ArticleiScience2026
Isolating error-based and reward-based learning in a real-world task via gradual perturbations in embodied VR.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Error-based and reward-based mechanisms act together in real-world motor learning. Laboratory tasks separate them by manipulating feedback, but we previously showed that in a real-world task the correction of a large error is rewarding in itself, and engages reward-based learning even without a reward signal. Here, we asked whether they separate when errors are kept small. We used embodied virtual reality of pool billiards, in which the visual scene is aligned with a physical table, and rotated the cue ball's seen trajectory gradually while haptics and proprioception stayed unchanged. Thirty-two participants played two sessions, learning the same rotation with error-only feedback in one and reward-only feedback in the other. Their exploration after failed shots and their post-movement beta rebound over the motor cortex differed between sessions. Gradual perturbations can therefore isolate the two mechanisms in a real-world task, and open a way to target them in rehabilitation and skill training.
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