Motor Planning Sensitivity to Affective Looming Sounds Within The Peri-personal Space: An Interplay of Exogenous and Endogenous Influences

10 Our brain maps the space immediately surrounding the body, the peripersonal space 11 (PPS), to sharpen sensory-motor coordination whenever an object enters it. Within PPS, 12 past research demonstrated how several factors influence motor readiness: from 13 exogenous factors, such as body -object distance and stimulus semantics, to 14 endogenous traits like personality traits. Nevertheless, most paradigms rely on vision or 15 touch, relegating hearing to a supporting role and leaving auditory -only contributions 16 unclear. Here, we tested whether a3ective content and individual traits modulate motor 17 planning for looming sounds that stop within PPS. Thirty -three adults completed three 18 auditory-only tasks in which positive, negative, or neutral sounds halted at five simulated 19 distances from the participant's ears (0.3 –0.7 m). We recorded anticipatory postural 20 adjustments, distance estimates, a3ective ratings, and sensory suggestibility via a 21 questionnaire. Motor responses were largely anticipated as sounds stopped nearer the 22 body, while delayed and less precise for semantic (positive or negative) than neutral 23 sounds. Higher suggestibility predicted longer and more variable premotor latencies, 24 particularly for non -semantic sounds. These findings show that auditory cues alone 25 engage flexible sensorimotor mechanisms within PPS, where exogenous (distance, 26 semantics) and endogenous (suggestibility) factors jointly shape motor readiness and 27 spatial perception. 28

Peripersonal space, Auditory looming stimuli , Spatial hearing, Anticipatory 29 postural adjustments, Semantic modulation, Sensory suggestibility 30 .CC-BY 4.0 International licensemade available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint 1. Introduction 31 To successfully navigate the environment, the brain maintains a dynamic representation 32 of the body's surrounding space by integrating sensory perception with action planning1. 33 This representation, known as peripersonal space (PPS), enables rapid responses to 34 potential threats , such as sidestepping an unseen car , and facilitates goal -directed 35 behaviours, like catching a buzzing mosquito in the dark. Importantly, perception-action 36 coupling within PPS boundaries is not fixed : they are shaped by both external 37 environmental factors, such as the characteristics of stimuli entering the PPS (e.g., their 38 nature, valence, or social relevance), and internally driven factors, including 39 interoceptive accuracy and personality traits2. Prior literature o3ers a wide range of 40 methodologies to investigate the factors influenced by the PPS, from reactive tasks that 41 measure physiological responses probing underlying neural mechanisms to decision -42 making tasks that engage higher cognitive functions 3. However, these exogenous and 43 endogenous factors (i.e. environment-dependent configurations such as object distance 44 and internal traits such as personality traits) and their e3ects on either low -level 45 perceptual processes or high -level cognitive functions are often studied in isolation, 46 limiting our understanding of how the brain operates as an integrated system4. Therefore, 47 this study leverages auditory looming stimuli entering the PPS to gather evidence from 48 physiological measurements, behavioural responses, and self-reported personality traits 49 to examine how endogenous and exogenous factors modulate participants’ responses. 50 When an object enters the PPS, the brain must infer object characteristics from sensory 51 inputs to enable a coherent interaction2. Much of our understanding of PPS comes from 52 multisensory research, demonstrating that auditory, visual, and tactile cues converge to 53 guide motor responses3. While hearing is often used as a facilitator and in support of 54 touch and vision 5,6, it is unclear how hearing alone provides a proxy for studying the 55 perception-action integration of objects entering the PPS. Among these, looming sounds 56 are particularly salient due to their ability to signal potential threats from a distance 7. 57 Empirical evidence shows that approaching sounds enhance spatial representation and 58 trigger fast er motor responses than those elicited by receding sounds 8,9. Moreover, a 59 sound’s meaning can influence the PPS’s size: negative sounds expand this boundary, so 60 our defensive system is triggered by objects farther away than those perceived as neutral 61 or positive sounds10. While these findings highlight the influence of both temporal, spatial 62 features and semantic meaning on PPS, the role of individual variability in shaping 63 auditory-motor coupling remains largely unexplored. 