{"paper_id":"100cc7bb-bf95-4d90-b90c-c135abe4af78","body_text":"Title 1 \nMotor Planning Sensitivity to A3ective Looming Sounds Within The Peri-personal Space: 2 \nAn Interplay of Exogenous and Endogenous Influences 3 \nAuthors 4 \nRoberto Barumerli1,*, ORCID, roberto.barumerli@univr.it  5 \nMichele Geronazzo2, ORCID 6 \nPaola Cesari1, ORCID 7 \n1 Department of Neurosciences, Biomedicine and Movement, University of Verona, Italy 8 \n2 Department of Engineering and Management, University of Padova, Italy 9 \nAbstract 10 \nOur brain maps the space immediately surrounding the body, the peripersonal space 11 \n(PPS), to sharpen sensory-motor coordination whenever an object enters it. Within PPS, 12 \npast research demonstrated how several factors influence motor readiness: from 13 \nexogenous factors, such as body -object distance and stimulus semantics, to 14 \nendogenous traits like personality traits. Nevertheless, most paradigms rely on vision or 15 \ntouch, relegating hearing to a supporting role and leaving auditory -only contributions 16 \nunclear. Here, we tested whether a3ective content and individual traits modulate motor 17 \nplanning for looming sounds that stop within PPS. Thirty -three adults completed three 18 \nauditory-only tasks in which positive, negative, or neutral sounds halted at five simulated 19 \ndistances from the participant's  ears (0.3 –0.7 m). We recorded anticipatory postural 20 \nadjustments, distance estimates, a3ective ratings, and sensory suggestibility via a 21 \nquestionnaire. Motor responses were largely anticipated as sounds stopped nearer the 22 \nbody, while delayed and less precise for semantic (positive or negative) than neutral 23 \nsounds. Higher suggestibility predicted longer and more variable premotor latencies, 24 \nparticularly for non -semantic sounds. These findings show that auditory cues alone 25 \nengage flexible sensorimotor mechanisms within PPS, where exogenous (distance, 26 \nsemantics) and endogenous (suggestibility) factors jointly shape motor readiness and 27 \nspatial perception. 28 \nKeywords: Peripersonal space, Auditory looming stimuli , Spatial hearing, Anticipatory 29 \npostural adjustments, Semantic modulation, Sensory suggestibility 30 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n1. Introduction  31 \nTo successfully navigate the environment, the brain maintains a dynamic representation 32 \nof the body's surrounding space by integrating sensory perception with action planning1. 33 \nThis representation, known as peripersonal space (PPS), enables rapid responses to 34 \npotential threats , such as sidestepping an unseen car , and facilitates goal -directed 35 \nbehaviours, like catching a buzzing mosquito in the dark. Importantly, perception-action 36 \ncoupling within PPS boundaries is not fixed : they are shaped by both external 37 \nenvironmental factors, such as the characteristics of stimuli entering the PPS (e.g., their 38 \nnature, valence, or social relevance), and internally driven factors, including 39 \ninteroceptive accuracy and personality traits2. Prior literature o3ers a wide range of 40 \nmethodologies to investigate the factors influenced by the PPS, from reactive tasks that 41 \nmeasure physiological responses probing underlying neural mechanisms to decision -42 \nmaking tasks that engage higher cognitive functions 3. However, these exogenous and 43 \nendogenous factors (i.e. environment-dependent configurations such as object distance 44 \nand internal traits such as personality traits) and their e3ects on either low -level 45 \nperceptual processes or high -level cognitive functions are often studied in isolation, 46 \nlimiting our understanding of how the brain operates as an integrated system4. Therefore, 47 \nthis study leverages auditory looming stimuli entering the PPS to gather evidence from 48 \nphysiological measurements, behavioural responses, and self-reported personality traits 49 \nto examine how endogenous and exogenous factors modulate participants’ responses. 50 \nWhen an object enters the PPS, the brain must infer object characteristics from sensory 51 \ninputs to enable a coherent interaction2. Much of our understanding of PPS comes from 52 \nmultisensory research, demonstrating that auditory, visual, and tactile cues converge to 53 \nguide motor responses3. While hearing is often used as a facilitator and in support of 54 \ntouch and vision 5,6, it is unclear how hearing alone provides a proxy for studying the 55 \nperception-action integration of objects entering the PPS. Among these, looming sounds 56 \nare particularly salient due to their ability to signal potential threats from a distance 7. 57 \nEmpirical evidence shows that approaching sounds enhance spatial representation and 58 \ntrigger fast er motor responses than those elicited by receding sounds 8,9. Moreover, a 59 \nsound’s meaning can influence the PPS’s size: negative sounds expand this boundary, so 60 \nour defensive system is triggered by objects farther away than those perceived as neutral 61 \nor positive sounds10. While these findings highlight the influence of both temporal, spatial 62 \nfeatures and semantic meaning on PPS, the role of individual variability in shaping 63 \nauditory-motor coupling remains largely unexplored.  64 \nAnticipatory postural adjustments (APAs) o3er a promising approach to investigate 65 \nperception–action mechanisms, revealing how motor commands from the central 66 \nnervous system prepare the body as a stimulus enters the peripersonal space11. APAs are 67 \nearly muscle activations that stabilise posture in preparation for movement. Their timing 68 \nand contraction strength reflect the duration and facilitation that the central nervous 69 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nsystem requires to process a perceptual event and initiate a motor response through the 70 \npre-motor corte x’s top -down modulation 12. Unlike methods such as transcranial 71 \nmagnetic stimulation, hand -blink reflex elicitation, or electroencephalogram, APA 72 \nmeasurement captures neural dynamics during natural movement, making it a more 73 \necological and functionally relevant tool for studying sensorimotor integration  in 74 \necological environments 13. In the auditory domain, APAs reveal feedforward motor 75 \nresponses: when looming sounds stop closer within the peripersonal space, APA onset 76 \noccurs earlier, but sounds stopping outside PPS produce no such e3ect, demonstrating 77 \nthat this timing modulation is specific to stimuli entering the PPS14. Moreover, looming 78 \nsounds with semantic content show valence -specific lateralisation: negative  sounds 79 \nstrongly trigger APAs, reflecting a tight coupling between auditory perception and motor 80 \nreadiness, likely due to preferential engagement of the left motor cortex for unpleasant 81 \nsounds and the right for pleasant ones 15–17. However, existing studies have primarily 82 \nfocused on group-level e3ects, leaving open the question of how individual di3erences  83 \nmodulate APA timing within PPS boundaries. 84 \nRecent work investigating the link between perception and action shows its flexibility 85 \nwithin PPS:  sensory information is uncertain, therefore, people can actively interpret 86 \nwhat they sense 18. With such ambigu ities, personal tendencies become evident : for 87 \ninstance, people with a high trait of anxiety lean more on their previous expectations 88 \nwhen judging unclear motion, making their trait di3erences observable in perceptual 89 \ndecision making19. With regard to PPS flexibility, trait-level factors highlight this plasticity: 90 \nhigher trait anxiety enlarges PPS, as indicated by a stronger hand -blink reflex when the 91 \nhand nears the face 20, whereas greater interoceptive accuracy narrows it, sharpening the 92 \nboundary between the embodied self and the external world 4. Although such studies 93 \nrelate PPS size to self -reported personality traits, they reveal little about how the 94 \nperception-action coupling itself varies across individuals within PPS boundaries. Clues 95 \ncome from multisensory illusions within PPS, where highly suggestible people are more 96 \nsusceptible to the rubber -hand illusion 21, possibly because enhanced multisensory 97 \nintegration and attentional engagement magnify their responsiveness 22. When 98 \nconsidering sensory suggestibility , defined as a  person’s susceptibility to external 99 \nsensory cues23, highly suggestible individuals might show di3erential activation within 100 \nsensory motor pathways, especially auditory processing brain regions linked to spatial 101 \nlocalisation and action preparation 24. Together, these findings indicate that personal 102 \ntraits shape not only how far PPS extends but also how we prepare and act when stimuli 103 \nenter this space. 