Motor prediction reduces beta-band power and enhances cerebellar-somatosensory connectivity before self-touch to enable its attenuation

Motor prediction reduces beta-band power and enhances cerebellar-somatosensory connectivity before self-touch to enable its attenuation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Motor prediction reduces beta-band power and enhances cerebellar-somatosensory connectivity before self-touch to enable its attenuation View ORCID Profile Xavier Job , View ORCID Profile Lau Møller Andersen , View ORCID Profile Mikkel C. Vinding , View ORCID Profile Noa Cemeljic , View ORCID Profile Daniel Lundqvist , View ORCID Profile Konstantina Kilteni doi: https://doi.org/10.1101/2025.07.29.667127 Xavier Job 1 Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xavier Job For correspondence: xavier.job{at}ki.se konstantina.kilteni{at}ki.se Lau Møller Andersen 2 Aarhus University , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lau Møller Andersen Mikkel C. Vinding 3 University of Copenhagen , Denmark Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mikkel C. Vinding Noa Cemeljic 1 Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Noa Cemeljic Daniel Lundqvist 1 Karolinska Institutet , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Lundqvist Konstantina Kilteni 1 Karolinska Institutet , Sweden 4 Donders Institute for Brain , Cognition C Behaviour, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Konstantina Kilteni For correspondence: xavier.job{at}ki.se konstantina.kilteni{at}ki.se Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Prevailing theories suggest that the brain uses an internal forward model to predict tactile input during voluntary movements, thereby reducing the intensity of the reafferent tactile sensation, a phenomenon known as self-touch attenuation. Although self-touch attenuation is a well- documented effect, it remains unclear how prediction-related neural mechanisms drive the attenuation prior to the actual self-touch input. In this study, we used magnetoencephalography (MEG) to examine the neural correlates of self-touch prediction. Participants performed a self-touch task with two control conditions. In one control, the touch was externally generated without any movement. In the other, the moving and the touched hands were spatially misaligned, thereby disrupting the sensorimotor alignment of self-touch. Self-touch evoked weaker early somatosensory activity (M50 component) than both control conditions. A psychophysics task also mirrored the pattern of neural attenuation, as the perception of self-touch was attenuated compared to the two control conditions. To isolate predictive neural mechanisms from general movement-related activity, we subtracted activity from corresponding stimulus-absent trials. To further refine the signal specific to predictive processing in self-touch, we compared self-touch with misaligned touch, the two conditions that both involved voluntary movement but differed in their prediction of self-touch. This revealed greater pre-stimulus beta-band desynchronization and increased cerebellar-to-somatosensory connectivity prior to self-touch compared to misaligned touch. Our results provide the first evidence of predictive neural activity that shapes the sensory consequences of self-touch, offering insight into the mechanisms through which predictive models modulate somatosensory processing. Significance statement The brain is thought to predict and attenuate the sensory consequences of self-generated actions, but neural evidence for such prediction before sensation has been limited. Using magnetoencephalography, we show that self-touch attenuation is preceded by beta-band desynchronization and increased directed connectivity from the cerebellum to the primary somatosensory cortex. These effects cannot be attributed to movement, as they were not observed in a control condition with similar motor output but without predicted self-touch consequences, suggesting they reflect predictive processing. Our study provides the first neural evidence of cerebellar influence on cortical sensory areas before self-touch. These pre-stimulus effects strongly support predictive forward models of sensorimotor control and shed new light on how the brain anticipates and modulates upcoming sensory input. Introduction For centuries, scientists have been intrigued by the question of why we cannot tickle ourselves ( Kilteni, 2025 ). In The Expression of the Emotions in Man and Animals , Darwin (1872) suggested that self-tickling fails to produce the expected sensation because we cannot surprise ourselves with our own movements. Modern motor control theories have since formalized this idea, proposing that the brain uses a copy of the motor command (“efference copy”) to predict the sensory consequences of self-generated actions via an internal forward model that is thought to be implemented in the cerebellum ( Blakemore et al., 2000 ; Kilteni & Ehrsson, 2020 ; McNamee & Wolpert, 2019 ; Miall & Wolpert, 1996 ). These predictions attenuate the resulting sensory feedback, thereby reducing the surprise and salience of self-generated input ( Blakemore et al., 2000 ; Franklin & Wolpert, 2011 ). As a result, self-touch feels less ticklish and less intense than identical touch caused by an external source ( Bays & Wolpert, 2008 ; Kilteni, 2023 ). Since the first formal experimental demonstration of self-touch attenuation in the 1970s ( Weiskrantz et al., 1971 ), numerous psychophysical studies have replicated this effect when participants moved one hand to touch the other ( Asimakidou et al., 2022 ; Bays et al., 2005 , 2006 ; Blakemore et al., 1999 ; Cemeljic et al., 2025 ; Kilteni et al., 2018 , 2019 , 2021 ; Kilteni & Ehrsson, 2017 , 2020 , 2022 ; Shergill et al., 2003 ). Behavioral findings support a causal role of motor prediction in self-touch attenuation, showing that this also occurs for expected self-touch, even in the absence of physical contact between the hands ( Bays et al., 2006 ; Job & Kilteni, 2023 ), and that attenuation increases as the predicted time of self-touch approaches ( Bays et al., 2005 ; Cemeljic et al., 2025 ). Recent computational modelling work has reinforced the motor prediction account by showing that variability in self-touch attenuation can be explained by noise in internal sensory predictions ( Valè et al., 2025 ). Neuroimaging studies have further corroborated these behavioral self-touch attenuation effects by showing reduced activity elicited by self-touch in the primary ( Hesse et al., 2010 ; Kilteni et al., 2023 ) and secondary ( Blakemore et al., 1998 ; Kilteni & Ehrsson, 2020 ) somatosensory cortices, as well as in the cerebellum ( Blakemore et al., 1998 , 2001 ; Kilteni & Ehrsson, 2020 ), compared to externally generated touch of identical intensity or delayed self-generated touch. Theoretically, if the attenuation of self-touch is driven by motor prediction, then neural correlates of this predictive process should emerge before the sensory input, consistent with a forward model that anticipates the somatosensory consequences of action. However, evidence for such pre-stimulus predictive activity remains elusive. Previous neuroimaging studies have either focused exclusively on stimulus-evoked activity ( Hesse et al., 2010 ), overlooking the pre-stimulus period, or have averaged neural activity across extended periods that include both the movement and the resulting touch ( Blakemore et al., 1998 ; Kilteni & Ehrsson, 2020 ), making it difficult to isolate anticipatory signals from post-stimulus activity. Two fMRI studies reported increased functional connectivity between the cerebellum (ipsilateral to the stimulated hand) and the contralateral somatosensory cortex during self-touch compared to external or delayed self-touch ( Kilteni et al., 2023 ; Kilteni & Ehrsson, 2020 ). While this may reflect cerebellar predictions modulating somatosensory activity, these studies could not determine whether the increase in connectivity preceded the touch or not, nor could they establish directionality (i.e., from cerebellum to somatosensory