Behavioural and Electrophysiological Correlates of Sensory Attenuation in the Somatosensory and Auditory modality within a Virtual Reality Setup

Behavioural and Electrophysiological Correlates of Sensory Attenuation in the Somatosensory and Auditory modality within a Virtual Reality Setup | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Behavioural and Electrophysiological Correlates of Sensory Attenuation in the Somatosensory and Auditory modality within a Virtual Reality Setup Gianluigi Giannini, Till Nierhaus, Polina Soldatova, Felix Blankenburg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7065290/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Sensory attenuation is the phenomenon that self-produced stimulations are suppressed compared to externally generated ones, both at the subjective and electrophysiological level. Despite the extensive literature on this phenomenon, it remains unclear whether electrophysiological attenuations are consistent across senses and whether they do reflect subjective attenuations of perceived intensity for self-produced sensations. Therefore, the aim of the present study is twofold: first we aimed to collect behavioural and electrophysiological measures of sensory attenuation in a controlled virtual reality setup, both in the auditory and somatosensory domain. Secondly, we correlated behavioural and electrophysiological indices of sensory attenuation to formally test whether the suppression for potentials evoked by self-generated stimulations reflects the sensory suppression revealed by behavioural measures. A total of 28 participants were included to compare the intensity of a first stimulation, which was self-generated or externally administered, to a second stimulation, which was administered at rest with varying intensity. The stimulations could be either electrical pulses at the fingertip or auditory clicks. Participants were also required to undergo a control task in which no stimulation was administered. The behavioural results indicate a reduced perceived intensity for self-produced compared to externally administered stimuli for the auditory domain. In contrast, no such difference was observed for the somatosensory domain. EEG results revealed suppression of the P2 for the auditory modality for the P200 in the somatosensory modality. Furthermore, a positive correlation between the P2 suppression and subjective intensity attenuation for the auditory modality. Together, our results suggest that electrophysiological suppression at mid-latency components reflect the perceived subjective attenuation of self-produced stimulation. This relationship, however, might be dependent on the sensory domain. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In our day-to-day interactions with the environment, our brain distinguishes what is done by ourselves and what by others [ 1 , 2 ]. The mechanisms responsible for this include the phenomenon of sensory attenuation, where self-generated sensations compared to externally induced ones are dampened, both at the neurophysiological [ 3 – 6 ] and subjective [ 7 – 11 ] level. Sensory attenuation is commonly investigated at the behavioural level by means of a comparison task. Participants are required to either self-generate or passively experience a first – test – stimulation, which is then compared to a second stimulation (the “comparison” stimulation) administered at rest with varying intensity. After the second stimulation, participants typically indicate which stimulation was felt stronger. This comparison task allows to calculate the value of the perceived stimulation intensity for which the test and comparison stimulations are felt identical in 50% of trials. This value is commonly referred to as threshold or point of subjective equality (PSE). When compared with the perceptual threshold at rest, lower thresholds for self-produced sensations indicate that the second stimulation is more likely to be judged as higher; i.e., the first stimulation is felt as weaker when produced by the subject [ 7 , 8 , 12 – 18 ]. Similar attenuations for self-produced stimuli have also been demonstrated by other studies that required participants to produce congruent or incongruent outcomes, respect to previously learned action-effects. Results that employed a similar paradigm usually report lower sensitivity for congruent action-effects, suggesting that the perception of outcomes that match motor predictions is dampened compared to outcomes that do not match the predicted action consequences [ 9 , 19 ]. However, the literature is not unanimous in reporting attenuation effects for self-produced sensations, with evidence ranging from null effects [ 20 , 21 ] to enhancement effects for self-produced sensations [ 16 , 20 , 22 – 24 ]. Concerning its electrophysiological correlates, sensory attenuation is usually investigated by means of contingent paradigms [ 25 ]. In those setups participants are required to: (i) perform a motor act that results in a sensory stimulation, (ii) passively attend a stimulation and (iii) perform similar motor sequences without any sensory consequences. The motor-only condition is commonly subtracted from the motor-and-sensory trial average to obtain a motor-corrected potential of self-generated stimulation that is then compared to the sensory-only potential perceived at rest. EEG findings have shown an attenuation of early and middle-latency sensory-evoked components at around 100 and 200ms, either in the auditory [ 6 , 14 , 26 – 33 ], visual [ 14 , 34 – 37 ] and somatosensory domain [ 38 – 40 ]. Although more scarce in comparison with behavioural evidence, some studies also reported contrasting findings [ 22 , 41 ]. Despite the extensive body of literature, it remains unclear whether the physiological attenuation reported by EEG studies corresponds to perceptual differences reflected in behavioural measures. To this end, only a handful of studies have simultaneously collected both behavioural and electrophysiological measures of attenuation within the same experimental setup [ 22 , 40 , 42 – 44 ] and only one scrutinised their statistical correlation, reporting a positive correlation between visual P2 amplitude attenuation and behavioural attenuation measured through an intensity comparison task [ 14 ]. It is thus still unclear, how behavioural measures of sensory attenuation might be reconciled with electrophysiological suppressions [ 45 ] and, more interestingly, which component might reflect the subjective intensity suppression. Regarding the mechanisms that may underlie this phenomenon, it is generally agreed that, upon action execution, motor areas generate an efference copy of the motor command that is used to predict the sensory consequences of an action [ 5 , 8 , 46 ], which are also referred to as “motor predictions” [ 47 ]. If the predicted action outputs match the effective action consequences, then the incoming sensory signal (the sensorial consequence of the action) is attenuated. This picture of sensory attenuation, however, seems too simplistic to take into account the growing body of evidence that is accumulating in recent years. For instance, recent findings indicate that different factors might modulate the attenuation driven by self-producing a stimulation, such as temporal predictability, temporal control and stimulus identity prediction [ 48 ]. Therefore, if an efference copy of a motor command underlies sensory attenuation, it appears that it does not merely encompasses the sensory consequences of the action itself, but also coveys more general information about the incoming – to be produced – sensory stimulation. To better understand this relationship, a previous study published by our group [ 38 ] advocated for the use of new technologies, such as Virtual Reality (VR) to control for a plethora of possible contributing factors in the attenuation for self-generated stimuli, and extended the previously scarce pool of literature on sensory attenuation in the somatosensory domain. To better generalize these results, however, it seems important to formally compare it to other modalities that have been more extensively explored in prior research, such as the auditory modality. The direct comparison of sensory attenuation across sensory modalities in a highly controlled setup would allow to further characterize this phenomenon, reducing the possibility of paradigm-specific differences. The first aim of the present study, therefore, was to investigate sensory attenuation both behaviourally and electrophysiologically through an intensity comparison task in VR, and to examine their statistical correlation. Secondly, to further substantiate our previous findings, we explored sensory attenuation in two different modalities: somatosensory and auditory. Our aim was to not only replicate previous evidence of sensory attenuation in the somatosensory domain, but also to strengthen it by demonstrating similar behavioural and electrophysiological attenuations in the auditory modality, which is a sensory modality that has been more extensively studied. We expected to find an ERP attenuation for self- compared to externally-generated stimuli in the N/P100 and P200 components for the somatosensory domain. Additionally, we hypothesized that the PSE for self-generated stimulations is significantly smaller than the PSE for externally-perceived ones. Lastly, we aimed to replicate these findings in the auditory domain. Methods The experiment consisted of an intensity comparison task in VR and concurrent EEG recording. The participants performed the experiment in a 3-dimensional virtual environment, in which they could either actively reach or passively be touched ( move or stay conditions) by a virtual ball that could give them an electrical somatosensory stimulation ( touch ), an auditory stimulation ( audio ) or control ( no-stimulation ), resulting in 6 possible conditions. One second after the administration of the first stimulation, participants received a second stimulus of the same modality always at rest. Participants then had to perform an intensity comparison task, in which they had to report whether the intensity of the second stimulation was higher or lower than the first one. Participants 29 healthy volunteers (20–37 years old, mean: 27.79, 13 females, all right-handed), recruited from the student body of the Freie Universität Berlin and the general public, participated for monetary compensation or an equivalent in course credit. The sample size was based on previous studies investigating sensory attenuation using a similar design [ 14 , 38 , 42 ]. Written informed consent was obtained from all subjects and/or their legal guardian(s) before participating to the experiment. The study was approved by the ethics committee at the Freie Universität Berlin (003/2021), and it was performed in accordance with the declaration of Helsinki. Experimental setup / apparatus The paradigm was presented in virtual reality (VR) using an Oculus Rift CV1 headset (Meta, Menlo Park, California, USA), mounted on top of a chinrest. This setup minimized electrical and mechanical artifacts generated by wearing the headset on the EEG cap [ 49 – 51 ]. The administration of electrical and auditory stimuli was controlled through a data acquisition card (National Instruments Corporation, Austin, Texas, USA). Somatosensory stimuli were delivered using a DS5 isolated bipolar constant current stimulation (Digitimer Limited, Welwyn Garden City, Hertfordshire, UK) via adhesive electrodes (GVB-geliMED GmbH, Bad Segeberg, Germany) attached to the tip of right index finger (cathode proximal, anode distal). Auditory stimuli were given through an amplifier (AS501, Dell, Round Rock, Texas, USA) connected to a pair of headphones (HD206, Sennheiser GmbH, Wedemark-Wennebostel, Germany). Both electrical and auditory stimuli consisted of rectangular pulses of 0.2 ms duration. Lastly, participants gave responses during the experiment through a set of foot pedals. The VR scene was built using Unity v.2020.3.26f1 (Unity Technologies, San Francisco, California, USA). The scene was identical to the one from a previous experiment [ 38 ] with the only difference that in the present scenario, a set of virtual pedals was also rendered at the bottom of the virtual environment, within field of view of participants (see below for a description of the paradigm). When subjects pressed a pedal anytime during the experiment, also the corresponding virtual pedal changed colour, resembling the pressure exerted in the real-world. Participants were instructed to keep their left foot on the left pedal and the right foot on the right pedal and to press only when prompted to do so. Throughout the experiment, participants could control the movement of a virtual right hand by moving along the real-world table an Oculus controller mounted on a sliding support. Hand position and rotation along the three axes were recorded throughout the whole experiment with a time resolution of 85.83 Hz (SD = 6.30 Hz). Calibration and setup At the start of each experimental session, participants’ somatosensory and auditory threshold was determined by manually changing the intensity of the stimulation until participants reported feeling 5 out of 10 stimuli (threshold somatosensory = 2.01 ± 1.32, threshold auditory = 66.86 ± 5.74 [mean ± SD]). Then, to determine a set of stimulus intensities to be used for experimental phase, participants underwent an intensity comparison task (almost identical to the one in the experimental phase) in which they received two subsequent stimuli at rest, 1 second apart. The first stimulus was always kept at the same intensity (2x threshold intensity for the somatosensory and + 30dB for the auditory modality), while the second stimulus could be one of possible 9 intensities, equally spaced around the intensity of the first stimulation. This meant that participants compared the first central stimulation to either: 4 increasingly lower intensities, 4 increasingly higher intensities or to the same intensity. One second after the second stimulation, a right-ward and left-ward arrow appeared on the screen with the labels “high” and “low”. Participants had to report whether the second stimulation was higher or lower compared to the first stimulus through the pedals. The initial comparison task comprised a total of 72 trials, i.e., 8 for each intensity level. The proportion of “high” responses was calculated for each stimulus intensity and a logistic function was fitted on the data points. The procedure was repeated by spacing the stimuli further apart or narrower until participants reached 0% and 100% of “high” responses for the lowest and highest intensity respectively and stimuli “high” proportions were equally distributed. The detection thresholds at 2% and 98% were estimated and the stimuli were equally spaced along these extremes. Participants underwent the same procedure for somatosensory and auditory stimuli, separately. The two sets of 9 stimuli, for the auditory and somatosensory modality, were then used in the subsequent training and experimental phase (thresholds at 2% and 98% for the somatosensory modality: T02 = 3.15 ± 0.99 mA, T98 = 4.84 ± 1.56; and for the auditory modality: T02 = 88.42 ± 5.78 dB, T98 = 103.61 ± 7.16 [mean ± SD]). After the initial calibration phase, the headset height and the lens focus were adjusted to obtain optimal visual resolution and the correspondence between the real-world controller and the virtual hand was calibrated so that left-wards and right-wards movements were equally comfortable and easy. Participants then underwent a short training phase in VR to familiarise with the experimental task. The training phase consisted of 32 trials, divided equally in stay and move trials. For each movement type, participants underwent 6 comparisons for the somatosensory modality, 6 comparisons for the auditory modality and 4 control tasks. After completing the training phase, participants were invited to an adjacent room where the EEG cap was fitted and the electrodes position was digitised through an Eximia neuronavigation system (Nexstim, Helsinki, Finland). The procedure took approximately 5 minutes to complete, after which the proper experimental phase could begin. Experimental design In each of the 4 experimental runs of approx. 15 min, participants underwent 160 trials, for a total of 640 trials per participant. On top of the virtual table was rendered a fixation cross, centred with the field of view of the camera as well as two indicator circles (distanced ± 0.2 Uu from the fixation cross and still within the field of view of each eye). Participants were instructed to keep their gaze on the fixation cross and to keep their index finger within one indicator circle or to move it towards the ball located in the circle located in the opposite side of the virtual surface, for the stay and move conditions respectively. At the beginning of each run, an arrow indicated the circle in which the participant had to put their index finger. Once the finger was in the circle, the new sequence started. At the beginning of each trial, a virtual ball appeared in the centre of the circle opposite from the participant’s finger. After a delay of 1 s, the fixation cross changed colour for 0.5 s. If the cross flashed green, participants were instructed to move as soon as the cross stopped flashing and to actively reach the ball ( move condition). If the cross flashed red, volunteers were required to stay still, and the virtual ball reached their immobile finger ( stay condition). As soon as the cross stopped flashing, the ball started moving with a velocity corresponding to the one of any of the previous trials in which a reaching movement was performed. In this way, we could minimise differences in trial time between stay and move conditions and we could personalise the ball velocity in stay conditions according to each participant’s moving pace. If participants moved during a stay condition or moved before the cross stopped flashing, a prompt appeared indicating the wrong execution of the trial. Once participants actively touched (or got touched by) the virtual ball, a somatosensory stimulation could have been administered in 40% of trials, an auditory stimulation in 40% of trials or no stimulation in 20% of trials ( touch and audio each were 256 out of 640 trials while the remaining 128 were control trials). The intensity of the first – test –stimulation (either self-generated or passively received – given upon contact with the virtual ball) was kept constant as the central intensity out of the 9 previously calculated during the calibration phase. Participants were instructed to keep their finger in the same position and one second after the first stimulus, they received a second – comparison – stimulation. The number of trials per intensity level was normally distributed so that the number of trials for the comparison of the central intensity against itself was maximised (48 out of 256 trials for the central intensity and 16 out of 256 trials for the lowest or highest intensity). In control trials, participants received no stimulation as a result of actively touching or passively getting touched by the virtual ball. One second after contact with the ball, the fixation cross in the middle of the screen changed colour (either darker or lighter) for 0.2 s. Half a second after the administration of the second stimulation (or control task), the virtual ball disappeared and two labels – “high” and “low”– appeared randomly on top of the pedals. Upon pedal press, the selected label increased in size before disappearing. If participants underwent the intensity comparison task (i.e., received a stimulation), they had to report whether the second stimulation was higher or lower in intensity than the first stimulus. If they were administered the control task (i.e., no-stimulation ), subjects were instructed to answer “high” if the fixation cross flashed lighter or “low” if it flashed darker. The response could be given within a 1.5 second time window. If the timeout was reached, a prompt indicating a missed response appeared on the screen, inviting participants to provide an answer faster on following trials. After the response was registered, a new trial started after a random inter-trial interval between 1.25 and 1.75 s. Subsequent trials always started with the virtual ball appearing in the opposite indicator circle respect to the participant’s hand. For a depiction of the experimental paradigm, please refer to Fig. 1 . Behavioural data analysis Movement velocities are defined as the average velocities during the time that elapsed from the start of the movement (either active hand movement or of the virtual ball) to the contact with the hand. Movement velocities outliers were defined as trials in which participants moved in less than 100 ms and longer than 2500 ms. Differences across conditions were calculated to test whether our velocity personalisation approach was successful and to exclude the possibility that differences in pre-stimulus movement characteristics might have impacted our electrophysiological results. A linear mixed effect model was fitted, having as fixed factors stimulation type ( touch , audio and no-stimulation ) and movement type ( move or stay ) and as random intercept the subjects. Since the intensity comparison task and the control task were substantially different, we kept the behavioural analyses separate for the calculation of response times and accuracy. Response time was defined as the time participants took to give an answer from the moment when the labels “high” and “low” appeared on top of the virtual pedals. Accuracies for the intensity comparison task were the percentage of correct comparisons (i.e., answered “low” when the second stimulus had a lower intensity than the first one) over the total number of possible comparisons (i.e., all intensities except for the middle intensity). Accuracies for the control tasks were simply