期刊
JOURNAL OF NEUROSCIENCE
卷 34, 期 41, 页码 13768-13779出版社
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.5306-13.2014
关键词
consolidation; mirror reversal; reaching movements; sensorimotor adaptation; skill learning; visual rotation
资金
- Marie Curie Initial Training Network Cerebellum-C7 within the 7th framework program of the European Union
- BBSRC [BB/J009458/1] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/J009458/1] Funding Source: researchfish
Motor learning tasks are often classified into adaptation tasks, which involve the recalibration of an existing control policy (the mapping that determines both feedforward and feedback commands), and skill-learning tasks, requiring the acquisition of new control policies. We show here that this distinction also applies to two different visuomotor transformations during reaching in humans: Mirror-reversal (left-right reversal over a mid-sagittal axis) of visual feedback versus rotation of visual feedback around the movement origin. During mirror-reversal learning, correct movement initiation (feedforward commands) and online corrections (feedback responses) were only generated at longer latencies. The earliest responses were directed into a nonmirrored direction, even after two training sessions. In contrast, for visual rotation learning, no dependency of directional error on reaction time emerged, and fast feedback responses to visual displacements of the cursor were immediately adapted. These results suggest that the motor system acquires a new control policy for mirror reversal, which initially requires extra processing time, while it recalibrates an existing control policy for visual rotations, exploiting established fast computational processes. Importantly, memory for visual rotation decayed between sessions, whereas memory for mirror reversals showed offline gains, leading to better performance at the beginning of the second session than in the end of the first. With shifts in time-accuracy tradeoff and offline gains, mirror-reversal learning shares common features with other skill-learning tasks. We suggest that different neuronal mechanisms underlie the recalibration of an existing versus acquisition of a new control policy and that offline gains between sessions are a characteristic of latter.
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