cortex, indicating predictive modulation, or vice versa, reflecting feedback from sensory processing). Thus, the key theoretical assumption of the forward model framework – that the brain predicts the expected somatosensory reafference prior to the actual touch and uses this prediction to suppress the reafferent input – has not yet received support at the neural level. To address this question, we recorded brain activity using magnetoencephalography (MEG) during a task in which participants used their right index finger to produce touch on their left index finger (self-touch). Neural activity during self-touch was compared to a condition in which participants received identical touch on their left index finger without any movement (external touch). An additional control condition was included, in which participants performed the same right-hand movement and received the same touch on their left hand as in the self-touch condition, but with their two hands spatially misaligned, thus not mimicking self-touch. This control condition (misaligned touch) has been previously shown to significantly reduce or even abolish attenuation ( Bays & Wolpert, 2008 ; Kilteni & Ehrsson, 2017 , 2020 ), and was included to control for several differences between self-touch and external touch conditions, including: i) the presence of simultaneous movement and touch, ii) simultaneous tactile stimulation on both index fingers, iii) temporal predictability of touch, iv) divided attention between the two hands, v) feelings of agency, and vi) general anticipation of touch. Participants also completed blocks of the same three conditions but without receiving any touch on their left index finger to control for any differences in hand posture or peripheral visual input between conditions. Finally, to quantify self-touch attenuation under conditions matched to the MEG task, participants completed a well-established psychophysical force discrimination task after the MEG session, outside the scanner. This behavioural paradigm typically contrasts self-generated and externally generated touch ( Kilteni, 2023 ), but here we extended it to also include a misaligned touch condition, thus mirroring the MEG task design. We first hypothesized that self-touch would elicit less somatosensory-evoked activity compared to externally generated and misaligned touch. Second, we expected modulations of pre-stimulus beta oscillations to signal the brain’s prediction for self-touch. Beta-band (13-30 Hz) oscillations are closely linked to motor control and the top-down modulation of sensory processing ( Engel et al., 2001 ; Engel & Fries, 2010 ; Saleh et al., 2010 ) and have been shown to reflect tactile expectations ( Andersen & Dalal, 2021 , 2024a ; Andersen & Lundqvist, 2019 ; Kimura, 2021 ; Kimura & Katayama, 2023 ; van Ede et al., 2010 , 2011). We therefore expected changes in pre-stimulus activity in the beta-band over the contralateral sensorimotor cortex. Third, we reasoned that if cerebellar activity underlies the predictive attenuation of self-touch, then connectivity in the beta band from the cerebellum to the somatosensory cortices may be increased prior to self-touch. We focused on left cerebellar lobule VI, based on earlier anatomical evidence that the cerebellum controls output to, and receives input from, the ipsilateral side of the body ( Grodd et al., 2001 ; Manni & Petrosini, 2004 ; Mottolese et al., 2013 ) and earlier fMRI evidence that this region is involved in self-touch attenuation when the right hand moves to touch the left hand ( Kilteni & Ehrsson, 2020 ). Although deeper structures like the cerebellum pose challenges for MEG recordings, recent simulations and empirical studies have demonstrated that cerebellar activity can be reliably detected with MEG ( Andersen et al., 2020 ; Samuelsson et al., 2020 ). We tested all these hypotheses with MEG because its millisecond temporal resolution allows dissociation of pre- and post-stimulus neural activity, crucial for identifying predictive processes. Finally, we hypothesized that like the neural self-touch attenuation effects, self-touch would feel weaker compared to externally generated and misaligned touch on the perceptual, force discrimination task. We found support for all these hypotheses. Results Decreased somatosensory cortical activity evoked by self-touch We first investigated attenuation of somatosensory activity time-locked to the touch. In the MEG session ( Figure 1a ), participants received tactile stimuli in three conditions (external touch, self-touch, and misaligned touch) ( Figure 1b ). Critically, control analyses confirmed no significant differences in movement acceleration ( Figure 1c ) or press intensity ( Figure 1d ) between the self-touch and misaligned touch conditions, which was supported by a Bayesian analysis ( Supplementary Text S1, Figure S1 ). Therefore, any differences in brain activity between the movement conditions cannot be driven by variations in movement kinematics between conditions. Download figure Open in new tab Figure 1. MEG stimulus-evoked results. (a) Illustration of the MEG setup, showing the custom device used for simulating self-generated touch. Participants pressed a lever with their right index finger, triggering a tactile stimulus on their left index finger. (b) Experimental conditions. External touch: The touch was delivered automatically after the visual cue. Self-touch: The touch was delivered when participants pressed a lever above their left index finger. Misaligned touch: Identical to the self-touch condition, except that the device was positioned 25 cm to the right of the left index finger. (c) The conditions with movement (self-touch and misaligned touch) did not significantly differ in their acceleration profiles for the moving hand or (d) the force exerted on the response device that triggered the tactile stimulus (see Supplementary Text S1 for details). Shaded error bands show ± standard error of the mean (s.e.m.). (e, f, g) MEG sensor level results. Butterfly plots of the 204 gradiometers, time-locked to the onset of the tactile stimulus on the left index finger, with topographical plots of the root mean square values of combined gradiometer pairs. Topographies show the M50 (50 ms) and M100 (110 ms) components of the event-related fields. Data are presented as the difference between stimulus-present and stimulus-absent blocks (see Supplementary Figure S2 ). Coloured heads in the bottom-left corners indicate sensor locations. (h, i, j) Linearly constrained minimum variance (LCMV) beamformer source reconstructions of the M50 component (45-65 ms) and (k, l, m) the M100 component (90-120 ms). (n, o, p) Grand averaged responses of the combined gradiometer pairs for each statistical comparison, with shaded error bands representing ± s.e.m. The grey areas mark the clusters that informed the rejection of the null-hypothesis in the cluster-based permutation tests. The topographical plots show the uncorrected mass-univariate t-values at the M50 and M100 time points, with cluster channels highlighted. Overall, the figure shows that the M50 response was significantly attenuated for the self-touch compared to external touch and misaligned touch conditions. Significant differences in the external touch vs. misaligned touch comparison emerged later (> 75 ms), peaking around the M100 component. Event-related fields (ERFs) ( Figure 1e-g ) were computed by averaging trials in each of the three conditions and subtracting the stimulus-absent waveforms, minimising movement-related activity and any postural or visual differences (See Methods – Stimulus-evoked activity & Supplementary Figure S2 ). The somatosensory M50 and the M100 components of the ERF were observed, as expected. The M50 component was source localised to the right postcentral gyrus (i.e., primary somatosensory cortex contralateral to the stimulated finger) ( Figure 1h-j ). The M100 component was localised to a broader area including bilateral sensorimotor areas and bilateral parietal operculum (i.e., bilateral secondary somatosensory cortices) ( Figure 1k-m ). Thus, the source localisation confirms previous