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

Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm | 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 Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm Gianluigi Giannini, Till Nierhaus, Felix Blankenburg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5281922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract We are not only passively immersed in a sensorial world, but we are active agents that directly produce stimulations. Understanding what’s unique about the sensory consequences can give valuable insight into the action-perception-cycle. Sensory attenuation is the phenomenon that self-produced stimulations are perceived as less intense compared to externally-generated ones. Studying this phenomenon, however, requires considering a plethora of factors that could otherwise interfere with its interpretation, such as differences in stimulus properties, attentional resources, or temporal predictability. We therefore developed a novel Virtual Reality (VR) setup that allows to control several of these confounding factors. Further, we modulated the expectation of receiving a somatosensory stimulation across self-production and passive perception through a simple probabilistic learning task, allowing us to test to what extent the electrophysiological correlates of sensory attenuation are impacted by stimulus expectation. We obtained electroencephalography (EEG) recordings of 26 participants. Results indicate that early (P100), mid-latency (P200) and later negative contralateral potentials were significantly attenuated by self-generated sensations, independently of the stimulus expectation. Moreover, a component around 200 ms post-stimulus at frontal sites was found to be enhanced for self-produced stimuli. The P300 was influenced by stimulus expectation, regardless of whether the stimulation was actively produced or passively attended. Together, our results indicate that VR opens up new possibilities to study sensory attenuation in more ecological, yet well-controlled paradigms, and that sensory attenuation is not significantly modulated by stimulus predictability. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Sensorimotor processing Biological sciences/Neuroscience/Somatosensory system Biological sciences/Psychology/Human behaviour Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Our sensorium appears to use internal models to predict future sensory data and thus also to filter environmental noise from salient information [ 1 – 3 ], so that our cognitive capacities can be allocated to optimally process information that is innovative or useful [ 4 ]. We are, however, not only passively immersed in a perceptual world. Every time we move or directly act on our environment, we generate a sequence of sensory data that can be predicted with a higher precision than external data [ 5 ]. This might be responsible, amongst others, for our ability to perceive ourselves as self-standing agents [ 6 , 7 ] or for distinguishing our speech from that produced by others [ 8 , 9 ]. Among the multiple phenomena emerging from this interaction between action and perception, sensory attenuation is the phenomenon that self-generated stimulations are suppressed compared to similar externally-generated stimuli [ 10 , 11 ], both at the subjective [ 12 – 18 ] and neurophysiological level [ 11 , 19 – 26 ]. It is suggested that at the core of this phenomenon lies a forward model: upon the generation of a motor command, an efference copy is generated to predict the sensory consequences of that movement [ 27 ]. These motor predictions and their observed sensory consequences are then compared. If the prediction matches the sensory re-afference, the self-generated stimulation is attenuated or cancelled-out and therefore perceived as less intense [ 10 , 13 , 28 ]. Historically, the investigation of the electrophysiological mechanisms underlying sensory attenuation has been largely studied using so-called contingent paradigms [ 29 ]. In these setups, participants undergo three conditions which require them to (i) perform sequences of prompted actions with a contingent sensory consequence, (ii) passively undergo similar sequences of stimuli generated by a computer and (iii) perform sequences of actions without any sensory consequence. Typically, the motor-only condition is subtracted from the motor-and-sensory condition to obtain a motor-corrected potential of self-generated stimulation that is then compared to the sensory-only potential of passively perceived stimuli. Electroencephalography (EEG) typically indicates that event-related potentials (ERPs) at around 100 ms and 200 ms post stimulus are attenuated for self- compared to externally-generated stimulations, either in the auditory [ 11 , 30 – 34 ], visual [ 35 – 39 ], and, although more scarce in number, in the somatosensory domain [ 40 , 41 ]. Despite the extensive work that has been published in recent years, the investigation of sensory attenuation has often revealed to be difficult in controlling for other explanatory factors. First of all, most studies investigating the electrophysiological correlates of sensory attenuation required participants to execute button presses to generate sensory action consequences [ 11 , 30 , 36 , 37 , 42 , 43 ]. It has been argued, however, that the additional mechanical stimulation from physically pressing a button – which is entirely perceived during motor-only conditions – would be masked by the self-generated sensory stimuli in the motor-and-sensory condition. The later subtraction of the two ERPs would then result in a smaller component, thus mimicking an attenuation for self-produced stimuli [ 29 , 44 ]. Moreover, albeit experimental stimulations in sensory attenuation paradigms are kept constant, additional mechanical stimulations exerted upon button press would be dependent on the velocity of the participants’ movements or the applied force and might therefore remain uncontrolled. Also, task differences in the classical contingent paradigm (i.e., passive listening compared to actively pressing a button to generate a sound) might induce an imbalance in attention requirements across conditions, which could account for the suppression effect reported between self- and externally-generated stimuli. In fact, as it is well known that attention requirements might reduce the electrophysiological responses evoked by a stimulation [ 45 – 51 ], it has been discussed that the simple execution of a motor task might require to allocate attentional resources away from stimulus perception, which might determine an ERP suppression for self-generated stimulations [ 29 , 52 ]. Another component that might affect the investigation of sensory attenuation is stimulus predictability [ 53 ]. Previous studies that manipulated stimuli predictions at rest suggested that stimuli that are predictable either in time [ 54 – 57 ], in the identity of the stimulation [ 58 – 63 ] or concerning the expectation of receiving a stimulus [ 64 , 65 ] exert an evoked electrophysiological response that is attenuated compared to the relative unpredictable counterpart. Studies that tried to control for these factors typically report that suppression of self-generated stimuli is resilient to temporal predictability [ 23 , 43 ] or stimulus identity predictions [ 14 , 20 , 42 , 66 ], despite some findings reporting mixed evidence [ 67 ] or proving the contrary [ 21 ]. In order to address these issues, new methodological instruments might be useful. In particular, Virtual Reality (VR) and head mounted displays are capable of creating immersive settings with a high degree of freedom [ 68 ] and are especially interesting for the exploration of action and perception interrelations. They allow the creation of setups in which different sensory and motor components are manipulated at will in ways that would not be feasible in other experimental settings. Only a handful of studies adopted such technologies in the investigation of sensory attenuation [ 69 – 72 ] and only one research integrated a VR setup with an EEG recording [ 73 ]. The current study expands on this work by collecting electrophysiological responses to self-generated and passively attended stimuli in a VR setting. The paradigm was designed to compare the electrophysiological potentials evoked by an electrical pulse at the fingertip resulting from a goal-directed action with the same potential when passively attended, net of a no-stimulation control condition (similar to a contingent paradigm). Our design also facilitated a broad and comprehensive control of a plethora of other explaining factors, such as stimulus properties (i.e., intensity and additional stimulations), temporal predictability and attentional requirements. Lastly, because the stimulations were administered in a probabilistic fashion either when self-produced or passively attended, we could also test for the influence of stimulus predictability on sensory attenuation. Materials and Methods The task consisted of a modified probabilistic learning paradigm in VR and concurrent EEG recording. The participants saw a 3-dimensional space with VR glasses in which they either actively reached or passively got touched ( move or stay conditions) by a virtual ball that could give them an electrical stimulation ( touch and no-touch ), in a probabilistic fashion (three levels of probability: low , equal , high ). This paradigm allowed us to directly compare the electrophysiological correlates evoked by a stimulus in both active and passive conditions, while controlling for the subjective expectation of receiving the stimulation. Participants 26 healthy volunteers (18–35 years old, mean: 24.36, 16 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 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. Written informed consent was obtained from all participants prior to the experiment. 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 allowed to minimise electrical and mechanical artifacts generated by wearing the headset directly on top of the EEG cap [ 74 – 76 ]. Somatosensory stimuli were administered with a DS7 isolated bipolar constant current stimulator (Digitimer Limited, Welwyn Garden City, Hertfordshire, UK) via adhesive electrodes (GVB-geliMED GmbH, Bad Segeberg, Germany) attached to the outer side of the right index finger (cathode proximal, anode distal, see Fig. 1 a, left). The stimuli consisted of electrical rectangular pulses of 0.2 ms duration. The VR scene was built using Unity v.2020.3.26f1 (Unity Technologies, San Francisco, California, USA). To mimic as accurately as possible the real-world setup, proportions of the virtual objects were scaled according to a ratio of 1 Unity units (Uu) to 1 real-world meter. A virtual white cube resembling a white table was positioned in a grey room. The camera from which participants could see the scene, was placed 0.35 Uu behind the table and 1.3 Uu from the ground, with a tilt of 66° degrees, resembling the same point of view as if participants were looking at their right hand moving on the table in the real world (see Fig. 1 a, left). In the real world, participants were asked to hold an Oculus controller in the right hand, which was mounted on a 3d-printed sliding support created ad hoc (see Fig. 1 a, right). Throughout the experiment, participants controlled the movements across the horizontal plane (x and y) and rotations (across the z axis) of a virtual hand by moving the controller in the real world. The virtual hand was also rendered in a similar position to how participants were holding on the controller and its movements were locked on the z-axis on top of the table (no vertical movements were possible, just horizontal slides). Hand position and rotation along the three axes were recorded throughout the whole experiment with a time resolution of 86.30 Hz (SD = 0.45 Hz). Calibration and setup At the beginning of a session, participant’s sensory threshold was determined by gradually increasing the stimulation intensity until they reported feeling the stimulation. Then, the amperage was modified until participants reported feeling 5 out of 10 pulses (mean: 1.96 mA, min: 1.20 mA, max: 3.40 mA). To ensure that each stimulation was clearly perceivable during the training and experimental sequence, participants received stimulation 1.5-2.0x their subjective threshold, adjusted based on comfortability (mean: 3.63 mA, min: 2.33 mA, max: 6.80 mA). The same intensity was used throughout the experiment. Participants were also asked to report the position at which they felt the stimulation. Since they could have seen the virtual ball touching their fingertip either from the left or from the right side, we tried to ensure that the stimulation was perceived in the centre of the finger. The height of the headset and the lenses focus were adjusted to obtain maximal visual resolution. Moreover, because participants were asked to perform leftwards and rightwards movements with their right hand (see later), we adjusted the correspondence between real-world and virtual-environment movement (i.e., calibration) so that movements in both directions were equally easy and comfortable. Then, the experimenter fitted the EEG cap to the participant and a short training phase of about 5 minutes started. The training phase consisted of 5 trials of the move condition, 5 trials of the stay condition and a full sequence of 25 trials. During the initial 10 sample trials, electrical stimulations were always administered to familiarise participants. During the full training sequence, stimulations were given in a probabilistic fashion, similarly as in the experimental phase. Lastly, during the training phase, we measured the velocity of participants’ movement and used it to adjust the velocity of the moving ball during stay conditions in the experimental phase. The average velocity and the variance across 14 move training trials (out of 25) was calculated. The virtual ball during stay trials moved at an average speed across participants of 0.41 Uu/s (SD = 0.08). Experimental Design In each of the 4 experimental runs of approx. 12 minutes, participants underwent 6 sequences of 25 trials, for a total of 600 trials per participant. A fixation cross appeared in the middle of the field of view of the camera, on the virtual table. To minimise horizontal eye movements, we instructed participants not to follow the moving ball or their hand but to keep their gaze on the cross. On the virtual table there were also two indicator circles (distance +/- 0.2 Uu from the fixation cross). Participants were instructed to either keep their index finger in the circle or move their finger towards the circle located in the opposite side of the virtual table, in the stay and move conditions respectively. At the beginning of each sequence of 25 trials, 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. Each trial began with the virtual ball appearing in the centre of the circle opposite from the participant’s finger. After a delay of 1 second, the fixation cross changed colour for 0.5 seconds. If the cross flashed green, participants were required to actively reach the ball positioned on the opposite side of the table ( move condition). Participants were instructed to move as soon as the cross stopped flashing. If the cross flashed red, they were required to stay still and wait for the ball to reach their fingertip ( stay condition). The ball started to move as soon as the cross stopped flashing. If participants moved during a stay condition, a prompt appeared indicating the wrong execution of the trial. Upon reaching and touching (or touched by) the virtual ball, an electrical shock at the fingertip could have been administered ( touch and no-touch conditions). For a depiction of the experimental paradigm, see Fig. 1 b. Electrical pulses were administered according to a simple probabilistic model with 25%, 50% and 75% probability (i.e., low , equal , high ). Within each sequence, the probability of incurring in a move or stay trial was at chance level. Therefore, participants underwent the same number of move and stay trials and, most importantly, the likelihood of receiving a stimulation was not associated with the probability of moving or staying still. Participants were explicitly informed about this. After subjects touched the virtual ball, they were instructed to stay still and keep their index finger in the newly reached indicator point. After they were touched by the virtual ball, they simply stayed in the same indicator point, waiting for a new trial. One second after touching the ball, the virtual object disappeared, and a new trial started after a random inter trial interval between 1.75 and 2.25 seconds. At the end of each sequence of 25 trials, three circles with the labels “25%”, “50%”, and “75%” appeared on the screen. Each percentage was associated with a probability condition, namely low , equal or high . Participants had to slide their finger in the circle corresponding to the probability condition that they thought was the one underlying the stimulus presentation during the sequence. Behavioural data analysis We analysed accuracy rates only via descriptive statistics, due to scarcity of data-points (only 8 responses per probabilistic state). Response times were calculated from the time of action or ball moving onset to the moment of ball touch. Outliers defined as trials exceeding 3 median absolute deviations were excluded. We fitted a linear mixed effect model having as fixed effects movement type (move or stay), stimulation (touch or no-touch) and probability condition (low, equal, high). Random intercepts were modelled by participants. This was done to check that our velocity personalisation approach across stay and move conditions was successful and that differences in trial lengths across conditions might have driven differences in the electrophysiological correlates. 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 [ 77 ], FieldTrip [ 78 ] and in-house MATLAB scripts. First, bad channels were identified manually and removed, the data were then referenced against the average reference, down-sampled to 512 Hz and high-pass filtered (0.01 Hz, firws, one-pass zero-phase, -6dB cut-off). Subsequently, eye-blinks and horizontal eye-movements were corrected using a topographical confound approach [ 79 , 80 ]. Next, we defined trials as the recording epochs going from at least half a second before instruction cue to at least one second after ball-touch. Due to the great variability in trial length in our experiment, this resulted in a trial definition that spanned from − 3s to 3s around ball-touch events. A low pass filter was applied (45 Hz, firws, one-pass zero-phase, -6dB cut-off) and EEG data were baseline corrected with respect to the pre-stimulus interval from − 10 to -5 ms. Finally, each trial was visually inspected and bad data segments were marked and excluded from the final dataset. 