Searching for visuomotor matches vs mismatches biases confidence and visual sampling strategies, but not performance

Searching for visuomotor matches vs mismatches biases confidence and visual sampling strategies, but not performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Searching for visuomotor matches vs mismatches biases confidence and visual sampling strategies, but not performance Jonathan Yi, Zhenyu Wang, Peng Wang, Jakub Limanowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7655357/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Visuomotor self-other distinction relies on the comparison of forward predictions from one’s motor system with visual movement data. Previous work suggests that matching kinematics may be preferentially processed, but at the same time, visuomotor mismatches are known to capture attention. Here, participants were presented four virtual hands, each reflecting their actual hand movements, conveyed via a data glove, with a unique added time delay. Participants had to identify which of these hands moved most similarly (search for match) or most differently (search for mismatch) to their actual movements, under varying degrees of task difficulty. We found that the instruction to identify visuomotor matches vs mismatches significantly biased the participants’ confidence; i.e., participants exhibited overconfidence when searching for matches. Furthermore, eye tracking showed participants relied significantly more on serial sampling of the hands when searching for matches, but more on central fixation and peripheral vision when searching for mismatches. These biases were not universally reflected in detection performance. However, performance when searching for visuomotor matches was less strongly affected by task difficulty than when searching for mismatches. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Action attention self-identification self-other distinction visuomotor conflict Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Our ability to accurately identify observed body movements as our own and distinguishing them from those of others is fundamental for the sense of self. Most accounts of (sensorimotor) self-other distinction build upon the notion of forward models in the brain, which can predict the sensations caused by one’s bodily movements based on signals from one’s motor system such as the “efference copy” (e.g., Wolpert & Flanagan, 2001 ). In short, if a sensation – an observed body movement – does not match the predictions generated by one’s forward models, the healthy brain is thought to attribute it to someone else; if it matched the predictions, it is self-attributed (Frith et al., 2000 ; Jeannerod, 2003 ; Haggard, 2017 ). A popular way to study the cognitive and neurobiological mechanisms underlying this kind of self-other distinction is through manipulation of the congruence of visual movement feedback and executed movements (see Limanowski, 2025 , for a review). This line of work has shown that introducing temporal offsets such as a delay between a participant's actual body movements and those of a corresponding virtual body part diminishes the sense of agency and control of the virtual movements (e.g., Krugwasser et al., 2019 ; Limanowski et al., 2017 ; Salomon et al., 2013 ). Along these lines, it has been shown that healthy people are very sensitive to sensorimotor mismatches; i.e., unpredicted sensory movement consequences (e.g., Krugwasser et al., 2019 ; Peters et al., 2025 ). Brain imaging work has shown that sensorimotor mismatches or sensory prediction errors – for instance, unpredicted visual movement feedback – strongly activate the brain’s attention (or reorienting) and error processing networks (Farrer et al., 2008 ; Ullsperger et al, 2014 ; van Kemenade et al., 2019 ; Quirmbach & Limanowski, 2022 ). On the other hand, many studies point to a preferential processing of matching visual kinematics (e.g., Tsakiris et al., 2005; Brass et al., 2009; Salomon et al., 2011 , 2013 ; Yon et al., 2018 , 2020 , 2021 ; Wen et al., 2018 , 2020). For instance, Wen et al. ( 2018 ) suggest that controlled objects particularly attract attention. One reason for these effects could be that self-produced (e.g. visual) stimuli can be processed faster precisely because they have been predicted by internal forward models (Kumar et al., 2015 ; Yon et al., 2020 ; Perrykkad et al. 2021 ). Indeed, studies by Salomon et al. ( 2011 , 2013 ) have demonstrated that self-identification among multiple moving ‘distractor’ avatars was faster when people moved actively than passively, supporting the assumed importance of forward sensory predictions for self-other distinction. The efficiency with which one can detect one’s own body movements can even be described in terms of a “self-pop out”, which is largely independent of e.g. the number of distractors (Salomon et al., 2013 ) In sum, visual movement information may be processed preferentially because it is recognized as “self” (predicted) or because it is recognized as “non-self” (unpredicted). Here, we directly contrasted these possible scenarios. Recent work has shown that cognitive-attentional factors such as task set or instructed behavioral relevance can bias the cortical processing of visual body movement feedback (Asai, 2015 ; Arslanova et al., 2019 ; Limanowski & Friston, 2020 ; Vigh & Limanowski, 2025 ). Following these works, here, we aimed to test whether the instruction to search for visuomotor matches (self-identification) vs mismatches (error identification) would be reflected by differences in performance, metacognition about this performance, and furthermore, by different sensory (i.e., visual) sampling strategies. The participants controlled four simultaneously presented virtual hands (VHs) using a data glove (Fig. 1 a); however, each of the VHs reflected the executed hand movements with a unique added time delay. The participants were instructed to identify which of the four VHs moved the most similarly (with the shortest delay) or most differently (with the longest delay) relative to their actual hand movements. This effectively induced a cognitive-attentional focus on identifying visuomotor matches vs mismatches; which can be related to the notion of “controllability” used in other studies (Wen et al., 2020, 2018 ). Crucially, all VHs were always delayed, and both search instructions contained trials with identical delay mappings. Therefore, any behavioral differences reflected the effects of the instructed search focus (match or mismatch identification). Participants had to perform the task under two levels of difficulty, i.e., relatively more or less similar delay mappings (Fig. 1 b). We tested whether participants would perform better; i.e., more accurate and faster when searching for visuomotor mappings (which would support a “self pop-out”) or when searching for visuomotor mismatches (which would speak for a particular perceptual saliency of sensorimotor prediction errors). Participants also had to provide confidence ratings in their responses (Fig. 1 c). It has been shown that peoples show overconfidence in the consequences of their own action, as e.g. observed in motor tasks (Wolpe et al., 2014 ; Charles et al., 2020 ; Fourneret & Jeannerod, 1998 ; Metcalfe & Greene, 2007 ; Arbuzova et al., 2021 ; Ciston et al., 2022 ; cf. Yon et al., 2021 ). Thus, we asked whether merely instructing to search for “own” actions (i.e., finding visuomotor matches > mismatches) would be reflected in participants’ confidence ratings—and whether this would relate to potential performance differences. Furthermore, we tracked participants’ gaze behavior during the search task, to test whether they would employ different sampling strategies depending on the search instruction. I.e., we tested whether participants would rely on individual (i.e., serial) sampling of individual hands, or on peripheral vision (i.e., showing a tendency to fixate centrally). Arguably, a serial sampling of the virtual hands’ movements (cf. Zelinsky, 2008 ) could improve detection accuracy. However, although it has reduced spatial resolution compared to foveal vision, peripheral visual sampling still remains highly sensitive to motion (Boff et al., 1986 ). Thus, we asked whether – and in which conditions specifically – participants would retreat to central fixation as a sampling strategy, which would allow them to use peripheral vision to compare the visual movements of all four hands simultaneously, as a form of “global monitoring” (cf. Cavanagh & Alvarez, 2005 ; Fehd, 2010 ; Fehd & Seiffert, 2008 ). Finally, following anxiety-related biased previously observed in other visuomotor tasks (e.g., Yi et al., 2021 ), we tested for potential influences of trait anxiety on detection performance and confidence. Results Searching for visuomotor matches vs mismatches yields comparable performance, but searching for matches is less affected by task difficulty We first investigated if Search Instruction and Delay Discrepancy affected performance as quantified by CR (Fig. 2 ) and RT (Fig. 3 ). Participants were able to identify the instructed target hand well above chance level in all conditions (Fig. 2 ). Our generalized linear mixed-effect model analyses (Gelman & Hill, 2006 ) revealed no significant main effect of Search Instruction on detection accuracy (B = 0.83, SE = 0.17, p = 0.347; cf. Table 1 and Supplementary Table 1). There was a significant main effect of task difficulty i.e., Delay Discrepancy (B = 10.22, SE = 2.13, p < 0.001), whereby participants performed worse in Low than in High-Discrepancy trials. Interestingly, however, there was a significant interaction between Search