104 \nIn this study, we investigated how participant responses are modulated by endogenous 105 \nand exogenous factors in response to looming auditory stimuli entering the PPS. We 106 \ncombined motor planning measures with an explicit distance estimation  task. 107 \nParticipants were presented with three looming sounds, selected from the International 108 \nA3ective Digitized Sounds (IADS -2) database25, in addition to the pink noise stimulus , 109 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \neach stopping at five simulated distances within PPS. The a3ective properties of these 110 \nsounds were validated using the Self -Assessment Manikin (SAM) 26, while the 111 \nMultidimensional Iowa Suggestibility Scale (MISS) quantified individual sensory 112 \nsuggestibility23. This dual approach allowed us to capture both rapid, pre -movement 113 \npostural adjustments and conscious spatial judgments. To accommodate individual 114 \ncontributions of reaction times and perceptual estimates, we employed a Bayesian 115 \nstatistical framework, including beta regressions, thereby o3ering a flexible and robust 116 \nanalysis strategy that can be potentially extended to other dynamic perception -action 117 \nparadigms27. By integrating neurophysiological measurements, crucial for understanding 118 \nmotor control in the premotor cortex , with explicit distance estimates, we aimed to 119 \nelucidate how individual traits shape the interplay between sensory processing, decision 120 \nmaking, and motor execution.  121 \nTherefore, we formulated two main hypotheses: (H1) when looming sounds entering the 122 \nPPS carry semantic content (for example, a baby crying) versus no meaning (pink noise), 123 \nexperimental measures will show di3erent levels of congruencies. In the cognitive 124 \ndistance estimation task , semantic sounds should be overestimated, whereas in the 125 \nphysiological task, the onset of initial muscle activation should be delayed, reflecting 126 \nextra processing needed to decode meaning 28. Second, we hypothesised that (H2) part 127 \nof the inter-individual variability of the acquired physiological and cognitive data could be 128 \nexplained through the individual quantified level of sensory suggestibility, particularly 129 \nwhen the incoming auditory stimuli carried distinct semantic connotations.   130 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n2. Results  131 \n \n \n(a) (b) \nFigure 1: characteristics of auditory stimuli and their affective evaluation. (a) Intensity curve \nof the experimental stimulus ( vertical bars represent the five virtual stopping distances) \nalong both time and distance axes. The participant’s ear canal was positioned at zero \ndistance. (b) Scatterplot of mean arousal ratings as a function of mean valence ratings for \nthree auditory stimuli. Error bars depict the standard error of the mean for both valence and \narousal over participants. \n 132 \nWith the general aim to investigate how auditory a3ective stimuli and individual 133 \nsuggestibility influence perceptual, spatial, and motor responses within the peripersonal 134 \nspace, we collected data from 33 participants (17 females; mean age = 23.3±3.2 years) 135 \nwho completed three auditory tasks and filled out a personality questionnaire.  136 \nParticipants self -reported normal hearing and no neurological or musculoskeletal 137 \nimpairments, a ll gave written informed consent, and the study was approved by the 138 \nEthics Committee of the University of Verona.  139 \nThe tasks assessed distinct perceptual and behavioural responses to three auditory 140 \nstimuli: two a3ective sounds selected from the IADS2  database25 (one with positive 141 \nvalence, ID 351 – Applause, and one with negative, ID 719 – Dentist Drill) and a neutral 142 \ncontrol stimulus ( Pink Noise) without semantic content.  An amplitude envelope 143 \nfollowing the inverse-square law was applied to simulate approaching sound sources29,30, 144 \nstarting 2.8 meters from the participant and moving at 0.7 m/s. Five stopping distances 145 \nwere defined relative to the participant's ear, ranging from 0.7 to 0.3 meters in 0.1-meter 146 \nsteps (see Fig. 1a).  147 \nData were collected at the Biomechanics Laboratory of the University of Verona using 148 \nmotion capture, electromyography (EMG), and audio delivery systems. Kinematic data 149 \nwere recorded at 250 Hz using a VICON MX Ultranet system with reflective markers 150 \nplaced on the head, shoulders, and index fingers. EMG signals were recorded from the 151 \nerector spinae muscles at 2000 Hz using a ZeroWire EMG system, synchronised with the 152 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nmotion capture via the Vicon control interface. Auditory stimuli were delivered through 153 \nindividually calibrated headphones, while the acoustic intensity was calibrated with a 154 \nsound level meter. All data streams  (including motion, EMG, and audio ) were 155 \nsynchronised to ensure precise temporal alignment (see Methods for further details). 156 \nTo combine neurophysiological data, behavioural measures, and self -reported 157 \nsuggestibility in a Bayesian model, we report results in three steps: (i) a3ective ratings of 158 \nthe sounds, (ii) estimates of each stimulus’s stopping distance, and (iii) reactive arm 159 \nmovements captured by EMG. The outcomes from distance estimation and muscle 160 \ncontraction timing are then related to individual scores of self-reported suggestibility 161 \nobtained from the MISS questionnaire23. 162 \nA*ective evaluation of looming sounds. The a3ective evaluation was conducted using 163 \nthe SAM mannequin26, following the original methodology of the IADS2 25, yielding strong 164 \nconcordance between our participants' ratings and the original scores  (see Fig.1b). An 165 \nAligned Rank Transform (ART) ANOVA31 revealed significant di3erences in valence across 166 \nstimulus categories (F(2, 405) = 303.56, p < .001) , but neither the distance factor (F(4, 167 \n405) = 1.99, p = .096) nor the interaction between stimulus category and distance showed 168 \nsignificant e3ects (F(8, 405) = .61, p = .767). The partial eta-squared for the stimulus type 169 \nwas ηp2=.6. Post-hoc contrasts computed by employing ART-C tests with Tukey 170 \ncorrection32 indicated that valence ratings di3ered significantly among all three sounds, 171 \nwith negative stimuli rated lowest, neutral stimuli intermediate, and positive stimuli 172 \nhighest ( see Fig .1b; all p < .001).  Importantly, these di3erences in the valence score 173 \nfollow the intrinsic a3ective salience of the stimuli, as observed in the original work25. 