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 response time outlier and (ii) were artifact free. On average, we excluded a total of 12.32% of trials (SD = 8.34%). 1.29% (SD = 1.10%) of the total trials were response time outliers and 11.28% (SD = 8.16%) contained artefactual segments, while 0.25% were both. After exclusion, an average of 526 trials (SD = 50) survived. Moreover, out of the initial 26 participants, one was excluded because they never chose the 75% probability condition as a response. The results presented later are therefore computed on 25 participants. EEG data analysis Main analyses were done within the SPM framework for M/EEG analysis. This method requires the preprocessed, epoched channel data to be linearly interpolated in a 32 X 32 grid for each time-point. Because we were specifically interested in the electrophysiological responses evoked by the electrical stimulation, we selected a window spanning from − 50 to 500 ms around ball-touch. In this way, we obtained one 3-D image of dimensions 32x32x283 (scalp space x intra-trial samples) per trial. First-level multiple regression models were then specified and estimated in SPM12, using dummy regressors for each possible condition combination (12 in total). This allowed for the regression of the EEG data over trials, separately for each voxel, which resulted in a 3-D β estimate for each condition with the same dimensionality as the initial images. Each β estimate, without the inclusion of further regressors or covariates, was mathematically equivalent to computing the ERPs of each condition. Second level analyses consisted of a mass-univariate multiple regression analysis of the individual β scalp-time images with a design matrix having 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 our model is equivalent to a 2x2x3 ANOVA. All analyses were performed with a cluster-forming threshold of p < .001; only clusters surviving at the cluster-level with family-wise error (FWE) corrected threshold of p FWE <0.05 [ 81 ] are reported here. The direct comparison of potentials evoked by an electrical stimulation after a movement or while staying still will lead to a spurious difference, driven by the additive effect of the motor act in the move condition. In contingent paradigms, a motor-only condition is subtracted from a motor-and-sensory condition to obtain a corrected ERP of self-produced stimulus perception to be compared against a sensory-only condition. In a similar fashion, this problem was addressed in our design by selectively exploring the interaction term across conditions (i.e., stimulation x movement, stimulation x probability, stimulation x movement x probability). In this way, we could directly compare the potentials evoked by the self- and externally-produced electrical pulses, net of move or stay no-touch control conditions (similar approaches were also adopted in: Kilteni & Ehrsson, 2020). Results Behavioural results Regarding accuracy measures, participants’ belief matched the real underlying state in 80% in the low condition, in the 65.5% of the equal and in the 69.5% of the high condition (see Fig. 2 a). Participants’ performance was well above chance level (33.3%), i.e., the participants were able to perform the task. We then fitted a linear mixed effect model to the response times to test for differences across condition. The model revealed a main effect of movement type ( F 1,13104 = 1646.9, p < 0.001) but no main effect of stimulation ( F 1,13104 = 0.008, p = 0.929) and probability condition ( F 2,13104 = 0.476, p = 0.621) or any interaction effect (see Fig. 2 b). Electrophysiological results The averaged electrophysiological activity was characterised by an increasing pre-stimulus negativity in both the stay and move conditions that reached its peak at stimulus onset. The negativity observed in stay trials before stimulus administration most likely reflects a process of tactile stimulus anticipation [ 82 – 85 ], while the negativity observed during move trials is due also to motor preparatory processes [ 86 , 87 ] and slow frequencies related to motor execution [ 88 – 90 ] (Fig. 3 a, top). Thus, in the pre-stimulus period, time-locked electrophysiological activity differed between move and stay conditions. These differences were accounted for by the inclusion of the no-touch control condition. From stimulus onset, our paradigm elicited the typical somatosensory responses [ 91 , 92 ] (Fig. 3 a, bottom). Figure 3 b shows the difference between the average touch trials across participants subtracted of the no-touch trials across electrodes with the expected evoked potentials, i.e., P50, N140 and P300 resulting from electrical stimulation of the right index finger. The corresponding topographic maps (Fig. 3 b, top) confirm the left lateralized voltage distribution of the somatosensory evoked potential (SEP) components on the scalp. The interaction stimulation x movement showed 4 main clusters reaching significance (see Fig. 4 a). The earliest significant difference that survived cluster-level FWE correction in the potentials evoked by the electrical stimulation, net of the corresponding no-touch control condition, was a positive deflection observed in frontal electrodes, starting at 0.08s and ending at 0.1s post stimulus (peak at 90ms, centroid: F4, p FWE = 0.048, F = 26.06). Our analyses also showed a cluster in contralateral parietal electrodes with an inverse amplitude as compared to the latter (but with the same trend, i.e., self-generated stimuli elicited greater suppression). This cluster did not survive correction for multiple comparison (peak at 88ms, centroid: TP7, p FWE = 0.100, F = 20.07) but was significant at the cluster-level uncorrected threshold ( p uncorr = 0.034). In this earlier window, the SEP generated during move conditions was suppressed compared to the stay condition. More specifically, in frontal electrodes, the positive potential (P100) generated as the consequence of a movement was reduced (less positive) compared to the corresponding potential generated while staying still. In parietal contralateral electrodes, the negative voltage (N100) generated in move trials was suppressed (less negative) when confronted with the same component evoked during stay trials (see supplementary Figure S1 ). Later in time, the analyses revealed two clusters surviving cluster-level FWE correction in frontal centro-parietal electrodes (peak: 158ms, centroid: C3, p FWE = 0.005, F = 17.85) and centro-frontal regions (peak = 221ms, centroid = Fz, p FWE = 0.009, F = 14.96) respectively. Within the first cluster, the potential evoked by self-generated electrical stimulations was reduced in amplitude compared to the corresponding response in passive trials. In the second cluster, the results indicated an enhancement effect, such that in centro-frontal electrodes the average SEP showed a greater voltage during move trials. At first look, it might seem that the two potentials share similar substrates, however, a detailed analysis of the scalp topographies and the centroid of the respective clusters (see supplementary Figure S2) indicates that the difference at around 200ms between self- and other-generated stimulations is greater in a centro-lateral electrode (C3) and then extends in time to central parietal bilateral sites. We will refer to this component as the P200, mostly because the centroid peaks much earlier in time (158 ms), compared to the effect observed at frontal sites (221 ms). Lastly, our results pointed out a cluster within parietal contralateral electrodes (peak: 436ms, centroid: C5, p FWE < 0.001, F = 25.54) in which actively generated stimuli generated a suppressed negative potential compared to passively attended ones. The interaction of stimulation x probability revealed one main cluster that reached significance at FWE correction for multiple comparison, located at centro-frontal electrodes over a time window between 0.348 and 0.387s after stimulus onset. This cluster will be referred to as the P300 (centroid: FCz, p FWE = 0.008, F = 11.94). The P300 showed a parametric modulation, in the way that low probability conditions exerted a greater amplitude, while the high probability condition resulted in a suppression of the waveform (see Fig. 4 b). The interaction stimulation x probability x movement resulted in no significant clusters. Discussion The investigation of sensory attenuation requires a multitude of control of concurring factors that could otherwise explain the attenuation normally observed between self- and externally-generated stimuli. With the present experiment we validated a novel VR paradigm that allowed for a more comprehensive control of stimulus properties, attentional requirements and other predictability factors during a modified probabilistic learning task. The electrophysiological results show that early- (P100 and N100, although not significant) and mid-latency (P200) ERP components are suppressed for self- compared to externally-generated stimulations, which is a well-known finding of sensory attenuation. Moreover, self-generated stimuli elicited an enhancement of a later component in fronto-central sites, as well as a suppression of a later waveform at contralateral electrodes. The contrasting nature of these effects to stimulus predictability indicates that the phenomenon of sensory attenuation prevailed when controlling for other factors. Stimulus predictability was instead associated with a later attenuation effect for high-probability stimuli of the fronto-central P300. Unlike previous studies investigating sensory attenuation, because we used VR to present somatosensory stimuli with different probabilities, we could obtain control move and stay trials that did not result in any electrical stimulation. This was important because the pre-stimulus waveform differed substantially across movement conditions. By specifically investigating the interaction term between movement and stimulation type, we could directly compare the evoked effect of touch in the different movement and probability conditions while controlling for the effects induced by the generation of a motor plan ( move condition) or by passively waiting for a stimulation to occur ( stay condition). Moreover, this approach also avoids the possible problem that directly subtracting a control condition from a condition of interest might induce a spurious difference driven by the control condition itself [ 22 ]. Thus, we can assume that the present analyses allowed to directly compare the potentials evoked by a stimulus when self- or externally-generated (net of a control no-touch move or stay condition) across different stimulus predictability conditions. Concerning the electrophysiological data, results revealed a suppression for self-generated stimuli at around 100 ms post-stimulus (P100 and N100, although not significant) and at around 200 ms (P200). Components within the same time window have been associated with conscious perception [ 93 – 95 ] and, more relevant to the scope of this paper, have also been demonstrated to be modulated by stimulus intensity [ 96 – 99 ], attentional requirements [ 45 – 51 ] and, despite evidence in the somatosensory modality is scarce, also by stimulus predictability in time [ 11 , 55 – 57 ]. These factors are also thought to impair the investigation of sensory attenuation as they might conflate the electrophysiological correlates of self-generated stimulations when left uncontrolled [ 29 , 43 , 53 ]. In trying to account for these aspects, we developed a VR setup that, similar to earlier studies in sensory attenuation in virtual environments, was designed to enable participants to perform naturalistic arm movement, reach and interact with virtual objects [ 70 , 73 ], while controlling for the intensity and timing of tactile stimuli presented to participants [ 71 , 72 ]. As in previous VR setups [ 69 ], stimuli were directly triggered by touching the virtual object and were independent from the velocity at which the ball approached (or got approached by) participants’ index finger. In this way, we could account for the additional mechanical stimulation that would otherwise be present when pressing a button to generate a stimulation [ 29 , 44 ]. Attentional resources were also controlled across conditions, not only because immersive VR settings might lead to higher levels of concentration when performing a task [ 100 , 101 ] but also, given that active hand and virtual ball movements occurred to and from both sides, we ensured spatial attention to be equal across conditions. More importantly, attentional resources were balanced across stay and move trials . Like previous studies that required participants to undergo interfering tasks such as counting the number of stimulations [ 43 , 102 ] or estimating the time interval between consecutive stimulations [ 103 ], in the present study volunteers needed to attend stimuli independently of whether they were presented in the move or stay conditions to form some expectations about their appearance. Volunteers could always predict the moment in time in which the ball would have administered (or not) the electrical pulse, not only in move trials as they were performing goal-directed actions, but also in stay trials because they could see the virtual ball approaching their index finger. Similar controls over stimulus temporal predictability have been adopted before [ 43 ]. Ball velocity was adjusted to each participants’ personal pace, as measured during the training phase. Response times comparison across conditions indicated that participants moved slower in move trials than the ball during stay trials. As a possible explanation, it is unlikely that participants kept the same movement pace from the training phase throughout the experiment, leading to an underestimation of the personalised response times during the training phase. However, it is unlikely that this difference affected the electrophysiological results. If the different trial lengths had led to a difference between the electrophysiological correlates at any point of the stay and move conditions, it would have affected both the touch and the respective control no-touch trials similarly, and therefore it should cancel out. Attenuation of components at around 100 ms post stimulus is a common result obtained by electrophysiological studies investigating sensory attenuation in the auditory [ 30 , 34 , 42 , 43 , 104 ], visual [ 37 – 39 , 105 , 106 ] and somatosensory domain [ 40 , 41 ]. Similarly, suppression of P200 has been often reported in electroencephalography studies that investigated sensory attenuation either in the auditory [ 34 ], visual [ 37 , 39 ] and somatosensory modality [ 41 ]. By replicating the electrophysiological suppression for self-generated movement, we believe to also validate the present novel VR paradigm, which adds on the scarce literature of existing VR studies that investigated this phenomenon via EEG [ 73 ]. Differently from previous evidence, however, our setup also holds the advantage of being able to control for a series of confounding factors, not only replicating previous electrophysiological findings of sensory attenuation but also substantiating the validity of this phenomenon. Assuming that sensory attenuation is not better explained by other concurring factors, it is likely that the electrophysiological attenuation for self-generated stimulations represents a reduction in the perceived intensity of the consequences of one’s own movements [ 10 ]. Corroborating this hypothesis, previous behavioural studies reported a reduced sensitivity [ 14 , 107 ] or intensity [ 12 , 13 ] in the perception of self-generated stimuli. Furthermore, previous results have demonstrated a direct link between subjective intensity reports and the somatosensory N140 and P200 [ 41 ] and the visual P2 [ 39 ], thus substantiating the idea that suppression of early- and mid- latency ERPs is accompanied by a decrease in subjective intensity. Although we didn’t collect stimulus intensity ratings and, thus, we couldn’t directly test these findings, it is plausible that our results reflect the same subjective attenuation. In trying to disentangle the underlying mechanisms of sensory attenuation, we did not only exert a comprehensive control over several intervening factors, but we also modulated the expectation of receiving a stimulation across conditions, regardless of whether it was self- or externally-generated. Previous studies that formally investigated this problem required participants to undergo sequences of highly predictable stimulation identities (constant or more predictable pitch) and totally unpredictable stimulations (variable pitch) [ 20 , 42 ] or by contrasting stimuli that were congruent or incongruent to previous learned action-effect contingencies [ 14 , 66 ]. Evidence indicates that sensory attenuation is not better explained by stimulus predictions, either according to electrophysiological measures [ 20 , 42 ] or subjective intensity rates [ 14 , 66 ]. Our results indicate that self-generated stimulations exert a greater electrophysiological suppression (in the N100/P100 complex and P200) compared to externally administered stimuli, independent of the expectation of stimulus administration. Therefore, sensory attenuation can’t be better explained by stimulation predictability and might indicate that this phenomenon is not based upon unspecific prediction mechanisms, but that it is rather driven by specific motor predictions that are put forward when performing an action [ 27 , 108 , 109 ]. Contrary to our expectation, however, later in time our results also pointed out another component at centro-frontal electrodes that showed an enhancement for self-produced compared to externally produced stimuli. Similar enhancements of centro-frontal positivities for self-generated stimuli, although sparse, have been described previously. Bednark and colleagues [ 42 ] reported