the proportion of correct responses (according to the instructions that participants received) over the total possible number of control responses. Two linear mixed effect models were fitted over response times and accuracies for the intensity comparison task, which comprised as factors stimulation type ( touch and audio ) and movement type ( move or stay ). Identical models were calculated using response times and accuracies for the control task, which had as factor movement type ( move or stay ). All models were fitted with a random intercept by participant. Lastly, to determine whether the perception of the first stimulation was affected by self-producing or passively perceiving it, we first calculated the proportion of trials in which the participants reported that the second stimulation was higher than the second one. For each participant, movement and stimulation condition, we fitted logistic psychometric functions on the proportion of “high” responses using the Psignifit toolbox [ 52 ]. This allowed to determine the point of subjective equality (PSE) across conditions, i.e., the stimulus intensity at which the participants would judge the test and comparison stimulus to be the same intensity in 50% of trials. PSE during move and stay trials were compared by fitting linear mixed effect models separately for the auditory and tactile modality. We expected the PSEs for self-produced stimuli to be significantly lower than the ones for externally-perceived stimulations, indicating that participants perceived the self-generated stimuli as lower in intensity than the same stimuli administered at rest. EEG data collection and preprocessing Data were collected using a 64-channel active electrode EEG system (ActiveTwo, BioSemi, Amsterdam, Netherlands) at a sampling rate of 2048 Hz, with head electrodes placed in accordance with the extended 10–20 system. Preprocessing of the EEG data was performed using SPM12 [ 53 ], FieldTrip [ 54 ] and in-house MATLAB scripts. We first visually identified and interpolated bad channels (5 ± 3 [mean ± SD]), then the continuous EEG recording for each participant was re-referenced against the average reference, down-sampled to 512 Hz and high-pass filtered (0.1 Hz, firws, one-pass zero-phase, -6 dB cut-off). Then, we corrected for eye-blinks and horizonal eye movements through a topographical confound approach [ 55 , 56 ]. Next, epochs were defined as windows spacing from − 2 s to 2 s around ball touch (i.e., when the first stimulus was presented or not) and a low pass filter was applied (45 Hz, firws, one-pass zero-phase, -6 dB cut-off). Epoched datasets were then visually inspected and bad data segments were marked and excluded. Each dataset was further denoised using Denoising Source Separation [DSS − 57–59] to maximise the reproducibility of stimulus evoked activity in response to tactile, auditory and no-stimulation conditions either during self-produced or externally-perceived trials. DSS is a semi-blind source separation technique that uses the time-locked electrophysiological activity to unmix the continuous recording into sources that gradually explain the time-locked signal. To determine the (cumulative) number of sources to retain, we calculated the log ratio between each experimental condition and the relative control condition, across channels and time-points before stimulation and after 500 ms. We then kept the number of components that minimised the differences between stimulation trials ( audio or touch ) and controls ( no-stimulation ). A total of 7 component for each subject was maintained. DSS therefore allowed us to denoise our signal to further prevent that differences between the recorded electrophysiological activity across conditions might impact our results (for a detailed discussion on this topic, please see Chap. 2.7). EEG data was then baseline corrected using a pre-stimulus window ranging from − 50 to − 5 ms. To ensure that the data were equivalent between the EEG and the behavioural analyses, in both datasets we kept only trials that (i) did not contain any movement velocity outlier, (ii) did not have any missed response and (iii) were clean EEG trials. On average, we excluded a total of 13.90% of trials (SD = 4.73%). 0.84% (SD = 1.18%) of the total trials were movement velocity outliers, 4.08 (SD = 3.25%) were missed responses and 9.90% (SD = 3.14%) contained artefactual trials, while 0.92% were shared. After exclusion, out of 640 trials we obtained on average 551 (SD = 31) trials. Lastly, out of the initial 29 participants, one participant was excluded because they couldn’t perform the task. The following results are therefore computed on 28 participants. EEG data analysis – Qualitative assessment It has been previously argued that a possible confound in the investigation of sensory attenuation is the violation of the assumption that the potential recorded during motor-only conditions is the same in motor-and-sensory trials. In our previous study [ 38 ] we excluded this possibility by investigating the pre-stimulus interval and pointing out that the difference between the motor potential recorded upon stimulus administration and when no stimulation was given resulted zero, strongly suggesting that the electrophysiological motor activity in the two conditions was compatible. Before reporting the formal statistical analyses, in the present work, we qualitatively assessed the time-locked electrophysiological activity to (i) verify that the tactile and auditory stimulation resulted in prototypical Somatosensory Evoked Potentials (SEP) and Auditory Evoked Potentials (AEP) and – more importantly – to (ii) confirm that the measured electrophysiological activity in the pre-stimulus interval for move and stay trials (either before a stimulation or not) was similar. EEG data analysis – Sensory Attenuation in somatosensory and auditory modalities Main analyses were performed according to the SPM framework for M/EEG analysis. This required the epoched electrophysiological data to be converted into a 3D image (scalp space x intra-trial samples). To achieve this, each subjective electrode localisation map was first projected onto a 2D space and re-scaled into a 32 x 32 mask, so that the position of the electrodes on the mask corresponded to the location of the sensors on the scalp. EEG data from each channel was then entered into the corresponding location on the mask and then linearly interpolated for each sample [ 60 ]. This procedure allowed to interpolate EEG 2D data arrays into 3D images to be further analysed with correction for multiple comparisons using random field theory [ 60 , 61 ]. Since we had strong hypotheses about the electrophysiological responses evoked by the tactile or auditory stimulation, we selected a window spanning from − 50 to 500 ms around ball-touch (i.e., the first stimulation, either self- or externally-produced). In this way, we obtained one 3D image of dimensions 32 × 32 × 283 (scalp space x intra-trial samples) for each trial. Then, all images were inserted into a first-level multiple regression model, which had one dummy regressor for each possible condition combination (6 in total). After model estimation, we obtained a 3D β estimate for each condition with the same dimensionality as the initial images, which corresponded to the averaged time-locked potential of each condition, for each participant. In the field of sensory attenuation, to test whether a self-produced stimulation evoked potential is attenuated compared to an externally perceived sensation, a control condition is required. In classical contingent paradigms, a movement-and-touch condition is subtracted of a movement-only condition before being compared to a touch-only condition [ 25 ]. In this way, any potential evoked by the movement alone can be accounted for, and only the potentials elicited by the stimulation (either self-generated or externally perceived) can be compared. In a similar fashion, our study allowed for the recording of two control conditions, i.e., conditions in which no stimulation was administered when actively reaching or passively being touched by the virtual ball. The additional advantage of our approach was that we could control not only movement-related evoked potentials, but also potentials evoked at rest, possibly due to stimulus expectation or attentional demands. To directly test whether the potential evoked by the self-elicited stimulation was attenuated compared to the one evoked by the externally-generated stimulation – while controlling for potentials elicited by movement or stimulus expectation – we implemented two mass-univariate multiple regression analysis for each stimulation modality. Each model included one regressor for each condition of interest, as well as for each subject. Mean differences across conditions were tested via F-tests and therefore, in its interpretation, both models were equivalent to a 2 x 2 ANOVA, having as factors: stimulation ( touch or audio and no-stimulation ) and movement type ( move or stay ). All analyses were performed with a cluster-forming threshold of p < 0.001; only clusters withstanding at the cluster-level with family-wise error (FWE) corrected threshold of p FWE <0.05 [ 62 ] are reported here. Usually, in contingent paradigms, a motor-only condition is subtracted from a motor-and-sensory condition to obtain a corrected potential of self-produced stimulus perception to be compared against a sensory-only condition. In a similar fashion, in the present study we tested differences between self-generated and externally-produced sensations, while controlling for a compatible control no-stimulation condition, by selectively exploring the interaction term across conditions, in each model independently (i.e., [ touch move – touch stay – no-stimulation move + no-stimulation stay ] and [ audio move – audio stay – no-stimulation move + no-stimulation stay ]). Control analysis – second stimulation To further assess the specificity of sensory attenuation, we also tested for electrophysiological attenuations upon administration of the second stimulation. We theorised to find no interaction effect between stimulation and movement type, excluding the presence of a sensory attenuation effect for the second stimulation and further substantiate the idea that our main results are not indeed based on the interaction between each movement and stimulation condition. To test this hypothesis, we first defined epochs around the second stimulation, ranging from − 50 to 500 ms and we baseline corrected the data using a window from − 50 to -5 ms before the stimulation. We then fitted models identical to the main analyses at the first and second level, separately for the tactile and auditory modalities. Identically to the main analyses, mean differences across conditions were tested via F-tests and therefore, in its interpretation, both models were equivalent to a 2 x 2 ANOVA, having as factors: stimulation ( touch or audio and no-stimulation ) and movement type ( move or stay ). All analyses were performed with a cluster-forming threshold of p < 0.001. We reported the clusters surviving FWE correction with a threshold of p FWE <0.05. Correlation between electrophysiological and behavioural indices of sensory attenuation To formally test whether behavioural attenuations for self-produced sensations were associated with electrophysiological indices of suppression, we performed a correlation analysis. First, for each subject and each sensory modality independently, we obtained a linear combination of the β estimates which resulted by subtracting the average of the move no-stimulation and stay stimulation trials to the average of the move stimulation and stay no-stimulation trials. These are also commonly referred to as contrast images. Positive differences in early-latency components (N100 or N1 for the somatosensory and auditory domain) or negative differences in mid-latency components (P200 or P2 for somatosensory and auditory modalities respectively), therefore, indicated an electrophysiological attenuation for self-produced compared to externally-generated stimulations (i.e., sensory attenuation). Similarly, for the behavioural domain, we calculated for each participant the difference between the PSE in the move and stay conditions. A negative difference would indicate that PSEs for self-generated stimuli were lower than externally perceived ones, i.e., stimuli resulting from move trials were perceived as less intense. The contrast images for each subject were then inserted into a second level mass-univariate multiple regression analysis, having as only regressor the behavioural index of attenuation. In its interpretation, the model is equivalent to a Pearson correlation between electrophysiological and behavioural indices of attenuation. Positive and negative correlations were then tested via t-statistics. Results Behavioural results Movement velocities were different across move and stay trials (ƞ 2 = 0.090, F 1,162 = 15.936, p < 0.001) but did not differ across stimulation type (ƞ 2 = 0.002, F 2,162 = 0.140, p = 0.869) and their interaction was not significant (ƞ 2 = 0.004, F 2,162 = 0.327, p = 0.721). Move trials were overall 0.019 Uu/s (circa 11.19 ms) faster than stay trials (see Fig. 2 a). Although this indicates that our velocity personalisation approach was not optimal, we consider it very unlikely that this limitation significantly influenced the subsequent results If the different trial lengths led to a difference between the electrophysiological correlates of stay and move conditions, it would have affected equally both the stimulation ( audio or touch ) and the respective control no-stimulation trials, thus cancelling out in the subsequent analyses. Response times for the comparison task were faster in move than in stay trials of 22.83 ms (ƞ 2 = 0.105, F 1,108 = 12.605, p < 0.001) but did not differ across touch or audio stimulations (ƞ 2 = 0.006, F 1,108 = 0.661, p = 0.418). The interaction between the two factors was also not significant (ƞ 2 = 0.001, F 1,108 = 0.121, p = 0.728). Response times for the control task instead showed no differences between stay and move conditions (ƞ 2 = 0.001, F 1,54 = 0.051, p = 0.821). See Fig. 2 b. Lastly, participants were more accurate on the auditory task compared to the tactile task (ƞ 2 = 0.166, F 1,108 = 21.488, p < 0.001) of 7.08% on average. Accuracy did not differ across movement conditions (ƞ 2 = 0.010, F 1,108 = 1.057, p = 0.306), nor was the interaction between the two factors significant (ƞ 2 = 0.000, F 1,108 = 0.010, p = 0.920). Accuracy for the control task also did not differ across move or stay trials (ƞ 2 = 0.010, F 1,54 = 0.571, p = 0.453). For illustration, see Fig. 2 c. Lastly, PSE for the auditory modality were significantly lower in the move than in the stay condition (ƞ 2 = 0.106, F 1,54 = 6.38, p = 0.014). For the tactile modality we observed no differences across movement types (ƞ 2 = 0.000, F 1,54 = 0.010, p = 0.921). For a depiction, see Fig. 2 d and e . Electrophysiological results – qualitative evaluation The averaged electrophysiological activity for the movement condition in all sensory modalities (either touch , audio or no-stimulation ) was characterised by an increasing pre-stimulus negativity that gradually increased before stimulus onset. This is likely due to stimulus anticipation [ 63 – 65 ], motor preparatory processes [ 66 , 67 ] and motor execution [ 68 – 70 ]. In stay conditions, across sensory modalities, the pre-stimulus activity was also characterised by a negativity, possibly reflecting a process of stimulus anticipation (see Fig. 3 a and b ). Differences between movement conditions were accounted for in our analyses by the inclusion of a control ( no-stimulation ) condition, for both move and stay trials. As mentioned before, the robustness of our results relies on the assumption that the measured electrophysiological activity across stimulation types is equivalent within move and stay trials. In support of this hypothesis, the pre-stimulus activity of the main effect of electrical or auditory stimulation ( touch or audio – no-stimulation ) averages around zero, giving aid to the assumption that – indeed – the measured electrophysiological activity across move or stay trials was equivalent. From stimulus onset, our paradigm elicited a typical SEP. Figure 3 c shows the difference between the average touch trials across participants subtracted of the control ( no-stimulation ) trials across electrodes with the expected evoked potentials, i.e., P50, N/P100 and P200 resulting from electrical stimulation of the right index finger. The corresponding topographic maps (Fig. 3 b, right) confirm the left lateralized voltage distribution of the somatosensory evoked potential (SEP) components on the scalp (N/P100). The auditory stimulation also elicited a typical AEP. As can be observed in Fig. 3 d, the difference between audio trials and no-stimulation trials elicited prototypical potentials, i.e., N1 and P2. Electrophysiological results – sensory attenuation To test for differences in the electrophysiological activity evoked by either self-producing or passively attending a stimulation, while controlling for the effects of a control condition, we investigated the interaction term between movement and stimulation type. For the somatosensory modality, one cluster survived cluster-level FWE correction in central electrodes and starting at 0.117 s to 0.168 s post stimulus (peak at 0.145 s, centroid: CP1, ƞ 2 = 0.229, F 1,81 = 24.11, p FWE = 0.010). This positive potential (P200) was reduced in amplitude when the stimulation resulted as a consequence of a movement compared to the corresponding potential administered at rest (Fig. 4 a). For the auditory modality, one cluster reached significance, located in centro-frontal electrodes and starting at 0.121 s to 0.166 s post stimulus (peak at 0.137 s, centroid: FC4, ƞ 2 = 0.331, F 1,81 = 40.04, p FWE < 0.001). This positive potential (P2) was suppressed for self- compared to other-generated auditory stimulations, when controlling for no-stimulation conditions (Fig. 4 b). Notably, concomitant to the fronto-central positivity, our analyses also revealed an occipital negativity that survived cluster-level FWE correction (peak at 0.135 s, centroid: O2, ƞ 2 = 0.303, F 1,81 = 35.25, p FWE = 0.001). This is consistent with a dipolar scalp distribution and, since typically auditory evoked potentials around 200 ms are reported in fronto-central electrodes [ 26 , 27 ], this cluster was ignored from further analysis. Electrophysiological results – control analysis To further corroborate our electrophysiological results, we performed a control analysis by investigating possible interaction effects for the potentials evoked by the second – comparison – stimulation. We expected at this timepoint no sensory attenuation effect since the stimuli administered after the first – test – stimulation (that could be either self- or externally-generated) were perceived at rest. If our analyses had pointed out any kind of interaction, this could have indicated that our results might also be explained by possible interactions between movement and the stimulation, i.e., the motor (or non-motor) potential across stimulation conditions is not equal. As expected, our control analysis on the second – comparison – stimulus revealed no interactions between movement type and stimulation. Electrophysiological results – correlation between electrophysiological and behavioural attenuation indices. To formally test whether behavioural indicators of sensory attenuation were related to electrophysiological suppressions, we performed a correlation analysis on the previously defined − 50 to 500 ms window and throughout electrodes. Results for the auditory modality revealed that one central cluster, peaking at 0.156 s (and ranging from 0.102 s to 0.178 s) was positively correlating with behavioural indices of attenuation (centroid: C2, ƞ 2 = 0.529, T 27 = 5.51, p FWE = 0.001). We identified this peak corresponding to the previously identified auditory P2. The present results therefore indicate that at larger auditory P2 suppressions corresponded larger PSE reductions for self-generated stimulations (for a depiction, see Fig. 4 c). The correlation analysis for the somatosensory modality revealed no significant clusters. Discussion Despite the extensive literature investigating sensory attenuation, it remains unclear whether behavioural attenuation of self-generated stimuli reflects electrophysiological suppression. In the present experiment, we employed a previously validated VR paradigm that allowed for more comprehensive control of stimulus properties, attentional requirements, and other predictability factors during an intensity comparison task, alongside concomitant EEG recording. In this way, we were able to probe behavioural and electrophysiological correlates of sensory attenuation both, in the somatosensory and auditory modalities. Behavioural analyses revealed that participants perceived the self-generated stimulations as less intense only in the auditory modality. This was accompanied by higher accuracy in the auditory comparison task than in the somatosensory one. The intensity comparison task in both sensory modalities, however, showed a faster response time for move than stay trials. At the electrophysiological domain, the P200 for somatosensory and the P2 for auditory stimulations were attenuated when the stimulations were self-generated compared to when they were passively administered, while controlling for move and stay conditions where no stimulation was administered. We found no evidence of attenuation for the second stimulation. Lastly, the correlation analysis revealed that the suppression at the auditory P2 positively correlated with the decrease in perceived intensity as indexed by the intensity comparison task. Behavioural measures of sensory attenuation Contrary to our expectations, we found evidence of behavioural sensory