Action enhances the detection of visuomotor incongruence: A comparison of matched spatial vs temporal manipulations along identical trajectories

Action enhances the detection of visuomotor incongruence: A comparison of matched spatial vs temporal manipulations along identical trajectories | 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 Action enhances the detection of visuomotor incongruence: A comparison of matched spatial vs temporal manipulations along identical trajectories Fanni Peters , Peng Wang , View ORCID Profile Jakub Limanowski doi: https://doi.org/10.1101/2025.06.18.660374 Fanni Peters Institut für Psychologie, Universität Greifswald , Greifswald, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peng Wang Institut für Psychologie, Universität Greifswald , Greifswald, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jakub Limanowski Institut für Psychologie, Universität Greifswald , Greifswald, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jakub Limanowski For correspondence: jakub.limanowski{at}uni-greifswald.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Introducing delays to visual movement feedback or displacing it in space is a common experimental manipulation to study the neurocomputational basis of flexible motor control and self-other distinction. While both manipulations imply positional mismatches, there are crucial differences between them, such as the preservation of kinematics (velocity, acceleration etc.) by spatial displacement but not time delay. The systematic comparison of the perceptual sensitivity to spatial vs temporal visuomotor mismatches is notoriously difficult. In this preregistered experiment, we addressed this question by using a continuous elliptical drawing task with a new, matched spatial (offset) vs temporal (delay) vs congruent manipulation of visual movement feedback along identical movement trajectories; and comparing detection performances during execution vs observation. The detection of deviations in visual movement feedback was significantly improved by action; especially at critical (near- threshold) mismatch levels. Temporal and spatial detection showed similar patterns in the active part. During observation, spatial, but not temporal deviations could also be detected above chance level, suggesting a visual sensitivity to violations of kinematic invariants. Introduction Distinguishing sensations that have been caused by one’s actions from those that have been caused by others is a key prerequisite for bodily self-other distinction and the experience of, broadly speaking, agency and control ( Tsakiris et al., 2005 ; Synofzik et al., 2008 ; Salomon et al., 2013 ; Haggard, 2017 ; Wen et al., 2018 ; Wen & Imamizu, 2022 ; Limanowski, 2022 ). The brain likely achieves this by calculating a prediction of the sensory consequences of (planned) actions by means of a “forward” model, and comparing these predictions with the actual sensory reafference (Miall et al., 1996; Wolpert & Miall, 1996 ): Matching (predicted) sensory feedback is perceived as a result of one’s own actions, mismatching feedback is perceived as having been externally caused. To understand the underlying processes and their neurocomputational foundations, one can artificially distort sensory movement feedback, so that it does not (immediately) correspond to the forward predictions. Two kinds of manipulations are especially prominent: Introducing temporal lags (delays) to visual movement feedback, or displacing it in space. Both manipulations imply positional mismatches between the moving body part and the visual feedback; the registration of both, spatial and temporal visuomotor mismatches is commonly interpreted as leading to a loss of perceived control or agency, and the attribution of the movement to someone else ( Frith et al., 2000 ; Farrer & Frith, 2002 ; Tsakiris & Haggard, 2005 ). However, there are some fundamental differences between spatial and temporal mismatches. A key difference is that spatial manipulations preserve all visual kinematics in “real-time”, merely requiring spatial remapping of coordinates ( Rohde & Ernst, 2016 ; Limanowski, 2025 ). In contrast, added delays imply mismatching motor and visual kinematics; e.g., a motor acceleration will produce a visual acceleration only after the time delay (ibd.). Several, yet unresolved questions arise from this: One is whether temporal and spatial visuomotor comparisons provide equally strong cues for self-other distinction (cf. Farrer et al., 2008 ; Krugwasser et al., 2019 ), or whether e.g. visual feedback delays may be a ‘stronger’ error signal than spatial displacement, because they are more closely tied to action timing and initiation and, thus, volition ( Rohde & Ernst, 2016 ; Tanaka & Imamizu, 2025 ). Another, related question is whether there are separate “forward” delay estimators, and whether temporal and spatial adaptation is qualitatively different ( Foulkes & Miall, 2000 ; Miall et al., 1993 ; Limanowski, 2025 ). A key first step in addressing these questions is to compare the sensitivity to visuomotor error signals in the spatial vs temporal domain ( Rohde & Ernst, 2016 ). While many studies have used either one of these manipulations, only few have attempted to combine—and compare— them in a single experiment: Farrer et al. (2008) used “qualitatively” different spatial vs temporal incongruencies, introducing angular deviations to visual cursor movement feedback; thus, the visual movement feedback in spatial vs temporal manipulations followed different trajectories—precluding clear inferences about sensitivity to visuomotor kinematic mismatches per se. Krugwasser et al. (2019) advanced on this by adding angular deviations vs delays to a single hand flexion movement. They showed comparable sensitivities to angular vs temporal deviations, but the brief, single movement trajectory did not allow for a detailed comparison of sensitivity to the different aspects of visual vs motor kinematics. In this preregistered study ( https://osf.io/39tch ), we addressed this question by using an elliptical drawing task with a new, matched manipulation that allowed a systematic