Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.647314
Title: Motor learning during reaching movements : model acquisition and recalibration
Author: Telgen, S. J.
ISNI:       0000 0004 5366 269X
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2015
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Abstract:
This thesis marks a departure from the traditional task-based distinction between sensorimotor adaptation and skill learning by focusing on the mechanisms that underlie adaptation and skill learning. I argue that adaptation is a recalibration of an existing control policy, whereas skill learning is the acquisition and subsequent automatization of a new control policy. A behavioral criterion to distinguish the two mechanisms is offered. The first empirical chapter contrasts learning in visuomotor rotations of 40° with learning left-right reversals during reaching movements. During left-right reversals, speed-accuracy trade-offs increased and offline gains emerged, whereas during visual rotations, speed-accuracy trade-offs remained constant and instead of offline gains, there was offline forgetting. I argue that these dissociations reflect differences in the underlying learning mechanisms: acquisition and recalibration. The second empirical chapter tests whether the dissociation based on time-accuracy trade-offs reveals a general property of recalibration or whether instead the interpretation is limited to the specific contrast between left-right reversals and visuomotor rotations. When the size of the prediction error– the difference between intended and perceived movement – was gradually increased participants switched from recalibration to control policy acquisition. This switching point can be derived by considering the role of internal models in recalibration: If the internal model that learns from errors and the environment are too dissimilar – e.g. in left-right reversal and large rotations– recalibration would cause the system to learn from errors in the wrong way, such that prediction errors would increase further. To address this problem the final empirical chapter explores if the way the system learns from errors can be reversed. In conclusion, the results provide behavioral criteria to differentiate between adaptation and skill learning. By exploring the boundaries of recalibration this thesis contributes to a more principled understanding of the mechanisms involved in adaptation and skill learning.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.647314  DOI: Not available
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