that the P3b was enhanced for self- compared to externally-produced stimuli, with a greater increase for stimulations that were incongruent to predictions. The authors attribute this enhancement effect to a possible link between the context updating hypothesis of the auditory P3 and the comparator model underlying sensory attenuation. More specifically, they hypothesise that in both mechanisms a stimulation is compared against an internal model which generates a prediction about said stimulation (either motor-specific and not); it is possible therefore that the anticipation of a stimulation is codetermined by both motor and non-motor predictions (see also Waszak & Herwig, 2007). Similar claims have also been advanced by Harrison and colleagues [ 43 ], who speculated the existence of an “additive” effect of different predictive information. This claim might be substantiated by evidence of enhanced stimulus processing due to movement [ 111 , 112 ]. Although speculative, our results of enhanced fronto-central positivities may align with these explanations. Lastly, another component that showed a modulation for self- compared to externally-generated stimulations was a negativity at parietal contralateral electrodes. Some findings suggest that similar potentials are related to somatosensory information maintenance in working memory [ 113 – 115 ]. Although participants were not actively required to rehearse any information during the task, it is plausible that they maintained some representations of the stimuli in memory to execute the learning task. Although, the reported suppression of the later potential for self- compared to externally generated stimuli might also represent a facilitation of stimulus processing specific to actively generated stimuli [ 111 , 112 ]. To our knowledge, the present study is the first to report similar effects in late-latency components for self- compared to externally-generated stimulations and its interpretations are to be considered exclusively as speculative, especially considering that similar potentials are known to be affected by methodological issues [ 116 ]. Concerning the effect of stimulus expectation, our analyses revealed another centro-frontal cluster (P300) that showed a parametric effect over probability conditions (greater voltages for low-probability stimulations), independently whether participants actively produced the stimulation or passively perceived it. In the literature, the P300 has been oftentimes associated to the detection of deviant stimuli [ 117 – 120 ] or, more generally, stimulus novelty or saliency [ 117 , 121 – 123 ] and it seems to be involved in more endogenous processes, such as post-perceptual stimulus evaluation [ 124 – 127 ]. In line with this evidence, our results indicated that the stimulation drove a stronger effect when it was more unexpected (low probability conditions). This ultimately indicates that stimulus probability still affects stimulus perception, independently of the movement type but in a much later time window. Conclusions Through our novel VR paradigm, we accounted for differences between self- and externally-produced stimuli in an ecological setup while controlling for other explaining factors that could impair the investigation of sensory attenuation, such as stimulus properties, attentional resources and stimulus predictability in time. Additionally, the setup was implemented to modulate stimulus expectation across conditions. Our results revealed an attenuation effect for self-generated stimulations in the N100, P100, P200, and late-latency SEPs, as well as an enhancement of mid-latency potentials. These effects were independent from stimulus predictability, which instead modulated the P300 component. Therefore, we can conclude that the attenuation effect observed in the present experiment is not better explained by other explaining factors and is resilient to predictability manipulations. Sensory attenuation, therefore, appears to represent a standalone phenomenon that is not otherwise explained by stimulus expectation. Declarations Acknowledgments We thank Marlon Esmeyer for comments on the manuscript. Author Contributions GG: Conceptualization, Data acquisition, Data curation, Methodology, Formal analysis, Writing – original draft. TN: Conceptualization, Methodology, Supervision, Writing – review and editing. FB: Conceptualization, Methodology, Supervision, Writing – review and editing. Data availability statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request and by 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_Pred Additional information Funding 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. Competing interests The author(s) declare no competing interests. References Friston, K. The free-energy principle: a unified brain theory? Nat Rev Neurosci 11 , 127–138 (2010). Rao, R. P. N. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2 , 79–87 (1999). Walsh, K. S., McGovern, D. P., Clark, A. & O’Connell, R. G. Evaluating the neurophysiological evidence for predictive processing as a model of perception. Annals of the New York Academy of Sciences 1464 , 242–268 (2020). Miall, R. C. & Wolpert, D. M. Forward Models for Physiological Motor Control. Neural Networks 9 , 1265–1279 (1996). Friston, K. J., Daunizeau, J., Kilner, J. & Kiebel, S. J. Action and behavior: a free-energy formulation. Biol Cybern 102 , 227–260 (2010). Tsakiris, M., Haggard, P., Franck, N., Mainy, N. & Sirigu, A. A specific role for efferent information in self-recognition. Cognition 96 , 215–231 (2005). Woźniak, M. How to grow a self: development of the self in a Bayesian brain. Preprint at https://doi.org/10.31234/osf.io/6e3ad (2019). Creutzfeldt, O., Ojemann, G. & Lettich, E. Neuronal activity in the human lateral temporal lobe. Exp Brain Res 77 , 476–489 (1989). Paus, T., Perry, D. W., Zatorre, R. J., Worsley, K. J. & Evans, A. C. Modulation of Cerebral Blood Flow in the Human Auditory Cortex During Speech: Role of Motor-to-sensory Discharges. European Journal of Neuroscience 8 , 2236–2246 (1996). 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). Shergill, S. S., Bays, P. M., Frith, C. D. & Wolpert, D. M. Two Eyes for an Eye: The Neuroscience of Force Escalation. Science 301 , 187–187 (2003). Walsh, L. D., Taylor, J. L. & Gandevia, S. C. Overestimation of force during matching of externally generated forces. The Journal of Physiology 589 , 547–557 (2011). Wolpe, N. et al. Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nat Commun 7 , 13034 (2016). 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. European Journal of Neuroscience 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. International Journal of Psychophysiology 70 , 137–143 (2008). Kaiser, J. & Schütz-Bosbach, S. Sensory attenuation of self-produced signals does not rely on self-specific motor predictions. European Journal of Neuroscience 47 , 1303–1310 (2018). Kilteni, K. & Ehrsson, H. H. Functional Connectivity between the Cerebellum and Somatosensory Areas Implements the Attenuation of Self-Generated Touch. J. Neurosci. 40 , 894–906 (2020). Klaffehn, A. L., Baess, P., Kunde, W. & Pfister, R. Sensory attenuation prevails when controlling for temporal predictability of self- and externally generated tones. Neuropsychologia 132 , 107145 (2019). Limanowski, J. et al. Action-Dependent Processing of Touch in the Human Parietal Operculum and Posterior Insula. Cerebral Cortex 30 , 607–617 (2020). Martikainen, M. H., Kaneko, K. & Hari, R. Suppressed Responses to Self-triggered Sounds in the Human Auditory Cortex. Cerebral Cortex 15 , 299–302 (2005). Shergill, S. S. et al. Modulation of somatosensory processing by action. Neuroimage 70 , 356–362 (2013). Wolpert, D. M. & Flanagan, J. R. Motor prediction. Current Biology 11 , R729–R732 (2001). Haggard, P., Clark, S. & Kalogeras, J. Voluntary action and conscious awareness. Nat Neurosci 5 , 382–385 (2002). Horváth, J. Action-related auditory ERP attenuation: Paradigms and hypotheses. Brain Research 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). 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’. International Journal of Psychophysiology 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). Gentsch, A. & Schütz-Bosbach, S. I Did It: Unconscious Expectation of Sensory Consequences Modulates the Experience of Self-agency and Its Functional Signature. Journal of Cognitive Neuroscience 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). 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). 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). Bednark, J. G., Poonian, S. K., Palghat, K., McFadyen, J. & Cunnington, R. Identity-specific predictions and implicit measures of agency. Psychology of Consciousness: Theory, Research, and Practice 2 , 253–268 (2015). Harrison, A. W. et al. Sensory attenuation is modulated by the contrasting effects of predictability and control. NeuroImage 237 , 118103 (2021). Horváth, J. The role of mechanical impact in action-related auditory attenuation. Cogn Affect Behav Neurosci 14 , 1392–1406 (2014). Clauwaert, A., Torta, D. M., Forster, B., Danneels, L. & Van Damme, S. Somatosensory attentional modulations during pain-related movement execution. Exp Brain Res 238 , 1169–1176 (2020). Fiorio, M. et al. Enhancing non-noxious perception: Behavioural and neurophysiological correlates of a placebo-like manipulation. Neuroscience 217 , 96–104 (2012). Forster, B. & Gillmeister, H. ERP investigation of transient attentional selection of single and multiple locations within touch. Psychophysiology 48 , 788–796 (2011). Fujiwara, N. et al. Second somatosensory area (SII) plays a significant role in selective somatosensory attention. Cognitive Brain Research 14 , 389–397 (2002). 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. Journal of Neurophysiology 80 , 2215–2221 (1998). Horváth, J., Maess, B., Baess, P. & Tóth, A. Action–Sound Coincidences Suppress Evoked Responses of the Human Auditory Cortex in EEG and MEG. Journal of Cognitive Neuroscience 24 , 1919–1931 (2012). 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. Psychological Bulletin 139 , 133–151 (2013). Schafer, E. W. P. & Marcus, M. M. Self-Stimulation Alters Human Sensory Brain Responses. Science 181 , 175–177 (1973). Costa-Faidella, J., Baldeweg, T., Grimm, S. & Escera, C. Interactions between “What” and “When” in the Auditory System: Temporal Predictability Enhances Repetition Suppression. J. Neurosci. 31 , 18590–18597 (2011). Nara, S. et al. Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli. NeuroImage 239 , 118314 (2021). Schwartze, M., Rothermich, K., Schmidt-Kassow, M. & Kotz, S. A. Temporal regularity effects on pre-attentive and attentive processing of deviance. Biological Psychology 87 , 146–151 (2011). Hsu, Y.-F., Hämäläinen, J. A. & Waszak, F. Temporal expectation and spectral expectation operate in distinct fashion on neuronal populations. Neuropsychologia 51 , 2548–2555 (2013). Hsu, Y.-F., Hämäläinen, J. A. & Waszak, F. Repetition suppression comprises both attention-independent and attention-dependent processes. NeuroImage 98 , 168–175 (2014). Hsu, Y.-F., Bars, S. L., Hämäläinen, J. A. & Waszak, F. Distinctive Representation of Mispredicted and Unpredicted Prediction Errors in Human Electroencephalography. J. Neurosci. 35 , 14653–14660 (2015). Hsu, Y.-F., Hämäläinen, J. A. & Waszak, F. Both attention and prediction are necessary for adaptive neuronal tuning in sensory processing. Front. Hum. Neurosci. 8 , (2014). Lange, K. Brain correlates of early auditory processing are attenuated by expectations for time and pitch. Brain and Cognition 69 , 127–137 (2009). Vroomen, J. & Stekelenburg, J. J. Visual Anticipatory Information Modulates Multisensory Interactions of Artificial Audiovisual Stimuli. Journal of Cognitive Neuroscience 22 , 1583–1596 (2010). Roth, W. T. Auditory Evoked Responses to Unpredictable Stimuli. Psychophysiology 10 , 125–138 (1973). Demaire, C. & Coquery, J.-M. Effects of selective attention on the late components of evoked potentials in man. Electroencephalography and Clinical Neurophysiology 42 , 702–704 (1977). Desantis, A., Mamassian, P., Lisi, M. & Waszak, F. The prediction of visual stimuli influences auditory loudness discrimination. Exp Brain Res 232 , 3317–3324 (2014). 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). Choi, J. W. et al. Neural Applications Using Immersive Virtual Reality: A Review on EEG Studies. IEEE Transactions on Neural Systems and Rehabilitation Engineering 31 , 1645–1658 (2023). Fritz, C., Flick, M. & Zimmermann, E. Tactile motor attention induces sensory attenuation for sounds. Consciousness and Cognition 104 , 103386 (2022). Dong, X. & Bao, M. The growing sensory suppression on visual perception during head-rotation preparation. PsyCh Journal 10 , 499–507 (2021). Kiepe, F., Kraus, N. & Hesselmann, G. Virtual occlusion effects on the perception of self-initiated visual stimuli. Consciousness and Cognition 107 , 103460 (2023). Vasser, M., Vuillaume, L., Cleeremans, A. & Aru, J. Waving goodbye to contrast: self-generated hand movements attenuate visual sensitivity. Neuroscience of Consciousness 2019 , niy013 (2019). Feder, S., Miksch, J., Grimm, S., Krems, J. F. & Bendixen, A. Using event-related brain potentials to evaluate motor-auditory latencies in virtual reality. Front. Neuroergonomics 4 , (2023). 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). Friston, K. Statistical Parametric Mapping: The Analysis of Funtional Brain Images . (Elsevier/Academic Press, Amsterdam ; Boston, 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 2011 , 1:1-1:9 (2011). Berg, P. & Scherg, M. A multiple source approach to the correction of eye artifacts. Electroencephalography and Clinical Neurophysiology 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. Journal of Clinical Neurophysiology 19 , 113 (2002). Flandin, G. & Friston, K. J. Topological Inference. in Brain Mapping (ed. Toga, A. W.) 495–500 (Academic Press, Waltham, 2015). doi:10.1016/B978-0-12-397025-1.00322-5. 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 Boxtel, G. J. M. & Böcker, K. B. E. Cortical Measures of Anticipation. Journal of Psychophysiology 18 , 61–76 (2004). van Ede, F., de Lange, F. P. & Maris, E. Anticipation Increases Tactile Stimulus Processing in the Ipsilateral Primary Somatosensory Cortex. Cerebral 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 Research 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. Clinical Neurophysiology 121 , 1285–1292 (2010). Leuthold, H. & Jentzsch, I. Distinguishing neural sources of movement preparation and execution: An electrophysiological analysis. Biological Psychology 60 , 173–198 (2002). Kalogianni, K. et al. Disentangling Somatosensory Evoked Potentials of the Fingers: Limitations and Clinical Potential. Brain Topogr 31 , 498–512 (2018). Schaefer, M., Mühlnickel, W., Grüsser, S. M. & Flor, H. Reproducibility and Stability of Neuroelectric Source Imaging in Primary Somatosensory Cortex. Brain Topogr 14 , 179–189 (2002). Schubert, R., Blankenburg, F., Lemm, S., Villringer, A. & Curio, G. Now you feel it—now you don’t: ERP correlates of somatosensory awareness. Psychophysiology 43 , 31–40 (2006). Auksztulewicz, R., Spitzer, B. & Blankenburg, F. Recurrent Neural Processing and Somatosensory Awareness. J. Neurosci. 32 , 799–805 (2012). Forschack, N., Nierhaus, T., Müller, M. M. & Villringer, A. Dissociable neural correlates of stimulation intensity and detection in somatosensation. NeuroImage 217 , 116908 (2020). 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). Onishi, H. et al. Effect of the number of pins and inter-pin distance on somatosensory evoked magnetic fields following mechanical tactile stimulation. Brain Research 1535 , 78–88 (2013). Torquati, K. et al. Comparison between SI and SII responses as a function of stimulus intensity. NeuroReport 13 , 813 (2002). Miltner, W., Johnson, R., Braun, C. & Larbig, W. Somatosensory event-related potentials to painful and non-painful stimuli: effects of attention. Pain 38 , 303–312 (1989). Iidal, Y. et al. The Effect of Immersive Head Mounted Display on a Brain Computer Interface Game. in Advances in Affective and Pleasurable Design (eds. Chung, W. & Shin, C. S.) 211–219 (Springer International Publishing, Cham, 2017). doi:10.1007/978-3-319-41661-8_21. Wan, B. et al. Measuring the Impacts of Virtual Reality Games on Cognitive Ability Using EEG Signals and Game Performance Data. IEEE Access 9 , 18326–18344 (2021). 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). Saupe, K., Widmann, A., Trujillo-Barreto, N. J. & Schröger, E. Sensorial suppression of self-generated sounds and its dependence on attention. International Journal of Psychophysiology 90 , 300–310 (2013). Heinks-Maldonado, T. H., Mathalon, D. H., Gray, M. & Ford, J. M. Fine-tuning of auditory cortex during speech production. Psychophysiology 42 , 180–190 (2005). Benazet, M., Thénault, F., Whittingstall, K. & Bernier, P.-M. Attenuation of visual reafferent signals in the parietal cortex during voluntary movement. J Neurophysiol 116 , 1831–1839 (2016). Csifcsák, G. et al. Action-associated modulation of visual event-related potentials evoked by abstract and ecological stimuli. Psychophysiology 56 , e13289 (2019). Roussel, C., Hughes, G. & Waszak, F. A preactivation account of sensory attenuation. Neuropsychologia 51 , 922–929 (2013). Cullen, K. E. Sensory signals during active versus passive movement. Current Opinion in Neurobiology 14 , 698–706 (2004). Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An Internal Model for Sensorimotor Integration. Science 269 , 1880–1882 (1995). Waszak, F. & Herwig, A. Effect anticipation modulates deviance processing in the brain. Brain Research 1183 , 74–82 (2007). Morillon, B. & Baillet, S. Motor origin of temporal predictions in auditory attention. Proceedings of the National Academy of Sciences 114 , E8913–E8921 (2017). Korka, B., Schröger, E. & Widmann, A. The encoding of stochastic regularities is facilitated by action-effect predictions. Sci Rep 11 , 6790 (2021). Jolicœur, P., Brisson, B. & Robitaille, N. Dissociation of the N2pc and sustained posterior contralateral negativity in a choice response task. Brain Research 1215 , 160–172 (2008). Katus, T., Grubert, A. & Eimer, M. Electrophysiological Evidence for a Sensory Recruitment Model of Somatosensory Working Memory. Cerebral Cortex 25 , 4697–4703 (2015). Katus, T., Müller, M. M. & Eimer, M. Sustained Maintenance of Somatotopic Information in Brain Regions Recruited by Tactile Working Memory. J. Neurosci. 