64 Anticipatory postural adjustments (APAs) o3er a promising approach to investigate 65 perception–action mechanisms, revealing how motor commands from the central 66 nervous system prepare the body as a stimulus enters the peripersonal space11. APAs are 67 early muscle activations that stabilise posture in preparation for movement. Their timing 68 and contraction strength reflect the duration and facilitation that the central nervous 69 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint system requires to process a perceptual event and initiate a motor response through the 70 pre-motor corte x’s top -down modulation 12. Unlike methods such as transcranial 71 magnetic stimulation, hand -blink reflex elicitation, or electroencephalogram, APA 72 measurement captures neural dynamics during natural movement, making it a more 73 ecological and functionally relevant tool for studying sensorimotor integration in 74 ecological environments 13. In the auditory domain, APAs reveal feedforward motor 75 responses: when looming sounds stop closer within the peripersonal space, APA onset 76 occurs earlier, but sounds stopping outside PPS produce no such e3ect, demonstrating 77 that this timing modulation is specific to stimuli entering the PPS14. Moreover, looming 78 sounds with semantic content show valence -specific lateralisation: negative sounds 79 strongly trigger APAs, reflecting a tight coupling between auditory perception and motor 80 readiness, likely due to preferential engagement of the left motor cortex for unpleasant 81 sounds and the right for pleasant ones 15–17. However, existing studies have primarily 82 focused on group-level e3ects, leaving open the question of how individual di3erences 83 modulate APA timing within PPS boundaries. 84 Recent work investigating the link between perception and action shows its flexibility 85 within PPS: sensory information is uncertain, therefore, people can actively interpret 86 what they sense 18. With such ambigu ities, personal tendencies become evident : for 87 instance, people with a high trait of anxiety lean more on their previous expectations 88 when judging unclear motion, making their trait di3erences observable in perceptual 89 decision making19. With regard to PPS flexibility, trait-level factors highlight this plasticity: 90 higher trait anxiety enlarges PPS, as indicated by a stronger hand -blink reflex when the 91 hand nears the face 20, whereas greater interoceptive accuracy narrows it, sharpening the 92 boundary between the embodied self and the external world 4. Although such studies 93 relate PPS size to self -reported personality traits, they reveal little about how the 94 perception-action coupling itself varies across individuals within PPS boundaries. Clues 95 come from multisensory illusions within PPS, where highly suggestible people are more 96 susceptible to the rubber -hand illusion 21, possibly because enhanced multisensory 97 integration and attentional engagement magnify their responsiveness 22. When 98 considering sensory suggestibility , defined as a person’s susceptibility to external 99 sensory cues23, highly suggestible individuals might show di3erential activation within 100 sensory motor pathways, especially auditory processing brain regions linked to spatial 101 localisation and action preparation 24. Together, these findings indicate that personal 102 traits shape not only how far PPS extends but also how we prepare and act when stimuli 103 enter this space. 104 In this study, we investigated how participant responses are modulated by endogenous 105 and exogenous factors in response to looming auditory stimuli entering the PPS. We 106 combined motor planning measures with an explicit distance estimation task. 107 Participants were presented with three looming sounds, selected from the International 108 A3ective Digitized Sounds (IADS -2) database25, in addition to the pink noise stimulus , 109 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint each stopping at five simulated distances within PPS. The a3ective properties of these 110 sounds were validated using the Self -Assessment Manikin (SAM) 26, while the 111 Multidimensional Iowa Suggestibility Scale (MISS) quantified individual sensory 112 suggestibility23. This dual approach allowed us to capture both rapid, pre -movement 113 postural adjustments and conscious spatial judgments. To accommodate individual 114 contributions of reaction times and perceptual estimates, we employed a Bayesian 115 statistical framework, including beta regressions, thereby o3ering a flexible and robust 116 analysis strategy that can be potentially extended to other dynamic perception -action 117 paradigms27. By integrating neurophysiological measurements, crucial for understanding 118 motor control in the premotor cortex , with explicit distance estimates, we aimed to 119 elucidate how individual traits shape the interplay between sensory processing, decision 120 making, and motor execution. 121 Therefore, we formulated two main hypotheses: (H1) when looming sounds entering the 122 PPS carry semantic content (for example, a baby crying) versus no meaning (pink noise), 123 experimental measures will show di3erent levels of congruencies. In the cognitive 124 distance estimation task , semantic sounds should be overestimated, whereas in the 125 physiological task, the onset of initial muscle activation should be delayed, reflecting 126 extra processing needed to decode meaning 28. Second, we hypothesised that (H2) part 127 of the inter-individual variability of the acquired physiological and cognitive data could be 128 explained through the individual quantified level of sensory suggestibility, particularly 129 when the incoming auditory stimuli carried distinct semantic connotations. 130 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint 2. Results 131 (a) (b) Figure 1: characteristics of auditory stimuli and their affective evaluation. (a) Intensity curve of the experimental stimulus ( vertical bars represent the five virtual stopping distances) along both time and distance axes. The participant’s ear canal was positioned at zero distance. (b) Scatterplot of mean arousal ratings as a function of mean valence ratings for three auditory stimuli. Error bars depict the standard error of the mean for both valence and arousal over participants. 