Dissociating neural representations of multiple learned sensorimotor mappings
Related Experiment Video
Updated: Oct 31, 2026

Decoding Natural Behavior from Neuroethological Embedding
Published on: October 3, 2025
Antoine Caraballo1, Sungshin Kim1,2,3,4
1Department of Data Science, Hanyang University, Seoul, Republic of Korea.
Introduction:
Previous behavioral studies demonstrate that humans can simultaneously learn to adapt to multiple conflicting rules for how our physical movements are related to the sensory feedback we receive from our environment (sensorimotor mappings). The preparation of movement (motor planning), rather than the physical movement itself (motor execution), has been shown to play a crucial role in keeping these conflicting rules distinct in our memory. However, it remains unclear how the human brain represents distinct planning and execution processes for multiple sensorimotor mappings.
Methods:
To address this, we designed a functional magnetic resonance imaging (fMRI) experiment to dissociate planning from execution during the learning of multiple visuomotor mappings. Crucially, our design compared brain activity when participants planned for different visual outcomes. Specifically, participants simultaneously adapted to opposite rotational perturbations (+30° and -30°) during a visuomotor task but ultimately executed the exact same physical movement.
Results:
Our univariate results demonstrate that the BOLD activity increases in the posterior parietal cortex (PPC) during rotational planning, supporting its involvement in processing learned sensorimotor mappings. However, using multivoxel pattern analysis (MVPA) to decode neural patterns, we found a dissociation between motor execution and rotational planning. Although the PPC and cerebellum showed distinct decodable neural patterns for opposing motor execution, we did not observe significant decoding for opposing rotational planning.
Discussion:
This suggests that the PPC may not represent opposing motor plans as distinct, though we cannot rule out a lack of robust decoding power due to our low trial count.
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