Dissociating the behavioral and computational features of implicit motor learning and explicit perturbation detection

Area of Science:

• Neuroscience
• Motor Control
• Human Perception

Background:

• The sensorimotor system distinguishes between externally-generated error (EGE) and internally-generated error (IGE).
• Previous research suggests a threshold for explicit detection of visuomotor rotations.

Purpose of the Study:

• To investigate the dissociation between implicit adaptation to perturbations and explicit detection of these errors.
• To elucidate the computational principles underlying these distinct sensorimotor behaviors.

Main Methods:

• A within-subjects study design combining behavioral experiments and computational modeling.
• Participants performed reaching movements with pseudo-randomized visuomotor rotations, with one session requiring explicit perturbation detection reports.

Main Results:

• Participants implicitly adapted to 1° externally-generated errors (EGEs) but required approximately 4° EGEs for reliable explicit detection.
• Computational models indicated distinct strategies for adaptation (cue comparison and error correction) and detection (Bayesian causal inference).

Conclusions:

• Implicit sensorimotor adaptation is dissociated from explicit perturbation detection.
• The sensorimotor system employs separate computational mechanisms for automatic error correction and conscious error awareness.