<p>The visual system continuously generates predictions to guide behavior, yet how visuomotor adaptation relates to sensory detection and motor variability remains unclear. We addressed this question using joystick-based tasks: a visuomotor interception task with angular or speed perturbations, a sensory detection task, and a no-feedback motor variability task. Participants showed robust within-task responses, with angular discrepancies engaging both external (target-based) and self-referential control, while speed discrepancies primarily involved self-referential strategies. Gaze behavior reflected distinct tracking modes depending on perturbation type. However, cross-task regression analyses revealed weak associations between detection, variability, and adaptation. These dissociations were not due to noise or low power but reflected consistent performance patterns. Notably, within-subject variability exceeded between-subject variability across all tasks, highlighting trial-to-trial fluctuations as key drivers of behavior. Together, these findings support the view that predictive control relies on specialized, context-dependent mechanisms, in which task-specific computations adaptively integrate context and internal state dynamics.</p>

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Dissociable sensory, motor, and visuomotor predictive functions

  • Inmaculada Márquez,
  • Mario Treviño

摘要

The visual system continuously generates predictions to guide behavior, yet how visuomotor adaptation relates to sensory detection and motor variability remains unclear. We addressed this question using joystick-based tasks: a visuomotor interception task with angular or speed perturbations, a sensory detection task, and a no-feedback motor variability task. Participants showed robust within-task responses, with angular discrepancies engaging both external (target-based) and self-referential control, while speed discrepancies primarily involved self-referential strategies. Gaze behavior reflected distinct tracking modes depending on perturbation type. However, cross-task regression analyses revealed weak associations between detection, variability, and adaptation. These dissociations were not due to noise or low power but reflected consistent performance patterns. Notably, within-subject variability exceeded between-subject variability across all tasks, highlighting trial-to-trial fluctuations as key drivers of behavior. Together, these findings support the view that predictive control relies on specialized, context-dependent mechanisms, in which task-specific computations adaptively integrate context and internal state dynamics.