comparison of detection thresholds for spatial (offset) and temporal (delay) incongruities in visual movement feedback, its kinematics, as well as the importance of motor signals for these processes. Participants continually and covertly drew ellipses on a tablet with a pen while manipulated visual movement feedback was displayed on a monitor. This feedback was either congruent or incongruent; i.e., either a constant time delay or a constant spatial offset was added to the visual movement (six levels of each). Participants were asked to indicate as quickly as possible whether the observed movement was congruent, temporally delayed or spatially offset with respect to their executed movement. The key novelty of our design was that movements with congruent, delayed, and displaced visual movement feedback were executed along an identical, elliptical trajectory—while the visual movement feedback followed the actual movement on the same trajectory (with a constant temporal lag or a constant spatial offset). Thus, in contrast to previous works (see above), our design allowed us to compare the perceptual sensitivity to spatial vs temporal mismatches when the visual movement trajectory was matched; furthermore, we matched spatial and temporal manipulations in terms of their average Euclidean distance. Thus, in our design, differences in detection performance could be related to the sensitivity of matching vs mismatching visual and motor kinematics, rather than confounding factors such as differences in visual movement trajectory. To manipulate the correspondence of visual and motor kinematics, we introduced regular variations in movement velocity by restricting the movements to an elliptical shape with a template. Thus, we capitalized on kinematic invariants in movement production as described by the “two-thirds power law” (Binet & Courtier, 1893); i.e., that biological movements tend to be slower at trajectories with stronger curvature (Box S1; cf. Viviani & Stucchi, 1992 ; de’Sperati & Stucchi, 1995; Dayan et al., 2007 ; Hun & Sejnowski, 2015). Crucially, in our experiment, these invariants were preserved in the delayed feedback condition, but spatially shifted along the elliptical trajectory in the offset feedback conditions. Previous work has shown that the visual system is sensitive to violations of kinematic invariants ( Salomon et al., 2016 ; Fraser et al., 2024 ; cf. Knoblich & Prinz, 2001 ; Knoblich et al., 2002 ). In short, the velocity profiles of offset vs delayed (and congruent) visual movement feedback contained some—in principle, perceptually detectable—systematic differences. Secondly, we aimed to establish the importance of motor predictions for this detection and classification. I.e., we tested whether mismatch detection and discrimination were more accurate when the visual feedback was the consequence of one’s own actions, than when observing the visual movement passively. To this end, we presented each participant with a replay of the movements they had performed in the first part, in a separate session one week later, using the same task and response format. Our main, preregistered ( https://osf.io/39tch ) hypotheses were: That incongruent visual feedback would be better detected in the active part than in the passive part (H1), and that the discrimination between the three types of visual action feedback would be more accurate in the active part than in the passive part (H2); i.e., above chance level in the active part (H2a). For the active part, we expected better detection performance with increasing incongruity of the visual action feedback (H3). Additionally, we hypothesised that, in the passive part, participants would be relatively better at detecting spatial offsets than temporal delays (H4a); i.e., because the kinematics of the reproduced offset movements violated the two-thirds power law, which should be perceptually detectable, in contrast to the playback of veridical but delayed kinematics. Furthermore, we expected the positive relationship between the degree of incongruity and recognition performance (cf. H3) to be greater in the active part than the passive part (H4b). In the passive > active part, the positive relationship between the degree of incongruity and recognition performance (cf. H3) should be stronger for offsets than delays (H4c, cf. H4a). Finally, following our preregistered exploratory analyses, we analysed the participants’ reaction times (i.e., trial durations) in the active part; we tested for a correlation of detection performance between the active and the passive parts; and we analysed the participants’ movement data (i.e., velocity and spectral profiles) to evaluate the comparability of the executed movements. Methods Unless explicitly noted otherwise, we preregistered our hypotheses and analytical methods ( https://osf.io/39tch ) and experimental design and procedures ( https://osf.io/udxth ) on OSF; we preregistered after having started data acquisition but before inspecting any data or performing any statistical analyses. Participants 33 right-handed volunteers (23 women; aged 18–42) completed the experiment. All participants provided written informed consent, had normal or corrected-to-normal vision, and reported no history of psychiatric or neurological conditions. The sample size was determined with a power analysis using G*Power, based on data from a pilot experiment ( N =5). We calculated an effect size of V=0.3 (Cramér’s V) relating to the difference between the two experimental parts. With a target power (1-β) of 0.8, the required sample size was 33 participants. The data of one participant had to be excluded, as no trials in one of the conditions survived our exclusion criteria (see below); resulting in a final sample size of 32. The experiment was approved by the ethics committee of the University Medicine of Greifswald. Experimental design and procedure The experimental environment was run on a personal computer using PsychoPy (version 2024.1.5; Peirce et al., 2019 ) and visualized on a computer screen (HP E27 G5 FHD, 27-inch IPS panel, 1920 x 1080 pixels resolution, 75 Hz refresh rate). Participants sat at a table at about 