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Updated: Dec 25, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Quantifying exploration in reward-based motor learning.

Nina M van Mastrigt¹, Jeroen B J Smeets¹, Katinka van der Kooij¹

¹Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

|April 3, 2020

Summary

This summary is machine-generated.

Quantifying exploration in motor learning requires careful estimation of sensorimotor noise. Our study shows that using rewarded trials to estimate noise provides a more accurate measure of exploration in reward-based learning.

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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

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Published on: June 1, 2015

Area of Science:

Motor Learning
Computational Neuroscience
Human Movement Science

Background:

Exploration, a key component of reward-based motor learning, manifests as increased movement variability.
Accurate quantification of exploration necessitates precise estimation of underlying sensorimotor noise.

Purpose of the Study:

To compare three distinct methods for estimating sensorimotor noise.
To evaluate how different trial types influence the quantification of exploration in a reward-based motor learning task.

Main Methods:

Participants performed a target-directed weight-shifting task with stochastic reward feedback.
Sensorimotor noise was estimated using trial-to-trial endpoint variability during baseline, no-feedback, and rewarded trials.
Exploration was calculated as the difference between variability after non-rewarded trials and the estimated sensorimotor noise.

Main Results:

Variability was significantly higher following non-rewarded trials compared to rewarded trials, confirming successful induction of exploration.
Estimates of sensorimotor noise varied significantly depending on the trial type used (rewarded vs. no-feedback trials).
Consequently, the calculated estimates of exploration also differed based on the sensorimotor noise estimation method.

Conclusions:

The method chosen for estimating sensorimotor noise critically impacts the quantification of exploration in reward-based motor learning.
Using variability from rewarded trials to estimate sensorimotor noise is recommended for more accurate exploration measurement.