attenuation in the auditory domain, but not in the tactile modality. It is usually assumed that our brain uses an efference copy of a motor command to predict motor outcomes and to estimate whether the effective sensory feedbacks generated by motor plans coincide with the predicted ones [ 47 ]. The consequence of this prediction is often believed to be an attenuation of action outcomes, i.e., already predicted or redundant information is kept away from our senses to leave space for more information rich afferents. This concept is supported by numerous studies demonstrating that self-generated sensory outcomes are perceived as less intense compared to identical stimulations administered at rest [ 8 , 17 ] or that congruent action-effects are suppressed rather than incongruent ones [ 9 , 19 ]. Moreover, these findings received support across different sensory modalities, such as tactile [ 7 , 8 , 12 , 19 ], auditory [ 16 – 18 , 24 , 71 , 72 ] and visual [ 9 , 73 , 74 ] substantiating even more this concept. However, the current state of the art suggests a more complex picture, as several studies indicate that motor efferents might adapt perception according to the different sensorial context or task requirement, rather than simply dampening it. For instance, Myers and colleagues [ 23 ] reported over a series of experiments that perceptual acuity is enhanced when actively generating a supra-threshold auditory stimulation during an intensity comparison task. Moreover, Reznik and colleagues [ 16 ] reported enhanced perceived loudness when the self-generated stimuli were at near-threshold level, in accordance to other studies that used similar stimuli intensities [ 23 , 75 ], while self-generated supra-threshold auditory stimulations were attenuated compared to passively perceived identical stimuli. This concept is further corroborated by studies utilizing a similar procedure and stimulus modality [ 72 ] and by evidence of larger suppression effects for greater stimulus intensities, although in the tactile domain [ 76 ]. In other words, self-generated sensations might be weighed differently based on the nature of the task at hand. If the task requires a higher precision, sensations that are self-produced might be enhanced rather than suppressed. The argument that predictable effects of a voluntary actions are not automatically suppressed is further supported by studies that did not point out attenuations for self-generated stimulations [ 20 , 21 ]. Concerning the present study, since both auditory and somatosensory modalities were probed through an identical comparison task, it is unlikely that the absence of an attenuation effect in the somatosensory domain is to be attributed to the task itself. In other words, if our comparison task was sufficient to induce a shift in perceptual threshold in the auditory modality, one would expect the same to happen for somatosensory stimuli. The reason behind this discrepancy, therefore, requires further investigation. One possibility is that sensory attenuation operates differently across senses, at least when conscious perception is involved. It is possible that, given the tight coupling of somatosensation with the body and the motor system, somatosensory stimuli are weighed differently by predictive mechanisms as compared to other senses [ 77 ]. This might be especially true in an ecological scenario such as our VR setup. Somatosensory stimulations, which were administered upon contact with a virtual ball, might have been attributed a special relevance given the ecological congruency with the task participants were required to undergo. Another possibility that could reconcile our behavioural findings is that the comparison task in the somatosensory modality was of greater hindrance. This is not only indexed by higher variances in the hit rates, but also by a significant overall lower accuracy obtained in the tactile comparison task compared to the auditory modality. Although speculatively, the greater precision required by the task might have induced participants not to experience a univalent effect, as a consequence of self-producing sensations. This hypothesis might be supported by recent findings that utilised a comparison task across two different sensory modalities (visual and auditory) in which an enhancement effect at the behavioural level is pointed out only for the visual modality, that had lower accuracies [ 15 ]. In other words, when the accuracy for the comparison task was even lower (possibly indicating a greater difficulty at the task) participants perceived self-generated stimuli as enhanced rather than attenuated. Further studies will be required to substantiate this hypothesis. Lastly, our findings also revealed a decreased response time at the comparison task when the stimulation to be compared was self-generated, rather than when it was externally administered, across sensory modalities. It has been previously demonstrated that predictable events are linked to decreased response times [ 78 – 81 ]. It is plausible that the perception of self-generated stimuli, being subject to qualitatively different predictive processes, might have been facilitated; thus, the following response at the comparison task was administered faster. Electrophysiological measures of sensory attenuation The electrophysiological results of attenuation for auditory P2 and somatosensory P200 are consistent across senses. Interestingly, our findings did not indicate attenuation of earlier ERP components, which is typically a common result in sensory attenuation, both in the auditory [ 6 , 14 , 26 – 33 ] and, despite scarce, somatosensory domain [ 38 – 40 ]. One possibility is that participants were required to estimate the intensity of the second stimulation – which had a varying intensity – to the intensity of a first self- or externally-produced stimulation, which had always the same intensity. Other studies that investigated sensory attenuation through comparison tasks required participants to indicate which one of the two stimulations was more intense rather than prompting a unilateral comparison [ 14 , 15 ]. Results from Ody and colleagues [ 14 ] showed an attenuation for auditory N1 and for visual N1 and P2 when the stimulation was actively produced in comparison to when it was passively generated. Similar findings were also replicated by another study from the same group [ 15 ]. Early ERP components have been demonstrated to be influenced by attentional factors, both in the auditory [ 82 – 84 ] and tactile domain [ 85 – 87 ]. Also, attentional factors have been demonstrated to influence the auditory N1 suppression that characterizes self-generated stimulations [ 27 ]. It is possible therefore that participants did not pay attention to the first stimulation as it was completely predictable both in its temporal and identity characteristics, resulting in an overall dampening of earlier electrophysiological components. On an additional note, the present electrophysiological results were obtained through a virtual reality setup validated in a previous study, which allowed us to control for other possible confounding effects that might impact on the attenuation for self-generated movements. More specifically, the experiment was designed to minimize the influence of stimulus properties, spatial attention and temporal expectation [ 38 ]. It is unlikely, therefore, that our results might be influenced by these aspects. Since participants were administered a second somatosensory or auditory stimulation at rest, we could further verify that our main analyses were not influenced by interactions between movement type and stimulation other than the sensory attenuation effect. The absence of any interaction effect at the second stimulation further corroborates this concept, i.e., the electrophysiological correlates of the motor (and non-motor) activity are identical across touch or audio and no-stimulation conditions. Bridging behavioural and electrophysiological suppression Lastly, our findings indicated a positive correlation between the auditory P2 suppression and the perceived reduced intensity for self-generated sensations. Contrary to our expectations, however, we did not find the same relation in the somatosensory modality. Similar contrasting findings across sensory modalities were also observed by another study by Ody and colleagues [ 14 ]. In their experiment – which was until now the only other study that formally tested a correlation between behavioural and electrophysiological suppressions – the authors reported an electrophysiological attenuation for self-generated sensations both in the auditory N1 and visual N1 and P2 components, but perceptual PSEs in both sensory modalities did not show a difference between active and passive conditions. Lastly, behavioural attenuation only in the visual task was correlating with visual P2 attenuation. Although in a different sensory modality, we replicate the previous findings obtained by Ody and colleagues, which suggest that the electrophysiological component underlying the subjective intensity reduction is a mid-latency ERP. Although the authors reported a correlation between behavioural and electrophysiological attenuations despite no evidence of behavioural suppression, it is still possible that in the present study the lack of a behavioural effect in the somatosensory domain (for the reasons discussed above) have also rendered the correlation with the electrophysiological suppression insignificant. The P2 for auditory stimuli has been demonstrated to be modulated by stimulus intensity [ 88 – 90 ], therefore it is plausible that attenuations of this component produced by self-generation of a stimulation might be responsible for the subjective reduction of perceived intensity of the same stimuli. Moreover, components within the same time-windows have also been theorised to be involved in selfhood attributions. For instance, Ghio and colleagues [ 91 ] found auditory N1 attenuation both for action observation and execution. However, P2 attenuations were stronger self-produced stimuli compared to observed tone production, suggesting that the P2 might be the electrophysiological correlate of authorship over the consequences of one’s own actions (also defined as the sense of agency). Similar conclusions were also advanced by Timm and collaborators [ 92 ] who reported that N1 suppressions happened irrespective of agency conditions, while P2 attenuations correlated with agency reports from participants. Sensory attenuation has often been acclaimed as one of the mechanisms that our brain utilizes to distinguish oneself from others [ 1 , 2 ] and it has been often considered as an implicit measure of the self of agency [ 42 , 93 ]. This interpretation might be consistent with our results, although it has not been formally tested in the present work. Therefore, N1 and P2 suppressions for self-generated stimulations are possibly two functionally distinct phenomena [ 94 ] which, more often than not, are existing as two consecutive steps. For instance, cerebellar patients exhibit an auditory N1 suppression for self-generated stimulations, but not P2 [ 95 , 96 ]. Mid-latency components seem also to be more resilient towards attention modulations [ 27 , 97 ] and could be overall a more direct measure of sensory-specific predictions [ 45 , 98 ]. Our results of a positive correlation between auditory P2 suppression and perceived intensity attenuation might indicate that mid-latency components alone are involved in higher-level stimulus processing and, possibly, in other meta-cognitive functions such as agency attributions. Declarations Funding Information This work was supported by Berlin School of Mind and Brain, Humboldt Universität zu Berlin ( http://www.mind-and-brain.de/home/ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contribution GG: Conceptualization, Data acquisition, Data curation, Methodology, Formal analysis, Writing – original draft. TN: Conceptualization, Methodology, Supervision, Writing – review and editing. PS: Data acquisition. FB: Conceptualization, Methodology, Supervision, Writing – review and editing. Data Availability The data that support the findings of this study are available from the corresponding author, GG, taking into account the data protection guidelines. The corresponding scripts for data analysis and for replicating figures are available here: https://github.com/Neurocomputation-and-Neuroimaging-Unit/SensAtt_Aud_Tact_Behav. References Tsakiris, M., Haggard, P., Franck, N., Mainy, N. & Sirigu A. A specific role for efferent information in self-recognition. Cognition 96 , 215–231 (2005). Wozniak, M. M. How to grow a self: development of the self in a Bayesian brain. (2019). https://doi.org/10.31234/osf.io/6e3ad Baess, P., Widmann, A., Roye, A., Schröger, E. & Jacobsen, T. Attenuated human auditory middle latency response and evoked 40-Hz response to self-initiated sounds. Eur. J. Neurosci. 29 , 1514–1521 (2009). Bäß, P., Jacobsen, T. & Schröger, E. Suppression of the auditory N1 event-related potential component with unpredictable self-initiated tones: Evidence for internal forward models with dynamic stimulation. Int. J. Psychophysiol. 70 , 137–143 (2008). Blakemore, Wolpert, D. M. & Frith, C. D. Central cancellation of self-produced tickle sensation. Nat. Neurosci. 1 , 635–640 (1998). Schafer, E. W. P. & Marcus, M. M. Self-Stimulation Alters Human Sensory Brain Responses. Science 181 , 175–177 (1973). Bays, P. M., Wolpert, D. M. & Flanagan, J. R. Perception of the consequences of self-action is temporally tuned and event driven. Curr. Biol. 15 , 1125–1128 (2005). Bays, P. M., Flanagan, J. R. & Wolpert, D. M. Attenuation of self-generated tactile sensations is predictive, not postdictive. PLoS Biol. 4 , e28 (2006). Cardoso-Leite, P., Mamassian, P., Schütz-Bosbach, S. & Waszak, F. A New Look at Sensory Attenuation: Action-Effect Anticipation Affects Sensitivity, Not Response Bias. Psychol. Sci. 21 , 1740–1745 (2010). Haggard, P. & Whitford, B. Supplementary motor area provides an efferent signal for sensory suppression. Brain Res. Cogn. Brain Res. 19 , 52–58 (2004). Walsh, L. D., Taylor, J. L. & Gandevia, S. C. Overestimation of force during matching of externally generated forces. J. Physiol. 589 , 547–557 (2011). Blakemore, Frith, C. D. & Wolpert, D. M. Spatio-temporal prediction modulates the perception of self-produced stimuli. J. Cogn. Neurosci. 11 , 551–559 (1999). Desantis, A., Weiss, C., Schütz-Bosbach, S. & Waszak, F. Believing and Perceiving: Authorship Belief Modulates Sensory Attenuation. PLOS ONE . 7 , e37959 (2012). Ody, E., Straube, B., He, Y. & Kircher, T. Perception of self-generated and externally-generated visual stimuli: Evidence from EEG and behavior. Psychophysiology 60 , e14295 (2023). Ody, E., Kircher, T., Straube, B. & He, Y. Voluntary action sharpens sensory prediction and facilitates neural processing of contingent sensory stimuli. Preprint at. https://doi.org/10.1101/2024.12.03.626576 (2024). Reznik, D., Henkin, Y., Levy, O. & Mukamel, R. Perceived Loudness of Self-Generated Sounds Is Differentially Modified by Expected Sound Intensity. PLOS ONE . 10 , e0127651 (2015). Weiss, C., Herwig, A. & Schütz-Bosbach, S. The self in action effects: Selective attenuation of self-generated sounds. Cognition 121 , 207–218 (2011). Weiss, C., Herwig, A. & Schütz-Bosbach, S. The Self in Social Interactions: Sensory Attenuation of Auditory Action Effects Is Stronger in Interactions with Others. PLOS ONE . 6 , e22723 (2011). Fuehrer, E., Voudouris, D., Lezkan, A., Drewing, K. & Fiehler, K. Tactile suppression stems from specific sensorimotor predictions. Proc. Natl. Acad. Sci. U S A . 119 , e2118445119 (2022). Desantis, A., Mamassian, P., Lisi, M. & Waszak, F. The prediction of visual stimuli influences auditory loudness discrimination. Exp. Brain Res. 232 , 3317–3324 (2014). Schwarz, K. A., Pfister, R., Kluge, M., Weller, L. & Kunde, W. Do we see it or not? Sensory attenuation in the visual domain. J. Exp. Psychol. Gen. 147 , 418–430 (2018). Dogge, M., Hofman, D., Custers, R. & Aarts, H. Exploring the role of motor and non-motor predictive mechanisms in sensory attenuation: Perceptual and neurophysiological findings. Neuropsychologia 124 , 216–225 (2019). Myers, J. C., Mock, J. R. & Golob, E. J. Sensorimotor Integration Can Enhance Auditory Perception. Sci. Rep. 10 , 1496 (2020). Sato, A. Action observation modulates auditory perception of the consequence of others’ actions. Conscious. Cogn. 17 , 1219–1227 (2008). Horváth, J. Action-related auditory ERP attenuation: Paradigms and hypotheses. Brain Res. 1626 , 54–65 (2015). Ford, J. M., Gray, M., Faustman, W. O., Roach, B. J. & Mathalon, D. H. Dissecting corollary discharge dysfunction in schizophrenia. Psychophysiology 44 , 522–529 (2007). Harrison, A. W. et al. Sensory attenuation is modulated by the contrasting effects of predictability and control. NeuroImage 237 , 118103 (2021). Martikainen, M. H., Kaneko, K. & Hari, R. Suppressed Responses to Self-triggered Sounds in the Human Auditory Cortex. Cereb. Cortex . 15 , 299–302 (2005). Mifsud, N. G. et al. Self-initiated actions result in suppressed auditory but amplified visual evoked components in healthy participants. Psychophysiology 53 , 723–732 (2016). Oestreich, L. K. L. et al. Subnormal sensory attenuation to self-generated speech in schizotypy: Electrophysiological evidence for a ‘continuum of psychosis’. Int. J. Psychophysiol. 97 , 131–138 (2015). Oestreich, L. K. L. et al. Cortical Suppression to Delayed Self-Initiated Auditory Stimuli in Schizotypy: Neurophysiological Evidence for a Continuum of Psychosis. Clin. EEG Neurosci. 47 , 3–10 (2016). Sowman, P. F., Kuusik, A. & Johnson, B. W. Self-initiation and temporal cueing of monaural tones reduce the auditory N1 and P2. Exp. Brain Res. 222 , 149–157 (2012). Timm, J., SanMiguel, I., Saupe, K. & Schröger, E. The N1-suppression effect for self-initiated sounds is independent of attention. BMC Neurosci. 14 , 2 (2013). Gentsch, A., Schütz-Bosbach, S. I. & Did It Unconscious Expectation of Sensory Consequences Modulates the Experience of Self-agency and Its Functional Signature. J. Cogn. Neurosci. 23 , 3817–3828 (2011). Hughes, G. & Waszak, F. ERP correlates of action effect prediction and visual sensory attenuation in voluntary action. NeuroImage 56 , 1632–1640 (2011). Hughes, G. & Waszak, F. Predicting faces and houses: Category-specific visual action-effect prediction modulates late stages of sensory processing. Neuropsychologia 61 , 11–18 (2014). Mifsud, N. G. et al. Attenuation of visual evoked responses to hand and saccade-initiated flashes. Cognition 179 , 14–22 (2018). Giannini, G., Nierhaus, T. & Blankenburg, F. Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm. Sci. Rep. 15 , 2819 (2025). Palmer, C. E., Davare, M. & Kilner, J. M. Physiological and Perceptual Sensory Attenuation Have Different Underlying Neurophysiological Correlates. J. Neurosci. 36 , 10803–10812 (2016). Pyasik, M. et al. I’m a believer: Illusory self-generated touch elicits sensory attenuation and somatosensory evoked potentials similar to the real self-touch. NeuroImage 229 , 117727 (2021). Kaiser, J. & Schütz-Bosbach, S. Sensory attenuation of self-produced signals does not rely on self-specific motor predictions. Eur. J. Neurosci. 47 , 1303–1310 (2018). Bednark, J. G., Poonian, S. K., Palghat, K., McFadyen, J. & Cunnington, R. Identity-specific predictions and implicit measures of agency. Psychol. Consciousness: Theory Res. Pract. 2 , 253–268 (2015). Hughes, G. ERP and behavioral evidence of increased sensory attenuation for fear-related action outcomes. Biol. Psychol. 111 , 8–13 (2015). Roussel, C., Hughes, G. & Waszak, F. Action prediction modulates both neurophysiological and psychophysical indices of sensory attenuation. Front Hum. Neurosci 8 , (2014). Korka, B., Widmann, A., Waszak, F., Darriba, Á. & Schröger, E. The auditory brain in action: Intention determines predictive processing in the auditory system—A review of current paradigms and findings. Psychon Bull. Rev. 29 , 321–342 (2022). Haggard, P., Clark, S. & Kalogeras, J. Voluntary action and conscious awareness. Nat. Neurosci. 5 , 382–385 (2002). Wolpert, D. M. & Flanagan, J. R. Motor prediction. Curr. Biol. 11 , R729–R732 (2001). Hughes, G., Desantis, A. & Waszak, F. Mechanisms of intentional binding and sensory attenuation: The role of temporal prediction, temporal control, identity prediction, and motor prediction. Psychol. Bull. 139 , 133–151 (2013). Kuziek, J. W. P. et al. Real brains in virtual worlds: Validating a novel oddball paradigm in virtual reality. Preprint at. https://doi.org/10.1101/749192 (2019). Stodt, B., Neudek, D., Martin, R. & Getzmann, S. Does Auditory Distance Perception Perform Similar in Real and Virtual Environments? - Results from an EEG Experiment. (2023). Wiens, S. et al. Electrophysiological correlates of in vivo and virtual reality exposure therapy in spider phobia. Psychophysiology 59 , e14117 (2022). Fründ, I., Haenel, N. V. & Wichmann, F. A. Inference for psychometric functions in the presence of nonstationary behavior. J. Vis. 11 , 16 (2011). Friston, K. Statistical Parametric Mapping: The Analysis of Funtional Brain Images (Elsevier/Academic, 2007). Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Intell. Neuroscience 1:1–1:9 (2011). (2011). Berg, P. & Scherg, M. A multiple source approach to the correction of eye artifacts. Electroencephalogr. Clin. Neurophysiol. 90 , 229–241 (1994). Ille, N., Berg, P. & Scherg, M. Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies. J. Clin. Neurophysiol. 19 , 113 (2002). de Cheveigné, A. & Parra, L. C. Joint decorrelation, a versatile tool for multichannel data analysis. NeuroImage 98 , 487–505 (2014). de Cheveigné, A. & Simon, J. Z. Denoising based on spatial filtering. J. Neurosci. Methods . 