Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm

Abstract

We are not only passively immersed in a sensorial world, but we are active agents that directly produce stimulations. Understanding what is unique about sensory consequences can give valuable insight into the action-perception-cycle. Sensory attenuation is the phenomenon that self-produced stimulations are perceived as less intense compared to externally-generated ones. Studying this phenomenon, however, requires considering a plethora of factors that could otherwise interfere with its interpretation, such as differences in stimulus properties, attentional resources, or temporal predictability. We therefore developed a novel Virtual Reality (VR) setup which allows control over several of these confounding factors. Furthermore, we modulated the expectation of receiving a somatosensory stimulation across self-production and passive perception through a simple probabilistic learning task, allowing us to test to what extent the electrophysiological correlates of sensory attenuation are impacted by stimulus expectation. Therefore, the aim of the present study was twofold: first we aimed validating a novel VR paradigm during electroencephalography (EEG) recoding to investigate sensory attenuation in a highly controlled setup; second, we tested whether electrophysiological differences between self- and externally-generated sensations could be better explained by stimulus predictability factors, corroborating the validity of sensory attenuation. Results of 26 participants indicate that early (P100), mid-latency (P200) and later negative contralateral potentials were significantly attenuated by self-generated sensations, independent of the stimulus expectation. Moreover, a component around 200 ms post-stimulus at frontal sites was found to be enhanced for self-produced stimuli. The P300 was influenced by stimulus expectation, regardless of whether the stimulation was actively produced or passively attended. Together, our results demonstrate that VR opens up new possibilities to study sensory attenuation in more ecological valid yet well-controlled paradigms, and that sensory attenuation is not significantly modulated by stimulus predictability, suggesting that sensory attenuation relies on motor-specific predictions about their sensory outcomes. This not only supports the phenomenon of sensory attenuation, but is also consistent with previous research and the concept that action actually plays a crucial role in perception.

Fig. 1

Experimental setup. (a) On the left, Oculus headset mounted on top of a chinrest, in which the participant puts his head while wearing the EEG cap. The volunteer is holding the oculus controller mounted on the 3D sliding support. On the computer monitor, Unity is displaying a replica of the scene rendered 3D in the VR-headset. In the middle, a participant holding on the oculus controller. On the index finger, typical electrode positioning that was used throughout the experiment for administration of electrical stimuli. On the right, rendered scene that was visible to each eye. (b) Depiction of an example of a sequence of trials in the experimental paradigm. At the start of the sequence, the participant saw at the centre of the screen an arrow indicating where to position their index finger (left-most frame). Once the indicated position was reached, the trial sequence could start. On the first trial of the new sequence, the participant was instructed to reach and touch the virtual ball positioned in the opposite indicator circle (first column), which administered no electrical stimulation (grey flash). After reaching the new position (right indicator circle), the participant was required to stay still. The next trial (second column) started with the appearance of the virtual ball in the indicator circle opposite to the hand. The participant was instructed to move again and touch the ball, which resulted in an electrical pulse at the fingertip (yellow flash) and the reach of a new position (left indicator circle). In the following trials (third and fourth columns), the participant was requested to stay still and passively wait for the ball to reach their index finger. Starting from the first column, the following trials were represented in order: move no-touch, move touch, stay touch, stay no-touch.

Fig. 2

Behavioural results (a) Average accuracy scores obtained by all participants and categorized by probability condition. Error bars represent standard errors; circles are the condition-specific averages obtained by each participant. (b) Average response times for each condition. Error bars represent standard errors. The asterisks represent significant differences at p < 0.001. LP, EP, HP = low, equal, high probability, respectively.

Fig. 3

Qualitative analysis of potentials evoked by stimulus onset. (a) On the left, ERPs for each condition for a subset of centro-lateral electrodes (marked as darker dots in the head shaped topography in the upper left corner of the lower ERP plot). Juxtaposed on top of the ERP plot, histogram of movement onsets. On the bottom, detail of the ERPs for each condition for a subset of centro-lateral electrodes (marked in the head-shape plot in the upper left corner) in the time window that was later brought at the group level analysis. (b) Superimposed plot of all electrodes (butterfly plot) of the difference between the averaged touch trials subtracted of the averaged no-touch trials. On top, scalp distributions of the difference between touch and no-touch trials at 50 ms and 110 ms post-stimulus. LP, EP, HP = low, equal, high probability, respectively.

Fig. 4

Electrophysiological results. (a) Interaction effect stimulation x probability; (b) Interaction effect stimulation x movement. All panels show the ERP plot of the subtraction of each touch condition to the corresponding movement specific average of no-touch conditions and then averaged per condition (averaged across movement types for panel a; averaged across probability conditions for panel b). The average of the electrodes comprising the cluster are plotted. Gray shaded areas represent significant time points with p_FWE < 0.05 and line contours are standard errors. Scalp distributions represent the difference between ERP plots across the significant time window. Bar-plots show the values of each condition across the significant time window, with standard errors. Legend on the upper right side of the image refers to all bar-plots in the panels. For a depiction of the ERPs before subtraction, please refer to Figure S2. LP, EP, HP = low, equal, high probability, respectively.