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. 2019 Jun 5;5(6):eaaw3121.
doi: 10.1126/sciadv.aaw3121. eCollection 2019 Jun.

Touch as an auxiliary proprioceptive cue for movement control

Affiliations

Touch as an auxiliary proprioceptive cue for movement control

A Moscatelli et al. Sci Adv. .

Abstract

Recent studies extended the classical view that touch is mainly devoted to the perception of the external world. Perceptual tasks where the hand was stationary demonstrated that cutaneous stimuli from contact with objects provide the illusion of hand displacement. Here, we tested the hypothesis that touch provides auxiliary proprioceptive feedback for guiding actions. We used a well-established perceptual phenomenon to dissociate the estimates of reaching direction from touch and musculoskeletal proprioception. Participants slid their fingertip on a ridged plate to move toward a target without any visual feedback on hand location. Tactile motion estimates were biased by ridge orientation, inducing a systematic deviation in hand trajectories in accordance with our hypothesis. Results are in agreement with an ideal observer model, where motion estimates from different somatosensory cues are optimally integrated for the control of movement. These outcomes shed new light on the interplay between proprioception and touch in active tasks.

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Figures

Fig. 1
Fig. 1. Experimental setup and protocol.
(A) The experimental setup, including the textured circular plate, the load cell, and the motion tracking system. In each trial, the servomotor placed under the plate (not visible in the picture) set the orientation of the plate. (B) Blindfolded participants were asked to slide their finger over the ridged plate along a straight direction away from their body. We assumed that extracutaneous proprioceptive cues provided an accurate measurement of motion direction (solid arrow). Instead, the cutaneous feedback produced an illusory sensation of bending toward a direction perpendicular to the ridges, in accordance with previous literature (dashed arrow). This eventually led to an adjustment of the motion trajectory toward the direction indicated by the dotted arrow. (C) Example of trajectories with different ridges. Data are from a single participant. (D) Plate orientations ranged from −60° to 60°. Photo credit: Matteo Bianchi, University of Pisa.
Fig. 2
Fig. 2. Results of the experiments 1 and 2.
(A) Experiment 1: The motion angle of the hand trajectory with respect to body midline regressed against the orientation of the textured plate. Positive y values are for a leftward deviation from the midline, whereas negative values are for a rightward deviation. In accordance with our predictions, there is a negative relationship (negative slope) between the error and the plate orientation. Data and linear fit are from a representative participant. (B) The slope of the linear relationship for 10 participants with group estimate and SD (LMM estimates). (C and D) Experiment 2: Conditions with and without glove are represented as orange and azure lines/bars, respectively. *P < 0.05, ***P < 0.001.
Fig. 3
Fig. 3. Stimuli and results in experiment 3.
(A) The virtual disk had the same size and position as the real plate. The visual target was arranged on the arc of an ideal circumference with a radius of 5 cm on the plate in one of the following angular positions: −15°, 0°, and 15°. The white arrow and labels were not visible during the experiment. Visual stimuli were displayed by means of an HMD. (B) The position error of the hand trajectory with respect to body midline. The color code is for the different target position, with light, medium, and dark purple corresponding to −15°, 0°, and 15°, respectively. Plate orientation is with respect to the position of the target. Data are from a representative participant. (C) The slope of the linear relationship for eight participants with group estimate and SD (LMM estimates). ***P < 0.001.
Fig. 4
Fig. 4. The Kalman filter model.
On the basis of the estimate of the current state and the motor command, a forward model predicts the following state of the limb. This internal estimate is compared to the sensory measurement, generating an error term. In our task, the sensory measurement is equal to the Bayesian integration of the proprioceptive and the tactile cues. This error term, weighted by a gain factor (the Kalman gain), is used to update the estimate of the system and eventually corrects the motor command.
Fig. 5
Fig. 5. Simulated data from the Kalman filter model.
(A) The simulated trajectory. (B) Simulation of experiment 2. The tactile weight, wT was set to 0.15 and 0.05 to simulate the with- and the without-glove condition, respectively (with wP = 1 − wT). We used the same color code as for experiment 2; with- and without-glove conditions were represented in orange and azure, respectively. (C) Simulation of experiment 3. The color code is for the different target position, with light, medium, and dark purple corresponding to −15°, 0°, and 15°, respectively.

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