles publicats en revistes (Cognició, Desenvolupament i Psicologia de l'Educació)
dc.subject
Integració sensoriomotora
dc.subject
Adaptació (Psicologia)
dc.subject
Soroll
dc.subject
Sensorimotor integration
dc.subject
Adaptability (Psychology)
dc.subject
Noise
dc.title
Decreased Temporal Sensorimotor Adaptation Due to Perturbation-Induced Measurement Noise
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion


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