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We can easily and without sight bring our fingertip to our nose, or swat a mosquito on our arm. These actions rely on proprioception, also known as kinesthesia, which classically has been attributed to processing of sensory inflow by the CNS. However, internal model theories of sensorimotor neuroscience propose that proprioceptive localization also involves a contribution from estimates of limb kinematics derived from motor commands. We tested this prediction in 19 subjects who moved the right index finger tip to touch the moving left index finger tip under three conditions: (1) vision allowed, active movement of the left hand (2) vision blocked, active movement of the left hand, and (3) vision blocked, passive movement of the left hand imposed by the experimenter. The target left index finger tip was moved in a wide range of directions by unrestricted movements of the arm. Mean errors in apposition of the right to the left index finger tips were small, averaging <2 cm between sensors fixed to the finger nails. Note that the average distance between the sensors was ~1.7 cm when the fingertips were brought together in "perfect" apposition under visual guidance. The 3D mean distance and variable distance errors were marginally lower by some 2 mm with eyes open compared to the eyes closed active condition. However, mean distance and variable distance errors did not differ between the active and passive conditions with eyes closed. Thus, proprioceptive localization of one's moving hand is very accurate, essentially as accurate as when vision is allowed. More importantly, our results demonstrate that hypothesized internal model derived estimates of arm kinematics do not contribute to localization accuracy beyond that provided by sensory signals, casting doubt on their existence.
Pubmed ID: 29867407
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
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