findings that the M50 and M100 components reflect activity in primary and secondary somatosensory cortices, respectively ( Hesse et al., 2010 ; Yamashiro et al., 2019 ). Cluster-based permutation tests ( Maris & Oostenveld, 2007 ) showed a significant difference between the self-touch condition and the external touch condition ( p < 0.001) ( Figure 1n ). The first cluster that informed the rejection of the null hypothesis indicated lower amplitudes in the self-touch condition compared to the external touch condition from 40 ms to 145 ms, including the M50 and M100 components and a later second cluster between 160 ms and 240 ms. Cluster-based permutation tests also showed a significant difference ( p = 0.015) when comparing the self-touch with the misaligned touch condition ( Figure 1o ), indicating lower amplitudes in the self-touch compared to the misaligned touch condition between 45 ms and 65 ms, corresponding to the M50 component. Finally, cluster-based permutation tests showed a significant difference between the misaligned touch and external touch condition ( p < 0.001) ( Figure 1p ), indicating lower amplitudes in the misaligned touch condition compared to the external touch condition between 75 ms and 160 ms corresponding to the M100 component, and between 155 ms and 220 ms. Together, these results show that both early (M50) and late (M100) somatosensory activity were attenuated for self-touch compared to externally generated touch of identical intensity. Importantly, when comparing the self-touch and the misaligned touch conditions that differ only in the prediction of the sensory consequences of the movement (i.e., self-touch), the attenuation of somatosensory activity was specific to the earlier M50 component. Later components were similarly attenuated in the self-touch and misaligned touch conditions relative to the external touch condition, likely reflecting additional effects between movement and non-movement conditions, including temporal predictability of touch, feelings of agency, and general anticipation of touch. Greater pre-stimulus beta desynchronization preceding self-touch To avoid confounding movement-related activity with predictive effects, we focused our pre-stimulus analysis on the self-touch and misaligned touch conditions, excluding the external touch condition. Notably, comparisons with the external touch condition (which lacked movement and had a fixed cue timing) gave similar results, but they are reported separately in the Supplementary Text S2 due to inherent differences in motor involvement and cue-locked activity. To test whether predictive mechanisms engage prior to self-touch, we analysed pre-stimulus (−1000 to 0 ms) beta-band (13-30 Hz) oscillatory activity in the self-touch and misaligned touch conditions ( Figure 2a-c ). Identically to the touch-evoked analyses, we first subtracted the activity from the stimulus-absent blocks (see Supplementary Figure S3 ). Source localisation identified the origin of the pre-stimulus beta desynchronisation in the sensorimotor cortex contralateral to the stimulated finger ( Figure 2d ). Download figure Open in new tab Figure 2. MEG pre-stimulus results. (a-c) Grand-averaged time-frequency power over channels contributing to the cluster showing a significant difference between conditions (self-touch vs. misaligned touch): (a) the self-touch condition, (b) the misaligned touch condition and (c) their difference (Δ = self-touch - misaligned touch). Stronger beta-band (13-30 Hz) desynchronisation was found in the self-touch condition prior to stimulus onset (dashed box). (d) Source localization of pre-stimulus beta activity revealed sensorimotor sources. (e) Line plot of mean beta power across time, averaged over the channels contributing to the cluster showing a significant difference between conditions. Timepoints contributing to the cluster-level effect are shown in grey. (f-h) Grand averaged beta-band coherence between the right primary somatosensory cortex (SIR) and left cerebellum for (f) the self-touch condition (g) and the misaligned touch condition and (h) their difference (Δ = self-touch – misaligned touch). The outlined region marks the cluster that informs the rejection of the null hypothesis. (I, j, k) Non-parametric Granger causality estimates of directed connectivity in the beta-band. (i) Connectivity in the pre-stimulus window (−0.5 to 0 ms) from the left cerebellar lobule VI to the right primary somatosensory cortex was significantly higher before self-touch compared to misaligned touch. Control analyses found no significant differences when (j) reversing the time axis and (k) reversing the direction (SIR to cerebellumL). Shaded error bands show ± standard error of the mean (s.e.m). A cluster-based permutation test on the sensor-level data showed a significant difference between the self-touch condition and the misaligned touch condition ( p = 0.0245), confirming significantly reduced beta power in the self-touch condition compared to the misaligned touch condition in the pre-stimulus window ( Figure 2e ). This finding suggests that the brain’s prediction of self-touch is associated with a desynchronisation of beta oscillations over the sensorimotor cortex before the touch occurs. Increased cerebellar-somatosensory connectivity preceding self-touch Having established condition-related differences in pre-stimulus beta desynchrony, we next examined changes in functional connectivity between two regions of interest (ROIs). Two ROIs were derived from previous fMRI studies investigating self-touch attenuation when the right hand moves to touch the left hand. The right primary somatosensory cortex (MNI: x = 48, y = −18, z = 60) was chosen based on Kilteni et al. (2023) and the left cerebellar lobule VI (MNI: x = −22, y = −58, z = −22) was chosen based on Kilteni and Ehrsson (2020) . Identically to all previous analyses, we first subtracted the activity from the stimulus-absent blocks to account for any movement, posture or visual differences between the conditions. A cluster-based permutation test on the coherence time course indicated significantly higher coherence in the beta band (13-30 Hz) between right SI and left cerebellum for the self-touch condition compared to the misaligned touch condition ( p = 0.019) ( Figure 2f-h ). Coherence in the stimulus-present and stimulus-absent blocks is shown separately in Supplementary Figure S4 . To further characterise the nature of the connectivity, we assessed directed connectivity using Granger causality, which provides complementary information to coherence by estimating the direction of influence between regions. A cluster-based permutation test on the Granger causality values confirmed significantly higher directed connectivity in the beta band (13-30 Hz) from the left cerebellum to the right SI in the self-touch condition compared to the misaligned touch condition ( p = 0.011) ( Figure 2i ). A control analysis involved reversing the time axis ( Figure 2j ), after which no positive clusters (self-touch > misaligned touch) were identified, suggesting that the effect is not due to spurious temporal correlations or non-causal dependencies. One negative cluster (misaligned touch > self-touch) was identified, but this did not reject the null hypothesis for the time-reversed Granger causality ( p = 0.271). A second control analysis involved reversing the direction of the connectivity from right SI to left cerebellum ( Figure 2k ), which found no significant effects ( p = 0.100), supporting the unidirectionality of the main result (from left cerebellum to right SI). Directed connectivity plots for stimulus-present and stimulus-absent blocks are shown separately in Supplementary Figure S5 . Decreased perceived intensity of self-touch Finally, participants performed the force-discrimination task that allowed us to quantify their self-touch attenuation ( Asimakidou et al., 2022 ; Bays et al., 2005 , 2006 ; Cemeljic et al., 2025 ; Job & Kilteni, 2023 ; Kilteni, 2023 ; Kilteni et al., 2019 , 2020 ; Kilteni & Ehrsson, 2022 ; Timar et al., 2023 ). The task consisted of a two-alternative forced-choice task, where