171 , 331–339 (2008). Särelä, J., Valpola, H. & Jordan, M. Denoising source separation. Journal Mach. Learn. research 6 , (2005). Litvak, V. et al. EEG and MEG Data Analysis in SPM8. Computational Intelligence and Neuroscience 852961 (2011). (2011). Ostwald, D., Schneider, S., Bruckner, R. & Horvath, L. Random field theory-based p-values: A review of the SPM implementation. Preprint at. https://doi.org/10.48550/arXiv.1808.04075 (2021). Flandin, G. & Friston, K. J. Topological Inference. in Brain Mapping (ed Toga, A. W.) 495–500 (Academic, Waltham, doi: 10.1016/B978-0-12-397025-1.00322-5 . (2015). Bianco, V. et al. Preparatory ERPs in visual, auditory, and somatosensory discriminative motor tasks. Psychophysiology 57 , e13687 (2020). Bianco, V. et al. Modality-specific sensory readiness for upcoming events revealed by slow cortical potentials. Brain Struct. Funct. 225 , 149–159 (2020). van Ede, F., de Lange, F. P. & Maris, E. Anticipation Increases Tactile Stimulus Processing in the Ipsilateral Primary Somatosensory Cortex. Cereb. Cortex . 24 , 2562–2571 (2014). Kornhuber, H. H. & Deecke, L. Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflügers Arch. 284 , 1–17 (1965). Kutas, M. & Donchin, E. Preparation to respond as manifested by movement-related brain potentials. Brain Res. 202 , 95–115 (1980). Kirsch, W., Hennighausen, E. & Rösler, F. ERP correlates of linear hand movements in a motor reproduction task. Psychophysiology 47 , 486–500 (2010). Kirsch, W. & Hennighausen, E. ERP correlates of linear hand movements: Distance dependent changes. Clin. Neurophysiol. 121 , 1285–1292 (2010). Leuthold, H. & Jentzsch, I. Distinguishing neural sources of movement preparation and execution: An electrophysiological analysis. Biol. Psychol. 60 , 173–198 (2002). Cao, L. & Gross, J. Attention Wins over Sensory Attenuation in a Sound Detection Task. PLOS ONE . 10 , e0136585 (2015). Paraskevoudi, N. & SanMiguel, I. Self-generation and sound intensity interactively modulate perceptual bias, but not perceptual sensitivity. Sci. Rep. 11 , 17103 (2021). Desantis, A., Roussel, C. & Waszak, F. The temporal dynamics of the perceptual consequences of action-effect prediction. Cognition 132 , 243–250 (2014). Yon, D. & Press, C. Predicted action consequences are perceptually facilitated before cancellation. J. Exp. Psychol. Hum. Percept. Perform. 43 , 1073–1083 (2017). Reznik, D., Henkin, Y., Schadel, N. & Mukamel, R. Lateralized enhancement of auditory cortex activity and increased sensitivity to self-generated sounds. Nat. Commun. 5 , 4059 (2014). Burin, D. et al. Comparing intensities and modalities within the sensory attenuation paradigm: Preliminary evidence. J. Adv. Res. 8 , 649–653 (2017). Kusnir, F., Pesin, S., Moscona, G. & Landau, A. N. When Temporal Certainty Doesn’t Help. J. Cogn. Neurosci. 32 , 315–325 (2020). Coy, N., Bendixen, A., Grimm, S., Roeber, U. & Schröger, E. Deviants violating higher-order auditory regularities can become predictive and facilitate behaviour. Atten. Percept. Psychophys . 85 , 2731–2750 (2023). Nobre, A., Correa, A. & Coull, J. The hazards of time. Curr. Opin. Neurobiol. 17 , 465–470 (2007). Rohenkohl, G., Cravo, A. M., Wyart, V. & Nobre, A. C. Temporal Expectation Improves the Quality of Sensory Information. J. Neurosci. 32 , 8424–8428 (2012). Rohenkohl, G., Gould, I. C., Pessoa, J. & Nobre, A. C. Combining spatial and temporal expectations to improve visual perception. J. Vis. 14 , 8 (2014). Luo, Y. & Wei, J. Cross-modal selective attention to visual and auditory stimuli modulates endogenous ERP components. Brain Res. 842 , 30–38 (1999). Näätanen, R., Teder, W., Alho, K. & Lavikainen, J. Auditory attention and selective input modulation: A topographical ERP study. NeuroReport 3 , 493 (1992). Tata, M. S., Prime, D. J., McDonald, J. J. & Ward, L. M. Transient spatial attention modulates distinct components of the auditory ERP. NeuroReport 12, 3679 (2001). Kida, T., Wasaka, T., Nakata, H., Akatsuka, K. & Kakigi, R. Active attention modulates passive attention-related neural responses to sudden somatosensory input against a silent background. Exp. Brain Res. 175 , 609–617 (2006). Lam, K., Kakigi, R., Mukai, T. & Yamasaki, H. Attention and visual interference stimulation affect somatosensory processing: a magnetoencephalographic study. Neuroscience 104 , 689–703 (2001). Mima, T., Nagamine, T., Nakamura, K. & Shibasaki, H. Attention Modulates Both Primary and Second Somatosensory Cortical Activities in Humans: A Magnetoencephalographic Study. J. Neurophysiol. 80 , 2215–2221 (1998). Barry, R. J. et al. Stimulus intensity effects and sequential processing in the passive auditory ERP. Int. J. Psychophysiol. 176 , 149–163 (2022). Mulert, C. et al. Sound level dependence of the primary auditory cortex: Simultaneous measurement with 61-channel EEG and fMRI. NeuroImage 28, 49–58 (2005). Picton, T. W., Woods, D. L., Baribeau-Braun, J. & Healey, T. M. Evoked potential audiometry. J. Otolaryngol. 6 , 90–119 (1976). Ghio, M., Scharmach, K. & Bellebaum, C. ERP correlates of processing the auditory consequences of own versus observed actions. Psychophysiology 55 , e13048 (2018). Timm, J., Schönwiesner, M., Schröger, E. & SanMiguel, I. Sensory suppression of brain responses to self-generated sounds is observed with and without the perception of agency. Cortex 80 , 5–20 (2016). Dewey, J. A. & Knoblich, G. Do Implicit and Explicit Measures of the Sense of Agency Measure the Same Thing? PLoS One . 9 , e110118 (2014). Crowley, K. E. & Colrain, I. M. A review of the evidence for P2 being an independent component process: age, sleep and modality. Clin. Neurophysiol. 115 , 732–744 (2004). Knolle, F., Schröger, E., Baess, P. & Kotz, S. A. The Cerebellum Generates Motor-to-Auditory Predictions: ERP Lesion Evidence. J. Cogn. Neurosci. 24 , 698–706 (2012). Knolle, F., Schröger, E. & Kotz, S. A. Cerebellar contribution to the prediction of self-initiated sounds. Cortex 49 , 2449–2461 (2013). Saupe, K., Widmann, A., Trujillo-Barreto, N. J. & Schröger, E. Sensorial suppression of self-generated sounds and its dependence on attention. Int. J. Psychophysiol. 90 , 300–310 (2013). Sanmiguel, I., Todd, J. & Schröger, E. Sensory suppression effects to self-initiated sounds reflect the attenuation of the unspecific N1 component of the auditory ERP. Psychophysiology 50 , 334–343 (2013). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7065290","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485697855,"identity":"c0e1565c-dd7d-4105-9e29-10368c4a242f","order_by":0,"name":"Gianluigi Giannini","email":"data:image/png;base64,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","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":true,"prefix":"","firstName":"Gianluigi","middleName":"","lastName":"Giannini","suffix":""},{"id":485697856,"identity":"14be53ab-c1d8-4bdc-9daf-6388133ba25c","order_by":1,"name":"Till Nierhaus","email":"","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Till","middleName":"","lastName":"Nierhaus","suffix":""},{"id":485697857,"identity":"26684258-44f4-450e-bdbe-deffaf1cd9b6","order_by":2,"name":"Polina Soldatova","email":"","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Polina","middleName":"","lastName":"Soldatova","suffix":""},{"id":485697858,"identity":"06f96879-ef77-48f7-9c16-45510b63a369","order_by":3,"name":"Felix Blankenburg","email":"","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Blankenburg","suffix":""}],"badges":[],"createdAt":"2025-07-07 12:08:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7065290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7065290/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30373-y","type":"published","date":"2025-12-23T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86889018,"identity":"33b290f1-ead7-47eb-8c12-44203ec331af","added_by":"auto","created_at":"2025-07-16 19:02:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":109012,"visible":true,"origin":"","legend":"\u003cp\u003eDepiction of an example of a sequence of trials in the experimental paradigm. On the first trial (first column), the participant was instructed to reach and touch the virtual ball positioned in the opposite indicator circle, which administered a first – \u003cem\u003etest\u003c/em\u003e – stimulation (somatosensory or auditory). After reaching the new position (right indicator circle), the participant was required to stay still and after 1 s a – \u003cem\u003ecomparison\u003c/em\u003e – stimulation of the same modality as the first one was administered. After 0.5 s, participants were required to estimate whether the second stimulation was higher or lower than the first one (comparison task). 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 not to move and wait for the ball to touch the finger, which resulted in a \u003cem\u003etest\u003c/em\u003estimulation (somatosensory or auditory). After the administration of a second stimulation of the same modality, the subject performed the same comparison task. During the following trial (third column), the participant was again instructed to move, but upon contact with the virtual ball, no stimulation was administered. After 1 s the fixation cross flashed darker or brighter and participants were instructed to respond “high” or “low” accordingly (control task). Participants could undergo control trials either in a \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003econdition.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7065290/v1/315f425259c5888fb7e586d8.png"},{"id":86889262,"identity":"80c342f9-1184-4fc0-8818-af08e13a7f4e","added_by":"auto","created_at":"2025-07-16 19:10:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127616,"visible":true,"origin":"","legend":"\u003cp\u003eBehavioural results. (a) Movement velocity, (b) response time and (c) accuracy across movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e) and stimulation conditions (\u003cem\u003etouch\u003c/em\u003e, \u003cem\u003eaudio\u003c/em\u003e or \u003cem\u003eno-stimulation\u003c/em\u003e). (d,e) Psychometric functions fitted over the average “high” response proportion for each stimulus intensity (absolute) and divided for movement conditions and stimulation type. Vertical lines originating from the psychometric functions represent PSEs. Error bars in all graphs represent standard error. s = \u003cem\u003estay\u003c/em\u003e, m = \u003cem\u003emove\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7065290/v1/3d832ef5d424e4ee891d0e39.png"},{"id":86890036,"identity":"5b327a07-eee1-4293-8cfb-bd2f8a70d65b","added_by":"auto","created_at":"2025-07-16 19:26:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":348779,"visible":true,"origin":"","legend":"\u003cp\u003eQualitative analysis of potentials evoked by stimulus onset. (a, b) ERPs for each condition for a subset of centro-lateral electrodes, divided for tactile and auditory stimulation in comparison to the control (\u003cem\u003eno-stimulation\u003c/em\u003e) trials. (c, d) Superimposed plot of all electrodes (butterfly plot) of the averaged differences between \u003cem\u003etouch \u003c/em\u003eor\u003cem\u003e audio\u003c/em\u003e trials and \u003cem\u003eno-stimulation\u003c/em\u003e trials matched by movement type. On the side, scalp distributions of the difference between \u003cem\u003eaudio \u003c/em\u003eor\u003cem\u003estay\u003c/em\u003e and \u003cem\u003eno-stimulation\u003c/em\u003e trials at the time points marked in the respective butterfly plots. In all panels, dotted lines at 0 s represent the moment at which the stimulation was administered or the control (\u003cem\u003eno-stimulation\u003c/em\u003e) condition was time-locked.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7065290/v1/2576ba1554c4b1733ebe4884.png"},{"id":86890100,"identity":"0e4899b0-bd3f-46e5-ad96-7015165d9dc3","added_by":"auto","created_at":"2025-07-16 19:34:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":239859,"visible":true,"origin":"","legend":"\u003cp\u003eElectrophysiological results. Interaction effect stimulation x movement for (a) the tactile modality and (b) the auditory modality. Both panels show the ERP plot of the subtraction of each stimulation condition to the corresponding \u003cem\u003eno-stimulation\u003c/em\u003e condition. The average of the electrodes comprising the cluster are plotted. Gray shaded areas represent significant time points with p\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.05 and line contours are standard errors. Dotted lines at 0 s represent the moment at which the auditory or somatosensory stimulation (or \u003cem\u003eno-stimulation\u003c/em\u003e) was administered. 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. (c) At the top, correlation between behavioural and electrophysiological measures in the auditory comparison task. On the x-axis is the difference between PSE across subjects, while on the y-axis the contrast image (\u003cem\u003eaudio move\u003c/em\u003e – \u003cem\u003eaudio stay\u003c/em\u003e – \u003cem\u003eno-stimulation move\u003c/em\u003e – \u003cem\u003eno-stimulation stay\u003c/em\u003e) is plotted, averaged over electrodes and timepoints as revealed by the whole-brain correlation analysis. The slope of the correlation line is the average βestimate from the GLM. Grey shaded areas are confidence intervals. At the bottom left, the time-course of the β estimate plotted for the significant cluster. Gray shaded area is the confidence interval while the darker shaded area is the time of significance. On the right, scalp distribution of the β estimate across the significant time-window. Highlighted electrodes are the electrodes included in the cluster at any time-point. s = \u003cem\u003estay\u003c/em\u003e, m = \u003cem\u003emove\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7065290/v1/b8afbf14bfcfc5787404bfaf.png"},{"id":99172620,"identity":"1757a8b3-8b24-431c-8d62-bb6d69e71d99","added_by":"auto","created_at":"2025-12-29 16:11:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1554575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7065290/v1/34bf629a-d21b-4479-b9c6-9dfe5fffa554.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Behavioural and Electrophysiological Correlates of Sensory Attenuation in the Somatosensory and Auditory modality within a Virtual Reality Setup","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn our day-to-day interactions with the environment, our brain distinguishes what is done by ourselves and what by others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mechanisms responsible for this include the phenomenon of sensory attenuation, where self-generated sensations compared to externally induced ones are dampened, both at the neurophysiological [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and subjective [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] level.\u003c/p\u003e\u003cp\u003eSensory attenuation is commonly investigated at the behavioural level by means of a comparison task. Participants are required to either self-generate or passively experience a first – test – stimulation, which is then compared to a second stimulation (the “comparison” stimulation) administered at rest with varying intensity. After the second stimulation, participants typically indicate which stimulation was felt stronger. This comparison task allows to calculate the value of the perceived stimulation intensity for which the test and comparison stimulations are felt identical in 50% of trials. This value is commonly referred to as threshold or point of subjective equality (PSE). When compared with the perceptual threshold at rest, lower thresholds for self-produced sensations indicate that the second stimulation is more likely to be judged as higher; i.e., the first stimulation is felt as weaker when produced by the subject [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similar attenuations for self-produced stimuli have also been demonstrated by other studies that required participants to produce congruent or incongruent outcomes, respect to previously learned action-effects. Results that employed a similar paradigm usually report lower sensitivity for congruent action-effects, suggesting that the perception of outcomes that match motor predictions is dampened compared to outcomes that do not match the predicted action consequences [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the literature is not unanimous in reporting attenuation effects for self-produced sensations, with evidence ranging from null effects [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] to enhancement effects for self-produced sensations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConcerning its electrophysiological correlates, sensory attenuation is usually investigated by means of \u003cem\u003econtingent paradigms\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In those setups participants are required to: (i) perform a motor act that results in a sensory stimulation, (ii) passively attend a stimulation and (iii) perform similar motor sequences without any sensory consequences. The \u003cem\u003emotor-only\u003c/em\u003e condition is commonly subtracted from the \u003cem\u003emotor-and-sensory\u003c/em\u003e trial average to obtain a motor-corrected potential of self-generated stimulation that is then compared to the sensory-only potential perceived at rest. EEG findings have shown an attenuation of early and middle-latency sensory-evoked components at around 100 and 200ms, either in the auditory [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], visual [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and somatosensory domain [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e–\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although more scarce in comparison with behavioural evidence, some studies also reported contrasting findings [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the extensive body of literature, it remains unclear whether the physiological attenuation reported by EEG studies corresponds to perceptual differences reflected in behavioural measures. To this end, only a handful of studies have simultaneously collected both behavioural and electrophysiological measures of attenuation within the same experimental setup [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and only one scrutinised their statistical correlation, reporting a positive correlation between visual P2 amplitude attenuation and behavioural attenuation measured through an intensity comparison task [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is thus still unclear, how behavioural measures of sensory attenuation might be reconciled with electrophysiological suppressions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] and, more interestingly, which component might reflect the subjective intensity suppression.\u003c/p\u003e\u003cp\u003eRegarding the mechanisms that may underlie this phenomenon, it is generally agreed that, upon action execution, motor areas generate an efference copy of the motor command that is used to predict the sensory consequences of an action [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which are also referred to as “motor predictions” [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. If the predicted action outputs match the effective action consequences, then the incoming sensory signal (the sensorial consequence of the action) is attenuated. This picture of sensory attenuation, however, seems too simplistic to take into account the growing body of evidence that is accumulating in recent years. For instance, recent findings indicate that different factors might modulate the attenuation driven by self-producing a stimulation, such as temporal predictability, temporal control and stimulus identity prediction [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, if an efference copy of a motor command underlies sensory attenuation, it appears that it does not merely encompasses the sensory consequences of the action itself, but also coveys more general information about the incoming – to be produced – sensory stimulation. To better understand this relationship, a previous study published by our group [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] advocated for the use of new technologies, such as Virtual Reality (VR) to control for a plethora of possible contributing factors in the attenuation for self-generated stimuli, and extended the previously scarce pool of literature on sensory attenuation in the somatosensory domain. To better generalize these results, however, it seems important to formally compare it to other modalities that have been more extensively explored in prior research, such as the auditory modality. The direct comparison of sensory attenuation across sensory modalities in a highly controlled setup would allow to further characterize this phenomenon, reducing the possibility of paradigm-specific differences.\u003c/p\u003e\u003cp\u003eThe first aim of the present study, therefore, was to investigate sensory attenuation both behaviourally and electrophysiologically through an intensity comparison task in VR, and to examine their statistical correlation. Secondly, to further substantiate our previous findings, we explored sensory attenuation in two different modalities: somatosensory and auditory. Our aim was to not only replicate previous evidence of sensory attenuation in the somatosensory domain, but also to strengthen it by demonstrating similar behavioural and electrophysiological attenuations in the auditory modality, which is a sensory modality that has been more extensively studied.\u003c/p\u003e\u003cp\u003eWe expected to find an ERP attenuation for self- compared to externally-generated stimuli in the N/P100 and P200 components for the somatosensory domain. Additionally, we hypothesized that the PSE for self-generated stimulations is significantly smaller than the PSE for externally-perceived ones. Lastly, we aimed to replicate these findings in the auditory domain.