50 cm distance from the computer screen ( Fig. 1A ). Download figure Open in new tab Figure 1. A: Experimental setup. Participants sat at a table in front of a computer monitor and performed elliptical pen movements on a graphics tablet, guided by a 3D-printed template, with visual movement feedback provided by on screen. The tablet, template, pen, and the participants’ right hand were occluded from view; to mask any auditory movement feedback, brown noise was played via speakers, also hidden from view. Participants gave rating responses with their left hand via a small key pad. B: Schematic trial structure. In the active part, the participants started each trial by moving to a predefined region of the ellipse (schematically indicated by the dashed yellow arrow), upon which a white dot appeared. Then they had to perform continuous elliptical movements at ∼0.33 Hz while receiving visual movement feedback via a white dot moving along the visible ellipse on screen. The movement of the dot could be either veridical (congruent with the executed movement, 1/3 of all trials) or manipulated (delayed with respect to the executed movement, 1/3 of all trials; or spatially offset with respect to the executed movement, 1/3 of all trials). There were 6 different levels of delays and offsets, respectively, which were matched in terms of their average distance between pen and dot on the ellipse (see Methods). Participants were instructed to perform continuous elliptical movements, and to end the trial by left-hand key press as soon as they were ready to rate the visual movement feedback: i.e., they had to rate whether each seen movement had been congruent, delayed, or offset with respect to their actual pen movement on the ellipse. C: The experiment consisted of an active part (as described above) and a passive part, separated by about one week. In the passive part, participants viewed a randomized playback of the visual movement (i.e., dot) feedback without moving themselves; they had to rate whether each seen movement had been from a congruent, delayed, or offset movement trial, as in the active part. Following the preregistered protocol ( https://osf.io/udxth ), the experiment consisted of two parts: an active (movement) part and a passive (observation) part. In the first, active part of the experiment, participants were asked to perform counter-clockwise elliptical movements on a tablet (One by Wacom, model no: CTL-672, connected to the computer via USB), using a pen held in their right hand. The elliptical shape of the movement was determined by a custom 3D printed template that guided the movements of the pen. I.e., the elliptical shape was cut out, preventing the pen to be lifted or to deviate to the outside. Visual movement feedback of the pen movements was provided as a white dot on screen (1.2° visual angle in diameter); participants were also shown the elliptical shape as a black outline on grey background on screen. The sizes of the physical and visual ellipses were matched in terms of degrees visual angle from the participant’s view. The size of the major axis of the ellipse was 22.5° visual angle, while the size of the minor axis was 10° visual angle. The eccentricity of the ellipse was Σ = 0.9 and was based on the shape used by Bidet-Ildei and colleagues (2008) . To avoid visual distractions, the tablet and the participant’s hand were hidden under a visual cover. To minimise potential auditory movement feedback, resulting e.g. from the pen scratching on the tablet, brown noise was played via speakers (also hidden from view under the cover) throughout the entire experiment. The instructed velocity for the movement to be performed was about 3 s per cycle (0.33 Hz). Participants practiced the task extensively to ensure consistent movement velocities (see below); we deliberately did not include any pacing signals, as these kinds of regular signals would have provided a strong cue to compare dot and/or pen positions against—which would have facilitated the detection of visuomotor manipulations. We recorded pen positions and the corresponding dot (feedback) positions on the target ellipse at a sampling rate of 75 Hz. To the left of the cover was a numeric keypad (LogiLink USB Numeric Keypad). Participants were asked to position their left hand so that the ring finger was on the “1” key, the middle finger was on the “2” key, the index finger was on the “3” key, and the thumb was on the “Enter” key. The setup is shown in Figure 1A . At the beginning of each trial, a blinking grey “starting point” appeared at the right apex of the ellipse ( Fig. 1B ). Participants started each feedback condition by reaching and crossing this point with a white dot that they controlled with their (counter-clockwise) pen movements. Upon crossing the starting point, the white dot (visual movement feedback) disappeared for 1500 ms, while the participants were instructed to perform the elliptical counter-clockwise movements at the trained regular pace. Briefly hiding the visual feedback was necessary to “buffer” the visual movement feedback in the delayed conditions. Three different visual movement feedback conditions were implemented: congruent, delayed, and offset visual feedback. In the congruent visual feedback condition, the movement of the dot on screen displayed the participants’ pen movement in “real-time” (bar intrinsic delays of our setup). In the delayed feedback condition, a time delay was introduced between the executed pen movements and the movements displayed via the dot, by buffering the recorded pen movements and replaying them after a constant time interval. Thus, the dot motion lagged behind the actual hand/pen movements with a constant delay. This means that the dot motion did not immediately reflect higher order kinematics, such as changes in velocity, of the actually executed movement. There were six possible levels of delay (13.3, 80, 160, 240, 320, and 400 ms) that we chose following previous related works ( Leube et al., 2003 ; Farrer et al., 2008 ; Limanowski et al., 2017 ; Krugwasser et al., 2019 ). One of our key novelties was the introduction of visual movement feedback that was