Understanding Strategic Motor Learning as a Process of Hypothesis Testing
- 1Carnegie Mellon University
Abstract
Multiple learning processes contribute to successful goal-directed actions under changing physiological states, biomechanical constraints, and environmental contexts. Among these, explicit strategies enable us to discover new movement patterns when existing ones no longer achieve the desired outcome. Yet, how strategies are discovered during motor learning remains unknown. To address this, we developed a novel behavioral paradigm that isolates strategy discovery in response to a range of visuomotor perturbations. This approach revealed that strategy discovery unfolds through an initial period of systematic exploration across multiple candidate strategies, followed by an 'aha' moment in which behavior converges on a stable solution. To account for these dynamics, we developed a computational model based on hypothesis testing in which learners generate and evaluate visuomotor rules to counteract the perturbation. This hypothesis testing model outperformed a range of alternative accounts, including gradual error reduction, win-stay lose-shift learning, and sudden one-shot insight. Together, these findings identify hypothesis testing as a computational mechanism by which humans discover new strategies during motor learning.
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BibTeX
@misc{niyogi2026understanding,
title = {{Understanding Strategic Motor Learning as a Process of Hypothesis Testing}},
author = {A. Niyogi and E. Cisneros and W. Ding and K. Allen and S. Vyas and R. Ivry and J. S. Tsay},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.05.749306},
url = {https://www.biorxiv.org/content/10.64898/2026.09.05.749306v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Understanding Strategic Motor Learning as a Process of Hypothesis Testing
AU - A. Niyogi
AU - E. Cisneros
AU - W. Ding
AU - K. Allen
AU - S. Vyas
AU - R. Ivry
AU - J. S. Tsay
PY - 2026
DA - 2026/09/05
PB - bioRxiv
DO - 10.64898/2026.09.05.749306
UR - https://www.biorxiv.org/content/10.64898/2026.09.05.749306v1
AB - Multiple learning processes contribute to successful goal-directed actions under changing physiological states, biomechanical constraints, and environmental contexts. Among these, explicit strategies enable us to discover new movement patterns when existing ones no longer achieve the desired outcome. Yet, how strategies are discovered during motor learning remains unknown. To address this, we developed a novel behavioral paradigm that isolates strategy discovery in response to a range of visuomotor perturbations. This approach revealed that strategy discovery unfolds through an initial period of systematic exploration across multiple candidate strategies, followed by an 'aha' moment in which behavior converges on a stable solution. To account for these dynamics, we developed a computational model based on hypothesis testing in which learners generate and evaluate visuomotor rules to counteract the perturbation. This hypothesis testing model outperformed a range of alternative accounts, including gradual error reduction, win-stay lose-shift learning, and sudden one-shot insight. Together, these findings identify hypothesis testing as a computational mechanism by which humans discover new strategies during motor learning.
ER -