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe experiment consisted of an intensity comparison task in VR and concurrent EEG recording. The participants performed the experiment in a 3-dimensional virtual environment, in which they could either actively reach or passively be touched (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e conditions) by a virtual ball that could give them an electrical somatosensory stimulation (\u003cem\u003etouch\u003c/em\u003e), an auditory stimulation (\u003cem\u003eaudio\u003c/em\u003e) or control (\u003cem\u003eno-stimulation\u003c/em\u003e), resulting in 6 possible conditions. One second after the administration of the first stimulation, participants received a second stimulus of the same modality always at rest. Participants then had to perform an intensity comparison task, in which they had to report whether the intensity of the second stimulation was higher or lower than the first one.\u003c/p\u003e\u003cp\u003eParticipants\u003c/p\u003e\u003cp\u003e 29 healthy volunteers (20–37 years old, mean: 27.79, 13 females, all right-handed), recruited from the student body of the Freie Universität Berlin and the general public, participated for monetary compensation or an equivalent in course credit. The sample size was based on previous studies investigating sensory attenuation using a similar design [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Written informed consent was obtained from all subjects and/or their legal guardian(s) before participating to the experiment. The study was approved by the ethics committee at the Freie Universität Berlin (003/2021), and it was performed in accordance with the declaration of Helsinki.\u003c/p\u003e\u003cp\u003eExperimental setup / apparatus\u003c/p\u003e\u003cp\u003eThe paradigm was presented in virtual reality (VR) using an Oculus Rift CV1 headset (Meta, Menlo Park, California, USA), mounted on top of a chinrest. This setup minimized electrical and mechanical artifacts generated by wearing the headset on the EEG cap [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e–\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe administration of electrical and auditory stimuli was controlled through a data acquisition card (National Instruments Corporation, Austin, Texas, USA). Somatosensory stimuli were delivered using a DS5 isolated bipolar constant current stimulation (Digitimer Limited, Welwyn Garden City, Hertfordshire, UK) via adhesive electrodes (GVB-geliMED GmbH, Bad Segeberg, Germany) attached to the tip of right index finger (cathode proximal, anode distal). Auditory stimuli were given through an amplifier (AS501, Dell, Round Rock, Texas, USA) connected to a pair of headphones (HD206, Sennheiser GmbH, Wedemark-Wennebostel, Germany). Both electrical and auditory stimuli consisted of rectangular pulses of 0.2 ms duration. Lastly, participants gave responses during the experiment through a set of foot pedals.\u003c/p\u003e\u003cp\u003eThe VR scene was built using Unity v.2020.3.26f1 (Unity Technologies, San Francisco, California, USA). The scene was identical to the one from a previous experiment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] with the only difference that in the present scenario, a set of virtual pedals was also rendered at the bottom of the virtual environment, within field of view of participants (see below for a description of the paradigm). When subjects pressed a pedal anytime during the experiment, also the corresponding virtual pedal changed colour, resembling the pressure exerted in the real-world. Participants were instructed to keep their left foot on the left pedal and the right foot on the right pedal and to press only when prompted to do so.\u003c/p\u003e\u003cp\u003eThroughout the experiment, participants could control the movement of a virtual right hand by moving along the real-world table an Oculus controller mounted on a sliding support. Hand position and rotation along the three axes were recorded throughout the whole experiment with a time resolution of 85.83 Hz (SD = 6.30 Hz).\u003c/p\u003e\u003cp\u003eCalibration and setup\u003c/p\u003e\u003cp\u003e At the start of each experimental session, participants’ somatosensory and auditory threshold was determined by manually changing the intensity of the stimulation until participants reported feeling 5 out of 10 stimuli (threshold somatosensory = 2.01 ± 1.32, threshold auditory = 66.86 ± 5.74 [mean ± SD]). Then, to determine a set of stimulus intensities to be used for experimental phase, participants underwent an intensity comparison task (almost identical to the one in the experimental phase) in which they received two subsequent stimuli at rest, 1 second apart. The first stimulus was always kept at the same intensity (2x threshold intensity for the somatosensory and + 30dB for the auditory modality), while the second stimulus could be one of possible 9 intensities, equally spaced around the intensity of the first stimulation. This meant that participants compared the first central stimulation to either: 4 increasingly lower intensities, 4 increasingly higher intensities or to the same intensity. One second after the second stimulation, a right-ward and left-ward arrow appeared on the screen with the labels “high” and “low”. Participants had to report whether the second stimulation was higher or lower compared to the first stimulus through the pedals. The initial comparison task comprised a total of 72 trials, i.e., 8 for each intensity level. The proportion of “high” responses was calculated for each stimulus intensity and a logistic function was fitted on the data points. The procedure was repeated by spacing the stimuli further apart or narrower until participants reached 0% and 100% of “high” responses for the lowest and highest intensity respectively and stimuli “high” proportions were equally distributed. The detection thresholds at 2% and 98% were estimated and the stimuli were equally spaced along these extremes. Participants underwent the same procedure for somatosensory and auditory stimuli, separately. The two sets of 9 stimuli, for the auditory and somatosensory modality, were then used in the subsequent training and experimental phase (thresholds at 2% and 98% for the somatosensory modality: T02 = 3.15 ± 0.99 mA, T98 = 4.84 ± 1.56; and for the auditory modality: T02 = 88.42 ± 5.78 dB, T98 = 103.61 ± 7.16 [mean ± SD]).\u003c/p\u003e\u003cp\u003eAfter the initial calibration phase, the headset height and the lens focus were adjusted to obtain optimal visual resolution and the correspondence between the real-world controller and the virtual hand was calibrated so that left-wards and right-wards movements were equally comfortable and easy. Participants then underwent a short training phase in VR to familiarise with the experimental task. The training phase consisted of 32 trials, divided equally in \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e trials. For each movement type, participants underwent 6 comparisons for the somatosensory modality, 6 comparisons for the auditory modality and 4 control tasks.\u003c/p\u003e\u003cp\u003eAfter completing the training phase, participants were invited to an adjacent room where the EEG cap was fitted and the electrodes position was digitised through an Eximia neuronavigation system (Nexstim, Helsinki, Finland). The procedure took approximately 5 minutes to complete, after which the proper experimental phase could begin.\u003c/p\u003e\u003cp\u003eExperimental design\u003c/p\u003e\u003cp\u003eIn each of the 4 experimental runs of approx. 15 min, participants underwent 160 trials, for a total of 640 trials per participant. On top of the virtual table was rendered a fixation cross, centred with the field of view of the camera as well as two indicator circles (distanced ± 0.2 Uu from the fixation cross and still within the field of view of each eye). Participants were instructed to keep their gaze on the fixation cross and to keep their index finger within one indicator circle or to move it towards the ball located in the circle located in the opposite side of the virtual surface, for the \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions respectively.\u003c/p\u003e\u003cp\u003eAt the beginning of each run, an arrow indicated the circle in which the participant had to put their index finger. Once the finger was in the circle, the new sequence started. At the beginning of each trial, a virtual ball appeared in the centre of the circle opposite from the participant’s finger. After a delay of 1 s, the fixation cross changed colour for 0.5 s. If the cross flashed green, participants were instructed to move as soon as the cross stopped flashing and to actively reach the ball (\u003cem\u003emove\u003c/em\u003e condition). If the cross flashed red, volunteers were required to stay still, and the virtual ball reached their immobile finger (\u003cem\u003estay\u003c/em\u003e condition). As soon as the cross stopped flashing, the ball started moving with a velocity corresponding to the one of any of the previous trials in which a reaching movement was performed. In this way, we could minimise differences in trial time between \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions and we could personalise the ball velocity in \u003cem\u003estay\u003c/em\u003e conditions according to each participant’s moving pace. If participants moved during a \u003cem\u003estay\u003c/em\u003e condition or moved before the cross stopped flashing, a prompt appeared indicating the wrong execution of the trial.\u003c/p\u003e\u003cp\u003eOnce participants actively touched (or got touched by) the virtual ball, a somatosensory stimulation could have been administered in 40% of trials, an auditory stimulation in 40% of trials or no stimulation in 20% of trials (\u003cem\u003etouch\u003c/em\u003e and \u003cem\u003eaudio\u003c/em\u003e each were 256 out of 640 trials while the remaining 128 were control trials).\u003c/p\u003e\u003cp\u003eThe intensity of the first – \u003cem\u003etest\u003c/em\u003e –stimulation (either self-generated or passively received – given upon contact with the virtual ball) was kept constant as the central intensity out of the 9 previously calculated during the calibration phase. Participants were instructed to keep their finger in the same position and one second after the first stimulus, they received a second – \u003cem\u003ecomparison\u003c/em\u003e – stimulation. The number of trials per intensity level was normally distributed so that the number of trials for the comparison of the central intensity against itself was maximised (48 out of 256 trials for the central intensity and 16 out of 256 trials for the lowest or highest intensity).\u003c/p\u003e\u003cp\u003e In control trials, participants received no stimulation as a result of actively touching or passively getting touched by the virtual ball. One second after contact with the ball, the fixation cross in the middle of the screen changed colour (either darker or lighter) for 0.2 s.\u003c/p\u003e\u003cp\u003eHalf a second after the administration of the second stimulation (or control task), the virtual ball disappeared and two labels – “high” and “low”– appeared randomly on top of the pedals. Upon pedal press, the selected label increased in size before disappearing. If participants underwent the intensity comparison task (i.e., received a stimulation), they had to report whether the second stimulation was higher or lower in intensity than the first stimulus. If they were administered the control task (i.e., \u003cem\u003eno-stimulation\u003c/em\u003e), subjects were instructed to answer “high” if the fixation cross flashed lighter or “low” if it flashed darker. The response could be given within a 1.5 second time window. If the timeout was reached, a prompt indicating a missed response appeared on the screen, inviting participants to provide an answer faster on following trials.\u003c/p\u003e\u003cp\u003eAfter the response was registered, a new trial started after a random inter-trial interval between 1.25 and 1.75 s. Subsequent trials always started with the virtual ball appearing in the opposite indicator circle respect to the participant’s hand. For a depiction of the experimental paradigm, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBehavioural data analysis\u003c/p\u003e\u003cp\u003eMovement velocities are defined as the average velocities during the time that elapsed from the start of the movement (either active hand movement or of the virtual ball) to the contact with the hand. Movement velocities outliers were defined as trials in which participants moved in less than 100 ms and longer than 2500 ms. Differences across conditions were calculated to test whether our velocity personalisation approach was successful and to exclude the possibility that differences in pre-stimulus movement characteristics might have impacted our electrophysiological results. A linear mixed effect model was fitted, having as fixed factors stimulation type (\u003cem\u003etouch\u003c/em\u003e, \u003cem\u003eaudio\u003c/em\u003e and \u003cem\u003eno-stimulation\u003c/em\u003e) and movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e) and as random intercept the subjects.\u003c/p\u003e\u003cp\u003eSince the intensity comparison task and the control task were substantially different, we kept the behavioural analyses separate for the calculation of response times and accuracy. Response time was defined as the time participants took to give an answer from the moment when the labels “high” and “low” appeared on top of the virtual pedals. Accuracies for the intensity comparison task were the percentage of correct comparisons (i.e., answered “low” when the second stimulus had a lower intensity than the first one) over the total number of possible comparisons (i.e., all intensities except for the middle intensity). Accuracies for the control tasks were simply the proportion of correct responses (according to the instructions that participants received) over the total possible number of control responses.\u003c/p\u003e\u003cp\u003eTwo linear mixed effect models were fitted over response times and accuracies for the intensity comparison task, which comprised as factors stimulation type (\u003cem\u003etouch\u003c/em\u003e and \u003cem\u003eaudio\u003c/em\u003e) and movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e). Identical models were calculated using response times and accuracies for the control task, which had as factor movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e). All models were fitted with a random intercept by participant.\u003c/p\u003e\u003cp\u003e Lastly, to determine whether the perception of the first stimulation was affected by self-producing or passively perceiving it, we first calculated the proportion of trials in which the participants reported that the second stimulation was higher than the second one. For each participant, movement and stimulation condition, we fitted logistic psychometric functions on the proportion of “high” responses using the Psignifit toolbox [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This allowed to determine the point of subjective equality (PSE) across conditions, i.e., the stimulus intensity at which the participants would judge the test and comparison stimulus to be the same intensity in 50% of trials. PSE during \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials were compared by fitting linear mixed effect models separately for the auditory and tactile modality. We expected the PSEs for self-produced stimuli to be significantly lower than the ones for externally-perceived stimulations, indicating that participants perceived the self-generated stimuli as lower in intensity than the same stimuli administered at rest.\u003c/p\u003e\u003cp\u003eEEG data collection and preprocessing\u003c/p\u003e\u003cp\u003eData were collected using a 64-channel active electrode EEG system (ActiveTwo, BioSemi, Amsterdam, Netherlands) at a sampling rate of 2048 Hz, with head electrodes placed in accordance with the extended 10–20 system. Preprocessing of the EEG data was performed using SPM12 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], FieldTrip [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and in-house MATLAB scripts. We first visually identified and interpolated bad channels (5 ± 3 [mean ± SD]), then the continuous EEG recording for each participant was re-referenced against the average reference, down-sampled to 512 Hz and high-pass filtered (0.1 Hz, firws, one-pass zero-phase, -6 dB cut-off). Then, we corrected for eye-blinks and horizonal eye movements through a topographical confound approach [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Next, epochs were defined as windows spacing from − 2 s to 2 s around ball touch (i.e., when the first stimulus was presented or not) and a low pass filter was applied (45 Hz, firws, one-pass zero-phase, -6 dB cut-off). Epoched datasets were then visually inspected and bad data segments were marked and excluded. Each dataset was further denoised using Denoising Source Separation [DSS − 57–59] to maximise the reproducibility of stimulus evoked activity in response to tactile, auditory and no-stimulation conditions either during self-produced or externally-perceived trials. DSS is a semi-blind source separation technique that uses the time-locked electrophysiological activity to unmix the continuous recording into sources that gradually explain the time-locked signal. To determine the (cumulative) number of sources to retain, we calculated the log ratio between each experimental condition and the relative control condition, across channels and time-points before stimulation and after 500 ms. We then kept the number of components that minimised the differences between stimulation trials (\u003cem\u003eaudio\u003c/em\u003e or \u003cem\u003etouch\u003c/em\u003e) and controls (\u003cem\u003eno-stimulation\u003c/em\u003e). A total of 7 component for each subject was maintained. DSS therefore allowed us to denoise our signal to further prevent that differences between the recorded electrophysiological activity across conditions might impact our results (for a detailed discussion on this topic, please see Chap.\u0026nbsp;2.7).\u003c/p\u003e\u003cp\u003eEEG data was then baseline corrected using a pre-stimulus window ranging from − 50 to − 5 ms.\u003c/p\u003e\u003cp\u003eTo ensure that the data were equivalent between the EEG and the behavioural analyses, in both datasets we kept only trials that (i) did not contain any movement velocity outlier, (ii) did not have any missed response and (iii) were clean EEG trials. On average, we excluded a total of 13.90% of trials (SD = 4.73%). 0.84% (SD = 1.18%) of the total trials were movement velocity outliers, 4.08 (SD = 3.25%) were missed responses and 9.90% (SD = 3.14%) contained artefactual trials, while 0.92% were shared. After exclusion, out of 640 trials we obtained on average 551 (SD = 31) trials. Lastly, out of the initial 29 participants, one participant was excluded because they couldn’t perform the task. The following results are therefore computed on 28 participants.\u003c/p\u003e\u003cp\u003eEEG data analysis – Qualitative assessment\u003c/p\u003e\u003cp\u003eIt has been previously argued that a possible confound in the investigation of sensory attenuation is the violation of the assumption that the potential recorded during \u003cem\u003emotor-only\u003c/em\u003e conditions is the same in \u003cem\u003emotor-and-sensory\u003c/em\u003e trials. In our previous study [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] we excluded this possibility by investigating the pre-stimulus interval and pointing out that the difference between the motor potential recorded upon stimulus administration and when no stimulation was given resulted zero, strongly suggesting that the electrophysiological motor activity in the two conditions was compatible. Before reporting the formal statistical analyses, in the present work, we qualitatively assessed the time-locked electrophysiological activity to (i) verify that the tactile and auditory stimulation resulted in prototypical Somatosensory Evoked Potentials (SEP) and Auditory Evoked Potentials (AEP) and – more importantly – to (ii) confirm that the measured electrophysiological activity in the pre-stimulus interval for \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials (either before a stimulation or not) was similar.\u003c/p\u003e\u003cp\u003eEEG data analysis – Sensory Attenuation in somatosensory and auditory modalities\u003c/p\u003e\u003cp\u003eMain analyses were performed according to the SPM framework for M/EEG analysis. This required the epoched electrophysiological data to be converted into a 3D image (scalp space x intra-trial samples). To achieve this, each subjective electrode localisation map was first projected onto a 2D space and re-scaled into a 32 x 32 mask, so that the position of the electrodes on the mask corresponded to the location of the sensors on the scalp. EEG data from each channel was then entered into the corresponding location on the mask and then linearly interpolated for each sample [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This procedure allowed to interpolate EEG 2D data arrays into 3D images to be further analysed with correction for multiple comparisons using random field theory [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSince we had strong hypotheses about the electrophysiological responses evoked by the tactile or auditory stimulation, we selected a window spanning from − 50 to 500 ms around ball-touch (i.e., the first stimulation, either self- or externally-produced). In this way, we obtained one 3D image of dimensions 32 × 32 × 283 (scalp space x intra-trial samples) for each trial.\u003c/p\u003e\u003cp\u003eThen, all images were inserted into a first-level multiple regression model, which had one dummy regressor for each possible condition combination (6 in total). After model estimation, we obtained a 3D β estimate for each condition with the same dimensionality as the initial images, which corresponded to the averaged time-locked potential of each condition, for each participant.