35 , 1390–1395 (2015). Alday, P. M. How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits. Psychophysiology 56 , e13451 (2019). Polich, J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol 118 , 2128–2148 (2007). Polich, J., Brock, T. & Geisler, M. W. P300 from auditory and somatosensory stimuli: probability and inter-stimulus interval. International Journal of Psychophysiology 11 , 219–223 (1991). Tarkka, I. M., Micheloyannis, S. & Stokić, D. S. Generators for human P300 elicited by somatosensory stimuli using multiple dipole source analysis. Neuroscience 75 , 275–287 (1996). Yamaguchi, S. & Knight, R. T. P300 generation by novel somatosensory stimuli. Electroencephalography and Clinical Neurophysiology 78 , 50–55 (1991). Horváth, J., Winkler, I. & Bendixen, A. Do N1/MMN, P3a, and RON form a strongly coupled chain reflecting the three stages of auditory distraction? Biological Psychology 79 , 139–147 (2008). Light, G. A., Swerdlow, N. R. & Braff, D. L. Preattentive sensory processing as indexed by the MMN and P3a brain responses is associated with cognitive and psychosocial functioning in healthy adults. J Cogn Neurosci 19 , 1624–1632 (2007). Nakajima, Y. & Imamura, N. Relationships between attention effects and intensity effects on the cognitive N140 and P300 components of somatosensory ERPs. Clinical Neurophysiology 111 , 1711–1718 (2000). Cohen, M. A., Ortego, K., Kyroudis, A. & Pitts, M. Distinguishing the Neural Correlates of Perceptual Awareness and Postperceptual Processing. J. Neurosci. 40 , 4925–4935 (2020). Förster, J., Koivisto, M. & Revonsuo, A. ERP and MEG correlates of visual consciousness: The second decade. Consciousness and Cognition 80 , 102917 (2020). Pitts, M. A., Padwal, J., Fennelly, D., Martínez, A. & Hillyard, S. A. Gamma band activity and the P3 reflect post-perceptual processes, not visual awareness. NeuroImage 101 , 337–350 (2014). Schröder, P., Nierhaus, T. & Blankenburg, F. Late cortical potentials are not a reliable marker of somatosensory awareness. 2020.10.01.322651 Preprint at https://doi.org/10.1101/2020.10.01.322651 (2020). Additional Declarations No competing interests reported. Supplementary Files GianniniNierhausBlankenburgSUPPLEMENTARYMATERIALS.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Dec, 2024 Reviews received at journal 09 Dec, 2024 Reviews received at journal 24 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviewers invited by journal 08 Nov, 2024 Editor assigned by journal 08 Nov, 2024 Editor invited by journal 04 Nov, 2024 Submission checks completed at journal 01 Nov, 2024 First submitted to journal 17 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5281922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375802164,"identity":"09d92f59-2ea5-42e6-b5f8-1dbc27b000b0","order_by":0,"name":"Gianluigi Giannini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNUlEQVRIie3SP0vEMBgG8LcE0uXtnuG0X6FFsArSjn6NlgNnpxu1Ukgndb3BL+GijjkyuEjFvUOPg04Kd4vg4J80LcJdzt0hD4QkbX88lATAxuY/hqjRAAOqZqEGAiFiqdeuk4N+voWk64RmU70mfxDQZC0YDqTbmMQ/Jm2T8YP4CtwX8fFQjyIXVwVCvRsVhDcwOdokoaRRkHE25oCns8unFg8L71aRdm8knTKA6sQgBeyzjlDAVHhcYiC9+4X3JbMpcThzuDSJ+/5LZp+a4Fy1yPOBfBv/QlC3xBRcIfsWdDqSsp6ITRIQnLC0Yikl6rMdTWh4cQNtqFvSamy0XD/eseXkLPHLcrF64zIJnuU8f4XaZ27Zqlex0TL0ZjnBwDgC48h0S97PibofzRZiY2NjYwPwA6FLaShWjsKeAAAAAElFTkSuQmCC","orcid":"","institution":"Neurocomputation and Neuroimaging Unit (NNU), Freie Universität Berlin","correspondingAuthor":true,"prefix":"","firstName":"Gianluigi","middleName":"","lastName":"Giannini","suffix":""},{"id":375802165,"identity":"6f5e0564-890f-43d6-a2c7-2842a8fb0d96","order_by":1,"name":"Till Nierhaus","email":"","orcid":"","institution":"Neurocomputation and Neuroimaging Unit (NNU), Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Till","middleName":"","lastName":"Nierhaus","suffix":""},{"id":375802166,"identity":"2a3c2aee-6e42-4d50-b5c6-732de93bd1ad","order_by":2,"name":"Felix Blankenburg","email":"","orcid":"","institution":"Neurocomputation and Neuroimaging Unit (NNU), Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Blankenburg","suffix":""}],"badges":[],"createdAt":"2024-10-17 10:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5281922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5281922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87244-9","type":"published","date":"2025-01-22T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70506774,"identity":"a66945ea-a10f-4960-a069-8a5c27e90228","added_by":"auto","created_at":"2024-12-03 23:42:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":501799,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental setup. a) On the left, Oculus headset mounted on top of a chinrest, in which the participant puts his head while wearing the EEG cap. The volunteer is holding the oculus controller mounted on the 3D sliding support. On the computer monitor, Unity is displaying a replica of the scene rendered 3D in the VR-headset. On the right, a participant holding on the oculus controller. On the index finger, typical electrode positioning that was used throughout the experiment for administration of electrical stimuli. b) representation of a sequence of trials in the experimental paradigm: at the start of each sequence, participants saw at the centre of the screen an arrow indicating where to position their index finger (left-most frame). Starting from the first row, a depiction of different trial types in the following order: \u003cem\u003emove no-touch\u003c/em\u003e,\u003cem\u003e move touch\u003c/em\u003e,\u003cem\u003estay touch\u003c/em\u003e,\u003cem\u003e stay no-touch\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/6a44434aadc0cbcd648da21e.png"},{"id":70507184,"identity":"e18d2a6c-6296-43b6-8721-22b426b3e19f","added_by":"auto","created_at":"2024-12-03 23:50:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268300,"visible":true,"origin":"","legend":"\u003cp\u003eBehavioural results a) Average accuracy scores obtained by all participants and categorized by probability condition. Error bars represent standard errors; circles are the condition-specific averages obtained by each participant. b) Average response times for each condition. Error bars represent standard errors. The asterisks represent significant differences at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/6edf5d9da512a37dfbfa8425.png"},{"id":70506777,"identity":"dd61d27e-45ac-437c-aa2d-add03bd96b2d","added_by":"auto","created_at":"2024-12-03 23:42:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":735729,"visible":true,"origin":"","legend":"\u003cp\u003eQualitative analysis of potentials evoked by stimulus onset. a) On the left, ERPs for each condition for a subset of centro-lateral electrodes (marked as darker dots in the head shaped topography in the upper left corner of the lower ERP plot). Juxtaposed on top of the ERP plot, histogram of movement onsets. On the bottom, detail of the ERPs for each condition for a subset of centro-lateral electrodes (marked in the head-shape plot in the upper left corner) in the time window that was later brought at the group level analysis. b) Superimposed plot of all electrodes (butterfly plot) of the difference between the averaged touch trials subtracted of the averaged no-touch trials. On top, scalp distributions of the difference between touch and no-touch trials at 50 ms and 110 ms post-stimulus.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/554d6a57677a9ca35fa56a07.png"},{"id":70506776,"identity":"e53fc9db-ea5c-4457-9a9f-f4f487e71d20","added_by":"auto","created_at":"2024-12-03 23:42:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":687337,"visible":true,"origin":"","legend":"\u003cp\u003eElectrophysiological results. (a) Interaction effect stimulation x probability; (b) Interaction effect stimulation x movement. All panels show the ERP plot of the subtraction of each touch condition to the corresponding movement specific average of no-touch conditions and then averaged per condition (averaged across movement types for panel a; averaged across probability conditions for panel b). The average of the electrodes comprising the cluster are plotted. Gray shaded areas represent significant time points with \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.05 and line contours are standard errors. Scalp distributions represent the difference between ERP plots across the significant time window. Bar-plots show the values of each condition across the significant time window, with standard errors. Legend on the upper right side of the image refers to all bar-plots in the panels.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/8252ceff777d5141ce210ba6.png"},{"id":74858380,"identity":"9be7e971-9a19-4b4e-9b4b-9e0786d7eb61","added_by":"auto","created_at":"2025-01-27 16:08:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3175993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/e6f2a50a-3250-4194-aaf4-6dc7b16a88b6.pdf"},{"id":70507185,"identity":"cc32d3ac-9fe3-48ea-ab93-9b0d22e84251","added_by":"auto","created_at":"2024-12-03 23:50:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":382310,"visible":true,"origin":"","legend":"","description":"","filename":"GianniniNierhausBlankenburgSUPPLEMENTARYMATERIALS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5281922/v1/e51b50615e8c24666a11bfb8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOur sensorium appears to use internal models to predict future sensory data and thus also to filter environmental noise from salient information [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], so that our cognitive capacities can be allocated to optimally process information that is innovative or useful [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. We are, however, not only passively immersed in a perceptual world. Every time we move or directly act on our environment, we generate a sequence of sensory data that can be predicted with a higher precision than external data [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This might be responsible, amongst others, for our ability to perceive ourselves as self-standing agents [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or for distinguishing our speech from that produced by others [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the multiple phenomena emerging from this interaction between action and perception, sensory attenuation is the phenomenon that self-generated stimulations are suppressed compared to similar externally-generated stimuli [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], both at the subjective [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and neurophysiological level [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is suggested that at the core of this phenomenon lies a forward model: upon the generation of a motor command, an efference copy is generated to predict the sensory consequences of that movement [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These motor predictions and their observed sensory consequences are then compared. If the prediction matches the sensory re-afference, the self-generated stimulation is attenuated or cancelled-out and therefore perceived as less intense [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, the investigation of the electrophysiological mechanisms underlying sensory attenuation has been largely studied using so-called contingent paradigms [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In these setups, participants undergo three conditions which require them to (i) perform sequences of prompted actions with a contingent sensory consequence, (ii) passively undergo similar sequences of stimuli generated by a computer and (iii) perform sequences of actions without any sensory consequence. Typically, the \u003cem\u003emotor-only\u003c/em\u003e condition is subtracted from the \u003cem\u003emotor-and-sensory\u003c/em\u003e condition to obtain a motor-corrected potential of self-generated stimulation that is then compared to the \u003cem\u003esensory-only\u003c/em\u003e potential of passively perceived stimuli. Electroencephalography (EEG) typically indicates that event-related potentials (ERPs) at around 100 ms and 200 ms post stimulus are attenuated for self- compared to externally-generated stimulations, either in the auditory [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32 CR33\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], visual [\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and, although more scarce in number, in the somatosensory domain [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the extensive work that has been published in recent years, the investigation of sensory attenuation has often revealed to be difficult in controlling for other explanatory factors. First of all, most studies investigating the electrophysiological correlates of sensory attenuation required participants to execute button presses to generate sensory action consequences [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It has been argued, however, that the additional mechanical stimulation from physically pressing a button \u0026ndash; which is entirely perceived during \u003cem\u003emotor-only\u003c/em\u003e conditions \u0026ndash; would be masked by the self-generated sensory stimuli in the \u003cem\u003emotor-and-sensory\u003c/em\u003e condition. The later subtraction of the two ERPs would then result in a smaller component, thus mimicking an attenuation for self-produced stimuli [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, albeit experimental stimulations in sensory attenuation paradigms are kept constant, additional mechanical stimulations exerted upon button press would be dependent on the velocity of the participants\u0026rsquo; movements or the applied force and might therefore remain uncontrolled. Also, task differences in the classical contingent paradigm (i.e., passive listening compared to actively pressing a button to generate a sound) might induce an imbalance in attention requirements across conditions, which could account for the suppression effect reported between self- and externally-generated stimuli. In fact, as it is well known that attention requirements might reduce the electrophysiological responses evoked by a stimulation [\u003cspan additionalcitationids=\"CR46 CR47 CR48 CR49 CR50\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], it has been discussed that the simple execution of a motor task might require to allocate attentional resources away from stimulus perception, which might determine an ERP suppression for self-generated stimulations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Another component that might affect the investigation of sensory attenuation is stimulus predictability [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Previous studies that manipulated stimuli predictions at rest suggested that stimuli that are predictable either in time [\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], in the identity of the stimulation [\u003cspan additionalcitationids=\"CR59 CR60 CR61 CR62\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] or concerning the expectation of receiving a stimulus [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] exert an evoked electrophysiological response that is attenuated compared to the relative unpredictable counterpart. Studies that tried to control for these factors typically report that suppression of self-generated stimuli is resilient to temporal predictability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] or stimulus identity predictions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], despite some findings reporting mixed evidence [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] or proving the contrary [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to address these issues, new methodological instruments might be useful. In particular, Virtual Reality (VR) and head mounted displays are capable of creating immersive settings with a high degree of freedom [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and are especially interesting for the exploration of action and perception interrelations. They allow the creation of setups in which different sensory and motor components are manipulated at will in ways that would not be feasible in other experimental settings. Only a handful of studies adopted such technologies in the investigation of sensory attenuation [\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] and only one research integrated a VR setup with an EEG recording [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study expands on this work by collecting electrophysiological responses to self-generated and passively attended stimuli in a VR setting. The paradigm was designed to compare the electrophysiological potentials evoked by an electrical pulse at the fingertip resulting from a goal-directed action with the same potential when passively attended, net of a no-stimulation control condition (similar to a contingent paradigm). Our design also facilitated a broad and comprehensive control of a plethora of other explaining factors, such as stimulus properties (i.e., intensity and additional stimulations), temporal predictability and attentional requirements. Lastly, because the stimulations were administered in a probabilistic fashion either when self-produced or passively attended, we could also test for the influence of stimulus predictability on sensory attenuation.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe task consisted of a modified probabilistic learning paradigm in VR and concurrent EEG recording. The participants saw a 3-dimensional space with VR glasses in which they either actively reached or passively got touched (\u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e conditions) by a virtual ball that could give them an electrical stimulation (\u003cem\u003etouch\u003c/em\u003e and \u003cem\u003eno-touch\u003c/em\u003e), in a probabilistic fashion (three levels of probability: \u003cem\u003elow\u003c/em\u003e, \u003cem\u003eequal\u003c/em\u003e, \u003cem\u003ehigh\u003c/em\u003e). This paradigm allowed us to directly compare the electrophysiological correlates evoked by a stimulus in both active and passive conditions, while controlling for the subjective expectation of receiving the stimulation.