132 With the general aim to investigate how auditory a3ective stimuli and individual 133 suggestibility influence perceptual, spatial, and motor responses within the peripersonal 134 space, we collected data from 33 participants (17 females; mean age = 23.3±3.2 years) 135 who completed three auditory tasks and filled out a personality questionnaire. 136 Participants self -reported normal hearing and no neurological or musculoskeletal 137 impairments, a ll gave written informed consent, and the study was approved by the 138 Ethics Committee of the University of Verona. 139 The tasks assessed distinct perceptual and behavioural responses to three auditory 140 stimuli: two a3ective sounds selected from the IADS2 database25 (one with positive 141 valence, ID 351 – Applause, and one with negative, ID 719 – Dentist Drill) and a neutral 142 control stimulus ( Pink Noise) without semantic content. An amplitude envelope 143 following the inverse-square law was applied to simulate approaching sound sources29,30, 144 starting 2.8 meters from the participant and moving at 0.7 m/s. Five stopping distances 145 were defined relative to the participant's ear, ranging from 0.7 to 0.3 meters in 0.1-meter 146 steps (see Fig. 1a). 147 Data were collected at the Biomechanics Laboratory of the University of Verona using 148 motion capture, electromyography (EMG), and audio delivery systems. Kinematic data 149 were recorded at 250 Hz using a VICON MX Ultranet system with reflective markers 150 placed on the head, shoulders, and index fingers. EMG signals were recorded from the 151 erector spinae muscles at 2000 Hz using a ZeroWire EMG system, synchronised with the 152 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint motion capture via the Vicon control interface. Auditory stimuli were delivered through 153 individually calibrated headphones, while the acoustic intensity was calibrated with a 154 sound level meter. All data streams (including motion, EMG, and audio ) were 155 synchronised to ensure precise temporal alignment (see Methods for further details). 156 To combine neurophysiological data, behavioural measures, and self -reported 157 suggestibility in a Bayesian model, we report results in three steps: (i) a3ective ratings of 158 the sounds, (ii) estimates of each stimulus’s stopping distance, and (iii) reactive arm 159 movements captured by EMG. The outcomes from distance estimation and muscle 160 contraction timing are then related to individual scores of self-reported suggestibility 161 obtained from the MISS questionnaire23. 162 A*ective evaluation of looming sounds. The a3ective evaluation was conducted using 163 the SAM mannequin26, following the original methodology of the IADS2 25, yielding strong 164 concordance between our participants' ratings and the original scores (see Fig.1b). An 165 Aligned Rank Transform (ART) ANOVA31 revealed significant di3erences in valence across 166 stimulus categories (F(2, 405) = 303.56, p < .001) , but neither the distance factor (F(4, 167 405) = 1.99, p = .096) nor the interaction between stimulus category and distance showed 168 significant e3ects (F(8, 405) = .61, p = .767). The partial eta-squared for the stimulus type 169 was ηp2=.6. Post-hoc contrasts computed by employing ART-C tests with Tukey 170 correction32 indicated that valence ratings di3ered significantly among all three sounds, 171 with negative stimuli rated lowest, neutral stimuli intermediate, and positive stimuli 172 highest ( see Fig .1b; all p < .001). Importantly, these di3erences in the valence score 173 follow the intrinsic a3ective salience of the stimuli, as observed in the original work25. 174 Similarly, statistical analysis of arousal values indicated significant main e3ects for both 175 stimulus categories (F(2, 405) = 5.40, p = .005 , ηp2=.03) and stopping distance (F(4, 405) 176 = 5.09, p < .001, ηp2=.05), but not for their interaction (F(8, 405) = .63, p = .751). Post-hoc 177 analysis revealed that the neutral stimulus was rated lower in arousal than the negative 178 stimulus (p = .003), and the positive stimulus did not di3er from both the neutral and 179 negative stimuli (see Fig.1b; all p > .05). This supports the notion that perceived intensity 180 modulates arousal and replicates findings that auditory intensity acts as a salient motion 181 cue mediating the e3ects of looming sounds 8. Further, all three sounds had tightly 182 clustered arousal ratings (mean 4.65 ± 0.96), a much narrower spread than the full IADS 183 arousal spectrum (mean 6.23 ± 2.16), ensuring comparable arousal across conditions. 184 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint (a) (b) (c) Figure 2 : distance estimation task and perceived distance of looming sounds . (a) Visualisation of a participant using her arm to provide the perceived final distance of the presented stimulus. (b) Estimated response of distance as a function of simulated ending distance for three auditory stimuli (Pink Noise, Applause, and Dentist Drill). Shaded areas represent 95% credible intervals of Bayesian beta regression while error bars report standard errors computed over participants . (c) Response dispersion as a function of distance, displayed with credible intervals at 50, 80 and 95% levels. 185 Stopping distance evaluation of looming sounds . The task required participants to 186 estimate the distance between the body and the sound at its endpoint using their left arm 187 as a scale as shown in Fig. 2a. Given the bounded support for responses that resulted in 188 lack of normality, we ran a repeated-measures ART-ANOVA that revealed significant main 189 e3ects of distance [F(4, 480) = 381.57, p < .001], stimulus type [F(2, 480) = 9.75, ηp2 = .27, 190 p < .001], and their interaction [F(8, 480) = 2.88, ηp2 = .05, p = .004]. Post-hoc comparisons 191 (ART-C tests with Tukey correction 32) indicated that participants successfully 192 discriminated ending distances across acoustic stopping points (mean ± se: .047 ± .004 193 m, p < .001 across distance levels). Further, neutral stimuli elicited significantly nearer 194 estimates (.261 ±.008 m) than both positive (.314 ± .006 m) and negative sounds (.309 ± 195 .014 m), with no significant di3erence between positive and negative stimuli (p > .05). 