participants judged the intensity of two forces, a test force of 2 N and a comparison force between 1 and 3 N ( Figure 3a ). The task was conducted under three conditions (external touch, self-touch and misaligned touch). A control analysis showed that there were no significant differences in the amount of force used to press during the self-touch and misaligned touch conditions ( Figure 3b ) ( t (23) = −0.17, p = 0.867 d = −0.035, CI S5 = [−0.23, 0.20]), with the absence of effects supported by a Bayesian analysis ( BF01 = 4.60). This ensured that any perceptual differences detected between the two movement conditions were not driven by differences in how participants pressed. Download figure Open in new tab Figure 3. Behavioural results. (a) Experimental conditions and trial procedure for the force discrimination task. In all three conditions, participants discriminated the intensity of a test force (2 N) from a comparison force (1-3 N) delivered to their left index finger on each trial. External touch: The test force was delivered automatically after the onset of an auditory cue on each trial. Self-touch: The test force was delivered when participants pressed a force sensor above their left index finger with their right index finger. Misaligned touch: Identical to the self-touch condition, except that the force sensor was positioned 25 cm to the right of the left index finger. (b) Participants pressed with approximately equal force in the self-touch and misaligned touch conditions. (c) Group psychometric curves for each condition using the average JND and PSE values across participants. The leftward shift in the self-touch condition indicates attenuated PSE values compared to the misaligned touch and external touch conditions. (d) Self-touch was perceived as weaker than external touch and (e) misaligned touch, whereas (f) external and misaligned touch did not significantly differ in perceived intensity. (b, d-f) Boxplots show the median and interquartile range. Raincloud plots show the individual participant values (dots) and their distributions. **p<0.001, *p<0.05. Responses on the force discrimination task (i.e., probability of reporting the second force as stronger than the first as a function of force level) were fitted with psychometric curves ( Figure 3c ), and all fits were considered very good ( Supplementary Figure S6 ). The just noticeable difference (JND) was extracted, which reflects the participants’ discrimination ability. The JNDs did not significantly differ between the conditions ( F (2, 46) = 0.57, p = 0.569, η 2 p = 0.024), with a Bayesian repeated measures ANOVA providing moderate evidence for the absence of an effect of condition ( BF01 = 5.39), indicating that the conditions were comparable in their level of perceptual difficulty ( Supplementary Figure S7 ). The point of subjective equality (PSE) was extracted, which represents the intensity at which the test force felt as strong as the comparison force and quantifies the perceived intensity (i.e. lower PSE values indicate lower perceived intensity). The PSEs significantly differed between the conditions ( F (2, 46) = 11.95, p < 0.001, η 2 p = 0.342). Bonferroni corrected post hoc comparisons revealed significantly lower PSEs in the self-touch condition compared to the external touch condition ( t (23) = −4.91, p < 0.001, d = −1.07, CI S5 = [−0.22, −0.07]) ( Figure 3d ). Self-touch, therefore, felt weaker than externally generated stimuli of identical intensity, replicating previous behavioural findings using the same force discrimination task ( Asimakidou et al., 2022 ; Bays et al., 2005 , 2006 ; Cemeljic et al., 2025 ; Job & Kilteni, 2023 ; Kilteni et al., 2021 ; Kilteni & Ehrsson, 2022 ; Timar et al., 2023 ). The PSEs in the self-touch condition were also significantly lower compared to the misaligned touch condition ( t (23) = −3.02, p = 0.018, d = −0.61, CI S5 = [−0.15, −0.01]) ( Figure 3e ), demonstrating that attenuation effects are not due to simultaneous touch on the two hands, temporal predictability, attentional demands, and dual-task requirements. Finally, the PSEs in the external touch condition did not significantly differ from the misaligned touch condition ( t (23) = 1.94, p = 0.194, d = 0.46, CI S5 = [−0.02, 0.14], BF01 = 0.942) ( Figure 3f ). Together, the behavioral results show that the touch was attenuated only when the sensorimotor context closely resembled self-touch (i.e., spatially aligned hands touching each other). Discussion In the present study, we found that self-touch elicits attenuated somatosensory-evoked activity compared to physically identical externally generated touch and touch that was spatially misaligned. In a separate offline behavioural task, designed to mirror its key conditions, self-touch attenuation was similarly reduced when the spatial alignment of self-touch was disrupted. Importantly, control analyses confirmed no significant differences in the movement kinematics between the self-touch and misaligned touch conditions for the MEG and behavioural tasks, ruling out any motor confounds that could explain the observed effects. Crucially, attenuation of somatosensory evoked activity was preceded by reduced pre-stimulus beta oscillatory activity and increased beta-band connectivity from cerebellar lobule VI to the primary somatosensory cortex (S1). Together, these findings provide evidence that self-touch attenuation is actively shaped by predictive processes and further indicate a cerebellar role in modulating self-generated somatosensory activity. Moreover, subtracting the activity from each condition’s stimulus-absent blocks minimised the influence of movement or posture-related signals and isolated predictive neural mechanisms from general movement-related activity. These controls strengthen the conclusion that the observed effects reflect predictive sensory processing of self-touch, rather than differences in sensory input, motor output or general task demands. The major contribution of our study is the identification of activity that uniquely precedes the actual self-touch input. Beta-band desynchronisation is widely associated with motor preparation and the top-down modulation of sensory processing ( Engel & Fries, 2010 ; Fries, 2015 ; Saleh et al., 2010 ). Beyond its role in maintaining ongoing cognitive states ( Engel & Fries, 2010 ), beta activity also appears to support the reactivation of task-relevant representations ( Spitzer & Haegens, 2017 ), indicating a broader function in anticipatory neural dynamics. We observed reduced beta power in the self-touch condition relative to the spatially misaligned condition, despite matched motor demands. This finding shows that beta oscillations not only index motor preparation but also play a role in predicting sensory feedback during self-touch movements. Previous studies have reported reduced beta power prior to predictable tactile stimuli outside the motor domain ( Forster et al., 2025 ; Kimura, 2021 ; Kimura & Katayama, 2023 ; van Ede et al., 2010 , 2011). Our findings extend this work by demonstrating that beta desynchronisation preceding self-touch encodes motor predictions about self-touch, likely modulating the flow of expected sensory input. These findings converge on the view that pre-stimulus beta modulations reflect a predictive neural state tailored to expected sensory events. In addition to the decrease in beta-band amplitude, we found increased pre-stimulus connectivity within the beta range between the primary somatosensory cortex (contralateral to the upcoming touch) and the ipsilateral cerebellum in the self-touch condition compared to the spatially misaligned condition. Crucially, this effect was evident not only in terms of increased coherence but also enhanced directed connectivity, as shown by Granger causality analysis, indicating information flow from the left cerebellar lobule VI to the contralateral primary somatosensory cortex. These findings provide novel empirical support for the forward model framework, which posits that the cerebellum generates predictive signals to modulate the somatosensory cortex in anticipation of self-generated