\u003c/p\u003e\u003cp\u003eIn the field of sensory attenuation, to test whether a self-produced stimulation evoked potential is attenuated compared to an externally perceived sensation, a control condition is required. In classical contingent paradigms, a movement-and-touch condition is subtracted of a movement-only condition before being compared to a touch-only condition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In this way, any potential evoked by the movement alone can be accounted for, and only the potentials elicited by the stimulation (either self-generated or externally perceived) can be compared. In a similar fashion, our study allowed for the recording of two control conditions, i.e., conditions in which no stimulation was administered when actively reaching or passively being touched by the virtual ball. The additional advantage of our approach was that we could control not only movement-related evoked potentials, but also potentials evoked at rest, possibly due to stimulus expectation or attentional demands.\u003c/p\u003e\u003cp\u003eTo directly test whether the potential evoked by the self-elicited stimulation was attenuated compared to the one evoked by the externally-generated stimulation – while controlling for potentials elicited by movement or stimulus expectation – we implemented two mass-univariate multiple regression analysis for each stimulation modality. Each model included one regressor for each condition of interest, as well as for each subject. Mean differences across conditions were tested via F-tests and therefore, in its interpretation, both models were equivalent to a 2 x 2 ANOVA, having as factors: stimulation (\u003cem\u003etouch\u003c/em\u003e or \u003cem\u003eaudio\u003c/em\u003e and \u003cem\u003eno-stimulation\u003c/em\u003e) and movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e). All analyses were performed with a cluster-forming threshold of p \u0026lt; 0.001; only clusters withstanding at the cluster-level with family-wise error (FWE) corrected threshold of p\u003csub\u003eFWE\u003c/sub\u003e \u0026lt;0.05 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] are reported here.\u003c/p\u003e\u003cp\u003eUsually, in contingent paradigms, a \u003cem\u003emotor-only\u003c/em\u003e condition is subtracted from a \u003cem\u003emotor-and-sensory\u003c/em\u003e condition to obtain a corrected potential of self-produced stimulus perception to be compared against a \u003cem\u003esensory-only\u003c/em\u003e condition. In a similar fashion, in the present study we tested differences between self-generated and externally-produced sensations, while controlling for a compatible control \u003cem\u003eno-stimulation\u003c/em\u003e condition, by selectively exploring the interaction term across conditions, in each model independently (i.e., [\u003cem\u003etouch move\u003c/em\u003e – \u003cem\u003etouch stay\u003c/em\u003e – \u003cem\u003eno-stimulation move\u003c/em\u003e + \u003cem\u003eno-stimulation stay\u003c/em\u003e] and [\u003cem\u003eaudio move\u003c/em\u003e – \u003cem\u003eaudio stay\u003c/em\u003e – \u003cem\u003eno-stimulation move\u003c/em\u003e + \u003cem\u003eno-stimulation stay\u003c/em\u003e]).\u003c/p\u003e\u003cp\u003eControl analysis – second stimulation\u003c/p\u003e\u003cp\u003eTo further assess the specificity of sensory attenuation, we also tested for electrophysiological attenuations upon administration of the second stimulation. We theorised to find no interaction effect between stimulation and movement type, excluding the presence of a sensory attenuation effect for the second stimulation and further substantiate the idea that our main results are not indeed based on the interaction between each movement and stimulation condition.\u003c/p\u003e\u003cp\u003eTo test this hypothesis, we first defined epochs around the second stimulation, ranging from − 50 to 500 ms and we baseline corrected the data using a window from − 50 to -5 ms before the stimulation. We then fitted models identical to the main analyses at the first and second level, separately for the tactile and auditory modalities. Identically to the main analyses, mean differences across conditions were tested via F-tests and therefore, in its interpretation, both models were equivalent to a 2 x 2 ANOVA, having as factors: stimulation (\u003cem\u003etouch\u003c/em\u003e or \u003cem\u003eaudio\u003c/em\u003e and \u003cem\u003eno-stimulation\u003c/em\u003e) and movement type (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e). All analyses were performed with a cluster-forming threshold of p \u0026lt; 0.001. We reported the clusters surviving FWE correction with a threshold of p\u003csub\u003eFWE\u003c/sub\u003e \u0026lt;0.05.\u003c/p\u003e\u003cp\u003eCorrelation between electrophysiological and behavioural indices of sensory attenuation\u003c/p\u003e\u003cp\u003eTo formally test whether behavioural attenuations for self-produced sensations were associated with electrophysiological indices of suppression, we performed a correlation analysis. First, for each subject and each sensory modality independently, we obtained a linear combination of the β estimates which resulted by subtracting the average of the \u003cem\u003emove no-stimulation\u003c/em\u003e and \u003cem\u003estay stimulation\u003c/em\u003e trials to the average of the \u003cem\u003emove stimulation\u003c/em\u003e and \u003cem\u003estay no-stimulation\u003c/em\u003e trials. These are also commonly referred to as contrast images. Positive differences in early-latency components (N100 or N1 for the somatosensory and auditory domain) or negative differences in mid-latency components (P200 or P2 for somatosensory and auditory modalities respectively), therefore, indicated an electrophysiological attenuation for self-produced compared to externally-generated stimulations (i.e., sensory attenuation).\u003c/p\u003e\u003cp\u003eSimilarly, for the behavioural domain, we calculated for each participant the difference between the PSE in the \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e conditions. A negative difference would indicate that PSEs for self-generated stimuli were lower than externally perceived ones, i.e., stimuli resulting from \u003cem\u003emove\u003c/em\u003e trials were perceived as less intense.\u003c/p\u003e\u003cp\u003eThe contrast images for each subject were then inserted into a second level mass-univariate multiple regression analysis, having as only regressor the behavioural index of attenuation. In its interpretation, the model is equivalent to a Pearson correlation between electrophysiological and behavioural indices of attenuation. Positive and negative correlations were then tested via t-statistics.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBehavioural results\u003c/p\u003e\u003cp\u003eMovement velocities were different across \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.090, F\u003csub\u003e1,162\u003c/sub\u003e = 15.936, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but did not differ across stimulation type (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.002, F\u003csub\u003e2,162\u003c/sub\u003e = 0.140, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.869) and their interaction was not significant (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.004, F\u003csub\u003e2,162\u003c/sub\u003e = 0.327, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.721). Move trials were overall 0.019 Uu/s (circa 11.19 ms) faster than \u003cem\u003estay\u003c/em\u003e trials (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Although this indicates that our velocity personalisation approach was not optimal, we consider it very unlikely that this limitation significantly influenced the subsequent results If the different trial lengths led to a difference between the electrophysiological correlates of \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions, it would have affected equally both the stimulation (\u003cem\u003eaudio\u003c/em\u003e or \u003cem\u003etouch\u003c/em\u003e) and the respective control \u003cem\u003eno-stimulation\u003c/em\u003e trials, thus cancelling out in the subsequent analyses.\u003c/p\u003e\u003cp\u003eResponse times for the comparison task were faster in \u003cem\u003emove\u003c/em\u003e than in \u003cem\u003estay\u003c/em\u003e trials of 22.83 ms (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.105, F\u003csub\u003e1,108\u003c/sub\u003e = 12.605, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but did not differ across \u003cem\u003etouch\u003c/em\u003e or \u003cem\u003eaudio\u003c/em\u003e stimulations (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.006, F\u003csub\u003e1,108\u003c/sub\u003e = 0.661, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.418). The interaction between the two factors was also not significant (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.001, F\u003csub\u003e1,108\u003c/sub\u003e = 0.121, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.728). Response times for the control task instead showed no differences between \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.001, F\u003csub\u003e1,54\u003c/sub\u003e = 0.051, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.821). See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb.\u003c/p\u003e\u003cp\u003eLastly, participants were more accurate on the auditory task compared to the tactile task (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.166, F\u003csub\u003e1,108\u003c/sub\u003e = 21.488, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) of 7.08% on average. Accuracy did not differ across movement conditions (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.010, F\u003csub\u003e1,108\u003c/sub\u003e = 1.057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.306), nor was the interaction between the two factors significant (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.000, F\u003csub\u003e1,108\u003c/sub\u003e = 0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.920). Accuracy for the control task also did not differ across \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e trials (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.010, F\u003csub\u003e1,54\u003c/sub\u003e = 0.571, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.453). For illustration, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec.\u003c/p\u003e\u003cp\u003eLastly, PSE for the auditory modality were significantly lower in the \u003cem\u003emove\u003c/em\u003e than in the \u003cem\u003estay\u003c/em\u003e condition (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.106, F\u003csub\u003e1,54\u003c/sub\u003e = 6.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). For the tactile modality we observed no differences across movement types (ƞ\u003csup\u003e2\u003c/sup\u003e = 0.000, F\u003csub\u003e1,54\u003c/sub\u003e = 0.010, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.921). For a depiction, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed and \u003cem\u003ee\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eElectrophysiological results \u0026ndash; qualitative evaluation\u003c/p\u003e\u003cp\u003eThe averaged electrophysiological activity for the movement condition in all sensory modalities (either \u003cem\u003etouch\u003c/em\u003e, \u003cem\u003eaudio\u003c/em\u003e or \u003cem\u003eno-stimulation\u003c/em\u003e) was characterised by an increasing pre-stimulus negativity that gradually increased before stimulus onset. This is likely due to stimulus anticipation [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], motor preparatory processes [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] and motor execution [\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. In \u003cem\u003estay\u003c/em\u003e conditions, across sensory modalities, the pre-stimulus activity was also characterised by a negativity, possibly reflecting a process of stimulus anticipation (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cem\u003eb\u003c/em\u003e). Differences between movement conditions were accounted for in our analyses by the inclusion of a control (\u003cem\u003eno-stimulation\u003c/em\u003e) condition, for both \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials. As mentioned before, the robustness of our results relies on the assumption that the measured electrophysiological activity across stimulation types is equivalent within \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials. In support of this hypothesis, the pre-stimulus activity of the main effect of electrical or auditory stimulation (\u003cem\u003etouch\u003c/em\u003e or \u003cem\u003eaudio\u003c/em\u003e \u0026ndash; \u003cem\u003eno-stimulation\u003c/em\u003e) averages around zero, giving aid to the assumption that \u0026ndash; indeed \u0026ndash; the measured electrophysiological activity across \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e trials was equivalent.\u003c/p\u003e\u003cp\u003eFrom stimulus onset, our paradigm elicited a typical SEP. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec shows the difference between the average \u003cem\u003etouch\u003c/em\u003e trials across participants subtracted of the control (\u003cem\u003eno-stimulation\u003c/em\u003e) trials across electrodes with the expected evoked potentials, i.e., P50, N/P100 and P200 resulting from electrical stimulation of the right index finger. The corresponding topographic maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, right) confirm the left lateralized voltage distribution of the somatosensory evoked potential (SEP) components on the scalp (N/P100).\u003c/p\u003e\u003cp\u003eThe auditory stimulation also elicited a typical AEP. As can be observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, the difference between \u003cem\u003eaudio\u003c/em\u003e trials and \u003cem\u003eno-stimulation\u003c/em\u003e trials elicited prototypical potentials, i.e., N1 and P2.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eElectrophysiological results \u0026ndash; sensory attenuation\u003c/p\u003e\u003cp\u003eTo test for differences in the electrophysiological activity evoked by either self-producing or passively attending a stimulation, while controlling for the effects of a control condition, we investigated the interaction term between movement and stimulation type.\u003c/p\u003e\u003cp\u003eFor the somatosensory modality, one cluster survived cluster-level FWE correction in central electrodes and starting at 0.117 s to 0.168 s post stimulus (peak at 0.145 s, centroid: CP1, ƞ\u003csup\u003e2\u003c/sup\u003e = 0.229, F\u003csub\u003e1,81\u003c/sub\u003e = 24.11, p\u003csub\u003eFWE\u003c/sub\u003e = 0.010). This positive potential (P200) was reduced in amplitude when the stimulation resulted as a consequence of a movement compared to the corresponding potential administered at rest (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eFor the auditory modality, one cluster reached significance, located in centro-frontal electrodes and starting at 0.121 s to 0.166 s post stimulus (peak at 0.137 s, centroid: FC4, ƞ\u003csup\u003e2\u003c/sup\u003e = 0.331, F\u003csub\u003e1,81\u003c/sub\u003e = 40.04, p\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.001). This positive potential (P2) was suppressed for self- compared to other-generated auditory stimulations, when controlling for \u003cem\u003eno-stimulation\u003c/em\u003e conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eNotably, concomitant to the fronto-central positivity, our analyses also revealed an occipital negativity that survived cluster-level FWE correction (peak at 0.135 s, centroid: O2, ƞ\u003csup\u003e2\u003c/sup\u003e = 0.303, F\u003csub\u003e1,81\u003c/sub\u003e = 35.25, p\u003csub\u003eFWE\u003c/sub\u003e = 0.001). This is consistent with a dipolar scalp distribution and, since typically auditory evoked potentials around 200 ms are reported in fronto-central electrodes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], this cluster was ignored from further analysis.\u003c/p\u003e\u003cp\u003eElectrophysiological results \u0026ndash; control analysis\u003c/p\u003e\u003cp\u003eTo further corroborate our electrophysiological results, we performed a control analysis by investigating possible interaction effects for the potentials evoked by the second \u0026ndash; comparison \u0026ndash; stimulation. We expected at this timepoint no sensory attenuation effect since the stimuli administered after the first \u0026ndash; test \u0026ndash; stimulation (that could be either self- or externally-generated) were perceived at rest. If our analyses had pointed out any kind of interaction, this could have indicated that our results might also be explained by possible interactions between movement and the stimulation, i.e., the motor (or non-motor) potential across stimulation conditions is not equal. As expected, our control analysis on the second \u0026ndash; comparison \u0026ndash; stimulus revealed no interactions between movement type and stimulation.\u003c/p\u003e\u003cp\u003eElectrophysiological results \u0026ndash; correlation between electrophysiological and behavioural attenuation indices.\u003c/p\u003e\u003cp\u003eTo formally test whether behavioural indicators of sensory attenuation were related to electrophysiological suppressions, we performed a correlation analysis on the previously defined \u0026minus;\u0026thinsp;50 to 500 ms window and throughout electrodes. Results for the auditory modality revealed that one central cluster, peaking at 0.156 s (and ranging from 0.102 s to 0.178 s) was positively correlating with behavioural indices of attenuation (centroid: C2, ƞ\u003csup\u003e2\u003c/sup\u003e = 0.529, T\u003csub\u003e27\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.51, p\u003csub\u003eFWE\u003c/sub\u003e = 0.001). We identified this peak corresponding to the previously identified auditory P2. The present results therefore indicate that at larger auditory P2 suppressions corresponded larger PSE reductions for self-generated stimulations (for a depiction, see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eThe correlation analysis for the somatosensory modality revealed no significant clusters.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the extensive literature investigating sensory attenuation, it remains unclear whether behavioural attenuation of self-generated stimuli reflects electrophysiological suppression. In the present experiment, we employed a previously validated VR paradigm that allowed for more comprehensive control of stimulus properties, attentional requirements, and other predictability factors during an intensity comparison task, alongside concomitant EEG recording. In this way, we were able to probe behavioural and electrophysiological correlates of sensory attenuation both, in the somatosensory and auditory modalities.\u003c/p\u003e\u003cp\u003e Behavioural analyses revealed that participants perceived the self-generated stimulations as less intense only in the auditory modality. This was accompanied by higher accuracy in the auditory comparison task than in the somatosensory one. The intensity comparison task in both sensory modalities, however, showed a faster response time for \u003cem\u003emove\u003c/em\u003e than \u003cem\u003estay\u003c/em\u003e trials.\u003c/p\u003e\u003cp\u003eAt the electrophysiological domain, the P200 for somatosensory and the P2 for auditory stimulations were attenuated when the stimulations were self-generated compared to when they were passively administered, while controlling for \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e conditions where no stimulation was administered. We found no evidence of attenuation for the second stimulation.\u003c/p\u003e\u003cp\u003eLastly, the correlation analysis revealed that the suppression at the auditory P2 positively correlated with the decrease in perceived intensity as indexed by the intensity comparison task.