196 Such results demonstrate systematic di3erences in responses when grouping them by 197 semantic category suggesting an interaction between auditory object recognition and 198 sound localisation33. 199 To further interpret these patterns in distance perception, we fitted a Bayesian beta 200 regression model with weakly informative priors (see Sec.4.3), which supported the 201 frequentist findings (see ribbons in Fig.2b) . The posterior distribution for the slope of 202 distance (.443 1/m, 95% -credible interval ( CI) [.409, .478]) confirmed with strong 203 evidence the participants' ability to discriminate changes in auditory distance, with the 204 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint (a) (b) (c) Figure 3: premotor reaction time (pmRT) and its timing variability for looming sounds . (a) Visualisation of the participant raising her arm after the stimulus presentation. (b) Premotor reaction times as a function of simulated ending distance for three auditory stimuli (Pink Noise, Applause, and Dentist Drill). Shaded areas represent 95% credible intervals of Bayesian regression and error bars representing standard errors over participants. (c) Timing dispersion (i.e. standard deviation) as a function of distance, displayed with credible intervals at 95% levels. 205 95%-CI not spanning zero. Additionally, systematic overestimations were observed for 206 positive stimuli (.048 m, 95%-CI [.038, .058]) and negative stimuli (.045 m, 95%-CI [.034, 207 .055]) compared to neutral stimuli. The Bayesian model allowed us to analyse response 208 dispersion (i.e. empirical standard deviation visualised in Fig.2c) across experimental 209 factors (distance and stimulus), demonstrating that dispersion linearly decreases with 210 distance (-.032 1/m, 95%-CI [-.062, -.005]; p(v < 0) = .986) , equating to a 1 cm decrease 211 over the distance interval. This e3ect could stem from participants compressing their 212 estimates near the shoulder because the arm’s finite length limits the measurement 213 range. 214 Premotor reaction time (pm-RT). In this task, we asked participants to raise their arms 215 as quickly as possible after the perceived end of the looming sound to measure postural 216 adjustments as the contraction initiation of postural muscles14 (as shown in Fig.3a). We 217 derived the premotor reaction time (pm -RT) as the activation time of the erector spinal 218 muscles, and the analysis of variance (ANOVA) demonstrated significance for main 219 e3ects of stimulus type (F(2, 480) = 90.754, p < .001) and distance (F(4, 480) = 381.571, p 220 < .00 1), and their interaction (F(8, 480) = 2.88 1, p < .004). Similarly, for the perceived 221 distance, pairwise t-tests with a Tukey correction revealed significantly faster pm -RT for 222 neutral (176 ± .003s) against both positive (0.187 ± 0.002; p = .028) and negative sounds 223 (0.191 ± .003; p < .001), highlighting the easier elaboration of such sounds (i.e., no need 224 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint to identify the content); instead, the contrast between positive and negative remained 225 inconclusive within the presented experimental data (p > .05). 226 We further investigated the trends using a Bayesian linear model (see Sec.4.3), which 227 supported and extended the ANOVA findings (see Fig.3b). First, the estimated posterior 228 distribution for the slope on the distance predictor was positive for neutral sounds (slope 229 0.046 1/s, 95% -CI [ .031, .061]), indicating that greater distance is associated with 230 increased pm-RT. Negative sounds systematically increased pm-RT timing compared to 231 neutral sounds (intercept .010 s, 95% -CI [ -.001, .022], p(v > 0) = .930), and positive 232 sounds positively interacted with the distance term ( .030 1/s, 95%-CI [.006, .054], p(v > 233 0) = .979). Second, we analysed how the variability of pm -RT, measured as standard 234 deviation, changed across experimental factors (visualised in Fig.3c). For neutral sounds, 235 distance introduced a negative e3ect (-.546s, 95%-CI [-.265, .164], p(v < 0) = .899), similar 236 to the perceived distance, where uncertainty decreases for far distances. In contrast, 237 semantic sounds showed increased uncertainty over distance, both for positive (1.201 238 1/s, 95%-CI [.188, 2.217], p(v > 0) = .975) and negative sounds ( .983 1/s, 95%-CI [-.002, 239 1.961], p(v > 0) = .950). 240 Suggestibility trait modules perception . Twenty-nine participants completed the 241 Physiological Reactivity (PHR) subscale from the MISS questionnaire, a 13-item 242 measure23. Unanticipated events during the experimental procedure led to incomplete 243 data for four participants, which could not be recovered post-session. We used the PHR 244 measure of self-assessed sensory suggestibility to evaluate the tendency to accept and 245 act on perceived physiological states, with either reduced critical evaluation (low 246 suggestibility) or independent judgment (high suggestibility). The subscale demonstrated 247 good internal consistency (i.e. all items contributed coherently to the same construct ), 248 yielding Cronbach’s α = .810 with a 95 % confidence interval of [.58 1, .897]. Further, the 249 distribution of raw scores was approximately normal (Shapiro -Wilk test, p = .65 4); 250 therefore, we standardised the scores by subtracting the sample mean and dividing by 251 the sample standard deviation. 