spatially displaced (offset) along the same, elliptical movement trajectory. Thus, in the spatially offset feedback condition, a constant spatial offset between the pen movement and the visual feedback (i.e., dot) was implemented; i.e., the dot followed the pen’s trajectory on the ellipse with a constant Euclidean distance. Importantly, the visual feedback was not delayed in this condition; i.e., despite a positional mismatch, the visual kinematics were preserved. In other words, the dot’s movements reflected changes in the executed movement kinematics in “real- time” (i.e., as in the congruent condition). This implied a shift of the visual velocity profiles along the trajectory of the ellipse, which violated the Two-thirds power law (see Fig. 2 for a display). Note that we expected participants to be perceptually sensitive to these violations (Hypothesis H4a), in line with previous work (see Introduction). Download figure Open in new tab Figure 2. Kinematics of the executed movements and the visual movement feedback in each condition. The left plots in each condition show the averaged power spectral densities (PSD, cf. Fig. S1). The PSDs show that participants exhibited very comparable movements in all conditions, with peak frequencies around 0.4-0.5 Hz. The elliptical plots show the averaged velocities of executed movements (left) and visual movement feedback (right) projected onto the elliptical trajectory (see Methods); darker parts indicate slower movement velocities. In all conditions, participants adhered to kinematic invariants described by the two-thirds power law; i.e., slowing down at stronger curvatures (left and right sides of the ellipse). The visual feedback also reflected this in the congruent (left) and the delayed (middle) conditions, the latter of which were effectively a simple lagging playback of the executed movements. In contrast, note the systematic shift of velocity profiles introduced by constant spatial offsets along the elliptical trajectory (right). To do this, for each recorded pen position, we determined the spatially displaced coordinates located behind the pen position on the elliptical trajectory (i.e., displaced clock-wise) at a fixed Euclidean distance, using a motion vector-based approach (along an up-sampled elliptical trajectory to increase accuracy, see Supplementary material for details). Note that, to implement a constant spatial offset following the participants elliptical movement, we had to restrict the visual movement to the ideal elliptical trajectory. In principle, participants could have noticed visual feedback following the ellipse while they actually deviated from the elliptical shape. To account for this, we eliminated hand movements that deviated substantially from the elliptical trajectory (see below). Again, there were six different levels of spatial offset (0.5, 2.9, 5.7, 8.5, 11.6, and 14.2° visual angle). Crucially, for comparability of the six delay and offset levels, each level was matched in terms of the average Euclidean distance between the physical (pen) and displayed feedback (dot) positions on the Ellipse. I.e., for each delay level, we calculated the average Euclidean distance based on a geometrical model assuming constant velocity movement along the ellipse at 0.33 Hz. This was then chosen as the respective spatial offset i.e., constant displacement of the dot relative to the pen position, in the respective “offset” levels. Participants completed 144 trials in total in this part, with 144/3 = 48 trials per condition (congruent, delay, offset). Each of the 6 delay and offset levels was presented 8 times, interspersed with congruent trials, in randomized order. The 144 trials in total were divided into six blocks of 24 trials each. The participants’ task was to decide, as quickly and correctly as possible, whether the white dot’s movements were congruent with their own movement, or whether there was an incongruence (in the form of a temporal delay or a spatial offset, see below). The participants could move until they reached a decision, end then each trial by pressing the enter key. If no decision was reached within 20 s, the trial ended automatically. The participants were then asked whether the visual feedback was congruent (in German: “identisch”), delayed (“zeitlich verzögert”) or offset (“räumlich versetzt”), see Figure 1B . The answers were selected using the numeric keys 1-3 (assignment to the response options randomized across participants) and confirmed with the Enter key. Prior to the actual experiment, participants completed a five-part training session to familiarize themselves with the experimental task and conditions. In the first training session, the movement took place under a congruent test condition. In the second part, a medium delay (240 ms) was introduced, and in the third part, a medium offset (8.5°) was introduced. In the fourth part, participants practiced all trial conditions in a randomized order and became familiar with the rating that took place after each trial. In the final part of the training, the participants practiced moving the pen continuously at a predetermined velocity (0.33 Hz i.e., 3 s per cycle). The training phase lasted approximately 20 minutes in total. In the second, passive part of the experiment, participants viewed a playback of the recorded visual movement feedback of their movements performed in the first (active) part; in randomized order. Here, no movement was executed ( Fig. 1C ). This part of the experiment served as a control condition in which visual sensitivity was examined in the absence of motor signals. The experimental setup with the covered tablet was identical to the first part of the experiment; brown noise was also played throughout the entire part. To avoid memory effects, we included an average interval of one week (7.12 ± 1.36 days) between the two experimental parts, (cf. Knoblich & Prinz, 2001 ). Furthermore, all playback trials were restricted to a maximum duration of 8 s, to avoid participants inferring feedback conditions from trial length alone. It should be noted that this affected about 40% of all trials, meaning that participants had comparably