Instruction and Delay Discrepancy (B = 5.25, SE = 1.69, p < 0.001). Post-hoc comparisons using Holm-correction (Holm, 1979 , Supplementary Table 2) showed that this interaction could be characterized in terms of a significantly poorer performance in FDLD than in FDHD trials (Δ = − 0.38 probability units, SE = 0.05, 95% CI [–0.52, − 0.28], z = − 8.02, p < .001), with no significant difference between FSLD and FSHD trials. In other words, increased task difficulty impaired performance when searching for visuomotor mismatches, but had no such effect when searching for visuomotor matches. On response times (RT), there likewise was no significant effect of Search Instruction (B = 0.09, SE = 0.05, p = .10), although participants were somewhat faster in the Find Similar than in Find Different trials (Table 1 , Fig. 3 ). There was a main effect of Delay Discrepancy on response times (B = -0.08, SE = 0.02, p High-Discrepancy trials; and participants were faster when giving correct than incorrect responses (main effect, B = -0.07, SE = 0.01, p < .001). Furthermore, we observed a significant Search Instruction × Delay Discrepancy interaction (B = 0.11, SE = 0.03, p < .001). Post-hoc comparisons showed that FSLD response times were significantly slower than FSHD (Δ = 0.14 log-s, SE = 0.02, 95% CI [0.08, 0.20], t(60.8) = 6.18, p mismatches There was a significant main effect of Search Instruction on CFR (B = -0.06, SE = 0.02, p = .004): on average, participants reported significantly higher confidence in their decisions in Find Similar than in Find Different trials (Fig. 4 a, Supplementary Table 5). This difference was significant for both levels of task difficulty (Supplementary Table 6). Furthermore, we observed a main effect of Delay Discrepancy (B = 0.02, SE = 0.01, p = .032) where participants were less confident in Low Discrepancy than High Discrepancy trials, likely reflecting the increased task difficulty. The interaction between Search Instruction and Delay Discrepancy was not significant. Furthermore, we observed a main effect of CR (B = 0.04, SE = 0.01, p < .001) where increasing CR values predicted higher CFRs. I.e., participants were more confident when they actually gave the correct response (Fig. 4 b). We also observed a main effect of RT (B = -0.23, SE = 0.01, p < .001) where lower RTs predicted higher CFRs. In other words, participants responded faster in trials where they reported higher confidence. See Fig. 4 c (cf. Supplementary Tables 5 and 6). Table 1 Summary of descriptive statistics of CR, log RT and CFR with associated standard deviations (in brackets). Search Instruction Delay Discrepancy Correct responses (%) Response time (s) Confidence rating (0–1) Find Similar High Discrepancy 57.3 (10.3) 1.32 (0.40) 0.754 (0.117) Find Similar Low Discrepancy 51.3 (11.4) 1.57 (0.44) 0.663 (0.152) Find Different High Discrepancy 69.2 (12.5) 1.60 (0.46) 0.625 (0.127) Find Different Low Discrepancy 43.1 (11.1) 1.70 (0.45) 0.579 (0.143) Gaze behavior suggests different visual sampling strategies associated with searching for visuomotor matches vs mismatches Next, we tested whether participants would rely on individual (i.e., serial) sampling of individual hands, or on peripheral vision (i.e., showing a tendency to fixate centrally). Based on the recorded eye tracking data, we calculated a “Gaze Bias” reflecting the average proportion spent fixating either of the VHs-the screen center (see Methods); i.e., positive values reflected a preference for visual sampling of individual hands, whereas negative values indicated a preference for central fixation and, likely, using peripheral vision to identify visuomotor (mis)matches. Overall, SJ spent more time looking at the VHs than at the center in each condition (Fig. 5 a). Crucially, we observed a main effect of Search Instruction (B = -0.023, SE = 0.009, p = 0.14) where participants spent significantly more time looking at the VHs > Center (higher Gaze Bias) in Find Similar > Find Different trials (Fig. 5 a, Supplementary Table 7). There was no significant main effect of Delay Discrepancy, but an interaction effect between Search Instruction × Delay Discrepancy (B = -0.012, SE = 0.002, p < 0.001, cf. Supplementary Table 8). Then, we looked at the Gaze Bias over the progression of single trials; i.e., over normalized trail time (see Methods). Firstly, we found a main effect of WTP (B = -0.012, SE = 0.001, p < 0.001), meaning that the participants’ gaze shifted relatively more towards the Center as trial time increased (Fig. 5 b). More importantly, we observed a significant interaction between Within Trial Progression × Search Instruction (B = 0.010, SE = 0.001, p < 0.001); i.e., the drift towards central fixation was significantly less pronounced in Find Similar than in Find Different trials (Supplementary Table 9). Thus, participants still sampled the VHs relatively more frequently closer to reaching a decision in the FS conditions, whereas at this point they had relatively retreated to central fixation in FD trials. See Fig. 3 B. Finally, there was an interaction between Within Trial Progression × Delay Discrepancy (B = − 0.008, SE = 0.001, p < 0.001), such that participants’ gaze shifted more strongly from the VHs to the Center ROI in High Discrepancy than in Low Discrepancy trials (i.e., a steeper slope cf. Figure 5 b). A supplementary analysis revealed that identified the “correct” hand significantly more frequently when they had fixated it more frequently (Fig. S1 and Table S10). Finally, participants with higher STAI-T scores were, on average, less accurate, slower, and less confident in the task; but none of these correlations reached significance (Fig. S2). Discussion The key finding of our study was that the instruction to search for visuomotor matches (i.e., self-identification) vs mismatches (prediction error identification) significantly affected the participants’ confidence ratings and gaze i.e. visual sampling behavior, but not detection performance (as quantified by accuracy and speed). This result can be unpacked as follows: Firstly, search instruction biased confidence ratings. It should be noted that, overall, higher confidence ratings were associated with better accuracy and faster response times, supporting the results of previous (visuo)motor studies (Locke et al., 2020; Sinanaj et al., 2015; Charles et al., 2020 ). Thus, confidence ratings seemed to adequately reflect decision uncertainty in principle. Crucially, however, confidence ratings were significantly higher when searching for visuomotor matches (Find Similar; i.e., self-identification) than when searching for mismatches (Find Different; i.e., prediction error identification) – despite no significant difference in performance between these conditions. In other words, participants seemed overconfident in their decisions when instructed to search for Similar > Different. This points towards a confidence bias when looking for oneself. Such a bias would align well with previous work showing a tendency for overconfidence in the sensory consequences of one’s own actions (Wolpe et al., 2014 ; Charles et al., 2020 ; Fourneret & Jeannerod, 1998 ; Metcalfe & Greene, 2007 ; Arbuzova et al., 2021 ; Ciston et al., 2022 ), and could be related to potential predictive mechanisms in the brain’s motor system, e.g., through oversensitivity to spurious sensorimotor correlations (Yon et al., 2020 , 2021 ; cf. Wen et al., 2018 ; Salomon et al., 2013 ). Secondly, eye tracking suggested that participants looked at the individual hands significantly more frequently (compared with simply fixating centrally) when searching for visuomotor matches. Conversely, they relied more on central fixation when searching for visuomotor mismatches. These gaze behavior differences significantly increased over the duration of trial time. Thus, the search instructions produced two different visual search strategies: when searching for visuomotor matches, participants seemed to favor serial sampling of the visual movements (hands); when searching for visuomotor mismatches, they seemed to predominantly rely on peripheral vision. This could mean that visuomotor mismatches were more salient than matches; i.e., that the unpredicted visual movement was more easily detectable through joint sampling of all four hands with peripheral vision – abolishing any need to use more costly serial sampling. This tentatively speaks to the perceptual salience of prediction errors hypothesis (see Introduction). However, these different search strategies did not result in significantly different performance; i.e., serial sampling was not significantly worse or slower than using peripheral vision (Participants were even somewhat, but non-significantly faster when searching for Similar > Different). Crucially, while the main effects were non-significant, we observed instruction-related performance differences depending on the current task difficulty. As expected, participants were more accurate, faster, and more confident when the task was easier (i.e., higher discrepancy between the four visuomotor delays; see Farrer et al., 2008 ; Shimada et al., 2010 for similar results). More interestingly, task difficulty interacted with search instruction to affect performance: Thus, detection accuracy when searching for mismatches (errors) was significantly impaired by increased task difficulty, whereas searching for matches (self-identification) was not (interaction effect, confirmed with post-hoc tests). This result shows an independence of self-identification from task difficulty. Here, task difficulty was related to the similarity of the distractors; in Salomon et al. ( 2013 ), a similar independence was shown