252 We analysed how the PHR score might explain between -participants variability in both 253 estimated distances and pm -RT. For both measurements, we included the normalised 254 PHR score in the Bayesian models as an independent factor, as well as its interaction with 255 remaining experimental factors (i.e., distance and stimulus semantics – see Methods). 256 For the perceived ending distance, the slope associated with the PHR score did not di3er 257 from zero (.004 m, 95% -CI [ -0.021, 0.030], p(v > 0) = 0. 625), and neither its e3ect on 258 response dispersion (.001 1/m, 95%-CI[-0.006, 0.009], p(v > 0) = .628)). When evaluating 259 how the suggestibility score influenced APA's timing, we identified a relevant impact in 260 predicting the accuracy and precision of pm-RTs as shown in Fig.4. In particular, the slope 261 associated with the PHR score was positive (0.015 s, 95%-CI [0.003, 0.027]), indicating 262 that individuals with higher suggestibility scores took longer to respond. The presence of 263 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint (a) (b) Figure 4: influence of suggestibility on premotor reaction time and its dispersion. a) The plot shows the linear relationship between normalised PHR score and pm-RT response for three stimulus categories. b) The plot reports the effect of the normalised PHR score on response dispersion is shown separately for three categories. Lines represent linear regressions, and shaded areas indicate the standard deviation of slope estimates. PHR score is reported on a normalised scale (arbitray unit – a.u.) 264 semantics also impacted the response through the suggestibility score, with the positive 265 semantics slowing down responses (0.0113, 95% -CI [0.006, 0.017]) as well as for 266 negatives (0.0159, 95% -CI [0.011, 0.021]) in comparison with the sound without 267 semantics (see Fig.4a). In addition, the e3ect of the PHR score on response dispersion 268 (i.e., standard deviation) di3ered from zero only when considering the di3erent types of 269 stimuli (refer to Fig.4b) : the variability increased only for the stimuli without semantics 270 (slope = .004 1/s, 95%-CI [.001, .008], p(v > 0) = 0.987), while it remains constant for both 271 negative and positive stimuli (max(p(v0)) < 0.800). Interestingly, for participants 272 with low suggestibility (PHR = −2), dispersion was significantly lower for non-semantic 273 sounds than for semantic ones (p = 0.994 for v < 0). This indicates that these individuals 274 responded more quickly and precisely when the sound lacked meaning. By contrast, for 275 highly suggestible participants (PHR = +2) , the dispersion did not di3er across sound 276 types (p = 0.832 for v > 0), showing that they remained slower and less precise regardless 277 of the stimulus content. 278 3. Discussion 279 We jointly investigated endogenous and exogenous factors impacting proximity 280 perception of looming sounds entering the PPS by combining physiological with 281 behavioural measurements and self -assessed questionnaires within a simulated 282 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint auditory environment. Overall, our results show that the endogenous factor (i.e. 283 individual suggestibility) influences sensorimotor integration, linking this personality trait 284 to the coupling between perception and action when a sound enters the peripersonal 285 space. We confirmed the first hypothesis (H1): the estimation of looming sound-stopping 286 distances (the cognitive task) and the timing of initial muscle activation in reaction to the 287 looming sound -stopping distances (the physiological task) showed di3erent levels of 288 congruencies when comparing the exogenous factor associated to the presence of 289 semantic (Applause and Dentist Drill) and without it (Pink Noise). For semantics sounds, 290 we found a n over-evaluation in the cognitive task and a retarded muscle activity in the 291 physiological task. The second hypothesis (H2), instead, was only partially supported: 292 individual levels of sensory suggestibility significantly influenced pm -RTs and their 293 variability, while distance perception remained una3ected across all stimulus types. 294 Interestingly, increased response variability in pm -RT measure was observed only for 295 neutral (Pink Noise) stimuli, suggesting a defined interplay between semantic content 296 and suggestibility in shaping reactive responses. Following, we discuss the results in 297 more detail. 298 In this study, we relied on auditory distance perception to demonstrate that 299 individualised looming sounds e3ectively modulate distance estimation and APA timing 300 within the PPS, as confirmed by both behavio ural and neurophysiological measures. In 301 the distance estimation task, participants correctly estimated the stopping distances of 302 the looming sound, demonstrating the participants’ ability to decode distance from rising 303 intensity levels. Second, the measurement of pm-RT successfully replicated the trends 304 observed in prior studies where sounds stopping nearer to the participant’s body elicit 305 quicker reactions 15. An important note is on the duration of our stimuli since it 306 represented a confounder to sound intensity, however, the original study from 307 Camponogara et al. 201514 demonstrated that stimuli with a flat envelope and modulated 308 duration did not elicit any modulation in the pm-RT indicating that participants did not 309 perceived such control stimuli as approaching auditory objects. Further, the approaching 310 stimuli were simulated through the inverse square law , following the concept that the 311 intensity cue is the most important to elicit auditory distance 34. Although the scientific 312 literature indicates that additional cues, such as reverberation or spectral cues , should 313 be considered to deliver an ecological percept 30, the results obtained from both tasks 314 demonstrate that participants were able to decode an estimate for auditory distance and 315 use it e3ectively despite the simplifications introduced in stimulus rendering. This was 316 evident both in the reactive task, where they modulated the pm-RT, and in the more 317 cognitive task involving distance estimation. 