sensory input. Importantly, unlike previous neuroimaging studies that were limited to post-stimulus activity ( Hesse et al., 2010 ), or averaged over pre- and post-stimulus activity ( Blakemore et al., 1998 ; Kilteni & Ehrsson, 2020 ), our data reveal that this cerebellar-somatosensory connectivity occurs prior to the sensory event and is directed from the cerebellum to the somatosensory cortex, two criteria important for establishing the predictive nature of the effect. This influence from the cerebellum could be anatomically mediated via cerebello-thalamo-cortical projections ( Aoki et al., 2019 ; Keser et al., 2015 ), which include pathways from the cerebellum to the thalamus and onward to motor and somatosensory cortical regions ( Andersen & Dalal, 2024b ; Bostan et al., 2013 ; Dum & Strick, 2003 ). Such a route would allow the cerebellum to relay predicted sensory consequences to the cortex prior to the tactile input. Together, these results underscore the cerebellum’s role in predicting reafferent somatosensory input and support the notion that forward-model mechanisms generate sensory predictions in advance during voluntary actions. In the psychophysical force discrimination task, participants perceived self-touch as weaker than externally generated touch, replicating previous findings ( Asimakidou et al., 2022 ; Bays et al., 2005 , 2006 ; Blakemore et al., 1999 ; Cemeljic et al., 2025 ; Kilteni et al., 2018 , 2019 , 2021 ; Kilteni & Ehrsson, 2017 , 2020 , 2022 ; Shergill et al., 2003 ). Crucially, this attenuation effect was significantly reduced when the hands were spatially misaligned, indicating that the effect depends on the spatial correspondence between the movement and the tactile feedback. A parallel pattern was observed in the somatosensory evoked activity, which was attenuated as early as the M50 component (45-65 ms post-stimulus) for self-touch compared to externally generated touch, consistent with prior neuroimaging findings ( Hesse et al., 2010 ). This attenuation of the M50 component was significantly diminished in the misaligned condition, suggesting that the early attenuation reflects the precision of motor predictions about self-touch rather than alternative non-predictive explanations. Such explanations include factors that differ between self-touch and external touch conditions, but are held constant in the misaligned condition, such as i) the co-occurrence of movement and touch, ii) bilateral stimulation (i.e. touch occurs on both fingers), iii) temporal predictability, iv) divided attention, v) agency, or vi) general expectation of touch. Notably, no significant difference was observed between self-touch and misaligned touch at the later M100 component, suggesting that spatial specificity of predictive attenuation may be limited to the early stages of somatosensory processing. Together, these converging behavioural and neural findings support the forward model framework, reinforcing the idea that attenuation depends on the correspondence between predicted and actual sensory feedback ( Bays & Wolpert, 2008 ; Blakemore et al., 2000 ; Wolpert & Flanagan, 2001 ). Our findings are consistent with animal studies showing reduced, or even cancelled, neural activity in response to self-generated stimuli, including vestibular input in non-human primates ( Brooks et al., 2015 ), self-generated sounds in mice ( Audette et al., 2022 ; Schneider et al., 2018 ; Singla et al., 2017 ), self-generated touch in mice ( Gupta et al., 2023 ), and self-generated electro-sensory and mechanosensory signals in fish ( Perks et al., 2020 ; Wallach & Sawtell, 2023 ), compared to externally generated stimuli. Notably, some studies have also identified neural signals that precede and predict the expected sensory consequences of action. For example, Audette et al., (2022) demonstrated that neural activity in the mouse auditory cortex reflects the expected timing and identity of self-generated sounds even before they occur. Previous studies also implicate cerebellar or cerebellar-like structures in predictive sensory attenuation. For example, Brooks et al., (2015) demonstrated that the cerebellum suppresses vestibular reafference during active head movements in monkeys. Similarly, Singla et al., (2017) showed that a cerebellum-like circuit in mice (the dorsal cochlear nucleus) selectively cancels self-generated sounds via non-auditory signals conveyed by mossy fibers. Likewise, Wallach & Sawtell (2023) described how a cerebellum-like circuit in weakly electric fish cancels predictable self-generated electrical signals. Perks et al., (2020) showed similar cerebellum-like effects for mechanosensory input caused by a fish’s own movements. These findings align with our observation of increased directed connectivity from the cerebellum to the primary somatosensory cortex in humans. Together, this suggests that a predictive cerebellar modulation of early sensory processing may be a conserved feature across species and sensory modalities. Disruptions in the integration of predictive signals with sensory input have been central to neurobiological accounts of psychosis spectrum disorders including schizophrenia ( Blakemore et al., 2000 ; Corlett et al., 2019 ; Shergill et al., 2005 , 2014 ) and schizotypy ( Asimakidou et al., 2022 ). Individuals with schizophrenia exhibit reduced attenuation of self-generated stimuli, including less attenuation of self-generated auditory evoked activity in EEG ( Whitford et al., 2011 ) and somatosensory activation in fMRI ( Shergill et al., 2014 ). These findings support the view that the symptoms of schizophrenia may arise from a deficit in predicting and processing self-generated sensations (C. Frith, 2005b ; C. D. Frith, 2019 ). Impairments in these predictive mechanisms are thought to disrupt the distinction between self-generated and externally generated events ( Fletcher & Frith, 2009 ), weaken the sense of agency ( Leptourgos & Corlett, 2020 ) and lead to perceptual disturbances such as delusions of control and hallucinations (C. Frith, 2005a ; C. D. Frith et al., 2000 ; Poletti et al., 2019 ). However, most previous neuroimaging studies have focused on post-stimulus activity, leaving open the question of whether earlier predictive processes are also compromised. Our results reveal pre-stimulus beta-band desynchronization and directed connectivity from the cerebellum to the somatosensory cortex, neural markers that may reflect intact predictive processing of self-touch. Future studies should investigate whether these pre-stimulus effects are diminished or absent in individuals with schizophrenia or elevated schizotypal traits, potentially offering early indicators of disrupted sensory prediction in clinical populations. Our study shows that self-touch is perceived as less intense, evokes weaker somatosensory activity, and is preceded by beta-band desynchronisation and enhanced directed cerebellar-to-somatosensory connectivity. Crucially, these predictive effects were reduced when the spatial alignment between movement and touch was disrupted, indicating that they depend on precise motor predictions. By isolating pre-stimulus activity and identifying the direction of information flow, from the cerebellum to the somatosensory cortex, our results extend previous work and provide a possible mechanism for how the brain anticipates self-generated tactile events. More broadly, predictive processes like those observed here may be fundamental to how the brain distinguishes self-from externally generated events. Given that these mechanisms are thought to be impaired in psychosis spectrum disorders, the pre-stimulus markers identified here offer promise for understanding disrupted sensory predictions in clinical populations. Methods Participants Twenty-four adults completed the experiment (aged 19-36 years, 12 female, 21 right-handed and 3 ambidextrous, M = 75.48, SD = 34.56). Two participants were replaced, one who did not attend the MRI session and one who did not attend the MEG session (a total of 26 participants recruited). Exclusion criteria were reports of any current or history of psychological or neurological conditions, as well as the use of any psychoactive drugs or medication to treat such conditions. Handedness was assessed using the Edinburgh Handedness Inventory ( Oldfield, 1971 ). All experiments were approved by the Swedish Ethical Review Authority (registration no. 2021-02319). All participants provided written informed consent and were compensated for their time. Structural MRI data acquisition Structural MRI data were acquired from each participant to co-register with the MEG data for source reconstruction. 