\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003e 26 healthy volunteers (18\u0026ndash;35 years old, mean: 24.36, 16 females, all right-handed), recruited from the student body of the Freie Universit\u0026auml;t Berlin and the general public, participated for monetary compensation or an equivalent in course credit. The study was approved by the ethics committee at the Freie Universit\u0026auml;t Berlin (003/2021), and it was performed in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants prior to the experiment.\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 allowed to minimise electrical and mechanical artifacts generated by wearing the headset directly on top of the EEG cap [\u003cspan additionalcitationids=\"CR75\" citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Somatosensory stimuli were administered with a DS7 isolated bipolar constant current stimulator (Digitimer Limited, Welwyn Garden City, Hertfordshire, UK) via adhesive electrodes (GVB-geliMED GmbH, Bad Segeberg, Germany) attached to the outer side of the right index finger (cathode proximal, anode distal, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, left). The stimuli consisted of electrical rectangular pulses of 0.2 ms duration.\u003c/p\u003e \u003cp\u003eThe VR scene was built using Unity v.2020.3.26f1 (Unity Technologies, San Francisco, California, USA). To mimic as accurately as possible the real-world setup, proportions of the virtual objects were scaled according to a ratio of 1 Unity units (Uu) to 1 real-world meter. A virtual white cube resembling a white table was positioned in a grey room. The camera from which participants could see the scene, was placed 0.35 Uu behind the table and 1.3 Uu from the ground, with a tilt of 66\u0026deg; degrees, resembling the same point of view as if participants were looking at their right hand moving on the table in the real world (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, left). In the real world, participants were asked to hold an Oculus controller in the right hand, which was mounted on a 3d-printed sliding support created ad hoc (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, right). Throughout the experiment, participants controlled the movements across the horizontal plane (x and y) and rotations (across the z axis) of a virtual hand by moving the controller in the real world. The virtual hand was also rendered in a similar position to how participants were holding on the controller and its movements were locked on the z-axis on top of the table (no vertical movements were possible, just horizontal slides). Hand position and rotation along the three axes were recorded throughout the whole experiment with a time resolution of 86.30 Hz (SD\u0026thinsp;=\u0026thinsp;0.45 Hz).\u003c/p\u003e \u003cp\u003eCalibration and setup\u003c/p\u003e \u003cp\u003e At the beginning of a session, participant\u0026rsquo;s sensory threshold was determined by gradually increasing the stimulation intensity until they reported feeling the stimulation. Then, the amperage was modified until participants reported feeling 5 out of 10 pulses (mean: 1.96 mA, min: 1.20 mA, max: 3.40 mA). To ensure that each stimulation was clearly perceivable during the training and experimental sequence, participants received stimulation 1.5-2.0x their subjective threshold, adjusted based on comfortability (mean: 3.63 mA, min: 2.33 mA, max: 6.80 mA). The same intensity was used throughout the experiment. Participants were also asked to report the position at which they felt the stimulation. Since they could have seen the virtual ball touching their fingertip either from the left or from the right side, we tried to ensure that the stimulation was perceived in the centre of the finger.\u003c/p\u003e \u003cp\u003eThe height of the headset and the lenses focus were adjusted to obtain maximal visual resolution. Moreover, because participants were asked to perform leftwards and rightwards movements with their right hand (see later), we adjusted the correspondence between real-world and virtual-environment movement (i.e., calibration) so that movements in both directions were equally easy and comfortable. Then, the experimenter fitted the EEG cap to the participant and a short training phase of about 5 minutes started. The training phase consisted of 5 trials of the \u003cem\u003emove\u003c/em\u003e condition, 5 trials of the \u003cem\u003estay\u003c/em\u003e condition and a full sequence of 25 trials. During the initial 10 sample trials, electrical stimulations were always administered to familiarise participants. During the full training sequence, stimulations were given in a probabilistic fashion, similarly as in the experimental phase. Lastly, during the training phase, we measured the velocity of participants\u0026rsquo; movement and used it to adjust the velocity of the moving ball during \u003cem\u003estay\u003c/em\u003e conditions in the experimental phase. The average velocity and the variance across 14 \u003cem\u003emove\u003c/em\u003e training trials (out of 25) was calculated. The virtual ball during \u003cem\u003estay\u003c/em\u003e trials moved at an average speed across participants of 0.41 Uu/s (SD\u0026thinsp;=\u0026thinsp;0.08).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExperimental Design\u003c/p\u003e \u003cp\u003eIn each of the 4 experimental runs of approx. 12 minutes, participants underwent 6 sequences of 25 trials, for a total of 600 trials per participant. A fixation cross appeared in the middle of the field of view of the camera, on the virtual table. To minimise horizontal eye movements, we instructed participants not to follow the moving ball or their hand but to keep their gaze on the cross. On the virtual table there were also two indicator circles (distance +/- 0.2 Uu from the fixation cross). Participants were instructed to either keep their index finger in the circle or move their finger towards the circle located in the opposite side of the virtual table, in the \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions respectively. At the beginning of each sequence of 25 trials, 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. Each trial began with the virtual ball appearing in the centre of the circle opposite from the participant\u0026rsquo;s finger. After a delay of 1 second, the fixation cross changed colour for 0.5 seconds. If the cross flashed green, participants were required to actively reach the ball positioned on the opposite side of the table (\u003cem\u003emove\u003c/em\u003e condition). Participants were instructed to move as soon as the cross stopped flashing. If the cross flashed red, they were required to stay still and wait for the ball to reach their fingertip (\u003cem\u003estay\u003c/em\u003e condition). The ball started to move as soon as the cross stopped flashing. If participants moved during a \u003cem\u003estay\u003c/em\u003e condition, a prompt appeared indicating the wrong execution of the trial. Upon reaching and touching (or touched by) the virtual ball, an electrical shock at the fingertip could have been administered (\u003cem\u003etouch\u003c/em\u003e and \u003cem\u003eno-touch\u003c/em\u003e conditions). For a depiction of the experimental paradigm, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Electrical pulses were administered according to a simple probabilistic model with 25%, 50% and 75% probability (i.e., \u003cem\u003elow\u003c/em\u003e, \u003cem\u003eequal\u003c/em\u003e, \u003cem\u003ehigh\u003c/em\u003e). Within each sequence, the probability of incurring in a \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e trial was at chance level. Therefore, participants underwent the same number of \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials and, most importantly, the likelihood of receiving a stimulation was not associated with the probability of moving or staying still. Participants were explicitly informed about this.\u003c/p\u003e \u003cp\u003eAfter subjects touched the virtual ball, they were instructed to stay still and keep their index finger in the newly reached indicator point. After they were touched by the virtual ball, they simply stayed in the same indicator point, waiting for a new trial. One second after touching the ball, the virtual object disappeared, and a new trial started after a random inter trial interval between 1.75 and 2.25 seconds.\u003c/p\u003e \u003cp\u003eAt the end of each sequence of 25 trials, three circles with the labels \u0026ldquo;25%\u0026rdquo;, \u0026ldquo;50%\u0026rdquo;, and \u0026ldquo;75%\u0026rdquo; appeared on the screen. Each percentage was associated with a probability condition, namely \u003cem\u003elow\u003c/em\u003e, \u003cem\u003eequal\u003c/em\u003e or \u003cem\u003ehigh\u003c/em\u003e. Participants had to slide their finger in the circle corresponding to the probability condition that they thought was the one underlying the stimulus presentation during the sequence.\u003c/p\u003e \u003cp\u003eBehavioural data analysis\u003c/p\u003e \u003cp\u003eWe analysed accuracy rates only via descriptive statistics, due to scarcity of data-points (only 8 responses per probabilistic state). Response times were calculated from the time of action or ball moving onset to the moment of ball touch. Outliers defined as trials exceeding 3 median absolute deviations were excluded. We fitted a linear mixed effect model having as fixed effects movement type (move or stay), stimulation (touch or no-touch) and probability condition (low, equal, high). Random intercepts were modelled by participants. This was done to check that our velocity personalisation approach across \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions was successful and that differences in trial lengths across conditions might have driven differences in the electrophysiological correlates.\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\u0026ndash;20 system.\u003c/p\u003e \u003cp\u003ePreprocessing of the EEG data was performed using SPM12 [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], FieldTrip [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e] and in-house MATLAB scripts. First, bad channels were identified manually and removed, the data were then referenced against the average reference, down-sampled to 512 Hz and high-pass filtered (0.01 Hz, firws, one-pass zero-phase, -6dB cut-off). Subsequently, eye-blinks and horizontal eye-movements were corrected using a topographical confound approach [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Next, we defined trials as the recording epochs going from at least half a second before instruction cue to at least one second after ball-touch. Due to the great variability in trial length in our experiment, this resulted in a trial definition that spanned from \u0026minus;\u0026thinsp;3s to 3s around ball-touch events. A low pass filter was applied (45 Hz, firws, one-pass zero-phase, -6dB cut-off) and EEG data were baseline corrected with respect to the pre-stimulus interval from \u0026minus;\u0026thinsp;10 to -5 ms. Finally, each trial was visually inspected and bad data segments were marked and excluded from the final dataset. 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 response time outlier and (ii) were artifact free. On average, we excluded a total of 12.32% of trials (SD\u0026thinsp;=\u0026thinsp;8.34%). 1.29% (SD\u0026thinsp;=\u0026thinsp;1.10%) of the total trials were response time outliers and 11.28% (SD\u0026thinsp;=\u0026thinsp;8.16%) contained artefactual segments, while 0.25% were both. After exclusion, an average of 526 trials (SD\u0026thinsp;=\u0026thinsp;50) survived. Moreover, out of the initial 26 participants, one was excluded because they never chose the 75% probability condition as a response. The results presented later are therefore computed on 25 participants.\u003c/p\u003e \u003cp\u003eEEG data analysis\u003c/p\u003e \u003cp\u003eMain analyses were done within the SPM framework for M/EEG analysis. This method requires the preprocessed, epoched channel data to be linearly interpolated in a 32 X 32 grid for each time-point. Because we were specifically interested in the electrophysiological responses evoked by the electrical stimulation, we selected a window spanning from \u0026minus;\u0026thinsp;50 to 500 ms around ball-touch. In this way, we obtained one 3-D image of dimensions 32x32x283 (scalp space x intra-trial samples) per trial. First-level multiple regression models were then specified and estimated in SPM12, using dummy regressors for each possible condition combination (12 in total). This allowed for the regression of the EEG data over trials, separately for each voxel, which resulted in a 3-D β estimate for each condition with the same dimensionality as the initial images. Each β estimate, without the inclusion of further regressors or covariates, was mathematically equivalent to computing the ERPs of each condition. Second level analyses consisted of a mass-univariate multiple regression analysis of the individual β scalp-time images with a design matrix having 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 our model is equivalent to a 2x2x3 ANOVA. All analyses were performed with a cluster-forming threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; only clusters surviving at the cluster-level with family-wise error (FWE) corrected threshold of \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e \u0026lt;0.05 [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] are reported here.\u003c/p\u003e \u003cp\u003eThe direct comparison of potentials evoked by an electrical stimulation after a movement or while staying still will lead to a spurious difference, driven by the additive effect of the motor act in the \u003cem\u003emove\u003c/em\u003e condition. 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 ERP of self-produced stimulus perception to be compared against a \u003cem\u003esensory-only\u003c/em\u003e condition. In a similar fashion, this problem was addressed in our design by selectively exploring the interaction term across conditions (i.e., stimulation x movement, stimulation x probability, stimulation x movement x probability). In this way, we could directly compare the potentials evoked by the self- and externally-produced electrical pulses, net of \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e no-touch control conditions (similar approaches were also adopted in: Kilteni \u0026amp; Ehrsson, 2020).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBehavioural results\u003c/p\u003e \u003cp\u003eRegarding accuracy measures, participants\u0026rsquo; belief matched the real underlying state in 80% in the low condition, in the 65.5% of the equal and in the 69.5% of the high condition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Participants\u0026rsquo; performance was well above chance level (33.3%), i.e., the participants were able to perform the task.\u003c/p\u003e \u003cp\u003eWe then fitted a linear mixed effect model to the response times to test for differences across condition. The model revealed a main effect of movement type (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e1,13104\u003c/sub\u003e = 1646.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but no main effect of stimulation (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e1,13104\u003c/sub\u003e = 0.008, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.929) and probability condition (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e2,13104\u003c/sub\u003e = 0.476, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.621) or any interaction effect (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eElectrophysiological results\u003c/p\u003e \u003cp\u003eThe averaged electrophysiological activity was characterised by an increasing pre-stimulus negativity in both the \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions that reached its peak at stimulus onset. The negativity observed in stay trials before stimulus administration most likely reflects a process of tactile stimulus anticipation [\u003cspan additionalcitationids=\"CR83 CR84\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], while the negativity observed during move trials is due also to motor preparatory processes [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] and slow frequencies related to motor execution [\u003cspan additionalcitationids=\"CR89\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, top). Thus, in the pre-stimulus period, time-locked electrophysiological activity differed between \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e conditions. These differences were accounted for by the inclusion of the \u003cem\u003eno-touch\u003c/em\u003e control condition.\u003c/p\u003e \u003cp\u003eFrom stimulus onset, our paradigm elicited the typical somatosensory responses [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, bottom). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the difference between the average \u003cem\u003etouch\u003c/em\u003e trials across participants subtracted of the \u003cem\u003eno-touch\u003c/em\u003e trials across electrodes with the expected evoked potentials, i.e., P50, N140 and P300 resulting from electrical stimulation of the right index finger. The corresponding topographic maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, top) confirm the left lateralized voltage distribution of the somatosensory evoked potential (SEP) components on the scalp.