fewer visual samples in the passive part. After the presentation of each trial, participants indicated whether they believed the observed movement had been from a congruent, delayed, or offset movement trial (in a format identical to the active part, see above). This part also consisted of a total of 144 trials, divided into six blocks of 24 trials each. Thus, the experimental design was 2x3 factorial with the factors Session (active, passive) and Feedback type (congruent, delay, offset). After completion of the experiment, the participants received compensation (10 € per hour or course credit). Movement data analysis We first resampled the data at 75 Hz to ensure that the measured data matched the theoretical sampling rate specified in PsychoPy; i.e., to account for occasional unstable frames that might have introduced jitter in the data. The resampled trials were then epoched according to the 13 experimental conditions. Trials in which participants made erroneous movements (i.e., clockwise, stopped for longer than 500 ms, or changed direction) were excluded from further data analysis; we also excluded trials in which the pen’s trajectory deviated more than 10% from the ideal elliptical trajectory—this could happen occasionally, as our template only guided the pen on the outside. A stringent exclusion of these trials was necessary, because these deviations could immediately reveal clues about which condition the participant was in: For instance, a reversal of motion would be reflected by visual feedback immediately in the “offset” condition, but only after some time in the “delay” condition. With these criteria, we rejected 17.7 % of all trials (see Table S1 for details); the number of excluded trials did not significantly differ across different conditions (χ 2 (2)=1.58, p = .45) and the different stimulus level (χ 2 (12) = 6.07, p = .912). The average movement velocity was determined by dividing the number of total cycles per trial by the total time per trial; and averaging these values across trials per condition. We used a two-sided t-test to test if the participants deviated from the instructed movement velocity of 0.33 Hz. Additionally, we conducted an analysis of variance (ANOVA) to determine whether the average velocity differed between conditions. To illustrate the movement kinematics in more detail, we plotted the power spectral densities (PSD) of the pen movements in a range from 0 to 4 Hz (cf. Foulkes & Miall, 2000 ). We plotted the PSDs for the x-axis and y-axis separately; note that these are basically redundant in an elliptical movement (see Supplementary material). Then, we looked at the participants’ velocity profiles; i.e., variations in movement speed along the elliptical trajectory, across conditions. We computed the movement velocity on the different location of the target ellipse by the derivative form: where 𝑣 𝑖 is the estimated instantaneous speed of the i-th time sample. ( xi, yi ) represents the pen/cursor position at the i-th time point, and 𝑑𝑡 is the temporal distance between the two samples (i.e., for 75Hz = 0.013 s). These estimates were smoothed using a Savitzky–Golay filter (15 time points window length, 0.2 s) to reduce noise. To enable a comparison of the velocity profiles across experimental conditions (see Fig. 2 ), we remapped the smoothed instantaneous speed profiles from each valid drawing round onto our elliptical geometry template ( a = 0.293, b = 0.1295, 500 points, see Supplementary material). Remapping was performed using a nearest-neighbor projection, i.e., each point on the template ellipse was assigned the speed value of the closest point from the participant’s original pen trajectory. This procedure yielded a resampled speed profile aligned to the idealized spatial structure of the ellipse. For each experimental condition, the remapped velocity profiles were first averaged across full rounds (i.e., completed elliptical cycles) in each trial, then averaged across trials per condition. This was done for executed (pen) movements and visual feedback (dot movements alike). Rating data analysis In the analysis of the rating responses (i.e., the classification of the visual feedback), we were interested in classification above chance (i.e., guessing) level; and in differences between feedback conditions and experimental parts. To determine classification performance above chance level (cf. in particular hypothesis H2a), we conducted binomial tests with a fixed value of 1/3; i.e., as each feedback trial could be rated as congruent, delayed, or offset (see above). We interpreted conditions with relative frequencies of correct answers significantly higher than this level as correctly classified by the participants. To analyse the correct responses in the experimental part, and their differences between conditions (cf. hypotheses H1 and H2), we used generalised linear mixed models (GLMM) with binomial distribution and logit link function. I.e., we conducted a logistic regression with nominal-scale predictors, which we embedded in a hierarchical model, due to the two experimental parts (active and passive part). This type of analysis accounted for the hierarchical structure of the data and the repeated measurements within subjects. Level 1 represented the subject level, while Level 2 represented the repeated measures within the subjects. The model assumed that measured values from the same person would be more similar the values from different people (Eid et al., 2017). The Intraclass Correlation Coefficient (ICC) was 0.058. This indicates that only about 6% of the variance was due to differences between the test subjects; 94% of the variance was due to differences within individuals (e.g. experimental part, stimulus conditions and stimulus level). In addition to the predictor “experimental part”, the model could include other predictors like stimulus condition or stimulus level and their interactions. The nominal-scale predictors (experimental part, condition, stimulus level) were dummy coded, with “active part” and “congruent” (“0”) as the reference category. Using this model, we evaluated the influence of the predictors experimental