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. 2020 Feb 21;23(2):100843.
doi: 10.1016/j.isci.2020.100843. Epub 2020 Jan 16.

Efference Copy Is Necessary for the Attenuation of Self-Generated Touch

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Free PMC article

Efference Copy Is Necessary for the Attenuation of Self-Generated Touch

Konstantina Kilteni et al. iScience. .
Free PMC article

Abstract

Self-generated touch feels less intense than external touch of the same intensity. According to theory, this is because the brain predicts and attenuates the somatosensory consequences of our movements using a copy of the motor command, i.e., the efference copy. However, whether the efference copy is necessary for this somatosensory attenuation is unclear. Alternatively, a predictable contact of two body parts could be sufficient. Here we quantified the attenuation of touch applied on the participants' left index finger when the touch was triggered by the active or passive movement of the right index finger and when it was externally generated. We observed attenuation only when the touch was triggered by the participants' active movement. In contrast, during the passive movement, the touch was perceived to be as strong as when the touch was externally triggered. Our results suggest that the efference copy is necessary for the attenuation of self-generated touch.

Keywords: Biological Sciences; Neuroscience; Sensory Neuroscience.

Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

Figures

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Figure 1
Figure 1
A Theoretical Model for Somatosensory Attenuation According to the efference-copy-based theoretical model, during the active movement of the right hand to touch the left hand, a copy of the motor command discharged to the right hand (the efference copy) is sent to the forward model that predicts the next state (e.g., position) of the right hand as well as the sensory consequences associated with that state (e.g., proprioceptive input). Similarly, the next state of the left hand is predicted, although this should remain motionless. Predicted and incoming information are combined in the state estimation process. If the predicted positions of the two hands are close, touch is additionally predicted and thus the incoming touch is attenuated. According to the alternative hypothesis describing a general predictive mechanism underlying somatosensory attenuation in the absence of the efference copy, during the passive movement of the right hand toward the left hand, there is no motor command and, thus, no efference copy (dark red part is absent from the model). The incoming sensory input (e.g., proprioception) is used in combination with prior beliefs from the forward models (“where I expect my hand to be”) to estimate the states of the two hands. The estimated states are fed back to the forward models. As before, if the predicted states of the two hands are close, touch is predicted and the incoming touch becomes attenuated. The present study investigated whether the motor command and thus the efference copy (the part of model denoted by the dark red dotted line) is a prerequisite of this predictive attenuation mechanism to dissociate between these two models.
Figure 2
Figure 2
Experimental Setup, Design and Analysis In all three conditions, the participants received two taps on their relaxed left index finger (a test tap and a comparison tap), and they were requested to indicate which tap felt stronger. In the active movement condition (A), the participants actively tapped a force sensor with their right index finger (blue sensor). This active tap simultaneously triggered the test tap on the participants' left index finger. In the passive movement condition (B), the participants' right index finger was left to fall on the force sensor (blue sensor) and passively tap it. This passive tap simultaneously triggered the test tap on the participants' left index finger. In the no movement condition (C), the participants remained relaxed, and the test tap was externally triggered. (D) Data from a representative participant. For each condition, the participant's responses were fitted with psychometric curves, and the point of subjective equality (PSE) and the just noticeable difference (JND) were extracted. We have horizontally jittered the points to avoid their overlapping. For further details, see Transparent Methods.
Figure 3
Figure 3
Results on the Points of Subjective Equality (PSEs) (A) Bar graphs show the PSEs (mean ± SEM) per condition (***p < 0.001, n.s. not significant; statistical significance revealed by using Wilcoxon signed rank tests and paired t-tests). Only the active movement condition produced somatosensory attenuation. In contrast, no changes were detected in the PSEs between the passive movement and the no movement condition. (B) Raincloud plots (Allen et al., 2019) show the raw PSEs as well as their distribution per condition. (C) Line plots illustrate the participants' paired responses per combination of conditions.
Figure 4
Figure 4
Results on the Just Noticeable Differences (JNDs) (A) Bar graphs show the JNDs (mean ± SEM) per condition (*p < 0.05, n.s. not significant; statistical significance revealed by using paired t-tests). The active and passive movement conditions showed higher JND than the no movement condition, although the Bayesian analysis did not provide any conclusive support for the existence of such differences. No changes were detected in the JNDs between the active and passive movement condition. (B) Raincloud plots show the raw JNDs as well as their distribution per condition. (C) Line plots illustrate the participants' paired responses per combination of conditions.
Figure 5
Figure 5
Group Psychometric Functions per Condition The plots were generated using the mean PSE and the mean JND across the thirty participants per condition. Significant attenuation with respect to the no movement condition was observed only in the active movement condition.
Figure 6
Figure 6
Results on the EMG RMS Amplitude (A) Bar graphs show the mean RMS amplitude (±SEM) per condition (***p < 0.001; statistical significance revealed by using Wilcoxon signed rank tests). (B) Raincloud plots show the raw amplitudes as well as their distributions per condition. (C) Line plots show the participants' paired responses per combination of conditions.

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