174 \nSimilarly, statistical analysis of arousal values indicated significant main e3ects for both 175 \nstimulus categories (F(2, 405) = 5.40, p = .005 , ηp2=.03) and stopping distance (F(4, 405) 176 \n= 5.09, p < .001, ηp2=.05), but not for their interaction (F(8, 405) = .63, p = .751). Post-hoc 177 \nanalysis revealed that the neutral stimulus was rated lower in arousal than the negative 178 \nstimulus (p = .003), and the positive stimulus did not di3er from both the neutral and 179 \nnegative stimuli (see Fig.1b; all p > .05). This supports the notion that perceived intensity 180 \nmodulates arousal and replicates findings that auditory intensity acts as a salient motion 181 \ncue mediating the e3ects of looming sounds 8. Further, all three sounds had tightly 182 \nclustered arousal ratings (mean 4.65 ± 0.96), a much narrower spread than the full IADS 183 \narousal spectrum (mean 6.23 ± 2.16), ensuring comparable arousal across conditions. 184 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n \n  \n(a) (b) (c) \nFigure 2 : distance estimation task and perceived distance of looming sounds . (a) \nVisualisation of a participant using her arm to provide the perceived final distance of the \npresented stimulus. (b) Estimated response of distance as a function of simulated ending \ndistance for three auditory stimuli (Pink Noise, Applause, and Dentist Drill).  Shaded areas \nrepresent 95% credible intervals of Bayesian beta regression  while error bars report \nstandard errors computed over participants . (c) Response dispersion  as a function of \ndistance, displayed with credible intervals at 50, 80 and 95% levels. \n 185 \nStopping distance evaluation of looming sounds . The task required participants to 186 \nestimate the distance between the body and the sound at its endpoint using their left arm 187 \nas a scale as shown in Fig. 2a. Given the bounded support for responses that resulted in 188 \nlack of normality, we ran a repeated-measures ART-ANOVA that revealed significant main 189 \ne3ects of distance [F(4, 480) = 381.57, p < .001], stimulus type [F(2, 480) = 9.75, ηp2 = .27, 190 \np < .001], and their interaction [F(8, 480) = 2.88, ηp2 = .05, p = .004]. Post-hoc comparisons 191 \n(ART-C tests with Tukey correction 32) indicated that participants successfully 192 \ndiscriminated ending distances across acoustic stopping points (mean ± se: .047 ± .004 193 \nm, p < .001 across distance levels). Further, neutral stimuli elicited significantly nearer 194 \nestimates (.261 ±.008 m) than both positive (.314 ± .006 m) and negative sounds (.309 ± 195 \n.014 m), with no significant di3erence between positive and negative stimuli (p > .05). 196 \nSuch results demonstrate systematic di3erences in responses when grouping them by 197 \nsemantic category  suggesting an interaction between auditory object recognition and 198 \nsound localisation33. 199 \nTo further interpret these patterns in distance perception, we fitted a Bayesian beta 200 \nregression model with weakly informative priors (see Sec.4.3), which supported the 201 \nfrequentist findings  (see ribbons in Fig.2b) . The posterior distribution for the slope of 202 \ndistance (.443  1/m, 95% -credible interval ( CI) [.409, .478]) confirmed with strong 203 \nevidence the participants' ability to discriminate changes in auditory distance, with the  204 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n \n  \n(a) (b) (c) \nFigure 3: premotor reaction time (pmRT) and its timing variability for looming sounds . (a) \nVisualisation of the participant raising her arm after the stimulus presentation. (b) Premotor \nreaction times as a function of simulated ending distance for three auditory stimuli (Pink \nNoise, Applause, and Dentist Drill).  Shaded areas represent 95% credible intervals of \nBayesian regression and error bars representing standard errors over participants. (c) \nTiming dispersion (i.e. standard deviation) as a function of distance, displayed with credible \nintervals at 95% levels.  \n 205 \n95%-CI not spanning zero.  Additionally, systematic overestimations were observed for 206 \npositive stimuli (.048 m, 95%-CI [.038, .058]) and negative stimuli (.045 m, 95%-CI [.034, 207 \n.055]) compared to neutral stimuli. The Bayesian model allowed us to analyse response 208 \ndispersion (i.e. empirical standard deviation visualised in Fig.2c) across experimental 209 \nfactors (distance and stimulus), demonstrating that dispersion linearly decreases with 210 \ndistance (-.032 1/m, 95%-CI [-.062, -.005]; p(v < 0) = .986) , equating to a 1 cm decrease 211 \nover the distance interval. This e3ect could stem from participants compressing their 212 \nestimates near the shoulder because the arm’s finite length limits the measurement 213 \nrange. 214 \nPremotor reaction time (pm-RT). In this task, we asked participants to raise their arms 215 \nas quickly as possible after the perceived end of the looming sound to measure postural 216 \nadjustments as the contraction initiation of postural muscles14 (as shown in Fig.3a). We 217 \nderived the premotor reaction time (pm -RT) as the activation  time of the erector spinal 218 \nmuscles, and the analysis of variance (ANOVA) demonstrated significance for main 219 \ne3ects of stimulus type (F(2, 480) = 90.754, p < .001) and distance (F(4, 480) = 381.571, p 220 \n< .00 1), and their interaction (F(8, 480) = 2.88 1, p < .004). Similarly, for the perceived 221 \ndistance, pairwise t-tests with a Tukey correction revealed significantly faster pm -RT for 222 \nneutral (176 ± .003s) against both positive (0.187 ± 0.002; p = .028) and negative sounds 223 \n(0.191 ± .003; p < .001), highlighting the easier elaboration of such sounds (i.e., no need 224 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nto identify the content); instead, the contrast between positive and negative remained 225 \ninconclusive within the presented experimental data (p > .05).  226 \nWe further investigated the trends using a Bayesian linear model (see Sec.4.3), which 227 \nsupported and extended the ANOVA findings  (see Fig.3b). First, the estimated posterior 228 \ndistribution for the slope on the distance predictor was positive for neutral sounds (slope 229 \n0.046 1/s, 95% -CI [ .031, .061]), indicating that greater distance is associated with 230 \nincreased pm-RT. Negative sounds systematically increased pm-RT timing compared to 231 \nneutral sounds (intercept .010 s, 95% -CI [ -.001, .022], p(v > 0) = .930), and positive 232 \nsounds positively interacted with the distance term ( .030 1/s, 95%-CI [.006, .054], p(v > 233 \n0) = .979). Second, we analysed how the variability of pm -RT, measured as standard 234 \ndeviation, changed across experimental factors (visualised in Fig.3c). For neutral sounds, 235 \ndistance introduced a negative e3ect (-.546s, 95%-CI [-.265, .164], p(v < 0) = .899), similar 236 \nto the perceived distance, where uncertainty decreases for far distances.  In contrast, 237 \nsemantic sounds showed increased uncertainty over distance, both for positive (1.201 238 \n1/s, 95%-CI [.188, 2.217], p(v > 0) = .975) and negative sounds ( .983 1/s, 95%-CI [-.002, 239 \n1.961], p(v > 0) = .950).  240 \nSuggestibility trait modules perception . Twenty-nine participants completed the 241 \nPhysiological Reactivity (PHR) subscale from the MISS questionnaire, a 13-item 242 \nmeasure23. Unanticipated events during the experimental procedure led to incomplete 243 \ndata for four participants, which could not be recovered post-session. We used the PHR 244 \nmeasure of self-assessed sensory suggestibility to evaluate the tendency to accept and 245 \nact on perceived physiological states, with either reduced critical evaluation (low 246 \nsuggestibility) or independent judgment (high suggestibility). The subscale demonstrated 247 \ngood internal consistency (i.e. all items contributed coherently to the same construct ), 248 \nyielding Cronbach’s α = .810 with a 95 % confidence interval of [.58 1, .897]. Further, the 249 \ndistribution of raw scores was approximately normal (Shapiro -Wilk test, p = .65 4); 250 \ntherefore, we standardised the scores by subtracting the sample mean and dividing by 251 \nthe sample standard deviation.  252 \nWe analysed how the PHR score might explain between -participants variability in both 253 \nestimated distances and pm -RT. For both measurements, we included the normalised 254 \nPHR score in the Bayesian models as an independent factor, as well as its interaction with 255 \nremaining experimental factors (i.e., distance and stimulus semantics – see Methods).  