from the number of distractors. In this light, our results tentatively support the “self pop out” effect (Salomon et al. 2013 ); i.e., a preferential, perhaps ‘automatic’ processing of matching visual kinematics. A limitation of our study is that our eye-tracking data was logged at 60 Hz, therefore, we were not able to investigate any fine grained oculomotor behavior such as (micro)saccades. The limited screen space also meant that even when one hand was fixated, all others were potentially visible in the periphery. This could be improved upon by future work using e.g. VR headsets. Finally, we did not collect any judgments of a sense of agency or control; future work could investigate whether those are affected by instructed search focus. Thus, our paradigm could also be extended to help understand difficulties in sensorimotor based self-other distinction in clinical populations such as schizophrenic patients (Synofzik et al., 2010 ; Schmitter et al., 2025 ; Frith et al., 2000 ). Methods Participants 25 healthy, right-handed participants (18 females, mean age = 24.56 years (SD = 4.13 range = 19–35 years) completed the experiment. The sample size was determined with a power analysis based on data from a pilot experiment (N = 8), targeting a medium-to-large effect size (Cohen’s d = 0.69) at a significance level of α = 0.05 and a desired power of 0.9. To reach this sample size, we had to recruit 33 participants, as 8 of those had to be excluded due to technical problems with the data glove, eye tracking calibration, or data logging errors. All participants provided written informed consent prior to participation; and received 10 € per hour or student credits as compensation. The study was approved by the local ethics committee of the Universitätsmedizin Greifswald and performed in accordance with the relevant guidelines and regulations and the Declaration of Helsinki. Experimental setup and procedure Participants sat in front of a computer screen (27”, 1920 x 1080 pixels resolution, 60 Hz refresh rate) with their chin on a rest at 62 cm distance. On their right hand (placed on their lap, and occluded from view by a black gown), the participants wore a data glove (5DT Data Glove 5 Ultra, https://5dt.com/5dt-data-glove-ultra/ ) with which they controlled the virtual hands’ movements (see below). Their left hand was placed on a key pad with tactile markers, with which participants provided the responses. We used an eye-tracker (Gaze-Point3 HD, https://www.gazept.com/ , 60 Hz sampling rate) to collect fixation data. During the experiment, participants had to move all four fingers except the thumb simultaneously in a simple grasping (i.e. closing-and-opening) motion paced by an auditory cue (a 250 Hz tone that grew louder and quieter following a sine wave function with 0.5 Hz frequency, cf. Vigh & Limanowski, 2025 ). Their hand movements were fed to four virtual hands presented on screen (with Unity, https://unity.com/ ), whereby the fingers of each VH received an average of all functioning glove sensors (cf. Limanowski et al., 2017 ). Importantly, we added a delay to the movements of each of the VHs; i.e., each VH reflected the movements executed by the participant after a unique temporal lag. The participants were instructed to identify (in each trial) which of four simultaneously presented VHs (Fig. 1 b) moved the most similarly (with the shortest delay) or most differently (with the longest delay) relative to their actual hand movements; and press a key with their left hand as soon as they were confident to report the decision. The Search Instructions were color coded (cyan or orange, counterbalances across participants), and represented with a colored border around the screen during the trials. After ending the trial by key press, the participant was then asked to indicate the chosen VH which they thought had moved the most similarly or differently to their actual movements. Then, participants were asked to provide a confidence rating (CFR) in their choice using a visual scale presented on screen (from 0–1 with discrete increments of 0.1). If the participants did not press ENTER within 30s after the start of a trial, the trial ended end the ratings were requested. The experiment consisted of two blocks, each of which contained 96 trials of one search instruction (Find Similar or Find Different). The block order was randomized across participants. In each block, half of the trials were High Discrepancy trials—in these trials, the difference in visuomotor delay between the hands was large (300 ms; i.e., the delays were: 50, 350, 650, 950 ms), which translated into an easier search task. The other half of the trials were Low discrepancy—here, the delay difference was smaller (i.e., 100ms), which corresponded to a harder task condition. To span the same range of delays as in the High Discrepancy condition, here, two delay sets were used (i.e., 50, 150, 250, 350 ms or 650, 750, 850, 950 ms). The VHs were placed equidistally from each other. In each trial, the delay levels were assigned to the hands in a novel combination, using 24 predetermined, unique arrangements. The participants completed a practice phase to familiarize themselves with the experimental task and design. Data analysis The data were analyzed using generalized linear mixed models (GLMMs) in R 4.4.3 (R Core Team, 2025) and lme4 (Bates et al., 2015 ). We adopted a stepwise model selection by initially including the total effect of our fixed effects and all their possible interactions and we then removed fixed effects and interaction terms by comparing the consistency of p-values of the fixed effects and Bayesian Information Criterion (BIC) values (Burnham et al., 2010). These values were obtained using the packages lme4 (Bates et al., 2015 ) and lmertest (Kuznetsova et al., 2017 ) in R. Models that failed to converge or were singular were aborted and not used. For all models, our experimental factors Search Instruction and Delay Discrepancy, when used as fixed or random effects, were centered around 0 by coding the levels as -0.5 and + 0.5 because this approach allows for the intercept to represent the grand mean, facilitating the interpretation of main effects and interactions (Gelman & Hill, 2006 ; Barr et al., 2013 ). For the models on CR, RT and CFR as the dependent variables, we always included the main effects and interaction terms of Search Instruction x Delay Discrepancy as fixed effects. We tested all combinations of main effects and interaction effects of CR, log RT and CFR of all models during model comparisons (see above). Furthermore, we always included the main effects of Search Instruction and Delay Discrepancy as random effects in order to control for random slopes of the variables at the participant level. For the eye-tracking models, as fixed effects we again included the main effects and interaction terms of Search Instruction x Delay Discrepancy. We also added the interaction term with WTP (see below). The main effects of Search Instruction and Delay Discrepancy were then added as random effects using Gaze Bias as the dependent variable. Correct responses were scored as a binary variable (1/0). The RT was defined as the elapsed time from trial onset until the participants’ pressed a key to end the trial (or if the trial duration expired, i.e., 30s). CFR was defined as the as value selected using the visual sliding scale, between 0–1 with 0.1 discrete increments. RT was always log-transformed when used both as a dependent variable and as a fixed effect due to it typically adhering to a right-skewed distribution (Ratcliff, 1993 ). Apart from RT, when used as fixed effect (not as dependent variables) all of our other continuous variables including the ones used for the eye-tracking analyses, were z-scored so that they also they were also centered around zero for easier interpretation (Schielzeth, 2010 ). For the eye tracking data analysis, we removed all data points deemed invalid by Gazepoint Controller (Gazepoint, Vancouver, Canada), i.e. when participants were blinking, not looking at the monitor or if the positions of the eyes and pupils could not be reliably identified. The valid data points were subsequently used in the several of our mixed effects model analyses. The VH ROIs were defined as the smallest possible rectangular area surrounding each VH when fully extended, and the Center ROI was defined as analogous area centered on screen. We calculated a Gaze Bias for each eye-tracking sample as that sample’s share of the trial duration spent looking at any of the VH ROIs vs the Center ROI (eye gaze samples Outside the VH ROIs and the Center ROI were excluded for this analysis since we wanted to test our hypothesis pertaining to whether participants relied on peripheral vs. serial sampling of visual information to solve the experimental task), coded positive when the eyes were predominantly on the VH ROIs and negative when they were on the Center ROI. In other words, Gaze Bias ranged from − 100% (gaze exclusively on the Center ROI of the entire duration of a trial) to + 100% (gaze exclusively on any of the VH ROIs) for each trial. To test for changes in Gaze Bias over time, we furthermore introduced Within Trial Progression (WTP: 0–100% of elapsed progression within a single trial, z-transformed for the mixed effects model analysis as a new fixed effect (Schielzeth, 2010 ). Finally, we used the State Trait Anxiety Inventory (STAI, Spielberger et al., 1983) scores to test whether individual differences in trait anxiety (state anxiety is not our focus here) would correlate with detection performance or confidence. We tested whether each of these four variables were normally distributed using Shapiro–Wilk tests (Shapiro & Wilk, 1965 ), and then applied Spearman or Pearson correlation analyses accordingly. Declarations Acknowledgments: This work was supported by a Freigeist Fellowship of the VolkswagenStiftung (AZ 97-932) to JL. We thank Jan Crusius for lending us the eye tracker, Samuel Yi for help with programming and Nina Standar for help with data acquisition. 