part (active vs. passive) and feedback condition (congruent, delay, offset) on the criterion of correct responses. We calculated pairwise contrasts between the average classification performances in the active vs passive parts for each stimulus condition. Additionally, we examined the extent to which the influence of the feedback condition on detection performance changed depending on the experimental part (i.e., interaction effect). To analyze the influence of the different mismatch levels (i.e., the 6 different levels of delay and offset, respectively) in the two parts of the test in more detail, we calculated a second model with an explicit interaction between the predictors of the experimental part and the stimulus level. The output of these analyses is a log odds ratio that is symmetric around zero—which corresponds to maximum uncertainty. In our case, for instance, a negative log odds ratio meant that a correct response in the active part was more likely than in the passive part, whereas a positive log odds ratio would mean that a correct response was more likely in passive > active. To test hypothesis H3; i.e., to test whether detection performance on the active part improved with increasing mismatch (delay or offset) level, we conducted linear regression analyses. Thereby, to enable better comparability of the resulting regression weights (slopes), we normalised the predictor “stimulus level”; i.e., the 6 levels of delay and offset were normalized between 0 and 1, respectively. To test hypotheses H4b and H4c (i.e., for conditional differences in the above linear relationships) we conducted a two-way repeated-measures ANOVA on the resulting regression weights for each participant in the delay and offset conditions in the active and passive parts. To test hypothesis H4a (i.e., whether participants were, on average, relatively better at detecting spatial offset than temporal delay), we calculated a two-way repeated-measures ANOVA with the factors experimental part (active, passive) and feedback condition (delay, offset). To evaluate differences in reaction times across all conditions, as per our preregistered exploratory analysis, a one-way repeated-measures ANOVA was performed. As the reaction time distribution was not normal, a log transformation was applied across all participants. This was also analogously calculated for rating duration; i.e., the time it took participants to select and confirm their answers after ending the trial (see above). Here, an ANOVA was conducted on the log-transformed reaction times with the factors experimental part (active or passive) and mismatch level (i.e., 6 levels per delay and offset). In the above ANOVAs, we tested for violations of the assumption of sphericity with Mauchley’s test, and applied Greenhouse-Geisser correction if required. Finally, for the exploratory analysis of a potential relationship between detection performance in the active vs passive parts, we conducted a linear regression analysis on the average correct responses of all participants across both experimental parts. Results Participants completed the task well, performing comparable movements across conditions at an average speed of about 0.4 Hz; there were no significant differences in average speed between conditions, ( F (12,403) = 1.06, p =.39, see Fig. 2 and Fig. S1 for the averaged spectral profiles). As expected from the known kinematic invariants of biological motion (Two-thirds power law, see Introduction), a systematic decrease in movement velocity at the stronger- curved points of the elliptical trajectory was consistently observed in each condition (see Fig. 2 ; also for a display of the shifted velocity profiles of the visual movement in the offset condition). Importantly, participants were significantly better at classifying the visual movement feedback during active > passive trials (log odds = −1.44, SE = 0.1, z = −14.61, p < .001), with performance well above chance level, see Figure 3A . Furthermore, performance in the active part significantly positively predicted performance in the passive part (β = 0.64, SE = 0.12, t = 5.28, p < .001; Fig. 3B ). This correlation also held when looking at detection of offsets and delays separately; furthermore, detection performances between delays and offsets were significant in the active and passive parts (see Figs. S2 and S3; both of these correlational analyses had not been preregistered). Thus, as intended, our task targeted a perceptual sensitivity to visual kinematic differences, consistently across both active and passive sessions. Download figure Open in new tab Figure 3. A: Average classification performance, with associated standard errors of the mean, in the active (movement) and passive (observation) parts. Participants were significantly better at classifying visual movement feedback in the active > passive part ( p <0.001). B: Average performances in the active and passive parts were significantly positively related ( p passive part was significant for congruent feedback (log-odds difference = 1.44, SE = 0.1, z = 14.61, p < .0001) and for the delayed feedback (log-odds difference = 0.74, SE = 0.08, z = 8.7, p < .0001). For the offset conditions, there was a statistical trend, but this effect was not significant (log-odds difference = 0.15, SE = 0.08, z = 1.82, p = .07). See Table 1 . These results supported our hypotheses H1 (partly, as the effect was non-significant for offsets) and H2. Furthermore, in line with our preregistered hypothesis H4a, there was a significant interaction effect between experimental part and mismatch type (repeated-measures ANOVA, F (1,31) = 14.52, p <. 