256 \nFor the perceived ending distance, the slope associated with the PHR score did not di3er 257 \nfrom zero (.004 m, 95% -CI [ -0.021, 0.030], p(v > 0) = 0. 625), and neither its e3ect on 258 \nresponse dispersion (.001 1/m, 95%-CI[-0.006, 0.009], p(v > 0) = .628)). When evaluating 259 \nhow the suggestibility score influenced APA's timing, we identified a relevant  impact in 260 \npredicting the accuracy and precision of pm-RTs as shown in Fig.4. In particular, the slope 261 \nassociated with the PHR score was positive (0.015  s, 95%-CI [0.003, 0.027]), indicating 262 \nthat individuals with higher suggestibility scores took longer to respond. The presence of  263 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n  \n(a) (b) \nFigure 4: influence of suggestibility on premotor reaction time and its dispersion. a) The plot \nshows the linear relationship between normalised PHR score and pm-RT response for three \nstimulus categories. b) The plot reports the effect of the normalised PHR score on response \ndispersion is shown separately for three categories. Lines represent linear regressions, and \nshaded areas indicate the standard deviation of slope estimates. PHR score is reported on \na normalised scale (arbitray unit – a.u.) \n 264 \nsemantics also impacted the response through the suggestibility score, with the positive 265 \nsemantics slowing down responses (0.0113, 95% -CI [0.006, 0.017]) as well as for 266 \nnegatives (0.0159, 95% -CI [0.011, 0.021]) in comparison with the sound without 267 \nsemantics (see Fig.4a). In addition, the e3ect of the PHR score on response dispersion 268 \n(i.e., standard deviation) di3ered from zero only when considering the di3erent types of 269 \nstimuli (refer to Fig.4b) : the variability increased only for the stimuli without semantics 270 \n(slope = .004 1/s, 95%-CI [.001, .008], p(v > 0) = 0.987), while it remains constant for both 271 \nnegative and positive stimuli  (max(p(v<0),p(v>0)) < 0.800). Interestingly, for participants 272 \nwith low suggestibility (PHR = −2), dispersion was significantly lower for non-semantic 273 \nsounds than for semantic ones (p = 0.994 for v < 0). This indicates that these individuals 274 \nresponded more quickly and precisely when the sound lacked meaning. By contrast, for 275 \nhighly suggestible participants (PHR = +2) , the dispersion did not di3er across sound 276 \ntypes (p = 0.832 for v > 0), showing that they remained slower and less precise regardless 277 \nof the stimulus content. 278 \n3. Discussion 279 \nWe jointly investigated endogenous and exogenous factors impacting proximity 280 \nperception of looming sounds entering the PPS by combining physiological with 281 \nbehavioural measurements and self -assessed questionnaires within a simulated 282 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nauditory environment. Overall, our results show that the endogenous factor (i.e. 283 \nindividual suggestibility) influences sensorimotor integration, linking this personality trait 284 \nto the coupling between perception and action when a sound enters the peripersonal 285 \nspace. We confirmed the first hypothesis (H1): the estimation of looming sound-stopping 286 \ndistances (the cognitive task) and the timing of initial muscle activation in reaction to the 287 \nlooming sound -stopping distances (the physiological task) showed di3erent levels of 288 \ncongruencies when comparing the exogenous factor associated to the presence of  289 \nsemantic (Applause and Dentist Drill) and without it (Pink Noise). For semantics sounds, 290 \nwe found a n over-evaluation in the cognitive task and a retarded muscle activity in the 291 \nphysiological task. The second hypothesis  (H2), instead, was only partially supported: 292 \nindividual levels of sensory suggestibility significantly influenced pm -RTs and their 293 \nvariability, while distance perception remained una3ected across all stimulus types. 294 \nInterestingly, increased response variability in pm -RT measure was observed only for 295 \nneutral (Pink Noise) stimuli, suggesting a defined interplay between semantic content 296 \nand suggestibility in shaping reactive responses. Following, we discuss the results in 297 \nmore detail. 298 \nIn this study, we relied on  auditory distance perception to demonstrate that 299 \nindividualised looming sounds e3ectively modulate distance estimation and APA timing 300 \nwithin the PPS, as confirmed by both behavio ural and neurophysiological measures. In 301 \nthe distance estimation task, participants correctly estimated the stopping distances of 302 \nthe looming sound, demonstrating the participants’ ability to decode distance from rising 303 \nintensity levels. Second, the measurement of pm-RT successfully replicated the trends 304 \nobserved in prior studies where sounds stopping nearer to the participant’s body elicit 305 \nquicker reactions 15. An important note is on the duration of our stimuli since it 306 \nrepresented a confounder to sound intensity, however, the original study from 307 \nCamponogara et al. 201514 demonstrated that stimuli with a flat envelope and modulated 308 \nduration did not elicit any modulation in the pm-RT indicating that participants did not 309 \nperceived such control stimuli as approaching auditory objects. Further, the approaching 310 \nstimuli were simulated through the inverse square law , following the concept that the 311 \nintensity cue is the most important to elicit auditory distance 34. Although the scientific 312 \nliterature indicates that additional cues, such as reverberation or spectral cues , should 313 \nbe considered to deliver an ecological percept 30, the results obtained from both tasks 314 \ndemonstrate that participants were able to decode an estimate for auditory distance and 315 \nuse it e3ectively despite the simplifications introduced in stimulus rendering. This was 316 \nevident both in the reactive task, where they modulated the pm-RT, and in the more 317 \ncognitive task involving distance estimation. 318 \nExtending previous findings, our study examined how semantic content influences 319 \nparticipant responses within the PPS, confirming anticipated relationships and 320 \nuncovering novel e3ects. Participants accurately distinguished the valence of auditory 321 \nstimuli, rating positive sounds higher than negative ones, with semantically neutral pink 322 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nnoise rated intermediately. Distance estimations and pm-RT revealed that meaningful 323 \n(positive and negative) stimuli were perceived as more distant and elicited slower motor 324 \nresponses compared to pink noise. This may be due to additional cognitive processing 325 \nrequired to decode semantic information, due to increased cognitive demand35, and with 326 \nsource localisation processing occurring more rapidly 28 than identification 33. While 327 \nprevious studies reported faster reactions to negative stimuli 10,36, their use of tactile 328 \ndetection tasks and focus on PPS boundaries di3er from our auditory -only, within-PPS 329 \ndesign. The slower pm-RTs to both negative and positive stimuli may reflect inhibitory 330 \nmotor mechanisms associated with defensive processing of stimuli perceived as 331 \nthreatening due to their proximity, regardless of semantic content8,17. Similar e3ects have 332 \nbeen observed in response to painful stimuli37 or unexpected, potentially threatening 333 \nacoustic stimuli 38. These results are  consistent with evidence that premotor neurons 334 \nsupport sensory-to-motor transformations1,6 and that threatening stimuli near the body 335 \nactivate cortical circuits involved in defensive behavio ur, which can bias or suppress 336 \nongoing motor output to maintain a margin of safety39. 