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Yon, D., Zainzinger, V., de Lange, F. P., Eimer, M., & Press, C. (2021). Action biases perceptual decisions toward expected outcomes. Journal of Experimental Psychology: General, 150(6), 1225. Zelinsky, G. J. (2008). A theory of eye movements during target acquisition. Psychological Review , 115 (4), 787–835. https://doi.org/10.1037/a0013118 Additional Declarations No competing interests reported. Supplementary Files YietalSupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:29:35","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138658,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/b29f5caa03c81c62de1cfe8a.html"},{"id":92575471,"identity":"91ebd71f-70c3-4877-925a-e49ba3ab8a2b","added_by":"auto","created_at":"2025-10-01 08:21:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":752076,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental setup and design. A: \u003c/strong\u003eExperimental setup. The participants sat in front of a computer screen and eye tracker, with the left hand placed on a key pad for responding. On the right hand (placed on their lap, and occluded by a black gown), the participants wore a data glove with which they controlled the virtual hands’ movements. The movements of the virtual hands were permanently delayed with respect to the actually executed hand movements, whereby each of the four virtual hands received a different amount of delay in each trial.\u003cstrong\u003e B: \u003c/strong\u003eThe experiment was a 2x2 factorial, within-participant design. The first factor Search Instruction meant that participants were either instructed to identify the virtual hand that moved most similarly (i.e. with the shortest added delay, corresponding to searching for visuomotor matches) or the one that moved most differently (i.e., with the longest added delay, searching for mismatches). The second factor determined task difficulty via the relative similarity of the virtual hands’ movements; i.e., whether the discrepancy between the individual delays was high or low (300 or 100 ms difference between the individual delay levels). The resulting conditions were: FSHD = find similar, high discrepancy; FSLD = find similar, low discrepancy; FDHD = find different, high discrepancy; FDLD = find different, low discrepancy. \u003cstrong\u003eC:\u003c/strong\u003e Example trial sequence. At the start of each trial, the participant was shown the current Search Instruction (Find Similar or Find Different, in German). In the trial shown here, the participant was instructed to search for the VH that moved the most similarly (i.e., with the least amount of delay); the instructions were color-coded, which was also indicated by a colored border around the visible portion of the screen. Participants were then able to move (“Exploration”) until they felt they had identified the correct (target) hand; whereby hand movements were paced by an auditory signal. The participants stopped the trial with left-hand key pressing, and then indicated which of the four VHs they had identified as the target. Subsequently, the participants were asked to provide a confidence rating in their choice.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/a8e64708a4c20c2694acd25b.png"},{"id":92574780,"identity":"044e6458-1aa8-4f0b-8b08-5b027720809e","added_by":"auto","created_at":"2025-10-01 08:13:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":205295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection accuracy. \u003c/strong\u003eThe bar plots show the average percentage of trials in which the participants identified the correct (target) hand. The dashed line indicates the chance i.e. guessing level of this study since the participants had to select between four different VHs in each given trial. Search Instruction had no significant effect on detection performance. However, participants performed better in High \u0026gt; Low Discrepancy trials (p\u0026lt;.001). Furthermore, there was a significant interaction effect (p\u0026lt;.001), with a significantly impaired performance in FDLD than in FDHD trials (p\u0026lt;.001), but not such effect in FSLD vs FSHD trials. The dots represent individual participant means; error bars represent Cousineau–Morey corrected standard errors (SE) of participant means. FSLD = Find Similar, Low Discrepancy; FSHD = Find Similar, High Discrepancy; FDLD = Find Different, Low Discrepancy; FDHD = Find Different, High Discrepancy. See Tables S1-2 for details.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/e960f0f6fb5f322000b45958.png"},{"id":92574778,"identity":"d35fbd3a-8ede-4eaa-b08b-7affc40897b0","added_by":"auto","created_at":"2025-10-01 08:13:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse Times. \u003c/strong\u003eThe bar plot shows the average time participants took until ending the trial to report their decision. Participants responded significantly slower in Low Discrepancy \u0026gt; High Discrepancy trials (p\u0026lt;.001), but Search Instruction had no significant effect. The dots represent individual participant means; error bars represent Cousineau–Morey corrected standard errors (SE) of participant means. FSLD = Find Similar, Low Discrepancy; FSHD = Find Similar, High Discrepancy; FDLD = Find Different, Low Discrepancy; FDHD = Find Different, High Discrepancy. See Tables S3-4 for details.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/6c8cedf23f57fbb671bab9f7.png"},{"id":92575474,"identity":"e0d49e04-6090-4a73-985c-f618f573bf3c","added_by":"auto","created_at":"2025-10-01 08:21:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":243074,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfidence Ratings. A:\u003c/strong\u003e Participants reported significantly higher confidence in their decisions in Find Similar \u0026gt; Find Different trials (p\u0026lt;.004), and in High \u0026gt; Low Discrepancy trials (p\u0026lt;.001). \u003cstrong\u003eB:\u003c/strong\u003e Participants were overall significantly more confident when they identified the correct hand (main effect, p\u0026lt;.001). \u003cstrong\u003eC:\u003c/strong\u003e Participants responded significantly faster (lower RT) when they were more confident (p\u0026lt;.001). For panels A and B, the error bars represent Cousineau–Morey corrected standard errors (SE) of participant means; dots represent individual-participant means. For panel C, the shaded areas depicts 95 % confidence intervals. FSLD = Find Similar, Low Discrepancy; FSHD = Find Similar, High Discrepancy; FDLD = Find Different, Low Discrepancy; FDHD = Find Different, High Discrepancy. See Tables S5-6 for details.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/ddc673e03274d06ffa3260be.png"},{"id":92575472,"identity":"f121a861-986c-4df0-a618-421a9d20f456","added_by":"auto","created_at":"2025-10-01 08:21:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":632721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGaze behavior.\u003c/strong\u003e \u003cstrong\u003eA: \u003c/strong\u003eEye tracking revealed that\u003cstrong\u003e \u003c/strong\u003eparticipants looked more on the VHs than the screen center in all conditions, indicating a preference for individual (i.e., serial) visual sampling over using peripheral vision to identify visuomotor (mis)matches. This preference was significantly stronger in the FS \u0026gt; FD conditions (p\u0026lt;.05). \u003cstrong\u003eB:\u003c/strong\u003e Over the course of single trials (plotted in normalized trial time, see Methods), the gaze bias towards the VHs decreased. This was significantly stronger in the FD \u0026gt; FS conditions (p\u0026lt;.001), where participants ultimately tended to fixate more centrally (indicated by negative y-axis values). Error bars represent Cousineau-Morey correct standard errors (SE) of participant means (Cousineau, 2005; Morey, 2008). The presented data in this figure consists of raw data and not any model fits. FSHD = Find Similar, High Discrepancy, FSLD = Find Similar, Low Discrepancy, FDHD = Find Similar, High Discrepancy, FDLD = Find Similar, Low Discrepancy. See Tables S7-9 for details.