318 Extending previous findings, our study examined how semantic content influences 319 participant responses within the PPS, confirming anticipated relationships and 320 uncovering novel e3ects. Participants accurately distinguished the valence of auditory 321 stimuli, rating positive sounds higher than negative ones, with semantically neutral pink 322 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint noise rated intermediately. Distance estimations and pm-RT revealed that meaningful 323 (positive and negative) stimuli were perceived as more distant and elicited slower motor 324 responses compared to pink noise. This may be due to additional cognitive processing 325 required to decode semantic information, due to increased cognitive demand35, and with 326 source localisation processing occurring more rapidly 28 than identification 33. While 327 previous studies reported faster reactions to negative stimuli 10,36, their use of tactile 328 detection tasks and focus on PPS boundaries di3er from our auditory -only, within-PPS 329 design. The slower pm-RTs to both negative and positive stimuli may reflect inhibitory 330 motor mechanisms associated with defensive processing of stimuli perceived as 331 threatening due to their proximity, regardless of semantic content8,17. Similar e3ects have 332 been observed in response to painful stimuli37 or unexpected, potentially threatening 333 acoustic stimuli 38. These results are consistent with evidence that premotor neurons 334 support sensory-to-motor transformations1,6 and that threatening stimuli near the body 335 activate cortical circuits involved in defensive behavio ur, which can bias or suppress 336 ongoing motor output to maintain a margin of safety39. 337 Prior literature has already linked how human defensive systems relate to personality 338 traits, most notably anxiety and fear40. Therefore, we explored the possibility of explaining 339 the variability observed in our data using the self -assessed MISS questionnaire , as 340 responses might also be influenced by endogenous (internal) traits. Our results 341 established suggestibility scores as a relevant predictor of participants' timing in 342 initiating APA (i.e. pm-RT). Specifically, participants with higher suggestibility showed 343 slower reactions, consistent with a “freeze” response rather than a fight or flight 344 reaction41. This pattern may reflect deeper engagement of the ventral auditory stream for 345 decoding semantic content, which then modulates motor readiness in the dorsal 346 pathway responsible for spatial and action processing 42. Moreover, the presence of 347 semantic content systematically increased pm-RT across participants, but notably, 348 highly suggestible individuals (normalised PHR scores > 1) did not di3erentiate their pm-349 RT between negative and positive semantic sounds. Once again, such a result might 350 indicate that increased suggestibility could a3ect top-down modulation from semantic 351 interpretations, uniformly impacting pre-frontal cortex excitability and potentially 352 reducing di3erentiation in motor responses based on sound valence12,43. 353 While the defensive -oriented role of PPS has been thoroughly explored, our results 354 extend previous findings by demonstrating that response dispersion measures are also 355 significantly modulated in the proposed within-PPS experimental design. Specifically, we 356 observed that the presence of semantic content interacted with individual suggestibility, 357 systematically influencing both response timing and precision. Recent studies have 358 indicated that predictability and emotional context can alter the sharpness or variability 359 of PPS boundaries44,45. In line with these observations, our data revealed that stimuli free 360 of semantic meaning elicited increased variability in participants with higher 361 suggestibility, suggesting broader or less precise PPS representations when meaningful 362 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint contextual cues are absent. Conversely, when semantic context was present, response 363 variability remained consistently higher irrespective of suggestibility, potentially due to 364 additional cognitive processing demands related to decoding stimulus meaning and 365 emotional significance35. This aligns with neural findings indicating high dorsal auditory 366 stream engagement under conditions requiring explicit spatial evaluation without 367 semantic clarity 28. Importantly, this modulation of residual dispersion emerged 368 exclusively in the pm-RT measure and not in the cognitively mediated estimated-distance 369 task, likely because the slower, deliberate pointing response allows participants more 370 cognitive control, reducing perceived threat and urgency46. 