337 \nPrior literature has already linked how human defensive systems relate to personality 338 \ntraits, most notably anxiety and fear40. Therefore, we explored the possibility of explaining 339 \nthe variability observed in our data using the self -assessed MISS questionnaire , as 340 \nresponses might also be influenced by endogenous (internal) traits. Our results 341 \nestablished suggestibility scores as a relevant predictor  of participants' timing in 342 \ninitiating APA (i.e. pm-RT). Specifically, participants with higher suggestibility showed 343 \nslower reactions, consistent with a “freeze” response rather than a fight or flight 344 \nreaction41. This pattern may reflect deeper engagement of the ventral auditory stream for 345 \ndecoding semantic content, which then modulates motor readiness in the dorsal 346 \npathway responsible for spatial and action processing 42. Moreover, the presence of 347 \nsemantic content systematically increased pm-RT across participants, but notably, 348 \nhighly suggestible individuals (normalised PHR scores > 1) did not di3erentiate their pm-349 \nRT between negative and positive semantic sounds. Once again, such a result might 350 \nindicate that increased suggestibility could a3ect  top-down modulation from semantic 351 \ninterpretations, uniformly impacting pre-frontal cortex excitability and potentially 352 \nreducing di3erentiation in motor responses based on sound valence12,43.  353 \nWhile the defensive -oriented role of PPS has been thoroughly explored, our results 354 \nextend previous findings by demonstrating that response dispersion measures are also 355 \nsignificantly modulated in the proposed within-PPS experimental design. Specifically, we 356 \nobserved that the presence of semantic content interacted with individual suggestibility, 357 \nsystematically influencing both response timing and precision. Recent studies have 358 \nindicated that predictability and emotional context can alter the sharpness or variability 359 \nof PPS boundaries44,45. In line with these observations, our data revealed that stimuli free 360 \nof semantic meaning elicited increased variability in participants with higher 361 \nsuggestibility, suggesting broader or less precise PPS representations when meaningful 362 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \ncontextual cues are absent. Conversely, when semantic context was present, response 363 \nvariability remained consistently higher irrespective of suggestibility, potentially due to 364 \nadditional cognitive processing demands related to decoding stimulus meaning and 365 \nemotional significance35. This aligns with neural findings indicating high dorsal auditory 366 \nstream engagement under conditions requiring explicit spatial evaluation without 367 \nsemantic clarity 28. Importantly, this modulation of residual dispersion emerged 368 \nexclusively in the pm-RT measure and not in the cognitively mediated estimated-distance 369 \ntask, likely because the slower, deliberate pointing response allows participants more 370 \ncognitive control, reducing perceived threat and urgency46. 371 \nDespite the extensive study of PPS using visual and tactile inputs, the potential of 372 \nauditory stimuli , especially in modulating motor readiness and spatial perception , 373 \nremains an open and promising avenue for investigation.  Our study highlights that 374 \nauditory information alone can e3ectively shape PPS representations through loudness-375 \nbased distance cues. Future research should further refine auditory simulations, 376 \nincorporating realistic spectral and binaural cues beyond intensity changes to better 377 \napproximate real-world acoustic dynamics 30,47. Moreover, given the known interactions 378 \nbetween interoceptive accuracy and PPS boundaries 4, it would be valuable to integrate 379 \nphysiological measures (e.g., heartbeat tracking) into our paradigm to examine how 380 \nindividual di3erences in interoceptive sensitivity modulate auditory PPS processing and 381 \npm-RT. Additionally, our personalised auditory rendering approach lends itself naturally 382 \nto implementations within immersive virtual reality13. Within these immersive scenarios, 383 \nfuture studies could systematically investigate not only the multisensory integration of 384 \nvisual and auditory stimuli but also how imaginative suggestibility and sense of presence 385 \ninfluence PPS boundary plasticity 48. Exploring emotional processing further, dynamic 386 \nmeasures of postural control 49 alongside pm-RT and PPS measurements could clarify 387 \nhow a3ective auditory stimuli shape behaviours (e.g., freezing or avoidance) within PPS16. 388 \nLastly, future investigations through neuroimaging methods (EEG/MEG) could examine 389 \nhow auditory \"what\" and \"where\" auditory pathways28 interact to modulate sensorimotor 390 \nintegration in response to looming auditory stimuli 33. Our Bayesian analyses and 391 \nresidual-based methodologies would provide e3ective tools for exploring how individual 392 \ndi3erences (e.g., personality, anxiety, interoceptive sensitivity) and stimulus properties 393 \njointly determine the variability in these neural and behavio ural responses, o3ering 394 \ndeeper insights into the underlying mechanisms shaping PPS representations. 395 \n 396 \nConclusions 397 \nOur findings demonstrate that auditory looming stimuli within the PPS  e3ectively 398 \nmodulate postural adjustments and distance estimation, influenced by both exogenous 399 \n(auditory distance and a3ective  content) and endogenous (suggestibility) factors.  We 400 \nconfirmed that stopping distances impacted our measures: as sounds stopped farther 401 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nwithin peripersonal space, participants responded more quickly and estimated distance 402 \nto be closer to the body . Instead, semantic information impacted experimental 403 \nmeasures, slowing motor responses and increasing the perceived auditory stopping 404 \ndistance, likely reflecting additional neural processing demands to address the stimuli’s 405 \nsemantic content. Notably, individual suggestibility shaped both reaction timing and  its 406 \ndispersion, highlighting the importance of considering internal personality traits when 407 \nstudying sensorimotor integration. These findings reinforce the dynamic and 408 \npersonalised nature of PPS representations, extending our understanding of how the 409 \nhuman brain integrates sensory, cognitive, and personality-driven processes to organise 410 \ndefensive and motor behavio urs in response to approaching stimuli.  In doing so, our 411 \nstudy extensively explores the suggestibility role in sensorimotor integration with the aim 412 \nof providing a blueprint for future research linking other personality factors, such as the 413 \nBig Five personality traits50, to perception-action coupling within the PPS.  414 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n4. Methods 415 \nParticipants 416 \nThirty-three right-handed adults ( 17 women; mean age: 23.4 ± 3.15 years) with self -417 \nreported normal hearing and no known neurological or musculoskeletal impairments 418 \nvolunteered for the study. Before participation, written informed consent was obtained 419 \nfrom each individual. The study protocol was approved by the Ethics Committee of the 420 \nDepartment of Neurosciences, Biomedicine, and Movement Sciences at the University 421 \nof Verona, and all procedures were carried out in accordance with the Declaration of 422 \nHelsinki. 423 \nStimulus generation  424 \nThree auditory stimuli were selected to represent distinct a3ective categories: Applause 425 \n(positive valence), Dentist Drill (negative valence), and Pink Noise (neutral control). The 426 \nemotional sounds —Applause (IADS ID 351) and Dentist Drill (IADS ID 719) —were 427 \nsourced from the International A3ective Digitised Sounds (IADS) database, which 428 \nprovides standardised ratings of valence and arousal25. The neutral stimulus, Pink Noise, 429 \nwas synthetically generated using MATLAB’s audio toolbox. Each stimulus was band -430 \npass filtered (0.25–9.5 kHz) and RMS amplitude-normalised using the Pink Noise level as 431 \nreference to ensure consistent loudness and spectral content across stimuli.  