\u003c/p\u003e \u003cp\u003eThe interaction stimulation x movement showed 4 main clusters reaching significance (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The earliest significant difference that survived cluster-level FWE correction in the potentials evoked by the electrical stimulation, net of the corresponding \u003cem\u003eno-touch\u003c/em\u003e control condition, was a positive deflection observed in frontal electrodes, starting at 0.08s and ending at 0.1s post stimulus (peak at 90ms, centroid: F4, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e = 0.048, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26.06). Our analyses also showed a cluster in contralateral parietal electrodes with an inverse amplitude as compared to the latter (but with the same trend, i.e., self-generated stimuli elicited greater suppression). This cluster did not survive correction for multiple comparison (peak at 88ms, centroid: TP7, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e = 0.100, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.07) but was significant at the cluster-level uncorrected threshold (\u003cem\u003ep\u003c/em\u003e\u003csub\u003euncorr\u003c/sub\u003e = 0.034). In this earlier window, the SEP generated during \u003cem\u003emove\u003c/em\u003e conditions was suppressed compared to the \u003cem\u003estay\u003c/em\u003e condition. More specifically, in frontal electrodes, the positive potential (P100) generated as the consequence of a movement was reduced (less positive) compared to the corresponding potential generated while staying still. In parietal contralateral electrodes, the negative voltage (N100) generated in \u003cem\u003emove\u003c/em\u003e trials was suppressed (less negative) when confronted with the same component evoked during \u003cem\u003estay\u003c/em\u003e trials (see supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Later in time, the analyses revealed two clusters surviving cluster-level FWE correction in frontal centro-parietal electrodes (peak: 158ms, centroid: C3, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e = 0.005, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17.85) and centro-frontal regions (peak\u0026thinsp;=\u0026thinsp;221ms, centroid\u0026thinsp;=\u0026thinsp;Fz, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e = 0.009, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14.96) respectively. Within the first cluster, the potential evoked by self-generated electrical stimulations was reduced in amplitude compared to the corresponding response in passive trials. In the second cluster, the results indicated an enhancement effect, such that in centro-frontal electrodes the average SEP showed a greater voltage during \u003cem\u003emove\u003c/em\u003e trials. At first look, it might seem that the two potentials share similar substrates, however, a detailed analysis of the scalp topographies and the centroid of the respective clusters (see supplementary Figure S2) indicates that the difference at around 200ms between self- and other-generated stimulations is greater in a centro-lateral electrode (C3) and then extends in time to central parietal bilateral sites. We will refer to this component as the P200, mostly because the centroid peaks much earlier in time (158 ms), compared to the effect observed at frontal sites (221 ms). Lastly, our results pointed out a cluster within parietal contralateral electrodes (peak: 436ms, centroid: C5, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e \u0026lt; 0.001, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25.54) in which actively generated stimuli generated a suppressed negative potential compared to passively attended ones.\u003c/p\u003e \u003cp\u003eThe interaction of stimulation x probability revealed one main cluster that reached significance at FWE correction for multiple comparison, located at centro-frontal electrodes over a time window between 0.348 and 0.387s after stimulus onset. This cluster will be referred to as the P300 (centroid: FCz, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eFWE\u003c/sub\u003e = 0.008, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11.94). The P300 showed a parametric modulation, in the way that low probability conditions exerted a greater amplitude, while the high probability condition resulted in a suppression of the waveform (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe interaction stimulation x probability x movement resulted in no significant clusters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe investigation of sensory attenuation requires a multitude of control of concurring factors that could otherwise explain the attenuation normally observed between self- and externally-generated stimuli. With the present experiment we validated a novel VR paradigm that allowed for a more comprehensive control of stimulus properties, attentional requirements and other predictability factors during a modified probabilistic learning task. The electrophysiological results show that early- (P100 and N100, although not significant) and mid-latency (P200) ERP components are suppressed for self- compared to externally-generated stimulations, which is a well-known finding of sensory attenuation. Moreover, self-generated stimuli elicited an enhancement of a later component in fronto-central sites, as well as a suppression of a later waveform at contralateral electrodes. The contrasting nature of these effects to stimulus predictability indicates that the phenomenon of sensory attenuation prevailed when controlling for other factors. Stimulus predictability was instead associated with a later attenuation effect for high-probability stimuli of the fronto-central P300.\u003c/p\u003e \u003cp\u003eUnlike previous studies investigating sensory attenuation, because we used VR to present somatosensory stimuli with different probabilities, we could obtain control \u003cem\u003emove\u003c/em\u003e and \u003cem\u003estay\u003c/em\u003e trials that did not result in any electrical stimulation. This was important because the pre-stimulus waveform differed substantially across movement conditions. By specifically investigating the interaction term between movement and stimulation type, we could directly compare the evoked effect of touch in the different movement and probability conditions while controlling for the effects induced by the generation of a motor plan (\u003cem\u003emove\u003c/em\u003e condition) or by passively waiting for a stimulation to occur (\u003cem\u003estay\u003c/em\u003e condition). Moreover, this approach also avoids the possible problem that directly subtracting a control condition from a condition of interest might induce a spurious difference driven by the control condition itself [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, we can assume that the present analyses allowed to directly compare the potentials evoked by a stimulus when self- or externally-generated (net of a control \u003cem\u003eno-touch move\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e condition) across different stimulus predictability conditions.\u003c/p\u003e \u003cp\u003eConcerning the electrophysiological data, results revealed a suppression for self-generated stimuli at around 100 ms post-stimulus (P100 and N100, although not significant) and at around 200 ms (P200). Components within the same time window have been associated with conscious perception [\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] and, more relevant to the scope of this paper, have also been demonstrated to be modulated by stimulus intensity [\u003cspan additionalcitationids=\"CR97 CR98\" citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], attentional requirements [\u003cspan additionalcitationids=\"CR46 CR47 CR48 CR49 CR50\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and, despite evidence in the somatosensory modality is scarce, also by stimulus predictability in time [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These factors are also thought to impair the investigation of sensory attenuation as they might conflate the electrophysiological correlates of self-generated stimulations when left uncontrolled [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In trying to account for these aspects, we developed a VR setup that, similar to earlier studies in sensory attenuation in virtual environments, was designed to enable participants to perform naturalistic arm movement, reach and interact with virtual objects [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], while controlling for the intensity and timing of tactile stimuli presented to participants [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs in previous VR setups [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], stimuli were directly triggered by touching the virtual object and were independent from the velocity at which the ball approached (or got approached by) participants\u0026rsquo; index finger. In this way, we could account for the additional mechanical stimulation that would otherwise be present when pressing a button to generate a stimulation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Attentional resources were also controlled across conditions, not only because immersive VR settings might lead to higher levels of concentration when performing a task [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e] but also, given that active hand and virtual ball movements occurred to and from both sides, we ensured spatial attention to be equal across conditions. More importantly, attentional resources were balanced across \u003cem\u003estay\u003c/em\u003e and move \u003cem\u003etrials\u003c/em\u003e. Like previous studies that required participants to undergo interfering tasks such as counting the number of stimulations [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e] or estimating the time interval between consecutive stimulations [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], in the present study volunteers needed to attend stimuli independently of whether they were presented in the \u003cem\u003emove\u003c/em\u003e or \u003cem\u003estay\u003c/em\u003e conditions to form some expectations about their appearance.\u003c/p\u003e \u003cp\u003eVolunteers could always predict the moment in time in which the ball would have administered (or not) the electrical pulse, not only in \u003cem\u003emove\u003c/em\u003e trials as they were performing goal-directed actions, but also in \u003cem\u003estay\u003c/em\u003e trials because they could see the virtual ball approaching their index finger. Similar controls over stimulus temporal predictability have been adopted before [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Ball velocity was adjusted to each participants\u0026rsquo; personal pace, as measured during the training phase. Response times comparison across conditions indicated that participants moved slower in \u003cem\u003emove\u003c/em\u003e trials than the ball during \u003cem\u003estay\u003c/em\u003e trials. As a possible explanation, it is unlikely that participants kept the same movement pace from the training phase throughout the experiment, leading to an underestimation of the personalised response times during the training phase. However, it is unlikely that this difference affected the electrophysiological results. If the different trial lengths had led to a difference between the electrophysiological correlates at any point of the \u003cem\u003estay\u003c/em\u003e and \u003cem\u003emove\u003c/em\u003e conditions, it would have affected both the \u003cem\u003etouch\u003c/em\u003e and the respective control \u003cem\u003eno-touch\u003c/em\u003e trials similarly, and therefore it should cancel out.\u003c/p\u003e \u003cp\u003eAttenuation of components at around 100 ms post stimulus is a common result obtained by electrophysiological studies investigating sensory attenuation in the auditory [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], visual [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e] and somatosensory domain [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Similarly, suppression of P200 has been often reported in electroencephalography studies that investigated sensory attenuation either in the auditory [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], visual [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and somatosensory modality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. By replicating the electrophysiological suppression for self-generated movement, we believe to also validate the present novel VR paradigm, which adds on the scarce literature of existing VR studies that investigated this phenomenon via EEG [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Differently from previous evidence, however, our setup also holds the advantage of being able to control for a series of confounding factors, not only replicating previous electrophysiological findings of sensory attenuation but also substantiating the validity of this phenomenon.\u003c/p\u003e \u003cp\u003eAssuming that sensory attenuation is not better explained by other concurring factors, it is likely that the electrophysiological attenuation for self-generated stimulations represents a reduction in the perceived intensity of the consequences of one\u0026rsquo;s own movements [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Corroborating this hypothesis, previous behavioural studies reported a reduced sensitivity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e] or intensity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in the perception of self-generated stimuli. Furthermore, previous results have demonstrated a direct link between subjective intensity reports and the somatosensory N140 and P200 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and the visual P2 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], thus substantiating the idea that suppression of early- and mid- latency ERPs is accompanied by a decrease in subjective intensity. Although we didn\u0026rsquo;t collect stimulus intensity ratings and, thus, we couldn\u0026rsquo;t directly test these findings, it is plausible that our results reflect the same subjective attenuation.\u003c/p\u003e \u003cp\u003eIn trying to disentangle the underlying mechanisms of sensory attenuation, we did not only exert a comprehensive control over several intervening factors, but we also modulated the expectation of receiving a stimulation across conditions, regardless of whether it was self- or externally-generated. Previous studies that formally investigated this problem required participants to undergo sequences of highly predictable stimulation identities (constant or more predictable pitch) and totally unpredictable stimulations (variable pitch) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] or by contrasting stimuli that were congruent or incongruent to previous learned action-effect contingencies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Evidence indicates that sensory attenuation is not better explained by stimulus predictions, either according to electrophysiological measures [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] or subjective intensity rates [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Our results indicate that self-generated stimulations exert a greater electrophysiological suppression (in the N100/P100 complex and P200) compared to externally administered stimuli, independent of the expectation of stimulus administration. Therefore, sensory attenuation can\u0026rsquo;t be better explained by stimulation predictability and might indicate that this phenomenon is not based upon unspecific prediction mechanisms, but that it is rather driven by specific motor predictions that are put forward when performing an action [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrary to our expectation, however, later in time our results also pointed out another component at centro-frontal electrodes that showed an enhancement for self-produced compared to externally produced stimuli. Similar enhancements of centro-frontal positivities for self-generated stimuli, although sparse, have been described previously. Bednark and colleagues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] reported that the P3b was enhanced for self- compared to externally-produced stimuli, with a greater increase for stimulations that were incongruent to predictions. The authors attribute this enhancement effect to a possible link between the context updating hypothesis of the auditory P3 and the comparator model underlying sensory attenuation. More specifically, they hypothesise that in both mechanisms a stimulation is compared against an internal model which generates a prediction about said stimulation (either motor-specific and not); it is possible therefore that the anticipation of a stimulation is codetermined by both motor and non-motor predictions (see also Waszak \u0026amp; Herwig, 2007). Similar claims have also been advanced by Harrison and colleagues [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], who speculated the existence of an \u0026ldquo;additive\u0026rdquo; effect of different predictive information. This claim might be substantiated by evidence of enhanced stimulus processing due to movement [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Although speculative, our results of enhanced fronto-central positivities may align with these explanations.\u003c/p\u003e \u003cp\u003eLastly, another component that showed a modulation for self- compared to externally-generated stimulations was a negativity at parietal contralateral electrodes. Some findings suggest that similar potentials are related to somatosensory information maintenance in working memory [\u003cspan additionalcitationids=\"CR114\" citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Although participants were not actively required to rehearse any information during the task, it is plausible that they maintained some representations of the stimuli in memory to execute the learning task. Although, the reported suppression of the later potential for self- compared to externally generated stimuli might also represent a facilitation of stimulus processing specific to actively generated stimuli [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. To our knowledge, the present study is the first to report similar effects in late-latency components for self- compared to externally-generated stimulations and its interpretations are to be considered exclusively as speculative, especially considering that similar potentials are known to be affected by methodological issues [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConcerning the effect of stimulus expectation, our analyses revealed another centro-frontal cluster (P300) that showed a parametric effect over probability conditions (greater voltages for low-probability stimulations), independently whether participants actively produced the stimulation or passively perceived it. In the literature, the P300 has been oftentimes associated to the detection of deviant stimuli [\u003cspan additionalcitationids=\"CR118 CR119\" citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e] or, more generally, stimulus novelty or saliency [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan additionalcitationids=\"CR122\" citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e] and it seems to be involved in more endogenous processes, such as post-perceptual stimulus evaluation [\u003cspan additionalcitationids=\"CR125 CR126\" citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. In line with this evidence, our results indicated that the stimulation drove a stronger effect when it was more unexpected (low probability conditions). This ultimately indicates that stimulus probability still affects stimulus perception, independently of the movement type but in a much later time window.