Hand position can be estimated by vision and proprioception (position sense). The brain is thought to weight and integrate these percepts to form a multisensory estimate of hand position with which to guide movement. Force field adaptation, a type of cerebellum-dependent motor learning, is associated with both motor and proprioceptive changes. The cerebellum has connections with multisensory parietal regions; however, it is unknown if force adaptation is associated with changes in multisensory perception. If force adaptation affects all relevant sensory modalities similarly, the brain’s weighting of vision vs. proprioception should be maintained. Alternatively, if force perturbation is interpreted as somatosensory unreliability, vision may be up-weighted relative to proprioception. We assessed visuo-proprioceptive weighting with a perceptual estimation task before and after subjects performed straight-ahead reaches grasping a robotic manipulandum. Each subject performed one session with a clockwise or counter-clockwise velocity-dependent force field, and one session in a null field. Subjects increased their weight of vision vs. proprioception in the force field session relative to the null session, regardless of force field direction, in the straight-ahead dimension (F1,44 = 5.13, p = 0.029). This suggests that force field adaptation is associated with an increase in the brain’s weighting of vision vs. proprioception.
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Abstract When visual and proprioceptive estimates of hand position disagree (e.g., viewing the hand underwater), the brain realigns them to reduce mismatch. This perceptual change is reflected in primary motor cortex (M1) excitability, suggesting potential relevance for hand movement. Here, we asked whether fingertip visuo-proprioceptive misalignment affects only the brain’s representation of that finger (somatotopically focal), or extends to other parts of the limb that would be needed to move the misaligned finger (somatotopically broad). In Experiments 1 and 2, before and after misaligned or veridical visuo-proprioceptive training at the index finger, we used transcranial magnetic stimulation to assess M1 representation of five hand and arm muscles. The index finger representation showed an association between M1 excitability and visuo-proprioceptive realignment, as did the pinkie finger representation to a lesser extent. Forearm flexors, forearm extensors, and biceps did not show any such relationship. In Experiment 3, participants indicated their proprioceptive estimate of the fingertip, knuckle, wrist, and elbow, before and after misalignment at the fingertip. Proprioceptive realignment at the knuckle, but not the wrist or elbow, was correlated with realignment at the fingertip. These results suggest the effects of visuo-proprioceptive mismatch are somatotopically focal in both sensory and motor domains.
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Trial-and-error motor adaptation has been linked to somatosensory plasticity and shifts in proprioception (limb position sense). The role of sensory processing in motor skill learning is less understood. Unlike adaptation, skill learning involves the acquisition of new movement patterns in the absence of perturbation, with performance limited by the speed-accuracy tradeoff. We investigated somatosensory changes during motor skill learning at the behavioral and neurophysiological level. Twenty-eight healthy young adults practiced a maze-tracing task, guiding a robotic manipulandum through an irregular 2D track featuring several abrupt turns. Practice occurred on days 1 and 2. Skill was assessed before practice on day 1 and again on day 3, with learning indicated by a shift in the speed-accuracy function between these assessments. Proprioceptive function was quantified with a passive two-alternative forced choice task. In a subset of 15 participants, we measured short latency afferent inhibition (SAI) to index somatosensory projections to motor cortex. We found that motor practice enhanced the speed-accuracy skill function (F 4,108 = 32.15, p < 0.001) and was associated with improved proprioceptive sensitivity at retention (t 22 = 24.75, p = 0.0031). Further, SAI increased after training (F 1,14 = 5.41, p = 0.036). Interestingly, individuals with larger increasesmore »