3D T1-weighted magnetisation prepared rapid gradient echo (MPRAGE) sequence structural images (voxel size: 1×1×1 mm) were obtained on a Siemens Prisma 3.0 T MR scanner. The pulse sequence parameters were: 1 mm isotropic resolution; field of view: 256 x 256 mm; 208 slices; slice thickness: 1 mm; bandwidth per pixel: 240 Hz/pixel; flip angle: 9°; inversion time (TI): 900 ms; echo time (TE): 2.98 ms; repetition time (TR): 2300 ms. Preparation of participants Before MEG measurement, each participant’s head shape was digitized using a Polhemus FASTRAK. Three fiducial points (the nasion, the left and right pre-auricular points) were digitized along with the positions of four head-position indicator coils (HPI-coils). Approximately 200 extra points were digitized over the head shape of each participant. Acquisition of MEG data MEG data were recorded with an Elekta Neuromag TRIUX 306-channel MEG system, with 102 magnetometers and 204 planar gradiometers. The MEG scanner was located inside a two-layer magnetically shielded room. Data were sampled at 1000 Hz with an online 0.1 Hz high-pass filter and 330 Hz low-pass filter. MEG stimuli, apparatus G task Tactile stimuli were generated using an inflatable membrane (MEG International Services Ltd., Coquitlam, Canada) attached to the left index fingertip. The membrane was part of a custom stimulation rig controlled by pneumatic valves (model SYJ712M-SMU-01F-Ǫ, SMC Corporation, Tokyo, Japan) using 1 bar of pressurized air. Figure 1a illustrates the experimental setup. Figure 1b shows the trial procedure for each condition. In the external touch condition, each trial began with the presentation of a blink instruction (the word “blink” in the centre of the screen). After 1200 ms, the blink instruction was replaced with a fixation cross cue that appeared in the centre of the screen. After 500 ms, the tactile stimulus of 100 ms duration was delivered to the pulp of the left index finger. Following the stimulation, an inter-trial interval was presented, which randomly varied in duration between 800, 1000, and 1200 ms. The fixation cross remained on the screen for the duration of the inter-trial interval before it was replaced with the blink instruction of the following trial. The inter-stimulus interval was therefore 2800 ± 200 ms. Participants were instructed to rest both hands during the external touch block. The left hand rested palm-up and its position was fixed using a vacuum pillow (Germa Vaccum Pillow). In the self-touch condition, the same blink instruction and fixation cross cue were presented on each trial. Participants were instructed to tap with their right index finger on a custom-made lever device placed directly above, but not in contact with, their left index finger when the fixation cross cue appeared. Participants had a maximum window of 3000 ms to press the lever. When the lever device was pressed with the right index finger, an optical fibre placed below the device detected the proximity of the finger, which triggered the onset of the tactile stimulus on the left index finger. The setup simulated pressing the left index finger with the right index finger through an object. Participants were instructed to keep their right index finger on a start position marker 5 cm from the lever device before and after the pressing action. The misaligned touch condition was identical to the self-touch condition, except that the device and start-position marker were placed 25 cm to the right of the left hand. Thus, the action of moving 5 cm to press the device in the self-touch and misaligned touch conditions was identical, but the spatial configuration no longer simulated touching one’s left index finger. The inter-stimulus interval did not significantly differ between conditions (see Supplementary Text S1 ). The average movement onset was 418 ms before stimulus onset (SD = 215) for the self-touch condition and 404 ms (SD = 160) for the misaligned touch condition, and no significant difference was found (see Supplementary Text S1 ). Participants completed 2 blocks of each condition. Each block consisted of 90 trials, resulting in a total of 180 trials per condition (self-touch, external touch and misaligned touch). Each condition was also completed in a ‘stimulus-absent’ block in which no stimulus was delivered. Participants were given self-timed breaks between blocks and instructions were presented on the screen before each block. The task lasted approximately 1 hour. The order of the conditions (external touch, self-touch, and misaligned touch) was counterbalanced across participants. The order of the stimulus-present and stimulus-absent blocks was randomised across participants, such that 50% of participants started with stimulus-present blocks and 50% of participants started with stimulus-absent blocks. White noise was played through sound tubes (model ADU1c, KAR Oy, Helsinki, Finland) into the ears of participants at approximately 65 dB throughout the task, making the tactile stimulation inaudible. An accelerometer, attached to the dorsal surface of the middle phalanx of the right index finger, measured the acceleration of the finger movements along three orthogonal axes. The continuous time course of the accelerometer was sampled with the MEG data and averaged offline by calculating the Euclidean norm. MEG Data processing Pre-processing MEG data were pre-processed offline by first applying temporal signal space separation (tSSS) to suppress artefacts from outside the scanner helmet and correct for head movement during the recordings ( Taulu et al., 2004 ; Taulu & Simola, 2006 ). Head origin was shifted to a position based on the initial position for each participant. The data analysis was performed using the FieldTrip toolbox for EEG/MEG-analysis ( Oostenveld et al., 2011 ); Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands. See http://fieldtriptoolbox.org ). The data was segmented into 3-second epochs from 1.5 seconds before stimulus onset to 1.5 seconds after stimulus onset, then down-sampled to 200 Hz and demeaned using the entire epoch. The delay between the digital trigger and the onset of the stimulation was assessed to be 41.0 ms via a separate recording using an accelerometer attached to the inflatable membrane. This delay was subtracted from each epoch. Data epochs were cleaned semi-manually using the ft_rejectvisual function, with segments showing large variances removed without applying a fixed threshold. The method was applied blind to the experimental condition. On average, 46 (SD = 83.01) trials were rejected per participant (< 5% of total trials). An ANOVA with a factor of condition (self-touch, misaligned touch and external touch) and stimulation (stimulus-present and stimulus-absent) was run on the number of rejected trials, for which no significant main effects or interactions were observed (all p-values > 0.05). To attenuate artifacts (e.g. cardiac activity, eye blinks and movements), we applied Independent Component Analysis (ICA). The data were decomposed into 60 independent components per participant using the runica algorithm (Infomax ICA). The resulting components were inspected visually using sensor-space topographies and time courses. An average of 9.0 components (SD = 6.3) were projected out of the data using linear back-projection. Stimulus-evoked activity Event-related Fields (ERFs) were extracted by averaging the trials from each of the three stimulus-present blocks (external, self, and misaligned) as well as the trials from the corresponding stimulus-absent blocks (external, self, and misaligned). The averaged stimulus-evoked