Predicting explorative motor learning using decision-making and motor noise

Research output: Contribution to journalArticlepeer-review

Standard

Predicting explorative motor learning using decision-making and motor noise. / Chen, Xiuli; Mohr, Kieran; Galea, Joseph M.

In: PLoS Computational Biology, Vol. 13, No. 4, e1005503, 24.04.2017.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Bibtex

@article{9e8b41544ce84eff94223810042424e7,
title = "Predicting explorative motor learning using decision-making and motor noise",
abstract = "A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.",
keywords = "Journal Article",
author = "Xiuli Chen and Kieran Mohr and Galea, {Joseph M}",
year = "2017",
month = apr,
day = "24",
doi = "10.1371/journal.pcbi.1005503",
language = "English",
volume = "13",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science (PLOS)",
number = "4",

}

RIS

TY - JOUR

T1 - Predicting explorative motor learning using decision-making and motor noise

AU - Chen, Xiuli

AU - Mohr, Kieran

AU - Galea, Joseph M

PY - 2017/4/24

Y1 - 2017/4/24

N2 - A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

AB - A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant's level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.

KW - Journal Article

U2 - 10.1371/journal.pcbi.1005503

DO - 10.1371/journal.pcbi.1005503

M3 - Article

C2 - 28437451

VL - 13

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 4

M1 - e1005503

ER -