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThrough our novel VR paradigm, we accounted for differences between self- and externally-produced stimuli in an ecological setup while controlling for other explaining factors that could impair the investigation of sensory attenuation, such as stimulus properties, attentional resources and stimulus predictability in time. Additionally, the setup was implemented to modulate stimulus expectation across conditions. Our results revealed an attenuation effect for self-generated stimulations in the N100, P100, P200, and late-latency SEPs, as well as an enhancement of mid-latency potentials. These effects were independent from stimulus predictability, which instead modulated the P300 component. Therefore, we can conclude that the attenuation effect observed in the present experiment is not better explained by other explaining factors and is resilient to predictability manipulations. Sensory attenuation, therefore, appears to represent a standalone phenomenon that is not otherwise explained by stimulus expectation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank Marlon Esmeyer for comments on the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eGG: Conceptualization, Data acquisition, Data curation, Methodology, Formal analysis, Writing \u0026ndash; original draft. TN: Conceptualization, Methodology, Supervision, Writing \u0026ndash; review and editing. FB: Conceptualization, Methodology, Supervision, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request and by taking into account the data protection guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe corresponding scripts for data analysis and for replicating figures are available here: https://github.com/Neurocomputation-and-Neuroimaging-Unit/SensAtt_Pred\u003c/p\u003e\n\u003cp\u003eAdditional information\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Berlin School of Mind and Brain, Humboldt Universit\u0026auml;t 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.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFriston, K. The free-energy principle: a unified brain theory? \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 127\u0026ndash;138 (2010).\u003c/li\u003e\n \u003cli\u003eRao, R. P. N. \u0026amp; Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 79\u0026ndash;87 (1999).\u003c/li\u003e\n \u003cli\u003eWalsh, K. S., McGovern, D. P., Clark, A. \u0026amp; O\u0026rsquo;Connell, R. G. Evaluating the neurophysiological evidence for predictive processing as a model of perception. \u003cem\u003eAnnals of the New York Academy of Sciences\u003c/em\u003e \u003cstrong\u003e1464\u003c/strong\u003e, 242\u0026ndash;268 (2020).\u003c/li\u003e\n \u003cli\u003eMiall, R. C. \u0026amp; Wolpert, D. M. Forward Models for Physiological Motor Control. \u003cem\u003eNeural Networks\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1265\u0026ndash;1279 (1996).\u003c/li\u003e\n \u003cli\u003eFriston, K. J., Daunizeau, J., Kilner, J. \u0026amp; Kiebel, S. J. Action and behavior: a free-energy formulation. \u003cem\u003eBiol Cybern\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 227\u0026ndash;260 (2010).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e96\u003c/strong\u003e, 215\u0026ndash;231 (2005).\u003c/li\u003e\n \u003cli\u003eWoźniak, M. How to grow a self: development of the self in a Bayesian brain. Preprint at https://doi.org/10.31234/osf.io/6e3ad (2019).\u003c/li\u003e\n \u003cli\u003eCreutzfeldt, O., Ojemann, G. \u0026amp; Lettich, E. Neuronal activity in the human lateral temporal lobe. \u003cem\u003eExp Brain Res\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 476\u0026ndash;489 (1989).\u003c/li\u003e\n \u003cli\u003ePaus, T., Perry, D. W., Zatorre, R. J., Worsley, K. J. \u0026amp; Evans, A. C. Modulation of Cerebral Blood Flow in the Human Auditory Cortex During Speech: Role of Motor-to-sensory Discharges. \u003cem\u003eEuropean Journal of Neuroscience\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2236\u0026ndash;2246 (1996).\u003c/li\u003e\n \u003cli\u003eBlakemore, Wolpert, D. M. \u0026amp; Frith, C. D. Central cancellation of self-produced tickle sensation. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 635\u0026ndash;640 (1998).\u003c/li\u003e\n \u003cli\u003eSchafer, E. W. P. \u0026amp; Marcus, M. M. Self-Stimulation Alters Human Sensory Brain Responses. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 175\u0026ndash;177 (1973).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e15\u003c/strong\u003e, 1125\u0026ndash;1128 (2005).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e4\u003c/strong\u003e, e28 (2006).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e21\u003c/strong\u003e, 1740\u0026ndash;1745 (2010).\u003c/li\u003e\n \u003cli\u003eHaggard, P. \u0026amp; Whitford, B. Supplementary motor area provides an efferent signal for sensory suppression. \u003cem\u003eBrain Res Cogn Brain Res\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 52\u0026ndash;58 (2004).\u003c/li\u003e\n \u003cli\u003eShergill, S. S., Bays, P. M., Frith, C. D. \u0026amp; Wolpert, D. M. Two Eyes for an Eye: The Neuroscience of Force Escalation. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e301\u003c/strong\u003e, 187\u0026ndash;187 (2003).\u003c/li\u003e\n \u003cli\u003eWalsh, L. D., Taylor, J. L. \u0026amp; Gandevia, S. C. Overestimation of force during matching of externally generated forces. \u003cem\u003eThe Journal of Physiology\u003c/em\u003e \u003cstrong\u003e589\u003c/strong\u003e, 547\u0026ndash;557 (2011).\u003c/li\u003e\n \u003cli\u003eWolpe, N. \u003cem\u003eet al.\u003c/em\u003e Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 13034 (2016).\u003c/li\u003e\n \u003cli\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\u003eEuropean Journal of Neuroscience\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 1514\u0026ndash;1521 (2009).\u003c/li\u003e\n \u003cli\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\u003eInternational Journal of Psychophysiology\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 137\u0026ndash;143 (2008).\u003c/li\u003e\n \u003cli\u003eKaiser, J. \u0026amp; Sch\u0026uuml;tz-Bosbach, S. Sensory attenuation of self-produced signals does not rely on self-specific motor predictions. \u003cem\u003eEuropean Journal of Neuroscience\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 1303\u0026ndash;1310 (2018).\u003c/li\u003e\n \u003cli\u003eKilteni, K. \u0026amp; Ehrsson, H. H. Functional Connectivity between the Cerebellum and Somatosensory Areas Implements the Attenuation of Self-Generated Touch. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 894\u0026ndash;906 (2020).\u003c/li\u003e\n \u003cli\u003eKlaffehn, A. L., Baess, P., Kunde, W. \u0026amp; Pfister, R. Sensory attenuation prevails when controlling for temporal predictability of self- and externally generated tones. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cstrong\u003e132\u003c/strong\u003e, 107145 (2019).\u003c/li\u003e\n \u003cli\u003eLimanowski, J. \u003cem\u003eet al.\u003c/em\u003e Action-Dependent Processing of Touch in the Human Parietal Operculum and Posterior Insula. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 607\u0026ndash;617 (2020).\u003c/li\u003e\n \u003cli\u003eMartikainen, M. H., Kaneko, K. \u0026amp; Hari, R. Suppressed Responses to Self-triggered Sounds in the Human Auditory Cortex. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 299\u0026ndash;302 (2005).\u003c/li\u003e\n \u003cli\u003eShergill, S. S. \u003cem\u003eet al.\u003c/em\u003e Modulation of somatosensory processing by action. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 356\u0026ndash;362 (2013).\u003c/li\u003e\n \u003cli\u003eWolpert, D. M. \u0026amp; Flanagan, J. R. Motor prediction. \u003cem\u003eCurrent Biology\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, R729\u0026ndash;R732 (2001).\u003c/li\u003e\n \u003cli\u003eHaggard, P., Clark, S. \u0026amp; Kalogeras, J. Voluntary action and conscious awareness. \u003cem\u003eNat Neurosci\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 382\u0026ndash;385 (2002).\u003c/li\u003e\n \u003cli\u003eHorv\u0026aacute;th, J. Action-related auditory ERP attenuation: Paradigms and hypotheses. \u003cem\u003eBrain Research\u003c/em\u003e \u003cstrong\u003e1626\u003c/strong\u003e, 54\u0026ndash;65 (2015).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e44\u003c/strong\u003e, 522\u0026ndash;529 (2007).\u003c/li\u003e\n \u003cli\u003eMifsud, N. G. \u003cem\u003eet al.\u003c/em\u003e Self-initiated actions result in suppressed auditory but amplified visual evoked components in healthy participants. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, 723\u0026ndash;732 (2016).\u003c/li\u003e\n \u003cli\u003eOestreich, L. K. L. \u003cem\u003eet al.\u003c/em\u003e Subnormal sensory attenuation to self-generated speech in schizotypy: Electrophysiological evidence for a \u0026lsquo;continuum of psychosis\u0026rsquo;. \u003cem\u003eInternational Journal of Psychophysiology\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 131\u0026ndash;138 (2015).\u003c/li\u003e\n \u003cli\u003eOestreich, L. K. L. \u003cem\u003eet al.\u003c/em\u003e Cortical Suppression to Delayed Self-Initiated Auditory Stimuli in Schizotypy: Neurophysiological Evidence for a Continuum of Psychosis. \u003cem\u003eClin EEG Neurosci\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 3\u0026ndash;10 (2016).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e222\u003c/strong\u003e, 149\u0026ndash;157 (2012).\u003c/li\u003e\n \u003cli\u003eGentsch, A. \u0026amp; Sch\u0026uuml;tz-Bosbach, S. I Did It: Unconscious Expectation of Sensory Consequences Modulates the Experience of Self-agency and Its Functional Signature. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 3817\u0026ndash;3828 (2011).\u003c/li\u003e\n \u003cli\u003eHughes, G. \u0026amp; Waszak, F. ERP correlates of action effect prediction and visual sensory attenuation in voluntary action. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 1632\u0026ndash;1640 (2011).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e61\u003c/strong\u003e, 11\u0026ndash;18 (2014).\u003c/li\u003e\n \u003cli\u003eMifsud, N. G. \u003cem\u003eet al.\u003c/em\u003e Attenuation of visual evoked responses to hand and saccade-initiated flashes. \u003cem\u003eCognition\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 14\u0026ndash;22 (2018).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e60\u003c/strong\u003e, e14295 (2023).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e36\u003c/strong\u003e, 10803\u0026ndash;10812 (2016).\u003c/li\u003e\n \u003cli\u003ePyasik, M. \u003cem\u003eet al.\u003c/em\u003e 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 \u003cstrong\u003e229\u003c/strong\u003e, 117727 (2021).\u003c/li\u003e\n \u003cli\u003eBednark, J. G., Poonian, S. K., Palghat, K., McFadyen, J. \u0026amp; Cunnington, R. Identity-specific predictions and implicit measures of agency. \u003cem\u003ePsychology of Consciousness: Theory, Research, and Practice\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 253\u0026ndash;268 (2015).\u003c/li\u003e\n \u003cli\u003eHarrison, A. W. \u003cem\u003eet al.\u003c/em\u003e Sensory attenuation is modulated by the contrasting effects of predictability and control. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e237\u003c/strong\u003e, 118103 (2021).\u003c/li\u003e\n \u003cli\u003eHorv\u0026aacute;th, J. The role of mechanical impact in action-related auditory attenuation. \u003cem\u003eCogn Affect Behav Neurosci\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1392\u0026ndash;1406 (2014).\u003c/li\u003e\n \u003cli\u003eClauwaert, A., Torta, D. M., Forster, B., Danneels, L. \u0026amp; Van Damme, S. Somatosensory attentional modulations during pain-related movement execution. \u003cem\u003eExp Brain Res\u003c/em\u003e \u003cstrong\u003e238\u003c/strong\u003e, 1169\u0026ndash;1176 (2020).\u003c/li\u003e\n \u003cli\u003eFiorio, M. \u003cem\u003eet al.\u003c/em\u003e Enhancing non-noxious perception: Behavioural and neurophysiological correlates of a placebo-like manipulation. \u003cem\u003eNeuroscience\u003c/em\u003e \u003cstrong\u003e217\u003c/strong\u003e, 96\u0026ndash;104 (2012).\u003c/li\u003e\n \u003cli\u003eForster, B. \u0026amp; Gillmeister, H. ERP investigation of transient attentional selection of single and multiple locations within touch. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 788\u0026ndash;796 (2011).\u003c/li\u003e\n \u003cli\u003eFujiwara, N. \u003cem\u003eet al.\u003c/em\u003e Second somatosensory area (SII) plays a significant role in selective somatosensory attention. \u003cem\u003eCognitive Brain Research\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 389\u0026ndash;397 (2002).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e175\u003c/strong\u003e, 609\u0026ndash;617 (2006).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e104\u003c/strong\u003e, 689\u0026ndash;703 (2001).\u003c/li\u003e\n \u003cli\u003eMima, T., Nagamine, T., Nakamura, K. \u0026amp; Shibasaki, H. Attention Modulates Both Primary and Second Somatosensory Cortical Activities in Humans: A Magnetoencephalographic Study. \u003cem\u003eJournal of Neurophysiology\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 2215\u0026ndash;2221 (1998).\u003c/li\u003e\n \u003cli\u003eHorv\u0026aacute;th, J., Maess, B., Baess, P. \u0026amp; T\u0026oacute;th, A. Action\u0026ndash;Sound Coincidences Suppress Evoked Responses of the Human Auditory Cortex in EEG and MEG. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1919\u0026ndash;1931 (2012).\u003c/li\u003e\n \u003cli\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\u003ePsychological Bulletin\u003c/em\u003e \u003cstrong\u003e139\u003c/strong\u003e, 133\u0026ndash;151 (2013).\u003c/li\u003e\n \u003cli\u003eSchafer, E. W. P. \u0026amp; Marcus, M. M. Self-Stimulation Alters Human Sensory Brain Responses. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e181\u003c/strong\u003e, 175\u0026ndash;177 (1973).\u003c/li\u003e\n \u003cli\u003eCosta-Faidella, J., Baldeweg, T., Grimm, S. \u0026amp; Escera, C. Interactions between \u0026ldquo;What\u0026rdquo; and \u0026ldquo;When\u0026rdquo; in the Auditory System: Temporal Predictability Enhances Repetition Suppression. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 18590\u0026ndash;18597 (2011).\u003c/li\u003e\n \u003cli\u003eNara, S. \u003cem\u003eet al.\u003c/em\u003e Temporal uncertainty enhances suppression of neural responses to predictable visual stimuli. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e239\u003c/strong\u003e, 118314 (2021).\u003c/li\u003e\n \u003cli\u003eSchwartze, M., Rothermich, K., Schmidt-Kassow, M. \u0026amp; Kotz, S. A. Temporal regularity effects on pre-attentive and attentive processing of deviance. \u003cem\u003eBiological Psychology\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 146\u0026ndash;151 (2011).\u003c/li\u003e\n \u003cli\u003eHsu, Y.-F., H\u0026auml;m\u0026auml;l\u0026auml;inen, J. A. \u0026amp; Waszak, F. Temporal expectation and spectral expectation operate in distinct fashion on neuronal populations. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 2548\u0026ndash;2555 (2013).\u003c/li\u003e\n \u003cli\u003eHsu, Y.-F., H\u0026auml;m\u0026auml;l\u0026auml;inen, J. A. \u0026amp; Waszak, F. Repetition suppression comprises both attention-independent and attention-dependent processes. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 168\u0026ndash;175 (2014).\u003c/li\u003e\n \u003cli\u003eHsu, Y.-F., Bars, S. L., H\u0026auml;m\u0026auml;l\u0026auml;inen, J. A. \u0026amp; Waszak, F. Distinctive Representation of Mispredicted and Unpredicted Prediction Errors in Human Electroencephalography. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 14653\u0026ndash;14660 (2015).\u003c/li\u003e\n \u003cli\u003eHsu, Y.-F., H\u0026auml;m\u0026auml;l\u0026auml;inen, J. A. \u0026amp; Waszak, F. Both attention and prediction are necessary for adaptive neuronal tuning in sensory processing. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2014).\u003c/li\u003e\n \u003cli\u003eLange, K. Brain correlates of early auditory processing are attenuated by expectations for time and pitch. \u003cem\u003eBrain and Cognition\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 127\u0026ndash;137 (2009).\u003c/li\u003e\n \u003cli\u003eVroomen, J. \u0026amp; Stekelenburg, J. J. Visual Anticipatory Information Modulates Multisensory Interactions of Artificial Audiovisual Stimuli. \u003cem\u003eJournal of Cognitive Neuroscience\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1583\u0026ndash;1596 (2010).\u003c/li\u003e\n \u003cli\u003eRoth, W. T. Auditory Evoked Responses to Unpredictable Stimuli. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 125\u0026ndash;138 (1973).\u003c/li\u003e\n \u003cli\u003eDemaire, C. \u0026amp; Coquery, J.-M. Effects of selective attention on the late components of evoked potentials in man. \u003cem\u003eElectroencephalography and Clinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 702\u0026ndash;704 (1977).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e232\u003c/strong\u003e, 3317\u0026ndash;3324 (2014).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e124\u003c/strong\u003e, 216\u0026ndash;225 (2019).\u003c/li\u003e\n \u003cli\u003eChoi, J. W. \u003cem\u003eet al.\u003c/em\u003e Neural Applications Using Immersive Virtual Reality: A Review on EEG Studies. \u003cem\u003eIEEE Transactions on Neural Systems and Rehabilitation Engineering\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1645\u0026ndash;1658 (2023).\u003c/li\u003e\n \u003cli\u003eFritz, C., Flick, M. \u0026amp; Zimmermann, E. Tactile motor attention induces sensory attenuation for sounds. \u003cem\u003eConsciousness and Cognition\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 103386 (2022).