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/849e925c3731fb0acdcd7fbf.png"},{"id":93705780,"identity":"e3c6a5e6-398d-499b-8e38-33d242bb305a","added_by":"auto","created_at":"2025-10-16 16:23:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2672533,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/ef672a6e-0e20-4d5f-b72d-090e1ab95474.pdf"},{"id":92574781,"identity":"bc2348e2-dfd3-49a1-b2ab-35400850441c","added_by":"auto","created_at":"2025-10-01 08:13:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":292561,"visible":true,"origin":"","legend":"","description":"","filename":"YietalSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7655357/v1/c57936f5f84df680ed1fb5bd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Searching for visuomotor matches vs mismatches biases confidence and visual sampling strategies, but not performance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOur ability to accurately identify observed body movements as our own and distinguishing them from those of others is fundamental for the sense of self. Most accounts of (sensorimotor) self-other distinction build upon the notion of forward models in the brain, which can predict the sensations caused by one\u0026rsquo;s bodily movements based on signals from one\u0026rsquo;s motor system such as the \u0026ldquo;efference copy\u0026rdquo; (e.g., Wolpert \u0026amp; Flanagan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In short, if a sensation \u0026ndash; an observed body movement \u0026ndash; does not match the predictions generated by one\u0026rsquo;s forward models, the healthy brain is thought to attribute it to someone else; if it matched the predictions, it is self-attributed (Frith et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jeannerod, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Haggard, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A popular way to study the cognitive and neurobiological mechanisms underlying this kind of self-other distinction is through manipulation of the congruence of visual movement feedback and executed movements (see Limanowski, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, for a review). This line of work has shown that introducing temporal offsets such as a delay between a participant's actual body movements and those of a corresponding virtual body part diminishes the sense of agency and control of the virtual movements (e.g., Krugwasser et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Limanowski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Salomon et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlong these lines, it has been shown that healthy people are very sensitive to sensorimotor mismatches; i.e., unpredicted sensory movement consequences (e.g., Krugwasser et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Brain imaging work has shown that sensorimotor mismatches or sensory prediction errors \u0026ndash; for instance, unpredicted visual movement feedback \u0026ndash; strongly activate the brain\u0026rsquo;s attention (or reorienting) and error processing networks (Farrer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ullsperger et al, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; van Kemenade et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Quirmbach \u0026amp; Limanowski, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand, many studies point to a preferential processing of \u003cem\u003ematching\u003c/em\u003e visual kinematics (e.g., Tsakiris et al., 2005; Brass et al., 2009; Salomon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yon et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, 2020). For instance, Wen et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that controlled objects particularly attract attention. One reason for these effects could be that self-produced (e.g. visual) stimuli can be processed faster precisely \u003cem\u003ebecause\u003c/em\u003e they have been predicted by internal forward models (Kumar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yon et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Perrykkad et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, studies by Salomon et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) have demonstrated that self-identification among multiple moving \u0026lsquo;distractor\u0026rsquo; avatars was faster when people moved actively than passively, supporting the assumed importance of forward sensory predictions for self-other distinction. The efficiency with which one can detect one\u0026rsquo;s own body movements can even be described in terms of a \u0026ldquo;self-pop out\u0026rdquo;, which is largely independent of e.g. the number of distractors (Salomon et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn sum, visual movement information may be processed preferentially because it is recognized as \u0026ldquo;self\u0026rdquo; (predicted) or because it is recognized as \u0026ldquo;non-self\u0026rdquo; (unpredicted). Here, we directly contrasted these possible scenarios. Recent work has shown that cognitive-attentional factors such as task set or instructed behavioral relevance can bias the cortical processing of visual body movement feedback (Asai, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arslanova et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Limanowski \u0026amp; Friston, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vigh \u0026amp; Limanowski, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Following these works, here, we aimed to test whether the instruction to search for visuomotor matches (self-identification) vs mismatches (error identification) would be reflected by differences in performance, metacognition about this performance, and furthermore, by different sensory (i.e., visual) sampling strategies.\u003c/p\u003e\u003cp\u003eThe participants controlled four simultaneously presented virtual hands (VHs) using a data glove (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea); however, each of the VHs reflected the executed hand movements with a unique added time delay. The participants were instructed to identify which of the four VHs moved the most similarly (with the shortest delay) or most differently (with the longest delay) relative to their actual hand movements. This effectively induced a cognitive-attentional focus on identifying visuomotor matches vs mismatches; which can be related to the notion of \u0026ldquo;controllability\u0026rdquo; used in other studies (Wen et al., 2020, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Crucially, all VHs were always delayed, and both search instructions contained trials with identical delay mappings. Therefore, any behavioral differences reflected the effects of the instructed search focus (match or mismatch identification). Participants had to perform the task under two levels of difficulty, i.e., relatively more or less similar delay mappings (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). We tested whether participants would perform better; i.e., more accurate and faster when searching for visuomotor mappings (which would support a \u0026ldquo;self pop-out\u0026rdquo;) or when searching for visuomotor mismatches (which would speak for a particular perceptual saliency of sensorimotor prediction errors).\u003c/p\u003e\u003cp\u003eParticipants also had to provide confidence ratings in their responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). It has been shown that peoples show overconfidence in the consequences of their own action, as e.g. observed in motor tasks (Wolpe et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Charles et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fourneret \u0026amp; Jeannerod, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Metcalfe \u0026amp; Greene, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Arbuzova et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ciston et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; cf. Yon et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, we asked whether merely instructing to search for \u0026ldquo;own\u0026rdquo; actions (i.e., finding visuomotor matches\u0026thinsp;\u0026gt;\u0026thinsp;mismatches) would be reflected in participants\u0026rsquo; confidence ratings\u0026mdash;and whether this would relate to potential performance differences.\u003c/p\u003e\u003cp\u003eFurthermore, we tracked participants\u0026rsquo; gaze behavior during the search task, to test whether they would employ different sampling strategies depending on the search instruction. I.e., we tested whether participants would rely on individual (i.e., serial) sampling of individual hands, or on peripheral vision (i.e., showing a tendency to fixate centrally). Arguably, a serial sampling of the virtual hands\u0026rsquo; movements (cf. Zelinsky, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) could improve detection accuracy. However, although it has reduced spatial resolution compared to foveal vision, peripheral visual sampling still remains highly sensitive to \u003cem\u003emotion\u003c/em\u003e (Boff et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Thus, we asked whether \u0026ndash; and in which conditions specifically \u0026ndash; participants would retreat to central fixation as a sampling strategy, which would allow them to use peripheral vision to compare the visual movements of all four hands simultaneously, as a form of \u0026ldquo;global monitoring\u0026rdquo; (cf. Cavanagh \u0026amp; Alvarez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Fehd, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Fehd \u0026amp; Seiffert, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Finally, following anxiety-related biased previously observed in other visuomotor tasks (e.g., Yi et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we tested for potential influences of trait anxiety on detection performance and confidence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSearching for visuomotor matches vs mismatches yields comparable performance, but searching for matches is less affected by task difficulty\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe first investigated if Search Instruction and Delay Discrepancy affected performance as quantified by CR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and RT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Participants were able to identify the instructed target hand well above chance level in all conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our generalized linear mixed-effect model analyses (Gelman \u0026amp; Hill, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) revealed no significant main effect of Search Instruction on detection accuracy (B\u0026thinsp;=\u0026thinsp;0.83, SE\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;=\u0026thinsp;0.347; cf. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1). There was a significant main effect of task difficulty i.e., Delay Discrepancy (B\u0026thinsp;=\u0026thinsp;10.22, SE\u0026thinsp;=\u0026thinsp;2.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereby participants performed worse in Low than in High-Discrepancy trials.\u003c/p\u003e\u003cp\u003eInterestingly, however, there was a significant interaction between Search Instruction and Delay Discrepancy (B\u0026thinsp;=\u0026thinsp;5.25, SE\u0026thinsp;=\u0026thinsp;1.69, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc comparisons using Holm-correction (Holm, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, Supplementary Table\u0026nbsp;2) showed that this interaction could be characterized in terms of a significantly poorer performance in FDLD than in FDHD trials (Δ = \u0026minus;\u0026thinsp;0.38 probability units, SE\u0026thinsp;=\u0026thinsp;0.05, 95% CI [\u0026ndash;0.52, \u0026minus;\u0026thinsp;0.28], z = \u0026minus;\u0026thinsp;8.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with no significant difference between FSLD and FSHD trials. In other words, increased task difficulty impaired performance when searching for visuomotor mismatches, but had no such effect when searching for visuomotor matches.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOn response times (RT), there likewise was no significant effect of Search Instruction (B\u0026thinsp;=\u0026thinsp;0.09, SE\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;=\u0026thinsp;.10), although participants were somewhat faster in the Find Similar than in Find Different trials (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was a main effect of Delay Discrepancy on response times (B = -0.08, SE\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), whereby participants responded slower in Low Discrepancy\u0026thinsp;\u0026gt;\u0026thinsp;High-Discrepancy trials; and participants were faster when giving correct than incorrect responses (main effect, B = -0.07, SE\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, we observed a significant Search Instruction \u0026times; Delay Discrepancy interaction (B\u0026thinsp;=\u0026thinsp;0.11, SE\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Post-hoc comparisons showed that FSLD response times were significantly slower than FSHD (Δ\u0026thinsp;=\u0026thinsp;0.14 log-s, SE\u0026thinsp;=\u0026thinsp;0.02, 95% CI [0.08, 0.20], t(60.8)\u0026thinsp;=\u0026thinsp;6.18, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), with no other comparison being significant. See Supplementary Tables\u0026nbsp;3 and 4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants are overconfident when searching for visuomotor matches\u0026thinsp;\u0026gt;\u0026thinsp;mismatches\u003c/h2\u003e\u003cp\u003eThere was a significant main effect of Search Instruction on CFR (B = -0.06, SE\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;.004): on average, participants reported significantly higher confidence in their decisions in Find Similar than in Find Different trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, Supplementary Table\u0026nbsp;5). This difference was significant for both levels of task difficulty (Supplementary Table\u0026nbsp;6). Furthermore, we observed a main effect of Delay Discrepancy (B\u0026thinsp;=\u0026thinsp;0.02, SE\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;.032) where participants were less confident in Low Discrepancy than High Discrepancy trials, likely reflecting the increased task difficulty. The interaction between Search Instruction and Delay Discrepancy was not significant.\u003c/p\u003e\u003cp\u003eFurthermore, we observed a main effect of CR (B\u0026thinsp;=\u0026thinsp;0.04, SE\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) where increasing CR values predicted higher CFRs. I.e., participants were more confident when they actually gave the correct response (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). We also observed a main effect of RT (B = -0.23, SE\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) where lower RTs predicted higher CFRs. In other words, participants responded faster in trials where they reported higher confidence. See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec (cf. Supplementary Tables\u0026nbsp;5 and 6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of descriptive statistics of CR, log RT and CFR with associated standard deviations (in brackets).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSearch Instruction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDelay\u003c/p\u003e\u003cp\u003eDiscrepancy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCorrect \u003c/p\u003e\u003cp\u003eresponses (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003cp\u003etime (s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConfidence\u003c/p\u003e\u003cp\u003erating (0\u0026ndash;1)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFind Similar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57.3 (10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.32 (0.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.754 (0.117)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFind Similar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow Discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.3 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.57 (0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.663 (0.152)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFind Different\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh Discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.2 (12.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.60 (0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.625 (0.127)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFind Different\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow Discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.1 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.70 (0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.579 (0.143)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGaze behavior suggests different visual sampling strategies associated with searching for visuomotor matches vs mismatches\u003c/h3\u003e\n\u003cp\u003eNext, we tested whether participants would rely on individual (i.e., serial) sampling of individual hands, or on peripheral vision (i.e., showing a tendency to fixate centrally). Based on the recorded eye tracking data, we calculated a \u0026ldquo;Gaze Bias\u0026rdquo; reflecting the average proportion spent fixating either of the VHs-the screen center (see Methods); i.e., positive values reflected a preference for visual sampling of individual hands, whereas negative values indicated a preference for central fixation and, likely, using peripheral vision to identify visuomotor (mis)matches.\u003c/p\u003e\u003cp\u003eOverall, SJ spent more time looking at the VHs than at the center in each condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Crucially, we observed a main effect of Search Instruction (B = -0.023, SE\u0026thinsp;=\u0026thinsp;0.009, p\u0026thinsp;=\u0026thinsp;0.14) where participants spent significantly more time looking at the VHs\u0026thinsp;\u0026gt;\u0026thinsp;Center (higher Gaze Bias) in Find Similar\u0026thinsp;\u0026gt;\u0026thinsp;Find Different trials (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, Supplementary Table\u0026nbsp;7). There was no significant main effect of Delay Discrepancy, but an interaction effect between Search Instruction \u0026times; Delay Discrepancy (B = -0.012, SE\u0026thinsp;=\u0026thinsp;0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, cf. Supplementary Table\u0026nbsp;8).\u003c/p\u003e\u003cp\u003eThen, we looked at the Gaze Bias over the progression of single trials; i.e., over normalized trail time (see Methods). Firstly, we found a main effect of WTP (B = -0.012, SE\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning that the participants\u0026rsquo; gaze shifted relatively more towards the Center as trial time increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). More importantly, we observed a significant interaction between Within Trial Progression \u0026times; Search Instruction (B\u0026thinsp;=\u0026thinsp;0.010, SE\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); i.e., the drift towards central fixation was significantly less pronounced in Find Similar than in Find Different trials (Supplementary Table\u0026nbsp;9). Thus, participants still sampled the VHs relatively more frequently closer to reaching a decision in the FS conditions, whereas at this point they had relatively retreated to central fixation in FD trials. See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. Finally, there was an interaction between Within Trial Progression \u0026times; Delay Discrepancy (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.008, SE\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), such that participants\u0026rsquo; gaze shifted more strongly from the VHs to the Center ROI in High Discrepancy than in Low Discrepancy trials (i.e., a steeper slope cf. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). A supplementary analysis revealed that identified the \u0026ldquo;correct\u0026rdquo; hand significantly more frequently when they had fixated it more frequently (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S10). Finally, participants with higher STAI-T scores were, on average, less accurate, slower, and less confident in the task; but none of these correlations reached significance (Fig. S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe key finding of our study was that the instruction to search for visuomotor matches (i.e., self-identification) vs mismatches (prediction error identification) significantly affected the participants\u0026rsquo; confidence ratings and gaze i.e. visual sampling behavior, but not detection performance (as quantified by accuracy and speed). This result can be unpacked as follows:\u003c/p\u003e\u003cp\u003eFirstly, search instruction biased confidence ratings. It should be noted that, overall, higher confidence ratings were associated with better accuracy and faster response times, supporting the results of previous (visuo)motor studies (Locke et al., 2020; Sinanaj et al., 2015; Charles et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, confidence ratings seemed to adequately reflect decision uncertainty in principle.\u003c/p\u003e\u003cp\u003eCrucially, however, confidence ratings were significantly higher when searching for visuomotor matches (Find Similar; i.e., self-identification) than when searching for mismatches (Find Different; i.e., prediction error identification) \u0026ndash; despite no significant difference in performance between these conditions. In other words, participants seemed overconfident in their decisions when instructed to search for Similar\u0026thinsp;\u0026gt;\u0026thinsp;Different. This points towards a confidence bias when looking for oneself. Such a bias would align well with previous work showing a tendency for overconfidence in the sensory consequences of one\u0026rsquo;s own actions (Wolpe et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Charles et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fourneret \u0026amp; Jeannerod, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Metcalfe \u0026amp; Greene, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Arbuzova et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ciston et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and could be related to potential predictive mechanisms in the brain\u0026rsquo;s motor system, e.g., through oversensitivity to spurious sensorimotor correlations (Yon et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; cf. Wen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Salomon et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecondly, eye tracking suggested that participants looked at the individual hands significantly more frequently (compared with simply fixating centrally) when searching for visuomotor matches. Conversely, they relied more on central fixation when searching for visuomotor mismatches. These gaze behavior differences significantly increased over the duration of trial time. Thus, the search instructions produced two different visual search strategies: when searching for visuomotor matches, participants seemed to favor serial sampling of the visual movements (hands); when searching for visuomotor mismatches, they seemed to predominantly rely on peripheral vision. This could mean that visuomotor mismatches were more salient than matches; i.e., that the unpredicted visual movement was more easily detectable through joint sampling of all four hands with peripheral vision \u0026ndash; abolishing any need to use more costly serial sampling. This tentatively speaks to the perceptual salience of prediction errors hypothesis (see Introduction). However, these different search strategies did not result in significantly different performance; i.e., serial sampling was not significantly worse or slower than using peripheral vision (Participants were even somewhat, but non-significantly faster when searching for Similar\u0026thinsp;\u0026gt;\u0026thinsp;Different).\u003c/p\u003e\u003cp\u003eCrucially, while the main effects were non-significant, we observed instruction-related performance differences depending on the current task difficulty. As expected, participants were more accurate, faster, and more confident when the task was easier (i.e., higher discrepancy between the four visuomotor delays; see Farrer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shimada et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e for similar results). More interestingly, task difficulty interacted with search instruction to affect performance: Thus, detection accuracy when searching for mismatches (errors) was significantly impaired by increased task difficulty, whereas searching for matches (self-identification) was not (interaction effect, confirmed with post-hoc tests). This result shows an independence of self-identification from task difficulty. Here, task difficulty was related to the similarity of the distractors; in Salomon et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), a similar independence was shown from the number of distractors. In this light, our results tentatively support the \u0026ldquo;self pop out\u0026rdquo; effect (Salomon et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); i.e., a preferential, perhaps \u0026lsquo;automatic\u0026rsquo; processing of matching visual kinematics.\u003c/p\u003e\u003cp\u003eA limitation of our study is that our eye-tracking data was logged at 60 Hz, therefore, we were not able to investigate any fine grained oculomotor behavior such as (micro)saccades. The limited screen space also meant that even when one hand was fixated, all others were potentially visible in the periphery. This could be improved upon by future work using e.g. VR headsets. Finally, we did not collect any judgments of a sense of agency or control; future work could investigate whether those are affected by instructed search focus. Thus, our paradigm could also be extended to help understand difficulties in sensorimotor based self-other distinction in clinical populations such as schizophrenic patients (Synofzik et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schmitter et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Frith et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003e25 healthy, right-handed participants (18 females, mean age\u0026thinsp;=\u0026thinsp;24.56 years (SD\u0026thinsp;=\u0026thinsp;4.13 range\u0026thinsp;=\u0026thinsp;19\u0026ndash;35 years) completed the experiment. The sample size was determined with a power analysis based on data from a pilot experiment (N\u0026thinsp;=\u0026thinsp;8), targeting a medium-to-large effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.69) at a significance level of α\u0026thinsp;=\u0026thinsp;0.05 and a desired power of 0.9. To reach this sample size, we had to recruit 33 participants, as 8 of those had to be excluded due to technical problems with the data glove, eye tracking calibration, or data logging errors. All participants provided written informed consent prior to participation; and received 10 \u0026euro; per hour or student credits as compensation. The study was approved by the local ethics committee of the Universit\u0026auml;tsmedizin Greifswald and performed in accordance with the relevant guidelines and regulations and the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eExperimental setup and procedure\u003c/h2\u003e\u003cp\u003eParticipants sat in front of a computer screen (27\u0026rdquo;, 1920 x 1080 pixels resolution, 60 Hz refresh rate) with their chin on a rest at 62 cm distance. On their right hand (placed on their lap, and occluded from view by a black gown), the participants wore a data glove (5DT Data Glove 5 Ultra, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://5dt.com/5dt-data-glove-ultra/\u003c/span\u003e\u003cspan address=\"https://5dt.com/5dt-data-glove-ultra/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with which they controlled the virtual hands\u0026rsquo; movements (see below). Their left hand was placed on a key pad with tactile markers, with which participants provided the responses. We used an eye-tracker (Gaze-Point3 HD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gazept.com/\u003c/span\u003e\u003cspan address=\"https://www.gazept.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, 60 Hz sampling rate) to collect fixation data.\u003c/p\u003e\u003cp\u003eDuring the experiment, participants had to move all four fingers except the thumb simultaneously in a simple grasping (i.e. closing-and-opening) motion paced by an auditory cue (a 250 Hz tone that grew louder and quieter following a sine wave function with 0.5 Hz frequency, cf. Vigh \u0026amp; Limanowski, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Their hand movements were fed to four virtual hands presented on screen (with Unity, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unity.com/\u003c/span\u003e\u003cspan address=\"https://unity.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), whereby the fingers of each VH received an average of all functioning glove sensors (cf. Limanowski et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Importantly, we added a delay to the movements of each of the VHs; i.e., each VH reflected the movements executed by the participant after a unique temporal lag.\u003c/p\u003e\u003cp\u003eThe participants were instructed to identify (in each trial) which of four simultaneously presented VHs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) moved the most similarly (with the shortest delay) or most differently (with the longest delay) relative to their actual hand movements; and press a key with their left hand as soon as they were confident to report the decision. The Search Instructions were color coded (cyan or orange, counterbalances across participants), and represented with a colored border around the screen during the trials. After ending the trial by key press, the participant was then asked to indicate the chosen VH which they thought had moved the most similarly or differently to their actual movements. Then, participants were asked to provide a confidence rating (CFR) in their choice using a visual scale presented on screen (from 0\u0026ndash;1 with discrete increments of 0.1). If the participants did not press ENTER within 30s after the start of a trial, the trial ended end the ratings were requested.