waveforms were then subtracted from the averaged waveforms of the corresponding stimulus-absent conditions. This subtraction minimizes non-stimulus-related activity, making the conditions more comparable. The subtraction resulted in three conditions of interest (self-touch, external touch, and misaligned touch). Source reconstruction of the M50 ERF component The time course of neural activity within regions of interest was reconstructed using a Linearly Constrained Minimum Variance (LCMV) beamformer technique (van Veen et al., 1997). T1-weighted MRI images were segmented and resliced to align with the headpoints obtained for each participant during the MEG preparation. A single-shell head model was computed based on the segmented MRI ( Nolte, 2003 ). A standard source model with a 10 mm grid, aligned to MNI space, was used for source reconstruction. The source model was registered to each participant’s MRI. A common spatial filter was calculated based on the data covariance matrix computed over a 0– 200 ms post-stimulus window, using combined trials from each stimulus-present condition and its respective stimulus-absent condition. A regularization parameter (lambda) of 5% was applied to the covariance matrix. The common spatial filter was then applied to the averaged evoked waveforms for each condition. A fixed orientation was determined using single value decomposition (SVD), and the dipole moment was projected onto this axis. Source strength was quantified by averaging the projected dipole moment over a latency window, either 45-65 ms (M50 component) or 90-120 ms (M100 component). Finally, power in the stimulus-present condition was then expressed relative to the stimulus-absent condition (e.g., external = (external stimulus present – external stimulus absent) / external stimulus absent). Pre-stimulus time-frequency activity For induced activity, the time-locked response of each condition was subtracted from each segment of the corresponding condition. This was done to minimize the presence of time-locked activity in the induced activity. Time-frequency representations (TFRs) of individual trials were then calculated using Morlet wavelet analysis with a wavelet width that linearly increased from 3 to 18 across a frequency range of 1-80 Hz. The planar gradient magnitude over both directions was computed by summing the two gradients at each sensor to a single positive-valued number. The averaged TFRs for the stimulus-present conditions were subtracted from the averaged TFRs of the corresponding stimulus-absent conditions. Source reconstruction of pre-stimulus beta Source reconstruction was performed using the Dynamic Imaging of Coherent Sources (DICS) ( Gross et al., 2001 ) beamforming method in FieldTrip ( Oostenveld et al., 2011 ). Cross-spectral density (CSD) matrices were computed for each condition over the 13-30 Hz frequency band in the pre-stimulus window (−500ms 0 ms). A common spatial filter was computed from the combined data of each stimulus-present and stimulus-absent condition. A regularization parameter (lambda) of 5% was applied to the CSD matrix. Source power was then estimated separately for each condition using this common filter. Finally, contrasts were calculated by expressing power in the stimulus-present condition relative to the stimulus-absent condition. Calculation of virtual sensors Virtual channel time series were estimated based on linearly constrained minimum variance beamformer (LCMV) projections to two ROIs derived from previous fMRI studies investigating self-touch attenuation when the right hand moves to touch the left hand. The right primary somatosensory cortex (MNI: x = 48, y = −18, z = 60) was chosen based on Kilteni et al. (2023) and the left cerebellar lobule VI (MNI: x = −22, y = −58, z = −22) was chosen based on Kilteni and Ehrsson (2020) . The template coordinates were transformed to individual coordinates. Coherence analysis Spectral coherence between the virtual sensors was computed using FieldTrip’s ft_connectivityanalysis, retaining only the imaginary component of coherence (‘ absimag ’ option) to minimise spurious zero-lag correlations. Time-frequency representations (TFRs) of power and cross-spectral density were computed using multitaper convolution with a Hanning taper. Frequency-dependent time windows of 7 cycles per frequency were used. Coherence estimates were calculated for each condition in the beta band (13-30 Hz), separately for each time point in the pre-stimulus window (−1 to 0 seconds). For each condition, the stimulus-absent block was subtracted from the corresponding stimulus-present block to isolate stimulus-specific coherence. Granger causality analysis Directed connectivity between the virtual sensors was assessed using non-parametric Granger causality using FieldTrip’s ft_connectivityanalysis function with the ‘ granger ’ method and ‘ bivariate ’ spectral factorization. To improve signal stationarity, the analysis was confined to a 500 ms pre-stimulus interval (−0.5 to 0 ms) that was closest to stimulus onset. Frequency decomposition was performed using the multitaper method with ±2 Hz spectral smoothing. For each participant and condition, stimulus-present Granger causality values were expressed relative to stimulus-absent values (see Supplementary Figure S5 ). Time-reversed trials were also analysed as a control to ensure directional asymmetries were not due to spurious temporal correlations or non-causal dependencies. Additional analyses of direction-reversed connectivity (from SI to the cerebellum) were conducted to assess the directional specificity. Behavioural force-discrimination task Following MEG data collection, participants completed the force discrimination task ( Kilteni, 2023 ). They rested their left hand, palm up, with their index finger placed on a moulded support ( Figure 3a ). On each trial, a motor (Maxon EC Motor EC 90 flat; Switzerland) delivered two forces (the test force and the comparison force) on the pulp of the left index finger through a cylindrical probe (25 mm height) with a flat aluminium surface (20 mm diameter) attached to a lever on the motor. A force sensor (FSG15N1A, Honeywell Inc.; diameter, 5 mm; minimum resolution, 0.01 N; response time, 1 ms; measurement range, 0–15 N) within the probe recorded the forces applied on the left index finger. Following the presentation of the two forces, participants verbally reported which of the two forces felt stronger, the first or the second. A second identical force sensor within an identical cylindrical probe (“active force sensor”) was placed on top of, but not in contact with, the probe of the left index finger. Participants judged the intensity of a test force and a comparison force (100 ms duration each) separated by a random interval between 800 ms and 1200 ms in a two-alternative forced-choice (2AFC) task. The intensity of the test force was 2 N, while the intensity of the comparison force was systematically varied among seven force levels (1, 1.5, 1.75, 2, 2.25, 2.5, or 3 N). In all the conditions, the forces were delivered by the same motor, in order to precisely control their magnitude, however, the source of the force was manipulated across conditions such that the force was triggered by the participant’s contact with a force sensor (self-touch condition and misaligned touch condition) or automatically by the stimulus computer (external touch condition). In the external touch condition, participants rested both hands and received the test and the comparison force on the left index finger. This external condition was used to assess somatosensory perception in the absence of any movement ( Bays et al., 2005 ; Kilteni et al., 2019 , 2020 ). Each trial began with an auditory cue (100 ms duration, 997 Hz) followed by the test force delivered to the participant’s left index finger 800 ms after the cue by the motor. The comparison force was then delivered at a random interval between 800 ms and 1200 ms after the test force. In the self-touch condition, the same auditory cue was presented as in the external touch condition, but participants now actively tapped on the force sensor