\u003c/li\u003e\n \u003cli\u003eDong, X. \u0026amp; Bao, M. The growing sensory suppression on visual perception during head-rotation preparation. \u003cem\u003ePsyCh Journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 499\u0026ndash;507 (2021).\u003c/li\u003e\n \u003cli\u003eKiepe, F., Kraus, N. \u0026amp; Hesselmann, G. Virtual occlusion effects on the perception of self-initiated visual stimuli. \u003cem\u003eConsciousness and Cognition\u003c/em\u003e \u003cstrong\u003e107\u003c/strong\u003e, 103460 (2023).\u003c/li\u003e\n \u003cli\u003eVasser, M., Vuillaume, L., Cleeremans, A. \u0026amp; Aru, J. Waving goodbye to contrast: self-generated hand movements attenuate visual sensitivity. \u003cem\u003eNeuroscience of Consciousness\u003c/em\u003e \u003cstrong\u003e2019\u003c/strong\u003e, niy013 (2019).\u003c/li\u003e\n \u003cli\u003eFeder, S., Miksch, J., Grimm, S., Krems, J. F. \u0026amp; Bendixen, A. Using event-related brain potentials to evaluate motor-auditory latencies in virtual reality. \u003cem\u003eFront. Neuroergonomics\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, (2023).\u003c/li\u003e\n \u003cli\u003eKuziek, J. W. P. \u003cem\u003eet al.\u003c/em\u003e Real brains in virtual worlds: Validating a novel oddball paradigm in virtual reality. Preprint at https://doi.org/10.1101/749192 (2019).\u003c/li\u003e\n \u003cli\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/li\u003e\n \u003cli\u003eWiens, S. \u003cem\u003eet al.\u003c/em\u003e Electrophysiological correlates of in vivo and virtual reality exposure therapy in spider phobia. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, e14117 (2022).\u003c/li\u003e\n \u003cli\u003eFriston, K. \u003cem\u003eStatistical Parametric Mapping: The Analysis of Funtional Brain Images\u003c/em\u003e. (Elsevier/Academic Press, Amsterdam ; Boston, 2007).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e2011\u003c/strong\u003e, 1:1-1:9 (2011).\u003c/li\u003e\n \u003cli\u003eBerg, P. \u0026amp; Scherg, M. A multiple source approach to the correction of eye artifacts. \u003cem\u003eElectroencephalography and Clinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 229\u0026ndash;241 (1994).\u003c/li\u003e\n \u003cli\u003eIlle, N., Berg, P. \u0026amp; Scherg, M. Artifact Correction of the Ongoing EEG Using Spatial Filters Based on Artifact and Brain Signal Topographies. \u003cem\u003eJournal of Clinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 113 (2002).\u003c/li\u003e\n \u003cli\u003eFlandin, G. \u0026amp; Friston, K. J. Topological Inference. in \u003cem\u003eBrain Mapping\u003c/em\u003e (ed. Toga, A. W.) 495\u0026ndash;500 (Academic Press, Waltham, 2015). doi:10.1016/B978-0-12-397025-1.00322-5.\u003c/li\u003e\n \u003cli\u003eBianco, V. \u003cem\u003eet al.\u003c/em\u003e Preparatory ERPs in visual, auditory, and somatosensory discriminative motor tasks. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, e13687 (2020).\u003c/li\u003e\n \u003cli\u003eBianco, V. \u003cem\u003eet al.\u003c/em\u003e Modality-specific sensory readiness for upcoming events revealed by slow cortical potentials. \u003cem\u003eBrain Struct Funct\u003c/em\u003e \u003cstrong\u003e225\u003c/strong\u003e, 149\u0026ndash;159 (2020).\u003c/li\u003e\n \u003cli\u003evan Boxtel, G. J. M. \u0026amp; B\u0026ouml;cker, K. B. E. Cortical Measures of Anticipation. \u003cem\u003eJournal of Psychophysiology\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 61\u0026ndash;76 (2004).\u003c/li\u003e\n \u003cli\u003evan Ede, F., de Lange, F. P. \u0026amp; Maris, E. Anticipation Increases Tactile Stimulus Processing in the Ipsilateral Primary Somatosensory Cortex. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 2562\u0026ndash;2571 (2014).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e284\u003c/strong\u003e, 1\u0026ndash;17 (1965).\u003c/li\u003e\n \u003cli\u003eKutas, M. \u0026amp; Donchin, E. Preparation to respond as manifested by movement-related brain potentials. \u003cem\u003eBrain Research\u003c/em\u003e \u003cstrong\u003e202\u003c/strong\u003e, 95\u0026ndash;115 (1980).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e47\u003c/strong\u003e, 486\u0026ndash;500 (2010).\u003c/li\u003e\n \u003cli\u003eKirsch, W. \u0026amp; Hennighausen, E. ERP correlates of linear hand movements: Distance dependent changes. \u003cem\u003eClinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 1285\u0026ndash;1292 (2010).\u003c/li\u003e\n \u003cli\u003eLeuthold, H. \u0026amp; Jentzsch, I. Distinguishing neural sources of movement preparation and execution: An electrophysiological analysis. \u003cem\u003eBiological Psychology\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 173\u0026ndash;198 (2002).\u003c/li\u003e\n \u003cli\u003eKalogianni, K. \u003cem\u003eet al.\u003c/em\u003e Disentangling Somatosensory Evoked Potentials of the Fingers: Limitations and Clinical Potential. \u003cem\u003eBrain Topogr\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 498\u0026ndash;512 (2018).\u003c/li\u003e\n \u003cli\u003eSchaefer, M., M\u0026uuml;hlnickel, W., Gr\u0026uuml;sser, S. M. \u0026amp; Flor, H. Reproducibility and Stability of Neuroelectric Source Imaging in Primary Somatosensory Cortex. \u003cem\u003eBrain Topogr\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 179\u0026ndash;189 (2002).\u003c/li\u003e\n \u003cli\u003eSchubert, R., Blankenburg, F., Lemm, S., Villringer, A. \u0026amp; Curio, G. Now you feel it\u0026mdash;now you don\u0026rsquo;t: ERP correlates of somatosensory awareness. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 31\u0026ndash;40 (2006).\u003c/li\u003e\n \u003cli\u003eAuksztulewicz, R., Spitzer, B. \u0026amp; Blankenburg, F. Recurrent Neural Processing and Somatosensory Awareness. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 799\u0026ndash;805 (2012).\u003c/li\u003e\n \u003cli\u003eForschack, N., Nierhaus, T., M\u0026uuml;ller, M. M. \u0026amp; Villringer, A. Dissociable neural correlates of stimulation intensity and detection in somatosensation. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e217\u003c/strong\u003e, 116908 (2020).\u003c/li\u003e\n \u003cli\u003eMulert, C. \u003cem\u003eet al.\u003c/em\u003e Sound level dependence of the primary auditory cortex: Simultaneous measurement with 61-channel EEG and fMRI. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 49\u0026ndash;58 (2005).\u003c/li\u003e\n \u003cli\u003eOnishi, H. \u003cem\u003eet al.\u003c/em\u003e Effect of the number of pins and inter-pin distance on somatosensory evoked magnetic fields following mechanical tactile stimulation. \u003cem\u003eBrain Research\u003c/em\u003e \u003cstrong\u003e1535\u003c/strong\u003e, 78\u0026ndash;88 (2013).\u003c/li\u003e\n \u003cli\u003eTorquati, K. \u003cem\u003eet al.\u003c/em\u003e Comparison between SI and SII responses as a function of stimulus intensity. \u003cem\u003eNeuroReport\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 813 (2002).\u003c/li\u003e\n \u003cli\u003eMiltner, W., Johnson, R., Braun, C. \u0026amp; Larbig, W. Somatosensory event-related potentials to painful and non-painful stimuli: effects of attention. \u003cem\u003ePain\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 303\u0026ndash;312 (1989).\u003c/li\u003e\n \u003cli\u003eIidal, Y. \u003cem\u003eet al.\u003c/em\u003e The Effect of Immersive Head Mounted Display on a Brain Computer Interface Game. in \u003cem\u003eAdvances in Affective and Pleasurable Design\u003c/em\u003e (eds. Chung, W. \u0026amp; Shin, C. S.) 211\u0026ndash;219 (Springer International Publishing, Cham, 2017). doi:10.1007/978-3-319-41661-8_21.\u003c/li\u003e\n \u003cli\u003eWan, B. \u003cem\u003eet al.\u003c/em\u003e Measuring the Impacts of Virtual Reality Games on Cognitive Ability Using EEG Signals and Game Performance Data. \u003cem\u003eIEEE Access\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 18326\u0026ndash;18344 (2021).\u003c/li\u003e\n \u003cli\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 \u003cstrong\u003e14\u003c/strong\u003e, 2 (2013).\u003c/li\u003e\n \u003cli\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\u003eInternational Journal of Psychophysiology\u003c/em\u003e \u003cstrong\u003e90\u003c/strong\u003e, 300\u0026ndash;310 (2013).\u003c/li\u003e\n \u003cli\u003eHeinks-Maldonado, T. H., Mathalon, D. H., Gray, M. \u0026amp; Ford, J. M. Fine-tuning of auditory cortex during speech production. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 180\u0026ndash;190 (2005).\u003c/li\u003e\n \u003cli\u003eBenazet, M., Th\u0026eacute;nault, F., Whittingstall, K. \u0026amp; Bernier, P.-M. Attenuation of visual reafferent signals in the parietal cortex during voluntary movement. \u003cem\u003eJ Neurophysiol\u003c/em\u003e \u003cstrong\u003e116\u003c/strong\u003e, 1831\u0026ndash;1839 (2016).\u003c/li\u003e\n \u003cli\u003eCsifcs\u0026aacute;k, G. \u003cem\u003eet al.\u003c/em\u003e Action-associated modulation of visual event-related potentials evoked by abstract and ecological stimuli. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, e13289 (2019).\u003c/li\u003e\n \u003cli\u003eRoussel, C., Hughes, G. \u0026amp; Waszak, F. A preactivation account of sensory attenuation. \u003cem\u003eNeuropsychologia\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 922\u0026ndash;929 (2013).\u003c/li\u003e\n \u003cli\u003eCullen, K. E. Sensory signals during active versus passive movement. \u003cem\u003eCurrent Opinion in Neurobiology\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 698\u0026ndash;706 (2004).\u003c/li\u003e\n \u003cli\u003eWolpert, D. M., Ghahramani, Z. \u0026amp; Jordan, M. I. An Internal Model for Sensorimotor Integration. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e269\u003c/strong\u003e, 1880\u0026ndash;1882 (1995).\u003c/li\u003e\n \u003cli\u003eWaszak, F. \u0026amp; Herwig, A. Effect anticipation modulates deviance processing in the brain. \u003cem\u003eBrain Research\u003c/em\u003e \u003cstrong\u003e1183\u003c/strong\u003e, 74\u0026ndash;82 (2007).\u003c/li\u003e\n \u003cli\u003eMorillon, B. \u0026amp; Baillet, S. Motor origin of temporal predictions in auditory attention. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, E8913\u0026ndash;E8921 (2017).\u003c/li\u003e\n \u003cli\u003eKorka, B., Schr\u0026ouml;ger, E. \u0026amp; Widmann, A. The encoding of stochastic regularities is facilitated by action-effect predictions. \u003cem\u003eSci Rep\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 6790 (2021).\u003c/li\u003e\n \u003cli\u003eJolic\u0026oelig;ur, P., Brisson, B. \u0026amp; Robitaille, N. Dissociation of the N2pc and sustained posterior contralateral negativity in a choice response task. \u003cem\u003eBrain Research\u003c/em\u003e \u003cstrong\u003e1215\u003c/strong\u003e, 160\u0026ndash;172 (2008).\u003c/li\u003e\n \u003cli\u003eKatus, T., Grubert, A. \u0026amp; Eimer, M. Electrophysiological Evidence for a Sensory Recruitment Model of Somatosensory Working Memory. \u003cem\u003eCerebral Cortex\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 4697\u0026ndash;4703 (2015).\u003c/li\u003e\n \u003cli\u003eKatus, T., M\u0026uuml;ller, M. M. \u0026amp; Eimer, M. Sustained Maintenance of Somatotopic Information in Brain Regions Recruited by Tactile Working Memory. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 1390\u0026ndash;1395 (2015).\u003c/li\u003e\n \u003cli\u003eAlday, P. M. How much baseline correction do we need in ERP research? Extended GLM model can replace baseline correction while lifting its limits. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, e13451 (2019).\u003c/li\u003e\n \u003cli\u003ePolich, J. Updating P300: an integrative theory of P3a and P3b. \u003cem\u003eClin Neurophysiol\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 2128\u0026ndash;2148 (2007).\u003c/li\u003e\n \u003cli\u003ePolich, J., Brock, T. \u0026amp; Geisler, M. W. P300 from auditory and somatosensory stimuli: probability and inter-stimulus interval. \u003cem\u003eInternational Journal of Psychophysiology\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 219\u0026ndash;223 (1991).\u003c/li\u003e\n \u003cli\u003eTarkka, I. M., Micheloyannis, S. \u0026amp; Stokić, D. S. Generators for human P300 elicited by somatosensory stimuli using multiple dipole source analysis. \u003cem\u003eNeuroscience\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 275\u0026ndash;287 (1996).\u003c/li\u003e\n \u003cli\u003eYamaguchi, S. \u0026amp; Knight, R. T. P300 generation by novel somatosensory stimuli. \u003cem\u003eElectroencephalography and Clinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 50\u0026ndash;55 (1991).\u003c/li\u003e\n \u003cli\u003eHorv\u0026aacute;th, J., Winkler, I. \u0026amp; Bendixen, A. Do N1/MMN, P3a, and RON form a strongly coupled chain reflecting the three stages of auditory distraction? \u003cem\u003eBiological Psychology\u003c/em\u003e \u003cstrong\u003e79\u003c/strong\u003e, 139\u0026ndash;147 (2008).\u003c/li\u003e\n \u003cli\u003eLight, G. A., Swerdlow, N. R. \u0026amp; Braff, D. L. Preattentive sensory processing as indexed by the MMN and P3a brain responses is associated with cognitive and psychosocial functioning in healthy adults. \u003cem\u003eJ Cogn Neurosci\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1624\u0026ndash;1632 (2007).\u003c/li\u003e\n \u003cli\u003eNakajima, Y. \u0026amp; Imamura, N. Relationships between attention effects and intensity effects on the cognitive N140 and P300 components of somatosensory ERPs. \u003cem\u003eClinical Neurophysiology\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 1711\u0026ndash;1718 (2000).\u003c/li\u003e\n \u003cli\u003eCohen, M. A., Ortego, K., Kyroudis, A. \u0026amp; Pitts, M. Distinguishing the Neural Correlates of Perceptual Awareness and Postperceptual Processing. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 4925\u0026ndash;4935 (2020).\u003c/li\u003e\n \u003cli\u003eF\u0026ouml;rster, J., Koivisto, M. \u0026amp; Revonsuo, A. ERP and MEG correlates of visual consciousness: The second decade. \u003cem\u003eConsciousness and Cognition\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 102917 (2020).\u003c/li\u003e\n \u003cli\u003ePitts, M. A., Padwal, J., Fennelly, D., Mart\u0026iacute;nez, A. \u0026amp; Hillyard, S. A. Gamma band activity and the P3 reflect post-perceptual processes, not visual awareness. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 337\u0026ndash;350 (2014).\u003c/li\u003e\n \u003cli\u003eSchr\u0026ouml;der, P., Nierhaus, T. \u0026amp; Blankenburg, F. Late cortical potentials are not a reliable marker of somatosensory awareness. 2020.10.01.322651 Preprint at https://doi.org/10.1101/2020.10.01.322651 (2020).\u003c/li\u003e\n\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-5281922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5281922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe are not only passively immersed in a sensorial world, but we are active agents that directly produce stimulations. Understanding what\u0026rsquo;s unique about the sensory consequences can give valuable insight into the action-perception-cycle. Sensory attenuation is the phenomenon that self-produced stimulations are perceived as less intense compared to externally-generated ones. Studying this phenomenon, however, requires considering a plethora of factors that could otherwise interfere with its interpretation, such as differences in stimulus properties, attentional resources, or temporal predictability. We therefore developed a novel Virtual Reality (VR) setup that allows to control several of these confounding factors. Further, we modulated the expectation of receiving a somatosensory stimulation across self-production and passive perception through a simple probabilistic learning task, allowing us to test to what extent the electrophysiological correlates of sensory attenuation are impacted by stimulus expectation. We obtained electroencephalography (EEG) recordings of 26 participants. Results indicate that early (P100), mid-latency (P200) and later negative contralateral potentials were significantly attenuated by self-generated sensations, independently of the stimulus expectation. Moreover, a component around 200 ms post-stimulus at frontal sites was found to be enhanced for self-produced stimuli. The P300 was influenced by stimulus expectation, regardless of whether the stimulation was actively produced or passively attended. Together, our results indicate that VR opens up new possibilities to study sensory attenuation in more ecological, yet well-controlled paradigms, and that sensory attenuation is not significantly modulated by stimulus predictability.\u003c/p\u003e","manuscriptTitle":"Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 23:42:45","doi":"10.21203/rs.3.rs-5281922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-11T05:34:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-09T15:00:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-24T14:30:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154131663819625169431287222982520836355","date":"2024-11-08T14:12:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267844231430878724846643734360435062407","date":"2024-11-08T13:57:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-08T13:53:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-08T10:09:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-04T11:27:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-01T09:33:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-17T10:18:15+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":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40011088,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":40011089,"name":"Biological sciences/Neuroscience/Sensorimotor processing"},{"id":40011090,"name":"Biological sciences/Neuroscience/Somatosensory system"},{"id":40011091,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-01-27T16:01:19+00:00","versionOfRecord":{"articleIdentity":"rs-5281922","link":"https://doi.org/10.1038/s41598-025-87244-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-22 15:57:30","publishedOnDateReadable":"January 22nd, 2025"},"versionCreatedAt":"2024-12-03 23:42:45","video":"","vorDoi":"10.1038/s41598-025-87244-9","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87244-9","workflowStages":[]},"version":"v1","identity":"rs-5281922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5281922","identity":"rs-5281922","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}