432 \nTo simulate dynamic proximity, all sounds were transformed into looming stimuli through 433 \namplitude modulation by applying the inverse-square law of sound intensity decay , 434 \nproducing an exponentially rising intensity that mimicked  a sound source approaching 435 \nthe body 29. Specifically, the  stimulus intensity 𝐼 followed the physical relation 𝐼\t ∝ \t\n!\n\"!, 436 \nwith 𝑑 being the simulated distance between the source and the listener.  437 \nEach looming sound began at a simulated distance of 2.8 m , maintained  a constant 438 \nvelocity of 0.7 m/s, and stopped at one of five predefined simulated endpoints within the 439 \nauditory PPS (0.3, 0.4, 0.5, 0.6, and 0.7 m) 13,14. The corresponding sound level increased 440 \nfrom 65 dBA to 95 dBA across the trajectory7,13. A 15-ms raised cosine onset ramp and 20-441 \nms o3set ramp were applied to each stimulus to suppress acoustic startle responses 442 \nand avoid o3-response artefacts. 443 \nApparatus 444 \nThe experimental procedures were conducted in the Biomechanics Laboratory of the 445 \nDepartment of Neurological, Biomedical and Movement Sciences, University of Verona. 446 \nMotion data were collected using a VICON MX Ultranet motion capture system (Oxford 447 \nMetrics, UK), comprising eight Vicon MX13 cameras operating at a 250 Hz sampling rate. 448 \nReflective markers were placed on the head (midsagittal plane), shoulders, and index 449 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nfingers to record postural and gestural kinematics. Custom MATLAB scripts enabled the 450 \nautomation of the experimental procedure and data collection.  451 \nElectromyographic (EMG) data were acquired from the erector spinae (ES) muscles using 452 \nthe ZeroWire EMG system ( Aurion, Italy ), with a sampling rate of 2000 Hz. The EMG 453 \nmultichannel analogue output was synchronised with the motion capture system via the 454 \nVicon MX control interface. 455 \nAuditory stimuli were delivered through Hefio One in-ear headphones, a research 456 \nprototype that  featured individual calibration of the ear canal for enhanced auditory 457 \nrendering (refer to Geronazzo et al. 2023 for technical specifications13). An external audio 458 \ninterface (Sa3ire LE, Focusrite, UK) was used to amplify the audio signal. Sound intensity 459 \nwas calibrated using a CESVA SC-2c sound level meter, and playback was synchronised 460 \nwith kinematic data via the Vicon system.  The Vicon MX control interface  guaranteed 461 \nsynchronised recording of the audio signal with the motion capture and EMG data 462 \nstreams. 463 \n1. Procedure 464 \nTask 1: Reactive Task 465 \nParticipants were  positioned at the centre of the recording space and  blindfolded 466 \nthroughout the entire task execution to reduce visual interference in the responses. They 467 \nsat upright with their hands resting comfortably  along their sides . With this task, we 468 \nmeasured APAs to quantify the timing of feedforward control in action anticipation, i.e., 469 \nchanges in muscle activation that occur before the initiation of a voluntary movement51. 470 \nTo elicit APAs, participants were instructed to quickly raise their arms forward in response 471 \nto the cessation of each sound, triggering postural perturbations caused by dynamic 472 \nintersegmental forces that shift the body’s center of mass forward, thus requiring 473 \npreparatory muscle adjustments to maintain vertical posture. Auditory stimuli, designed 474 \nto simulate looming sound sources, were presented binaurally and stopped at five 475 \nsimulated distances from the participant’s head. Each sound was presented with five 476 \nrepetitions per distance, resulting in a total of 75 randomised trials (3 sounds × 5 477 \ndistances × 5 repetitions). Each block of 25 trials was followed by a 2-minute rest interval 478 \nto minimise fatigue. Prior to the test, a training phase comprising 10 trials (5 distances as 479 \nin the main tasks × 2 repetitions) with complex tones (100, 450, 1450, 2450 Hz) was 480 \nconducted to familiarise participants with the response paradigm. 481 \nTask 2: Auditory Distance Estimation 482 \nThe second task was made to assess the spatial perception of auditory stimuli. The same 483 \nthree sound types and five distances were presented (4 repetitions per condition; 60 trials 484 \ntotal) in randomised order. Still blindfolded, participants were instructed to extend their 485 \ndominant upper arm horizontally as a proprioceptive metric for sound distance, with the 486 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nshoulder being the most proximal point of reference and the middle fingertip as the most 487 \ndistal point of reference. Then, participants were instructed to approach  their 488 \nnondominant index finger towards the dominant upper arm until it physically reached the 489 \nperceived stopping position. Experimental blocks, rest intervals, and training procedure 490 \nwere identical to Task 1.  491 \nTask 3: A*ective Evaluation of Sounds 492 \nThe third task assessed the subjective emotional evaluation of the stimuli. The blindfold 493 \nwas removed, and participants used a touchscreen laptop with a screen of 15 inches to 494 \nprovide a3ective ratings using the Self -Assessment Manikin (SAM) scale 26. Each 15 495 \nstimulus (3 sounds × 5 distances) was rated twice for two a3ective dimensions: Valence 496 \n(1 = most negative; 9 = most positive) and Arousal (1 = least arousing; 9 = most arousing). 497 \nStimuli were presented in randomised order across all combinations of sound type and 498 \ndistance.  499 \nTask 4: MISS  500 \nTo gauge individual suggestibility to embodied auditory cues, participants were asked to 501 \nrespond to the Multidimensional Iowa Suggestibility Scale (MISS )23, a validated self -502 \nreport inventory that assesses trait suggestibility across several domains rather than a 503 \nsingle context (e.g., hypnosis). Among its five subscales, we focused on Physiological 504 \nReactivity (PHR), a 13-item measure of automatic bodily responses to internal or external 505 \ncues (e.g., “Thinking about something scary can make my heart pound”), because it most 506 \nclosely aligns with our perceptual tasks. 507 \n2. Data Analysis and Preparation 508 \nTask 1: Premotor Reaction Time  509 \nFor each trial, the p remotor reaction time ( pm-RT) defined the initiation of muscle 510 \ncontraction (i.e. the timing of feedforward motor commands) by measuring the interval 511 \nbetween the auditory stimulus o3set and the onset of muscle activity in the erector 512 \nspinae, recorded bilaterally 13–15. The end of the auditory stimulus (trigger o3set) was 513 \ndetermined from the analogue trigger signal recorded during playback. After DC o3set 514 \ncorrection (mean of the first 200 samples), the signal was scanned to detect the global 515 \npeak, followed by the signal descent below a dynamic threshold set at 10% of the peak 516 \namplitude. Instead, erector spinae onset was identified from EMG signals first low-pass 517 \nfiltered at 200 Hz (4th-order Butterworth) and then rectified. The envelope was computed 518 \nvia a secondary low -pass filter at 0.02 Hz (5th -order Butterworth). A dynamic threshold 519 \nfor activation was set as the mean baseline activity plus three standard deviations, 520 \ncalculated over a 100-sample window (i.e., 50 ms at 2000 Hz sampling rate) immediately 521 \npreceding movement onset14,51.  