371 Despite the extensive study of PPS using visual and tactile inputs, the potential of 372 auditory stimuli , especially in modulating motor readiness and spatial perception , 373 remains an open and promising avenue for investigation. Our study highlights that 374 auditory information alone can e3ectively shape PPS representations through loudness-375 based distance cues. Future research should further refine auditory simulations, 376 incorporating realistic spectral and binaural cues beyond intensity changes to better 377 approximate real-world acoustic dynamics 30,47. Moreover, given the known interactions 378 between interoceptive accuracy and PPS boundaries 4, it would be valuable to integrate 379 physiological measures (e.g., heartbeat tracking) into our paradigm to examine how 380 individual di3erences in interoceptive sensitivity modulate auditory PPS processing and 381 pm-RT. Additionally, our personalised auditory rendering approach lends itself naturally 382 to implementations within immersive virtual reality13. Within these immersive scenarios, 383 future studies could systematically investigate not only the multisensory integration of 384 visual and auditory stimuli but also how imaginative suggestibility and sense of presence 385 influence PPS boundary plasticity 48. Exploring emotional processing further, dynamic 386 measures of postural control 49 alongside pm-RT and PPS measurements could clarify 387 how a3ective auditory stimuli shape behaviours (e.g., freezing or avoidance) within PPS16. 388 Lastly, future investigations through neuroimaging methods (EEG/MEG) could examine 389 how auditory "what" and "where" auditory pathways28 interact to modulate sensorimotor 390 integration in response to looming auditory stimuli 33. Our Bayesian analyses and 391 residual-based methodologies would provide e3ective tools for exploring how individual 392 di3erences (e.g., personality, anxiety, interoceptive sensitivity) and stimulus properties 393 jointly determine the variability in these neural and behavio ural responses, o3ering 394 deeper insights into the underlying mechanisms shaping PPS representations. 395 396

397 Our findings demonstrate that auditory looming stimuli within the PPS e3ectively 398 modulate postural adjustments and distance estimation, influenced by both exogenous 399 (auditory distance and a3ective content) and endogenous (suggestibility) factors. We 400 confirmed that stopping distances impacted our measures: as sounds stopped farther 401 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint within peripersonal space, participants responded more quickly and estimated distance 402 to be closer to the body . Instead, semantic information impacted experimental 403 measures, slowing motor responses and increasing the perceived auditory stopping 404 distance, likely reflecting additional neural processing demands to address the stimuli’s 405 semantic content. Notably, individual suggestibility shaped both reaction timing and its 406 dispersion, highlighting the importance of considering internal personality traits when 407 studying sensorimotor integration. These findings reinforce the dynamic and 408 personalised nature of PPS representations, extending our understanding of how the 409 human brain integrates sensory, cognitive, and personality-driven processes to organise 410 defensive and motor behavio urs in response to approaching stimuli. In doing so, our 411 study extensively explores the suggestibility role in sensorimotor integration with the aim 412 of providing a blueprint for future research linking other personality factors, such as the 413 Big Five personality traits50, to perception-action coupling within the PPS. 414 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint 4. Methods 415 Participants 416 Thirty-three right-handed adults ( 17 women; mean age: 23.4 ± 3.15 years) with self -417 reported normal hearing and no known neurological or musculoskeletal impairments 418 volunteered for the study. Before participation, written informed consent was obtained 419 from each individual. The study protocol was approved by the Ethics Committee of the 420 Department of Neurosciences, Biomedicine, and Movement Sciences at the University 421 of Verona, and all procedures were carried out in accordance with the Declaration of 422 Helsinki. 423 Stimulus generation 424 Three auditory stimuli were selected to represent distinct a3ective categories: Applause 425 (positive valence), Dentist Drill (negative valence), and Pink Noise (neutral control). The 426 emotional sounds —Applause (IADS ID 351) and Dentist Drill (IADS ID 719) —were 427 sourced from the International A3ective Digitised Sounds (IADS) database, which 428 provides standardised ratings of valence and arousal25. The neutral stimulus, Pink Noise, 429 was synthetically generated using MATLAB’s audio toolbox. Each stimulus was band -430 pass filtered (0.25–9.5 kHz) and RMS amplitude-normalised using the Pink Noise level as 431

