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Target size matters: target errors contribute to the generalization of implicit visuomotor learning
Maayan Reichenthal1, Guy Avraham2, Amir Karniel2
1Department of Biomedical Engineering, Ben-Gurion University of the Negev, Beersheva, Israel; Department of Physiology and Cell Biology, Ben-Gurion University of the Negev, Beersheva, Israel; and.
Journal of Neurophysiology
|April 29, 2016
Summary
Target errors, not just sensory prediction errors, influence implicit sensorimotor adaptation. This study shows that smaller targets, increasing target error, enhance implicit learning during dynamic reaching tasks.
Area of Science:
- Motor control
- Human sensorimotor adaptation
- Motor learning
Background:
- Sensorimotor adaptation is typically driven by sensory prediction errors, influencing implicit learning.
- Target errors are traditionally linked to explicit learning mechanisms.
- Previous research focused on static reaching tasks with constant targets.
Purpose of the Study:
- To investigate the role of target errors in implicit sensorimotor adaptation within a dynamic reaching environment.
- To examine if implicit learning benefits from target errors when hitting moving targets.
- To test the hypothesis that implicit processes are sensitive to target errors in dynamic tasks.
Main Methods:
- Subjects played a dynamic Pong game with a gradually introduced rotational perturbation (25°).
- Two groups were formed: high-target error (small ball) and low-target error (big ball).
- Open-loop reaching movements to static targets were assessed before and after the game.
Main Results:
- Both groups adapted to the rotational perturbation.
- Only the high-target error group exhibited directional bias in post-rotation movements.
- This suggests implicit adaptation is influenced by the magnitude of target error.
Conclusions:
- Implicit sensorimotor adaptation is sensitive to target errors, even in dynamic tasks.
- Target errors can modulate implicit learning processes, challenging previous distinctions.
- Dynamic environments may require a re-evaluation of error-driven learning mechanisms.