placed on top of, but not in contact with, the probe. The active tap on the force sensor triggered the motor to apply the test force on the left index finger (threshold 0.2 N). The tap of their right index finger triggered the test force on their left index finger with an intrinsic delay of 36 ms. The comparison force was then delivered at a random interval between 800ms and 1200ms after the test force. Participants were asked to tap, neither too weakly nor too strongly, with their right index finger, “as if tapping the screen of their smartphone”, as instructed in previous studies ( Asimakidou et al., 2022 ; Kilteni et al., 2021 ; Kilteni & Ehrsson, 2022 ). The misaligned touch condition was identical to the self-touch condition, except that the force sensor that participants tapped to trigger the touch on their left index finger was placed 25 cm to the right of the left index finger, rather than directly above the left index finger. While previous studies investigating spatial manipulations of self-touch have used force-matching paradigms ( Bays & Wolpert, 2008 ; Kilteni & Ehrsson, 2017 , 2020 ). These typically involve sustained force stimuli lasting several seconds, unlike the brief tactile stimuli used in the MEG task. By contrast, the force discrimination task used stimuli of identical duration to those in the MEG task, enabling a more closely matched behavioural task. To our knowledge, this is the first study to apply a spatial manipulation of self-touch within the force discrimination task. White noise was played throughout the task to mask any sounds made by the motor that could serve as a cue for the task. Each block consisted of 70 trials, resulting in 210 trials per participant. The order of the conditions (self-touch, external touch, and misaligned touch) was counterbalanced across participants. Preprocessing of psychophysical trials The behavioural task included 5040 trials in total (24 participants * 70 trials * 3 conditions). 20 trials were excluded because of 16 missing responses and 4 trials in which the force was not applied correctly (1.85 N < test force < 2.15 N) Fitting of psychophysical responses For each experiment and each condition, the responses were fitted with a generalized linear model using a logit link function ( Equation 1 ): We extracted two parameters of interest: the Point of Subjective Equality (PSE) , which represents the intensity at which the test force felt as strong as the comparison force ( p = 0.5) and quantifies the perceived intensity, and the JND , which reflects the participants’ discrimination ability. Before fitting the responses, the values of the applied comparison forces were binned to the closest value to their theoretical values (1, 1.5, 1.75, 2, 2.25, 2.5, or 3 N). See Supplementary Figure S6 for individual participant psychometric curves. The fitted logistic models were very good, with McFadden’s R-squared measures ranging between 0.600 to 0.970. After the psychophysical task, participants completed the Schizotypal Personality Ǫuestionnaire (SPǪ) ( Raine, 1991 ) which addressed a separate research question and will be reported in a different manuscript. Statistical analyses For movement-related data from the MEG session (i.e., movement onsets, maximum acceleration, and press intensity), repeated measures analysis of variance (rmANOVA) was used with two factors: condition (self-touch vs. misaligned touch) and stimulation block (stimulus-present vs. stimulus-absent). For behavioural data generated from the force discrimination task (i.e. PSEs and JNDs), rmANOVA was used to compare the external touch, self-touch, and misaligned touch conditions. For the peak force exerted with the right index finger on the force sensor, a paired t-test was used to compare the self-touch and misaligned touch conditions. Significant main effects from the rmANOVAs were followed by Bonferroni-corrected post hoc comparisons. Effect sizes are reported as partial eta squared η 2 for F-tests and Cohen’s d for t-tests. 95% confidence intervals ( CI S5 ) are reported for each statistical test. Bayesian factor analyses using default Cauchy priors with a scale of 0.707 were performed for tests that led to statistically non-significant effects. This provided information about the level of support for the null hypothesis compared to the alternative hypothesis ( BF01 ) based on the data. We interpret a Bayes factor from 1 to 3 as providing ‘anecdotal’ support for the null hypothesis, a Bayes factor from 3 to 10 as providing ‘moderate’ support for the null hypothesis, and a Bayes factor above 10 as providing ‘strong’ support for the null hypothesis ( Ǫuintana & Williams, 2018 ; van Doorn et al., 2021 ), indicating that support for either the preferred or null hypotheses is insufficient. All tests were two-tailed. For stimulus-evoked activity, comparisons were made between the self-touch and external touch conditions, as well as self-touch and misaligned touch, and misaligned touch and external touch conditions. For pre-stimulus activity, only conditions that involved action were compared (i.e., self-touch and misaligned touch), given that comparisons between a condition that involves action and the external touch condition without action would likely reflect the presence of movement within the pre-stimulus window, or activity locked to the fixation cross cue. Non-parametric cluster permutation ( Maris & Oostenveld, 2007 ) was used to compare the conditions. The following steps are taken for significance testing with cluster-based permutation tests: 1) perform mass-univariate dependent samples t-statistics comparing each condition for each of the samples in the multidimensional data structure; 2) neighbouring data points in the multidimensional data structure below a threshold of p < 0.05 are summed to calculate the cluster level statistic; 3) the procedure is repeated for 2000 permutations with the condition labels shuffled on each permutation; 4) The maximum cluster statistic was evaluated under its permutation distribution (shuffled data). The cluster-level significance threshold was set at the two-tailed level of 0.025. For the analysis of stimulus-evoked activity, the test window spanned 0 to 400 ms post-stimulus. For the analysis of pre-stimulus oscillatory activity, the test window covered −1000 to 0 ms relative to stimulus onset and was restricted to the beta band (13-30 Hz). Author Contributions X.J. and K.K. conceived and designed research; X.J. and N.C. performed research; X.J. analysed data; X.J. and K.K. wrote the manuscript. L.M.A., M.V., N.C., and D.L. read, edited and approved the final version. Declaration of interests The authors declare no competing interests. Acknowledgments We thank Lili Timar for her assistance with collecting the behavioural data. Experimental costs were covered by the Swedish Research Council (VR 2019-01909, and VR 2024-00906). X.J. was supported by the Marie Skłodowska-Curie Intra-European Individual Fellowship (grant number 101059348) and The Strategic Research Area Neuroscience (StratNeuro). K.K was supported by the European Research Council (ERC 101039152). Funder Information Declared Swedish Research Council, https://ror.org/03zttf063 , VR 2019-01909 , VR 2024-00906 European Commission, https://ror.org/00k4n6c32 , Marie Skłodowska-Curie Actions (101059348) StratNeuro Footnotes Classification: Biological Sciences/Neuroscience; Social Sciences/Psychological and Cognitive Sciences References ↵ Andersen , L. M. , & Dalal , S. S. ( 2021 ). The cerebellar clock: Predicting and timing somatosensory touch . NeuroImage , 238 , 118202 doi: 10.1016/j.neuroimage.2021.118202 OpenUrl CrossRef PubMed ↵ Andersen , L. M. , & Dalal , S. S. ( 2024a ). Detection of threshold-level stimuli modulated by temporal predictions of the cerebellum . ENeuro , 11 ( 4 ). doi: 10.1523/ENEURO.0070-24.2024 OpenUrl Abstract / FREE Full Text ↵ Andersen , L. M. , & Dalal , S. S. ( 2024b ). The role of the cerebellum in timing . Current Opinion in Behavioral Sciences , 5S , 101427 . doi: 10.1016/j.cobeha.2024.101427 OpenUrl CrossRef ↵ Andersen , L. 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