001); i.e., participants were relatively better at detecting offset than delayed playback in the passive part (post-hoc t-test, t (56.6) = -3.31, p < .01), compared with the active part (post-hoc t-test, t (56.6) = 1.17, p = .65). This suggested that, as anticipated, participants could not reliably identify delayed visual movement feedback by passive observation alone, but could detect violations of kinematic laws in the offset playback conditions. View this table: View inline View popup Download powerpoint Table 1. Relative response probabilities for all conditions, with performance against chance level and differences between active and passive tasks. Next, we examined classification performances at the different mismatch (i.e., delay vs offset) levels, testing our hypotheses H2a, H3, and H4b-c. In the active part, participants classified congruent feedback, delay levels >= 160 ms, and offsets >= 5.7° significantly above chance level ( Fig. 4 and Table 1 ). This overall supported our hypothesis H2a; however, very small mismatches were not detected above chance (in line with previous work, Leube et al., 2003 ; Farrer et al., 2008 ; Limanowski et al., 2017 ), which suggested a perceptual threshold for visuomotor mismatch detection. Thereby very low mismatch levels were mostly rated as “congruent”; but, with increasing mismatch (delay or offset), the number of “congruent” responses systematically decreased. Classification in the passive part was overall closer to chance level, but still significant for congruent feedback, and for offsets above 5.7° ( Table 1 ). Download figure Open in new tab Figure 4. Mean response frequencies in each condition for the active (i.e., movement, left) and passive (i.e., observation, right) parts. Each column shows the stacked average response frequencies in each condition (green = “congruent”, yellow = “delayed”, blue = “offset”). The correct response option is always displayed at the bottom and in a stronger saturation. The grey dashed line indicates the chance i.e. guessing level (33%). White asterisks denote significance above chance level; coloured asterisks denote significant differences between active and passive parts; solid lines indicate a significantly better classification performance in the active > passive part (vs dashed line: passive > active); * p <.05. ** p <.01. *** p <.001. Overall, classification performance was significantly enhanced by motor signals in the congruent visual movement feedback condition, and at medium-to-high levels of visual movement feedback delay and offset; classification was generally closer to chance-level in the passive part. See Results and Table 1 for details. Furthermore, participants showed significantly better classification during the active > passive part at delays >= 160 ms, and offsets of 5.7° and 8.5°. Significant differences in favour of the passive part were observed at the two lowest delay levels, i.e., 13.3 and 80 ms; and at the smallest offset level i.e. 0.5°. See Figure 4 and Table 1 . Note that at the very low mismatch levels, classification in the passive part was actually closer to chance level than in the active part—where participants (falsely) classified the visual feedback most frequently as “congruent”. No significant differences between the experimental parts were observed at the remaining offset levels, although performance was better at each of them in the active > passive part ( Table 1 ). We then tested for linear relationships between the amount of delay or offset and classification performance in the active part. Indeed, the performance improvement with increasing amount of visual feedback incongruence could be linearly approximated for delays (β = 0.66, SE = 0.06, t = 11.06, p < .001) and offsets (β = 0.58, SE = 0.07, t = 8.24, p < .001, see Fig. 5A ). In sum, these results suggested that higher visuomotor incongruences (i.e., in the active part) were consistently detected better; with comparable performance increases for delays and offsets, supporting our hypothesis H3. Download figure Open in new tab Figure 5. Linear relationships between the amount of incongruence and classification performance. A: Results of linear regressions between performance vs delays and offsets, respectively, in the active (top) and passive (bottom) parts. The mismatch (delay and offset) levels were normalized for better comparability of the regression weights, see Methods. Each dot represents a single participant’s condition average. B: Results of a repeated- measures ANOVA on the participants’ average regression weights (slopes). The bar plots show the mean regression weights with associated standard errors of the mean, with significant differences marked by asterisks (*** p passive part; and a significant interaction effect, which was driven by a significantly steeper slope for offsets > delays in the passive part. See Results for details. Furthermore, as expected (H4b), this relationship was stronger in the active > passive part (repeated-measures ANOVA on regression weights, main effect, F (1,31) = 42.38, p active part, the linear relation was stronger for offsets than for delays (interaction effect, F (1,31) = 21.83, p delays in the passive part ( t (52.7) = 4.33, p < .001), whereas this difference was not significant in the active part ( t(52.7) = -0.71, p = 0.89). This supported our hypothesis H4c. Following our preregistered exploratory analysis, we also analysed how long it took the subjects to reach a decision in each trial (i.e., the “reaction times” to end the trial per key press). Table 2 shows the average reaction times per condition. Linear regressions revealed significant negative correlations between mismatch level and trial duration in the delay (β = - 0.01, SE = 0.001, t = -5.7, p < .001) and offset conditions (β = -0.35, SE = 0.03, t = -10.0, p < .001). Thus, participants required consistently less time to reach a decision as the visuomotor mismatch levels increased. View this table: View inline View popup Download powerpoint Table 2. Reaction times (RT) per condition in the active part. After ending the trial, participants took only about 1 s to select and confirm their rating in all conditions (Table S1). There was a statistically significant effect of mismatch level on rating times (repeated-measures ANOVA, F (7.34, 227.56) = 2.1, p = 0.042, η²p = 0.011), but none of the individual pos-hoc comparisons with Bonferroni correction reached significance. Importantly, there was no significant main effect for experimental part ( F (1, 31) = 2.21, p = .147, η²p = .011); the interaction between stimulus level and experimental part was also not significant ( F (8.58, 266.04) = 1.4, p = .192, η²p = .006). This suggested that, as intended, the main decision process was reached during the trial itself; and that the rating selection process did not qualitatively differ between the active and passive parts. Discussion In line with our preregistered hypotheses, we found that the detection of temporal and spatial deviations in visual movement feedback was improved by the presence of motor signals; i.e., when executing the movement, compared to simply observing the visual feedback. This result supports the general idea that motor signals such as the efference copy play a significant role in the detection of unpredicted reafference and, thus, in self-other distinction (see Introduction). In the active part of the experiment, a significant increase in classification performance and an associated decrease in reaction time were observed with increasing stimulus level. This finding is consistent with previous results showing that larger visuomotor incongruences are easier to detect ( Leube et al., 2003 ; Farrer et al., 2008 ; Limanowski et al., 2017 ; Krugwasser et al., 2019 ). Advancing on these works, our results demonstrate very similar perceptual sensitivities (above-chance classifications and linear relations with the amount of mismatch) to spatial and temporal visuomotor mismatches (i.e., in the active part). This result was enabled by our novel setup, which allowed us to implement spatial and temporal deviations, matched in terms of average visuomotor incongruence, along the same elliptical movement trajectory. Another key result were the steeper slopes of classification performance depending on mismatch (delay or offset) level in the active > passive part. We propose this results suggests a stronger differentiation between self and non-self (i.e., predicted vs unpredicted visual feedback) during movement. I.e., during movement, participants rated very low mismatches mostly (incorrectly) as “congruent”; i.e., these visual deviations from motor predictions were not reliably detected. During passive movements, classification performance was generally closer to guessing (including classification of very low mismatches closer to chance levels), resulting in less stringent “self-other distinction”. Furthermore, our results demonstrate that violations of visual kinematics—here, through spatial offset of the visual movement feedback along the ellipse—can be reliably detected during passive observation alone; increasingly so depending on the amount of offset, as during execution. This result supports previous findings that the visual system is highly sensitive to motor kinematics; particularly to alterations in biological movement such as the covariation between velocity and curvature described by the two-thirds power law ( Johansson, 1973 ; Viviani & Stucchi, 1992 ; de’Sperati & Stucchi, 1995; Dayan et al., 2007 ; Salomon et al., 2016 ; Fraser et al., 2024 ; Rolfs et al., 2025 ). Crucially, however, our results show that action significantly enhanced the correct detection of spatially manipulated visual movement feedback at critical levels. I.e., these intermediate-offset (mismatch) levels were the first with above chance-level performance (while performance seemed to plateau at larger offsets). Conversely, the chance-level detection of delays in the passive part was unsurprising, as without motor signals, the difference between real-time and delayed identical visual movements was impossible to tell. In sum, we have demonstrated both commonalities (i.e., similar detection performances and slopes for matches levels) and key differences (i.e., in the reliance on motor signals vs visual sensitivity to kinematic invariants) in the detection of experimentally matched levels of spatially vs temporally altered visual movement feedback, along identical (elliptical) movement trajectories. Future work could try to match the active and passive tasks better. Limiting the playback duration (for within-session comparability) resulted in fewer perceptual samples available in the passive, compared with the active part. Furthermore, the absence of movements in our passive part could have freed more cognitive resources. Both could be mitigated by evoking passive movements—another benefit of such a control condition would be the production of comparable somatosensory feedback to the active part. Future work could also add assessments of confidence, which could strengthen the interpretation of potential guessing tendencies driving classification during low-mismatch trials in the passive part. Physiological measures of brain activity such as EEG could shed light onto the momentary sensitivity to spatial vs temporal mismatches; this should also be extended to scenarios in which both kinds of visuomotor mismatches are combined, and to different movement trajectories. Conflict of interest statement The authors declare no competing financial interests. Data availability statement The data will be made available upon request. The data are not available publicly due to ethical and privacy restrictions. Acknowledgments This work was supported by a Freigeist Fellowship of the VolkswagenStiftung (AZ 97-932) to JL. 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Share Action enhances the detection of visuomotor incongruence: A comparison of matched spatial vs temporal manipulations along identical trajectories Fanni Peters , Peng Wang , Jakub Limanowski bioRxiv 2025.06.18.660374; doi: https://doi.org/10.1101/2025.06.18.660374 Share This Article: Copy Citation Tools Action enhances the detection of visuomotor incongruence: A comparison of matched spatial vs temporal manipulations along identical trajectories Fanni Peters , Peng Wang , Jakub Limanowski bioRxiv 2025.06.18.660374; doi: https://doi.org/10.1101/2025.06.18.660374 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Animal Behavior and Cognition Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13895) Bioinformatics (41951) Biophysics (21456) Cancer Biology (18594) Cell Biology (25520) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22510) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88622) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)