522 \nTask 2: Distance estimation  523 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nTo estimate perceived sound location, we computed the Euclidean 3D distance between 524 \nthe participant’s pointing index finger and the ipsilateral shoulder, based on VICON 525 \nmotion capture data recorded during the final posture of the distance estimation task. 526 \nThe analysis window began 3 seconds after the sound o3set , capturing the response 527 \nphase. Within this window, a 15-sample moving average filter was applied to reduce 528 \nmeasurement noise, and finger stabilisation was identified as periods where the finger’s 529 \n3D velocity remained below 0.01 m/s for at least 40 ms13. The final distance estimate was 530 \ncalculated as the Euclidean distance between the mean position of the stabilised 531 \npointing finger and the dominant-side shoulder. 532 \nTask 3: A*ective ratings 533 \nFor each participant, the valence and arousal ratings collected by employing the SAM 534 \nmannequin were averaged over repetitions, yielding one valence and one arousal score 535 \nper stimulus type and distance pair.  536 \nTask 4: PHR score 537 \nParticipants’ raw PHR scores were obtained by summing the 13 items (each rated 1 to 5), 538 \nwhich were used as a continuous predictor in the Bayesian models for Tasks 1 and 2. 539 \n3. Statistical analysis  540 \nThe statistical inference workflow combined frequentist and Bayesian approaches to 541 \nevaluate the e3ects of stimulus type and sound distance on behavioural and 542 \nphysiological responses. The section begins with an analysis of group -level di3erences 543 \nusing repeated-measures ANOVA. The analysis of di3erences is followed by Bayesian 544 \nlinear and generalised regression models tailored to the distributional characteristics of 545 \neach dependent variable, allowing us to quantify uncertainty and test directed 546 \nhypotheses on the relationships between measured quantities and experimental factors. 547 \nThen, we explore how suggestibility traits, as captured by the MISS questionnaire, 548 \nmodulate perceptual outcomes.  549 \nAnalysis of the di*erences 550 \nPrior to statistical analysis, measurements were averaged over repetitions to guarantee 551 \nstable estimates of individual performance and reduce the influence of trial -level noise 552 \nor outliers.  When reporting m eans and standard errors for each stimulus type , we 553 \ncomputed them using a correction method for within-subjects52. 554 \nFor the measurements of valence and arousal as well as for perceived distance metric, 555 \ngiven the lack of normality on the residuals, we run a  two-way aligned rank transform  556 \n(ART) ANOVA31 on with the within-subject factors: stimulus type (Applause, Dentist Drill, 557 \nPink Noise) and distance (.3,.4,.5,.6,.7 m). E3ect sizes (generalised eta squared, η²) were 558 \ncomputed for all significant e3ects. For significant main e3ects or interactions, post hoc 559 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \npairwise comparisons were conducted using sum -to-zero contrasts  with Tukey 560 \ncorrection for multiple comparisons.  Post hoc contrasts were performed using the ART 561 \nprocedure for contrasts (ART -C)32. This method allows for valid nonparametric 562 \ncomparisons of factor levels following an ART ANOVA by applying linear contrasts to 563 \naligned-and-ranked data, while preserving the structure of factorial designs.  564 \nStatistical evaluation for the pm-RT metric was performed following a two-way ANOVA 565 \nwith two within-group factors, with five levels of distance and three levels for stimulus 566 \ntype (as for the previously described metrics). The data were processed and normalised 567 \nper participant . Sphericity corrections ( i.e., Greenhouse-Geisser adjustment) were 568 \nintroduced, and linear model residuals followed the normality assumption (p > .05). 569 \nE3ect sizes (generalised eta squared, η²) were computed for all significant e3ects. Post 570 \nhoc comparisons were conducted using the estimated marginal means (EMMs) 571 \nframework to follow up significant main e3ects and interactions identified in the ANOVA. 572 \nPairwise contrasts were applied to the EMMs for both stimulus type and distance, with 573 \nadjustments for multiple comparisons using the Tukey method. Only contrasts with 574 \nstatistically significant adjusted p-values (p ≤ 0.05) were considered in the interpretation 575 \nof results. 576 \nBayesian Statistical Analysis 577 \nWith the aim of quantifying  the relationships between experimental factors and 578 \nmeasurements, we adopted a fully probabilistic modelling approach for inference based 579 \non Bayesian statistics27. For this analysis, we focused on the relationships between 580 \nperceived distance, pm-RT, sound type, and the PHR score.  581 \nModel design 582 \nBayesian models were fitted for each behavioural metric, mirroring the full factorial 583 \ndesign used in the ANOVA. This allowed for direct comparability between frequentist and 584 \nBayesian approaches and ensured that all models captured the key experimental 585 \nmanipulations: stimulus type, distance, and their interaction, along with random 586 \nintercepts for participant ID to account for repeated measures. 587 \nThe analysis of perceived distance  employed a Bayesian beta regression, which 588 \nappropriately accounted for the bounded and non -Gaussian nature of this measure 53. 589 \nPerceived distances were expressed by pointing on the dominant arm, resulting in values 590 \nbetween 0 and 1  m. Modelling both the mean and precision as a function of distance, 591 \nstimulus type, and their interaction enabled the assessment of how both perceived 592 \nproximity and response variability changed across conditions.  593 \nInstead, a  Bayesian linear regression with a Gaussian likelihood  described the pm-RT 594 \nvariability across experimental conditions . As with the distance model, this model 595 \nincluded the full factorial fixed e3ects and participant -level random intercepts and  596 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nmodelled the residual standard deviation as a function of the same predictors to account 597 \nfor possible heteroscedasticity.  598 \nAs a last step, w e incorporated the normalised PHR score alongside the existing fixed 599 \ne3ects (stimulus type, distance, and their interaction), as well as all two - and three-way 600 \ninteractions involving PHR. This extended structure was applied to both the mean \t𝜇 and 601 \nprecision 𝜙 (or residual 𝜎) components of the models, allowing the assessment of how 602 \nindividual di3erences in suggestibility influenced not only response magnitude but also 603 \nresponse variability. Using the Wilkison notation and defining 𝑓(∙) and 𝑔(∙) as linking 604 \nfunctions (i.e. identity for the Gaussian family, or 𝑙𝑜𝑔𝑖𝑡 and 𝑙𝑜𝑔 respectively for a Beta 605 \nregression53), the models followed this structure:  606 \n𝑓(𝜇) = \t (𝑆𝑡𝑖𝑚\t ∗ \t𝐷𝑖𝑠𝑡\t ∗ \t𝑃𝐻𝑅) + (1|𝐼𝐷), 607 \n𝑔(𝜙) = \t (𝑆𝑡𝑖𝑚\t ∗ \t𝐷𝑖𝑠𝑡\t ∗ \t𝑃𝐻𝑅) + (1|𝐼𝐷), 608 \nincorporating stimulus type (𝑆𝑡𝑖𝑚), distance (𝐷𝑖𝑠𝑡), and normalised PHR score (𝑃𝐻𝑅) as 609 \nfixed e3ects. Participant identifiers 𝐼𝐷 were included as a random intercept. 610 \nParameter estimation and model evaluation 611 \nModel’s parameters were estimated using four Markov Chain Monte Carlo (MCMC) 612 \nchains, with 3000 iterations per chain and 1000 warm-up steps. We increased iterations 613 \nto 4000 and 2000 warm-up steps for models including PHR scores to reach convergence. 614 \nSampling employed Stan, a probabilistic programming language for Bayesian inference 615 \nand statistical modelling54 and its default sampling scheme , No-U-Turn (NUTS), an 616 \nadaptive form of Hamiltonian Monte Carlo designed to e3iciently explore complex 617 \nposterior distributions without manual tuning 55. Default weakly informative priors 618 \n(normal distribution, mean = 0, SD = 1). Convergence diagnostics (R̂  < 1.01) and e3ective 619 \nsample sizes were verified for all parameters27.  