\u003c/p\u003e\u003cp\u003eBehavioural measures of sensory attenuation\u003c/p\u003e\u003cp\u003eContrary to our expectations, we found evidence of behavioural sensory attenuation in the auditory domain, but not in the tactile modality. It is usually assumed that our brain uses an efference copy of a motor command to predict motor outcomes and to estimate whether the effective sensory feedbacks generated by motor plans coincide with the predicted ones [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The consequence of this prediction is often believed to be an attenuation of action outcomes, i.e., already predicted or redundant information is kept away from our senses to leave space for more information rich afferents. This concept is supported by numerous studies demonstrating that self-generated sensory outcomes are perceived as less intense compared to identical stimulations administered at rest [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] or that congruent action-effects are suppressed rather than incongruent ones [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, these findings received support across different sensory modalities, such as tactile [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], auditory [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and visual [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] substantiating even more this concept. However, the current state of the art suggests a more complex picture, as several studies indicate that motor efferents might adapt perception according to the different sensorial context or task requirement, rather than simply dampening it. For instance, Myers and colleagues [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported over a series of experiments that perceptual acuity is enhanced when actively generating a supra-threshold auditory stimulation during an intensity comparison task. Moreover, Reznik and colleagues [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] reported enhanced perceived loudness when the self-generated stimuli were at near-threshold level, in accordance to other studies that used similar stimuli intensities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], while self-generated supra-threshold auditory stimulations were attenuated compared to passively perceived identical stimuli. This concept is further corroborated by studies utilizing a similar procedure and stimulus modality [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and by evidence of larger suppression effects for greater stimulus intensities, although in the tactile domain [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. In other words, self-generated sensations might be weighed differently based on the nature of the task at hand. If the task requires a higher precision, sensations that are self-produced might be enhanced rather than suppressed. The argument that predictable effects of a voluntary actions are not automatically suppressed is further supported by studies that did not point out attenuations for self-generated stimulations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConcerning the present study, since both auditory and somatosensory modalities were probed through an identical comparison task, it is unlikely that the absence of an attenuation effect in the somatosensory domain is to be attributed to the task itself. In other words, if our comparison task was sufficient to induce a shift in perceptual threshold in the auditory modality, one would expect the same to happen for somatosensory stimuli. The reason behind this discrepancy, therefore, requires further investigation. One possibility is that sensory attenuation operates differently across senses, at least when conscious perception is involved. It is possible that, given the tight coupling of somatosensation with the body and the motor system, somatosensory stimuli are weighed differently by predictive mechanisms as compared to other senses [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. This might be especially true in an ecological scenario such as our VR setup. Somatosensory stimulations, which were administered upon contact with a virtual ball, might have been attributed a special relevance given the ecological congruency with the task participants were required to undergo.\u003c/p\u003e\u003cp\u003eAnother possibility that could reconcile our behavioural findings is that the comparison task in the somatosensory modality was of greater hindrance. This is not only indexed by higher variances in the hit rates, but also by a significant overall lower accuracy obtained in the tactile comparison task compared to the auditory modality. Although speculatively, the greater precision required by the task might have induced participants not to experience a univalent effect, as a consequence of self-producing sensations. This hypothesis might be supported by recent findings that utilised a comparison task across two different sensory modalities (visual and auditory) in which an enhancement effect at the behavioural level is pointed out only for the visual modality, that had lower accuracies [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In other words, when the accuracy for the comparison task was even lower (possibly indicating a greater difficulty at the task) participants perceived self-generated stimuli as enhanced rather than attenuated. Further studies will be required to substantiate this hypothesis.\u003c/p\u003e\u003cp\u003eLastly, our findings also revealed a decreased response time at the comparison task when the stimulation to be compared was self-generated, rather than when it was externally administered, across sensory modalities. It has been previously demonstrated that predictable events are linked to decreased response times [\u003cspan additionalcitationids=\"CR79 CR80\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. It is plausible that the perception of self-generated stimuli, being subject to qualitatively different predictive processes, might have been facilitated; thus, the following response at the comparison task was administered faster.\u003c/p\u003e\u003cp\u003eElectrophysiological measures of sensory attenuation\u003c/p\u003e\u003cp\u003eThe electrophysiological results of attenuation for auditory P2 and somatosensory P200 are consistent across senses. Interestingly, our findings did not indicate attenuation of earlier ERP components, which is typically a common result in sensory attenuation, both in the auditory [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and, despite scarce, somatosensory domain [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. One possibility is that participants were required to estimate the intensity of the second stimulation \u0026ndash; which had a varying intensity \u0026ndash; to the intensity of a first self- or externally-produced stimulation, which had always the same intensity. Other studies that investigated sensory attenuation through comparison tasks required participants to indicate which one of the two stimulations was more intense rather than prompting a unilateral comparison [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Results from Ody and colleagues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] showed an attenuation for auditory N1 and for visual N1 and P2 when the stimulation was actively produced in comparison to when it was passively generated. Similar findings were also replicated by another study from the same group [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Early ERP components have been demonstrated to be influenced by attentional factors, both in the auditory [\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] and tactile domain [\u003cspan additionalcitationids=\"CR86\" citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Also, attentional factors have been demonstrated to influence the auditory N1 suppression that characterizes self-generated stimulations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is possible therefore that participants did not pay attention to the first stimulation as it was completely predictable both in its temporal and identity characteristics, resulting in an overall dampening of earlier electrophysiological components.\u003c/p\u003e\u003cp\u003eOn an additional note, the present electrophysiological results were obtained through a virtual reality setup validated in a previous study, which allowed us to control for other possible confounding effects that might impact on the attenuation for self-generated movements. More specifically, the experiment was designed to minimize the influence of stimulus properties, spatial attention and temporal expectation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is unlikely, therefore, that our results might be influenced by these aspects.\u003c/p\u003e\u003cp\u003e Since participants were administered a second somatosensory or auditory stimulation at rest, we could further verify that our main analyses were not influenced by interactions between movement type and stimulation other than the sensory attenuation effect. The absence of any interaction effect at the second stimulation further corroborates this concept, i.e., the electrophysiological correlates of the motor (and non-motor) activity are identical across \u003cem\u003etouch\u003c/em\u003e or \u003cem\u003eaudio\u003c/em\u003e and \u003cem\u003eno-stimulation\u003c/em\u003e conditions.\u003c/p\u003e\u003cp\u003eBridging behavioural and electrophysiological suppression\u003c/p\u003e\u003cp\u003eLastly, our findings indicated a positive correlation between the auditory P2 suppression and the perceived reduced intensity for self-generated sensations. Contrary to our expectations, however, we did not find the same relation in the somatosensory modality. Similar contrasting findings across sensory modalities were also observed by another study by Ody and colleagues [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In their experiment \u0026ndash; which was until now the only other study that formally tested a correlation between behavioural and electrophysiological suppressions \u0026ndash; the authors reported an electrophysiological attenuation for self-generated sensations both in the auditory N1 and visual N1 and P2 components, but perceptual PSEs in both sensory modalities did not show a difference between active and passive conditions. Lastly, behavioural attenuation only in the visual task was correlating with visual P2 attenuation. Although in a different sensory modality, we replicate the previous findings obtained by Ody and colleagues, which suggest that the electrophysiological component underlying the subjective intensity reduction is a mid-latency ERP. Although the authors reported a correlation between behavioural and electrophysiological attenuations despite no evidence of behavioural suppression, it is still possible that in the present study the lack of a behavioural effect in the somatosensory domain (for the reasons discussed above) have also rendered the correlation with the electrophysiological suppression insignificant.\u003c/p\u003e\u003cp\u003eThe P2 for auditory stimuli has been demonstrated to be modulated by stimulus intensity [\u003cspan additionalcitationids=\"CR89\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], therefore it is plausible that attenuations of this component produced by self-generation of a stimulation might be responsible for the subjective reduction of perceived intensity of the same stimuli. Moreover, components within the same time-windows have also been theorised to be involved in selfhood attributions. For instance, Ghio and colleagues [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] found auditory N1 attenuation both for action observation and execution. However, P2 attenuations were stronger self-produced stimuli compared to observed tone production, suggesting that the P2 might be the electrophysiological correlate of authorship over the consequences of one\u0026rsquo;s own actions (also defined as the sense of agency). Similar conclusions were also advanced by Timm and collaborators [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] who reported that N1 suppressions happened irrespective of agency conditions, while P2 attenuations correlated with agency reports from participants. Sensory attenuation has often been acclaimed as one of the mechanisms that our brain utilizes to distinguish oneself from others [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and it has been often considered as an implicit measure of the self of agency [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. This interpretation might be consistent with our results, although it has not been formally tested in the present work.\u003c/p\u003e\u003cp\u003eTherefore, N1 and P2 suppressions for self-generated stimulations are possibly two functionally distinct phenomena [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] which, more often than not, are existing as two consecutive steps. For instance, cerebellar patients exhibit an auditory N1 suppression for self-generated stimulations, but not P2 [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Mid-latency components seem also to be more resilient towards attention modulations [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] and could be overall a more direct measure of sensory-specific predictions [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur results of a positive correlation between auditory P2 suppression and perceived intensity attenuation might indicate that mid-latency components alone are involved in higher-level stimulus processing and, possibly, in other meta-cognitive functions such as agency attributions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Information\u003c/h2\u003e\u003cp\u003eThis work was supported by Berlin School of Mind and Brain, Humboldt Universit\u0026auml;t zu Berlin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mind-and-brain.de/home/\u003c/span\u003e\u003cspan address=\"http://www.mind-and-brain.de/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGG: Conceptualization, Data acquisition, Data curation, Methodology, Formal analysis, Writing \u0026ndash; original draft. TN: Conceptualization, Methodology, Supervision, Writing \u0026ndash; review and editing. PS: Data acquisition. FB: Conceptualization, Methodology, Supervision, Writing \u0026ndash; review and editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003e The data that support the findings of this study are available from the corresponding author, GG, taking into account the data protection guidelines. The corresponding scripts for data analysis and for replicating figures are available here: https://github.com/Neurocomputation-and-Neuroimaging-Unit/SensAtt_Aud_Tact_Behav.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTsakiris, M., Haggard, P., Franck, N., Mainy, N. \u0026amp; Sirigu A. A specific role for efferent information in self-recognition. \u003cem\u003eCognition\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e, 215\u0026ndash;231 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWozniak, M. M. How to grow a self: development of the self in a Bayesian brain. (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31234/osf.io/6e3ad\u003c/span\u003e\u003cspan address=\"10.31234/osf.io/6e3ad\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaess, P., Widmann, A., Roye, A., Schr\u0026ouml;ger, E. \u0026amp; Jacobsen, T. Attenuated human auditory middle latency response and evoked 40-Hz response to self-initiated sounds. \u003cem\u003eEur. J. Neurosci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 1514\u0026ndash;1521 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eB\u0026auml;\u0026szlig;, P., Jacobsen, T. \u0026amp; Schr\u0026ouml;ger, E. Suppression of the auditory N1 event-related potential component with unpredictable self-initiated tones: Evidence for internal forward models with dynamic stimulation. \u003cem\u003eInt. J. Psychophysiol.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 137\u0026ndash;143 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlakemore, Wolpert, D. M. \u0026amp; Frith, C. D. Central cancellation of self-produced tickle sensation. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 635\u0026ndash;640 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchafer, E. W. P. \u0026amp; Marcus, M. M. Self-Stimulation Alters Human Sensory Brain Responses. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e181\u003c/b\u003e, 175\u0026ndash;177 (1973).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBays, P. M., Wolpert, D. M. \u0026amp; Flanagan, J. R. Perception of the consequences of self-action is temporally tuned and event driven. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1125\u0026ndash;1128 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBays, P. M., Flanagan, J. R. \u0026amp; Wolpert, D. M. Attenuation of self-generated tactile sensations is predictive, not postdictive. \u003cem\u003ePLoS Biol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, e28 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCardoso-Leite, P., Mamassian, P., Sch\u0026uuml;tz-Bosbach, S. \u0026amp; Waszak, F. A New Look at Sensory Attenuation: Action-Effect Anticipation Affects Sensitivity, Not Response Bias. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1740\u0026ndash;1745 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaggard, P. \u0026amp; Whitford, B. Supplementary motor area provides an efferent signal for sensory suppression. \u003cem\u003eBrain Res. Cogn. Brain Res.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 52\u0026ndash;58 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalsh, L. D., Taylor, J. L. \u0026amp; Gandevia, S. C. Overestimation of force during matching of externally generated forces. \u003cem\u003eJ. Physiol.\u003c/em\u003e \u003cb\u003e589\u003c/b\u003e, 547\u0026ndash;557 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlakemore, Frith, C. D. \u0026amp; Wolpert, D. M. Spatio-temporal prediction modulates the perception of self-produced stimuli. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 551\u0026ndash;559 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesantis, A., Weiss, C., Sch\u0026uuml;tz-Bosbach, S. \u0026amp; Waszak, F. Believing and Perceiving: Authorship Belief Modulates Sensory Attenuation. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, e37959 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOdy, E., Straube, B., He, Y. \u0026amp; Kircher, T. Perception of self-generated and externally-generated visual stimuli: Evidence from EEG and behavior. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, e14295 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOdy, E., Kircher, T., Straube, B. \u0026amp; He, Y. Voluntary action sharpens sensory prediction and facilitates neural processing of contingent sensory stimuli. \u003cem\u003ePreprint at.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2024.12.03.626576\u003c/span\u003e\u003cspan address=\"10.1101/2024.12.03.626576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReznik, D., Henkin, Y., Levy, O. \u0026amp; Mukamel, R. Perceived Loudness of Self-Generated Sounds Is Differentially Modified by Expected Sound Intensity. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, e0127651 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeiss, C., Herwig, A. \u0026amp; Sch\u0026uuml;tz-Bosbach, S. The self in action effects: Selective attenuation of self-generated sounds. \u003cem\u003eCognition\u003c/em\u003e \u003cb\u003e121\u003c/b\u003e, 207\u0026ndash;218 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeiss, C., Herwig, A. \u0026amp; Sch\u0026uuml;tz-Bosbach, S. The Self in Social Interactions: Sensory Attenuation of Auditory Action Effects Is Stronger in Interactions with Others. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e6\u003c/b\u003e, e22723 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFuehrer, E., Voudouris, D., Lezkan, A., Drewing, K. \u0026amp; Fiehler, K. Tactile suppression stems from specific sensorimotor predictions. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e119\u003c/b\u003e, e2118445119 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesantis, A., Mamassian, P., Lisi, M. \u0026amp; Waszak, F. The prediction of visual stimuli influences auditory loudness discrimination. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e232\u003c/b\u003e, 3317\u0026ndash;3324 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwarz, K. A., Pfister, R., Kluge, M., Weller, L. \u0026amp; Kunde, W. Do we see it or not? Sensory attenuation in the visual domain. \u003cem\u003eJ. Exp. Psychol. Gen.\u003c/em\u003e \u003cb\u003e147\u003c/b\u003e, 418\u0026ndash;430 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDogge, M., Hofman, D., Custers, R. \u0026amp; Aarts, H. Exploring the role of motor and non-motor predictive mechanisms in sensory attenuation: Perceptual and neurophysiological findings. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e, 216\u0026ndash;225 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMyers, J. C., Mock, J. R. \u0026amp; Golob, E. J. Sensorimotor Integration Can Enhance Auditory Perception. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 1496 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSato, A. Action observation modulates auditory perception of the consequence of others\u0026rsquo; actions. \u003cem\u003eConscious. Cogn.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 1219\u0026ndash;1227 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorv\u0026aacute;th, J. Action-related auditory ERP attenuation: Paradigms and hypotheses. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cb\u003e1626\u003c/b\u003e, 54\u0026ndash;65 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFord, J. M., Gray, M., Faustman, W. O., Roach, B. J. \u0026amp; Mathalon, D. H. Dissecting corollary discharge dysfunction in schizophrenia. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 522\u0026ndash;529 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison, A. W. et al. Sensory attenuation is modulated by the contrasting effects of predictability and control. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e237\u003c/b\u003e, 118103 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMartikainen, M. H., Kaneko, K. \u0026amp; Hari, R. Suppressed Responses to Self-triggered Sounds in the Human Auditory Cortex. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 299\u0026ndash;302 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMifsud, N. G. et al. Self-initiated actions result in suppressed auditory but amplified visual evoked components in healthy participants. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 723\u0026ndash;732 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOestreich, L. K. L. et al. Subnormal sensory attenuation to self-generated speech in schizotypy: Electrophysiological evidence for a \u0026lsquo;continuum of psychosis\u0026rsquo;. \u003cem\u003eInt. J. Psychophysiol.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e, 131\u0026ndash;138 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOestreich, L. K. L. et al. Cortical Suppression to Delayed Self-Initiated Auditory Stimuli in Schizotypy: Neurophysiological Evidence for a Continuum of Psychosis. \u003cem\u003eClin. EEG Neurosci.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 3\u0026ndash;10 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSowman, P. F., Kuusik, A. \u0026amp; Johnson, B. W. Self-initiation and temporal cueing of monaural tones reduce the auditory N1 and P2. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e222\u003c/b\u003e, 149\u0026ndash;157 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTimm, J., SanMiguel, I., Saupe, K. \u0026amp; Schr\u0026ouml;ger, E. The N1-suppression effect for self-initiated sounds is independent of attention. \u003cem\u003eBMC Neurosci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 2 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGentsch, A., Sch\u0026uuml;tz-Bosbach, S. I. \u0026amp; Did It Unconscious Expectation of Sensory Consequences Modulates the Experience of Self-agency and Its Functional Signature. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e, 3817\u0026ndash;3828 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHughes, G. \u0026amp; Waszak, F. ERP correlates of action effect prediction and visual sensory attenuation in voluntary action. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e, 1632\u0026ndash;1640 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHughes, G. \u0026amp; Waszak, F. Predicting faces and houses: Category-specific visual action-effect prediction modulates late stages of sensory processing. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 11\u0026ndash;18 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMifsud, N. G. et al. Attenuation of visual evoked responses to hand and saccade-initiated flashes. \u003cem\u003eCognition\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e, 14\u0026ndash;22 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGiannini, G., Nierhaus, T. \u0026amp; Blankenburg, F. Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 2819 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalmer, C. E., Davare, M. \u0026amp; Kilner, J. M. Physiological and Perceptual Sensory Attenuation Have Different Underlying Neurophysiological Correlates. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 10803\u0026ndash;10812 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePyasik, M. et al. I\u0026rsquo;m a believer: Illusory self-generated touch elicits sensory attenuation and somatosensory evoked potentials similar to the real self-touch. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e229\u003c/b\u003e, 117727 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaiser, J. \u0026amp; Sch\u0026uuml;tz-Bosbach, S. Sensory attenuation of self-produced signals does not rely on self-specific motor predictions. \u003cem\u003eEur. J. Neurosci.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 1303\u0026ndash;1310 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBednark, J. G., Poonian, S. K., Palghat, K., McFadyen, J. \u0026amp; Cunnington, R. Identity-specific predictions and implicit measures of agency. \u003cem\u003ePsychol. Consciousness: Theory Res. Pract.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, 253\u0026ndash;268 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHughes, G. ERP and behavioral evidence of increased sensory attenuation for fear-related action outcomes. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e111\u003c/b\u003e, 8\u0026ndash;13 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoussel, C., Hughes, G. \u0026amp; Waszak, F. Action prediction modulates both neurophysiological and psychophysical indices of sensory attenuation. \u003cem\u003eFront Hum. Neurosci\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorka, B., Widmann, A., Waszak, F., Darriba, \u0026Aacute;. \u0026amp; Schr\u0026ouml;ger, E. The auditory brain in action: Intention determines predictive processing in the auditory system\u0026mdash;A review of current paradigms and findings. \u003cem\u003ePsychon Bull. Rev.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 321\u0026ndash;342 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaggard, P., Clark, S. \u0026amp; Kalogeras, J. Voluntary action and conscious awareness. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 382\u0026ndash;385 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWolpert, D. M. \u0026amp; Flanagan, J. R. Motor prediction. \u003cem\u003eCurr. Biol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, R729\u0026ndash;R732 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHughes, G., Desantis, A. \u0026amp; Waszak, F. Mechanisms of intentional binding and sensory attenuation: The role of temporal prediction, temporal control, identity prediction, and motor prediction. \u003cem\u003ePsychol. Bull.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e, 133\u0026ndash;151 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKuziek, J. W. P. et al. Real brains in virtual worlds: Validating a novel oddball paradigm in virtual reality. \u003cem\u003ePreprint at.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/749192\u003c/span\u003e\u003cspan address=\"10.1101/749192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStodt, B., Neudek, D., Martin, R. \u0026amp; Getzmann, S. Does Auditory Distance Perception Perform Similar in Real and Virtual Environments? - Results from an EEG Experiment. (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWiens, S. et al. Electrophysiological correlates of in vivo and virtual reality exposure therapy in spider phobia. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e, e14117 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFr\u0026uuml;nd, I., Haenel, N. V. \u0026amp; Wichmann, F. A. Inference for psychometric functions in the presence of nonstationary behavior. \u003cem\u003eJ. Vis.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 16 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFriston, K. \u003cem\u003eStatistical Parametric Mapping: The Analysis of Funtional Brain Images\u003c/em\u003e (Elsevier/Academic, 2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOostenveld, R., Fries, P., Maris, E. \u0026amp; Schoffelen, J. M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. \u003cem\u003eIntell. Neuroscience\u003c/em\u003e 1:1\u0026ndash;1:9 (2011). (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerg, P. \u0026amp; Scherg, M. A multiple source approach to the correction of eye artifacts. \u003cem\u003eElectroencephalogr. Clin. Neurophysiol.\u003c/em\u003e \u003cb\u003e90\u003c/b\u003e, 229\u0026ndash;241 (1994).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIlle, N., Berg, P. \u0026amp; Scherg, M. Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies. \u003cem\u003eJ. Clin. Neurophysiol.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 113 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Cheveign\u0026eacute;, A. \u0026amp; Parra, L. C. Joint decorrelation, a versatile tool for multichannel data analysis. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 487\u0026ndash;505 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Cheveign\u0026eacute;, A. \u0026amp; Simon, J. Z. Denoising based on spatial filtering. \u003cem\u003eJ. Neurosci. Methods\u003c/em\u003e. \u003cb\u003e171\u003c/b\u003e, 331\u0026ndash;339 (2008).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eS\u0026auml;rel\u0026auml;, J., Valpola, H. \u0026amp; Jordan, M. Denoising source separation. \u003cem\u003eJournal Mach. Learn. research\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLitvak, V. et al. EEG and MEG Data Analysis in SPM8. \u003cem\u003eComputational Intelligence and Neuroscience\u003c/em\u003e 852961 (2011). (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOstwald, D., Schneider, S., Bruckner, R. \u0026amp; Horvath, L. Random field theory-based p-values: A review of the SPM implementation. \u003cem\u003ePreprint at.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.1808.04075\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.1808.04075\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlandin, G. \u0026amp; Friston, K. J. Topological Inference. in Brain Mapping (ed Toga, A. W.) 495\u0026ndash;500 (Academic, Waltham, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/B978-0-12-397025-1.00322-5\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-397025-1.00322-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBianco, V. et al. Preparatory ERPs in visual, auditory, and somatosensory discriminative motor tasks. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e57\u003c/b\u003e, e13687 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBianco, V. et al. Modality-specific sensory readiness for upcoming events revealed by slow cortical potentials. \u003cem\u003eBrain Struct. Funct.\u003c/em\u003e \u003cb\u003e225\u003c/b\u003e, 149\u0026ndash;159 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Ede, F., de Lange, F. P. \u0026amp; Maris, E. Anticipation Increases Tactile Stimulus Processing in the Ipsilateral Primary Somatosensory Cortex. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 2562\u0026ndash;2571 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKornhuber, H. H. \u0026amp; Deecke, L. Hirnpotential\u0026auml;nderungen bei Willk\u0026uuml;rbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. \u003cem\u003ePfl\u0026uuml;gers Arch.\u003c/em\u003e \u003cb\u003e284\u003c/b\u003e, 1\u0026ndash;17 (1965).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKutas, M. \u0026amp; Donchin, E. Preparation to respond as manifested by movement-related brain potentials. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cb\u003e202\u003c/b\u003e, 95\u0026ndash;115 (1980).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirsch, W., Hennighausen, E. \u0026amp; R\u0026ouml;sler, F. ERP correlates of linear hand movements in a motor reproduction task. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 486\u0026ndash;500 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirsch, W. \u0026amp; Hennighausen, E. ERP correlates of linear hand movements: Distance dependent changes. \u003cem\u003eClin. Neurophysiol.\u003c/em\u003e \u003cb\u003e121\u003c/b\u003e, 1285\u0026ndash;1292 (2010).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeuthold, H. \u0026amp; Jentzsch, I. Distinguishing neural sources of movement preparation and execution: An electrophysiological analysis. \u003cem\u003eBiol. Psychol.\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 173\u0026ndash;198 (2002).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao, L. \u0026amp; Gross, J. Attention Wins over Sensory Attenuation in a Sound Detection Task. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, e0136585 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParaskevoudi, N. \u0026amp; SanMiguel, I. Self-generation and sound intensity interactively modulate perceptual bias, but not perceptual sensitivity. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 17103 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDesantis, A., Roussel, C. \u0026amp; Waszak, F. The temporal dynamics of the perceptual consequences of action-effect prediction. \u003cem\u003eCognition\u003c/em\u003e \u003cb\u003e132\u003c/b\u003e, 243\u0026ndash;250 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYon, D. \u0026amp; Press, C. Predicted action consequences are perceptually facilitated before cancellation. \u003cem\u003eJ. Exp. Psychol. Hum. Percept. Perform.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 1073\u0026ndash;1083 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReznik, D., Henkin, Y., Schadel, N. \u0026amp; Mukamel, R. Lateralized enhancement of auditory cortex activity and increased sensitivity to self-generated sounds. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 4059 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurin, D. et al. Comparing intensities and modalities within the sensory attenuation paradigm: Preliminary evidence. \u003cem\u003eJ. Adv. Res.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 649\u0026ndash;653 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKusnir, F., Pesin, S., Moscona, G. \u0026amp; Landau, A. N. When Temporal Certainty Doesn\u0026rsquo;t Help. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 315\u0026ndash;325 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoy, N., Bendixen, A., Grimm, S., Roeber, U. \u0026amp; Schr\u0026ouml;ger, E. Deviants violating higher-order auditory regularities can become predictive and facilitate behaviour. \u003cem\u003eAtten. Percept. Psychophys\u003c/em\u003e. \u003cb\u003e85\u003c/b\u003e, 2731\u0026ndash;2750 (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNobre, A., Correa, A. \u0026amp; Coull, J. The hazards of time. \u003cem\u003eCurr. Opin. Neurobiol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 465\u0026ndash;470 (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRohenkohl, G., Cravo, A. M., Wyart, V. \u0026amp; Nobre, A. C. Temporal Expectation Improves the Quality of Sensory Information. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 8424\u0026ndash;8428 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRohenkohl, G., Gould, I. C., Pessoa, J. \u0026amp; Nobre, A. C. Combining spatial and temporal expectations to improve visual perception. \u003cem\u003eJ. Vis.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 8 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuo, Y. \u0026amp; Wei, J. Cross-modal selective attention to visual and auditory stimuli modulates endogenous ERP components. \u003cem\u003eBrain Res.\u003c/em\u003e \u003cb\u003e842\u003c/b\u003e, 30\u0026ndash;38 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN\u0026auml;\u0026auml;tanen, R., Teder, W., Alho, K. \u0026amp; Lavikainen, J. Auditory attention and selective input modulation: A topographical ERP study. \u003cem\u003eNeuroReport\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 493 (1992).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTata, M. S., Prime, D. J., McDonald, J. J. \u0026amp; Ward, L. M. Transient spatial attention modulates distinct components of the auditory ERP. \u003cem\u003eNeuroReport\u003c/em\u003e 12, 3679 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKida, T., Wasaka, T., Nakata, H., Akatsuka, K. \u0026amp; Kakigi, R. Active attention modulates passive attention-related neural responses to sudden somatosensory input against a silent background. \u003cem\u003eExp. Brain Res.\u003c/em\u003e \u003cb\u003e175\u003c/b\u003e, 609\u0026ndash;617 (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLam, K., Kakigi, R., Mukai, T. \u0026amp; Yamasaki, H. Attention and visual interference stimulation affect somatosensory processing: a magnetoencephalographic study. \u003cem\u003eNeuroscience\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e, 689\u0026ndash;703 (2001).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMima, T., Nagamine, T., Nakamura, K. \u0026amp; Shibasaki, H. Attention Modulates Both Primary and Second Somatosensory Cortical Activities in Humans: A Magnetoencephalographic Study. \u003cem\u003eJ. Neurophysiol.\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 2215\u0026ndash;2221 (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarry, R. J. et al. Stimulus intensity effects and sequential processing in the passive auditory ERP. \u003cem\u003eInt. J. Psychophysiol.\u003c/em\u003e \u003cb\u003e176\u003c/b\u003e, 149\u0026ndash;163 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMulert, C. et al. Sound level dependence of the primary auditory cortex: Simultaneous measurement with 61-channel EEG and fMRI. \u003cem\u003eNeuroImage\u003c/em\u003e 28, 49\u0026ndash;58 (2005).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePicton, T. W., Woods, D. L., Baribeau-Braun, J. \u0026amp; Healey, T. M. Evoked potential audiometry. \u003cem\u003eJ. Otolaryngol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 90\u0026ndash;119 (1976).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhio, M., Scharmach, K. \u0026amp; Bellebaum, C. ERP correlates of processing the auditory consequences of own versus observed actions. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e55\u003c/b\u003e, e13048 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTimm, J., Sch\u0026ouml;nwiesner, M., Schr\u0026ouml;ger, E. \u0026amp; SanMiguel, I. Sensory suppression of brain responses to self-generated sounds is observed with and without the perception of agency. \u003cem\u003eCortex\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e, 5\u0026ndash;20 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDewey, J. A. \u0026amp; Knoblich, G. Do Implicit and Explicit Measures of the Sense of Agency Measure the Same Thing? \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, e110118 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCrowley, K. E. \u0026amp; Colrain, I. M. A review of the evidence for P2 being an independent component process: age, sleep and modality. \u003cem\u003eClin. Neurophysiol.\u003c/em\u003e \u003cb\u003e115\u003c/b\u003e, 732\u0026ndash;744 (2004).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnolle, F., Schr\u0026ouml;ger, E., Baess, P. \u0026amp; Kotz, S. A. The Cerebellum Generates Motor-to-Auditory Predictions: ERP Lesion Evidence. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 698\u0026ndash;706 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnolle, F., Schr\u0026ouml;ger, E. \u0026amp; Kotz, S. A. Cerebellar contribution to the prediction of self-initiated sounds. \u003cem\u003eCortex\u003c/em\u003e \u003cb\u003e49\u003c/b\u003e, 2449\u0026ndash;2461 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaupe, K., Widmann, A., Trujillo-Barreto, N. J. \u0026amp; Schr\u0026ouml;ger, E. Sensorial suppression of self-generated sounds and its dependence on attention. \u003cem\u003eInt. J. Psychophysiol.\u003c/em\u003e \u003cb\u003e90\u003c/b\u003e, 300\u0026ndash;310 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSanmiguel, I., Todd, J. \u0026amp; Schr\u0026ouml;ger, E. Sensory suppression effects to self-initiated sounds reflect the attenuation of the unspecific N1 component of the auditory ERP. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 334\u0026ndash;343 (2013).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7065290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7065290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSensory attenuation is the phenomenon that self-produced stimulations are suppressed compared to externally generated ones, both at the subjective and electrophysiological level. Despite the extensive literature on this phenomenon, it remains unclear whether electrophysiological attenuations are consistent across senses and whether they do reflect subjective attenuations of perceived intensity for self-produced sensations. Therefore, the aim of the present study is twofold: first we aimed to collect behavioural and electrophysiological measures of sensory attenuation in a controlled virtual reality setup, both in the auditory and somatosensory domain. Secondly, we correlated behavioural and electrophysiological indices of sensory attenuation to formally test whether the suppression for potentials evoked by self-generated stimulations reflects the sensory suppression revealed by behavioural measures.\u003c/p\u003e\u003cp\u003e A total of 28 participants were included to compare the intensity of a first stimulation, which was self-generated or externally administered, to a second stimulation, which was administered at rest with varying intensity. The stimulations could be either electrical pulses at the fingertip or auditory clicks. Participants were also required to undergo a control task in which no stimulation was administered.\u003c/p\u003e\u003cp\u003eThe behavioural results indicate a reduced perceived intensity for self-produced compared to externally administered stimuli for the auditory domain. In contrast, no such difference was observed for the somatosensory domain. EEG results revealed suppression of the P2 for the auditory modality for the P200 in the somatosensory modality. Furthermore, a positive correlation between the P2 suppression and subjective intensity attenuation for the auditory modality.\u003c/p\u003e\u003cp\u003eTogether, our results suggest that electrophysiological suppression at mid-latency components reflect the perceived subjective attenuation of self-produced stimulation. This relationship, however, might be dependent on the sensory domain.\u003c/p\u003e","manuscriptTitle":"Behavioural and Electrophysiological Correlates of Sensory Attenuation in the Somatosensory and Auditory modality within a Virtual Reality Setup","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-16 19:01:57","doi":"10.21203/rs.3.rs-7065290/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-07T10:23:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T07:19:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T06:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252598049943661290193349521647803546307","date":"2025-07-14T23:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87484016586463105188412057193805850851","date":"2025-07-14T12:07:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T11:47:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T11:31:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-08T11:05:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-08T08:32:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-07T12:00:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f9ec44b-5d51-45b3-a851-98463c438c0e","owner":[],"postedDate":"July 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51559295,"name":"Biological sciences/Neuroscience"},{"id":51559296,"name":"Biological sciences/Psychology"},{"id":51559297,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-12-29T16:08:09+00:00","versionOfRecord":{"articleIdentity":"rs-7065290","link":"https://doi.org/10.1038/s41598-025-30373-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-23 15:58:09","publishedOnDateReadable":"December 23rd, 2025"},"versionCreatedAt":"2025-07-16 19:01:57","video":"","vorDoi":"10.1038/s41598-025-30373-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30373-y","workflowStages":[]},"version":"v1","identity":"rs-7065290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7065290","identity":"rs-7065290","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}