\u003c/p\u003e\u003cp\u003eThe experiment consisted of two blocks, each of which contained 96 trials of one search instruction (Find Similar or Find Different). The block order was randomized across participants. In each block, half of the trials were High Discrepancy trials\u0026mdash;in these trials, the difference in visuomotor delay between the hands was large (300 ms; i.e., the delays were: 50, 350, 650, 950 ms), which translated into an easier search task. The other half of the trials were Low discrepancy\u0026mdash;here, the delay difference was smaller (i.e., 100ms), which corresponded to a harder task condition. To span the same range of delays as in the High Discrepancy condition, here, two delay sets were used (i.e., 50, 150, 250, 350 ms or 650, 750, 850, 950 ms). The VHs were placed equidistally from each other. In each trial, the delay levels were assigned to the hands in a novel combination, using 24 predetermined, unique arrangements. The participants completed a practice phase to familiarize themselves with the experimental task and design.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe data were analyzed using generalized linear mixed models (GLMMs) in R 4.4.3 (R Core Team, 2025) and lme4 (Bates et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). We adopted a stepwise model selection by initially including the total effect of our fixed effects and all their possible interactions and we then removed fixed effects and interaction terms by comparing the consistency of p-values of the fixed effects and Bayesian Information Criterion (BIC) values (Burnham et al., 2010). These values were obtained using the packages lme4 (Bates et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and lmertest (Kuznetsova et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in R. Models that failed to converge or were singular were aborted and not used. For all models, our experimental factors Search Instruction and Delay Discrepancy, when used as fixed or random effects, were centered around 0 by coding the levels as -0.5 and +\u0026thinsp;0.5 because this approach allows for the intercept to represent the grand mean, facilitating the interpretation of main effects and interactions (Gelman \u0026amp; Hill, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Barr et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For the models on CR, RT and CFR as the dependent variables, we always included the main effects and interaction terms of Search Instruction x Delay Discrepancy as fixed effects. We tested all combinations of main effects and interaction effects of CR, log RT and CFR of all models during model comparisons (see above). Furthermore, we always included the main effects of Search Instruction and Delay Discrepancy as random effects in order to control for random slopes of the variables at the participant level. For the eye-tracking models, as fixed effects we again included the main effects and interaction terms of Search Instruction x Delay Discrepancy. We also added the interaction term with WTP (see below). The main effects of Search Instruction and Delay Discrepancy were then added as random effects using Gaze Bias as the dependent variable.\u003c/p\u003e\u003cp\u003eCorrect responses were scored as a binary variable (1/0). The RT was defined as the elapsed time from trial onset until the participants\u0026rsquo; pressed a key to end the trial (or if the trial duration expired, i.e., 30s). CFR was defined as the as value selected using the visual sliding scale, between 0\u0026ndash;1 with 0.1 discrete increments. RT was always log-transformed when used both as a dependent variable and as a fixed effect due to it typically adhering to a right-skewed distribution (Ratcliff, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Apart from RT, when used as fixed effect (not as dependent variables) all of our other continuous variables including the ones used for the eye-tracking analyses, were z-scored so that they also they were also centered around zero for easier interpretation (Schielzeth, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the eye tracking data analysis, we removed all data points deemed invalid by Gazepoint Controller (Gazepoint, Vancouver, Canada), i.e. when participants were blinking, not looking at the monitor or if the positions of the eyes and pupils could not be reliably identified. The valid data points were subsequently used in the several of our mixed effects model analyses. The VH ROIs were defined as the smallest possible rectangular area surrounding each VH when fully extended, and the Center ROI was defined as analogous area centered on screen. We calculated a Gaze Bias for each eye-tracking sample as that sample\u0026rsquo;s share of the trial duration spent looking at any of the VH ROIs vs the Center ROI (eye gaze samples Outside the VH ROIs and the Center ROI were excluded for this analysis since we wanted to test our hypothesis pertaining to whether participants relied on peripheral vs. serial sampling of visual information to solve the experimental task), coded positive when the eyes were predominantly on the VH ROIs and negative when they were on the Center ROI. In other words, Gaze Bias ranged from \u0026minus;\u0026thinsp;100% (gaze exclusively on the Center ROI of the entire duration of a trial) to +\u0026thinsp;100% (gaze exclusively on any of the VH ROIs) for each trial. To test for changes in Gaze Bias over time, we furthermore introduced Within Trial Progression (WTP: 0\u0026ndash;100% of elapsed progression within a single trial, z-transformed for the mixed effects model analysis as a new fixed effect (Schielzeth, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, we used the State Trait Anxiety Inventory (STAI, Spielberger et al., 1983) scores to test whether individual differences in trait anxiety (state anxiety is not our focus here) would correlate with detection performance or confidence. We tested whether each of these four variables were normally distributed using Shapiro\u0026ndash;Wilk tests (Shapiro \u0026amp; Wilk, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1965\u003c/span\u003e), and then applied Spearman or Pearson correlation analyses accordingly.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis work was supported by a Freigeist Fellowship of the VolkswagenStiftung (AZ 97-932) to JL. We thank Jan Crusius for lending us the eye tracker, Samuel Yi for help with programming and Nina Standar for help with data acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data will be made available upon request. 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A theory of eye movements during target acquisition. \u003cem\u003ePsychological Review\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(4), 787\u0026ndash;835. https://doi.org/10.1037/a0013118\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Action, attention, self-identification, self-other distinction, visuomotor conflict","lastPublishedDoi":"10.21203/rs.3.rs-7655357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7655357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVisuomotor self-other distinction relies on the comparison of forward predictions from one\u0026rsquo;s motor system with visual movement data. Previous work suggests that matching kinematics may be preferentially processed, but at the same time, visuomotor mismatches are known to capture attention. Here, participants were presented four virtual hands, each reflecting their actual hand movements, conveyed via a data glove, with a unique added time delay. Participants had to identify which of these hands moved most similarly (search for match) or most differently (search for mismatch) to their actual movements, under varying degrees of task difficulty. We found that the instruction to identify visuomotor matches vs mismatches significantly biased the participants\u0026rsquo; confidence; i.e., participants exhibited overconfidence when searching for matches. Furthermore, eye tracking showed participants relied significantly more on serial sampling of the hands when searching for matches, but more on central fixation and peripheral vision when searching for mismatches. These biases were not universally reflected in detection performance. However, performance when searching for visuomotor matches was less strongly affected by task difficulty than when searching for mismatches.\u003c/p\u003e","manuscriptTitle":"Searching for visuomotor matches vs mismatches biases confidence and visual sampling strategies, but not performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 08:13:30","doi":"10.21203/rs.3.rs-7655357/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"569a88c8-3f67-4128-8391-f0c622526e89","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55483796,"name":"Biological sciences/Neuroscience"},{"id":55483797,"name":"Biological sciences/Psychology"},{"id":55483798,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-10-16T16:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 08:13:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7655357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7655357","identity":"rs-7655357","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}