620 \nModel adequacy was evaluated using posterior predictive checks, drawing between 2000 621 \nsamples from the fitted posterior distributions to compare model predictions with 622 \nobserved data across key experimental conditions. We compared the model’s average 623 \npredictions and how much they varied from the real data. This approach enables the 624 \npossibility to assess if the model reproduced the patterns observed in the data by visually 625 \ninspecting summary plots and by employing aggregated statistics. 626 \nProbabilistic queries 627 \nExperimental e3ects  were assessed  through probabilistic queries of the posterior 628 \ndistributions of fixed e3ects. Directional and comparative hypotheses were tested with 629 \nposterior probabilities for targeted hypotheses (e.g., whether the e3ect of stimulus Pink 630 \nNoise was reliably smaller than Dentist Drill in proximity estimation). Posterior 631 \ndistributions were summarised using means and 95% credible intervals (95%-CI), giving 632 \na clear range of plausible e3ect values 27. To further quantify certainty regarding the 633 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \ndirection of these e3ects, we computed the probability of direction, representing the 634 \nproportion of posterior samples consistent with the sign of the posterior mean. These 635 \nmetrics allowed us to interpret e3ect magnitudes and robustness without relying on 636 \nbinary significance thresholds. 637 \nTo facilitate meaningful comparison between models using di3erent likelihood families 638 \n(i.e., Gaussian versus Beta regression), we standardised measures of variability. 639 \nSpecifically, for the beta regression, we computed the posterior standard deviation from 640 \nthe precision parameter to allow consistent interpretation of variability across di3erent 641 \ndistributional families53. 642 \nWe computed marginal e3ects to quantify how changes in predictors influenced 643 \noutcome variables across their posterior distributions. For continuous predictors (e.g., 644 \ndistance), we estimated average marginal slopes, reflecting the instantaneous rate of 645 \nchange in the outcome per unit increase in the predictor. For categorical predictors (e.g., 646 \nstimulus type), we computed average contrasts between categories, summarising 647 \ndi3erences in predicted outcomes across experimental conditions. Marginal e3ect 648 \nestimates were summarised as posterior means and 95%-CI, providing robust 649 \npopulation-level inferences integrated over observed covariate distributions. 650 \nFinally, we conducted post hoc exploratory analyses on model -derived parameters not 651 \ndirectly estimated within the primary Bayesian models. Specifically, we extracted 652 \nparticipant-level model estimates or their dispersion from posterior draws and then 653 \nanalysed their relationship with individual -level covariates (e.g., standardised PHR 654 \nscores). For this procedure, we sampled 1000 draws and summarised them with mean 655 \nand standard deviation over levels. These analyses allowed us to explore whether 656 \nparticipant di3erences in suggestibility explained systematic variation in model 657 \nuncertainty or sensitivity to experimental manipulations.  658 \n4. Software tools 659 \nData acquisition and preprocessing were conducted using custom -written scripts in 660 \nMATLAB. All subsequent statistical analyses were performed in R (version 4.4.2). For data 661 \nmanipulation and visualisation, we used packages including data.table, ggplot2, car, and 662 \nRmisc. Frequentist analyses (e.g., repeated -measures ANOVA, post hoc contrasts, and 663 \ne3ect size estimation) were implemented using afex, ARTool, emmeans, and e>ectsize. 664 \nBayesian analyses relied on Stan via the interfaces provided by brms and rstan, 665 \ncomplemented by marginale>ects, tidybayes, and posterior for marginal e3ects 666 \nestimation, posterior exploration, model checking, and hypothesis evaluation. 667 \n  668 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \nFunding declaration 669 \nThis study was financially supported by the European Community Program 670 \nNextGenerationEU in the form of a grant (PNRR M4-C2.1.1 PRIN 2022 project “S-TWIN” - 671 \n533 2022F9FWZ8 003 - CUP G53D23002840006) received by MG and PC. 672 \nAcknowledgements 673 \nFigures 1a, 2a, and 3a include icons designed using resources from Flaticon.com. 674 \nAuthor Contributions 675 \nRoberto Barumerli:  Formal analysis; Visuali sation; Writing – original draft; Writing – 676 \nreview & editing.  677 \nMichele Geronazzo: Funding acquisition; Conceptuali sation; Methodology; Data 678 \nAcquisition; Writing – review & editing. 679 \nPaola Cesari: Funding acquisition; Supervision; Conceptuali sation; Methodology; 680 \nWriting – review & editing. 681 \nAll authors have read and approved the submitted version of the manuscript and agree 682 \nto be personally accountable for their own contributions and for ensuring the integrity of 683 \nany part of the work. 684 \nConflict of Interest 685 \nThe authors declare no competing financial or non-financial interests. 686 \nAdditional Information 687 \nThe authors declare that they have no competing financial or non -financial interests in 688 \nrelation to this work. 689 \nData availability 690 \nThe datasets for the current study, as well as the analysis scripts, are available from the 691 \ncorresponding author on request.  692 \n 693 \n  694 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint \n\n \n \n55. Hoffman, M. D. & Gelman, A. The No -U-Turn Sampler: Adaptively Setting Path 828 \nLengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research  15, 1593–829 \n1623 (2014). 830 \n 831 \n 832 \nLegends 833 \nFigure 1: characteristics of auditory stimuli and their a3ective evaluation. (a) Intensity 834 \ncurve of the experimental stimulus (vertical bars represent the five virtual stopping 835 \ndistances) along both time and distance axes. The participant’s ear canal was positioned 836 \nat zero distance. (b) Scatterplot of mean arousal ratings as a function of mean valence 837 \nratings for three auditory stimuli. Error bars depict the standard error of the mean for both 838 \nvalence and arousal over participants. 839 \nFigure 2 : d istance estimation task and perceived distance of looming sounds . (a) 840 \nVisualisation of a participant using her arm to provide the perceived final distance of the 841 \npresented stimulus. (b) Estimated response of distance as a function of simulated ending 842 \ndistance for three auditory stimuli (Pink Noise, Applause, and Dentist Drill). Shaded areas 843 \nrepresent 95% credible intervals of Bayesian beta regression while error bars report 844 \nstandard errors computed over participants. (c) Response dispersion  as a function of 845 \ndistance, displayed with credible intervals at 50, 80 and 95% levels. 846 \nFigure 3: premotor reaction time (pmRT) and its timing variability for looming sounds. (a) 847 \nVisualisation of the participant raising her arm after the stimulus presentation . (b) 848 \nPremotor reaction times as a function of simulated ending distance for three auditory 849 \nstimuli (Pink Noise, Applause, and Dentist Drill).  Shaded areas represent 95% credible 850 \nintervals of Bayesian regression and error bars representing standard errors over 851 \nparticipants. (c) Timing dispersion (i.e. standard deviation)  as a function of distance, 852 \ndisplayed with credible intervals at 95% levels.  853 \nFigure 4: influence of suggestibility on premotor reaction time and its dispersion. a) The 854 \nplot shows the linear relationship between normalised PHR score and pm-RT response 855 \nfor three stimulus categories. b) The plot reports the e3ect of the normalised PHR score 856 \non response dispersion is shown separately for three categories. Lines represent linear 857 \nregressions, and shaded areas indicate the standard deviation of slope estimates . PHR 858 \nscore is reported on a normalised scale (arbitray unit – a.u.). 859 \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}