to ensure consistent loudness and spectral content across stimuli. 432 To simulate dynamic proximity, all sounds were transformed into looming stimuli through 433 amplitude modulation by applying the inverse-square law of sound intensity decay , 434 producing an exponentially rising intensity that mimicked a sound source approaching 435 the body 29. Specifically, the stimulus intensity 𝐼 followed the physical relation 𝐼 ∝ ! "!, 436 with 𝑑 being the simulated distance between the source and the listener. 437 Each looming sound began at a simulated distance of 2.8 m , maintained a constant 438 velocity of 0.7 m/s, and stopped at one of five predefined simulated endpoints within the 439 auditory PPS (0.3, 0.4, 0.5, 0.6, and 0.7 m) 13,14. The corresponding sound level increased 440 from 65 dBA to 95 dBA across the trajectory7,13. A 15-ms raised cosine onset ramp and 20-441 ms o3set ramp were applied to each stimulus to suppress acoustic startle responses 442 and avoid o3-response artefacts. 443 Apparatus 444 The experimental procedures were conducted in the Biomechanics Laboratory of the 445 Department of Neurological, Biomedical and Movement Sciences, University of Verona. 446 Motion data were collected using a VICON MX Ultranet motion capture system (Oxford 447 Metrics, UK), comprising eight Vicon MX13 cameras operating at a 250 Hz sampling rate. 448 Reflective markers were placed on the head (midsagittal plane), shoulders, and index 449 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint fingers to record postural and gestural kinematics. Custom MATLAB scripts enabled the 450 automation of the experimental procedure and data collection. 451 Electromyographic (EMG) data were acquired from the erector spinae (ES) muscles using 452 the ZeroWire EMG system ( Aurion, Italy ), with a sampling rate of 2000 Hz. The EMG 453 multichannel analogue output was synchronised with the motion capture system via the 454 Vicon MX control interface. 455 Auditory stimuli were delivered through Hefio One in-ear headphones, a research 456 prototype that featured individual calibration of the ear canal for enhanced auditory 457 rendering (refer to Geronazzo et al. 2023 for technical specifications13). An external audio 458 interface (Sa3ire LE, Focusrite, UK) was used to amplify the audio signal. Sound intensity 459 was calibrated using a CESVA SC-2c sound level meter, and playback was synchronised 460 with kinematic data via the Vicon system. The Vicon MX control interface guaranteed 461 synchronised recording of the audio signal with the motion capture and EMG data 462 streams. 463 1. Procedure 464 Task 1: Reactive Task 465 Participants were positioned at the centre of the recording space and blindfolded 466 throughout the entire task execution to reduce visual interference in the responses. They 467 sat upright with their hands resting comfortably along their sides . With this task, we 468 measured APAs to quantify the timing of feedforward control in action anticipation, i.e., 469 changes in muscle activation that occur before the initiation of a voluntary movement51. 470 To elicit APAs, participants were instructed to quickly raise their arms forward in response 471 to the cessation of each sound, triggering postural perturbations caused by dynamic 472 intersegmental forces that shift the body’s center of mass forward, thus requiring 473 preparatory muscle adjustments to maintain vertical posture. Auditory stimuli, designed 474 to simulate looming sound sources, were presented binaurally and stopped at five 475 simulated distances from the participant’s head. Each sound was presented with five 476 repetitions per distance, resulting in a total of 75 randomised trials (3 sounds × 5 477 distances × 5 repetitions). Each block of 25 trials was followed by a 2-minute rest interval 478 to minimise fatigue. Prior to the test, a training phase comprising 10 trials (5 distances as 479 in the main tasks × 2 repetitions) with complex tones (100, 450, 1450, 2450 Hz) was 480 conducted to familiarise participants with the response paradigm. 481 Task 2: Auditory Distance Estimation 482 The second task was made to assess the spatial perception of auditory stimuli. The same 483 three sound types and five distances were presented (4 repetitions per condition; 60 trials 484 total) in randomised order. Still blindfolded, participants were instructed to extend their 485 dominant upper arm horizontally as a proprioceptive metric for sound distance, with the 486 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint shoulder being the most proximal point of reference and the middle fingertip as the most 487 distal point of reference. Then, participants were instructed to approach their 488 nondominant index finger towards the dominant upper arm until it physically reached the 489 perceived stopping position. Experimental blocks, rest intervals, and training procedure 490 were identical to Task 1. 491 Task 3: A*ective Evaluation of Sounds 492 The third task assessed the subjective emotional evaluation of the stimuli. The blindfold 493 was removed, and participants used a touchscreen laptop with a screen of 15 inches to 494 provide a3ective ratings using the Self -Assessment Manikin (SAM) scale 26. Each 15 495 stimulus (3 sounds × 5 distances) was rated twice for two a3ective dimensions: Valence 496 (1 = most negative; 9 = most positive) and Arousal (1 = least arousing; 9 = most arousing). 497 Stimuli were presented in randomised order across all combinations of sound type and 498 distance. 499 Task 4: MISS 500 To gauge individual suggestibility to embodied auditory cues, participants were asked to 501 respond to the Multidimensional Iowa Suggestibility Scale (MISS )23, a validated self -502 report inventory that assesses trait suggestibility across several domains rather than a 503 single context (e.g., hypnosis). Among its five subscales, we focused on Physiological 504 Reactivity (PHR), a 13-item measure of automatic bodily responses to internal or external 505 cues (e.g., “Thinking about something scary can make my heart pound”), because it most 506 closely aligns with our perceptual tasks. 507 2. Data Analysis and Preparation 508 Task 1: Premotor Reaction Time 509 For each trial, the p remotor reaction time ( pm-RT) defined the initiation of muscle 510 contraction (i.e. the timing of feedforward motor commands) by measuring the interval 511 between the auditory stimulus o3set and the onset of muscle activity in the erector 512 spinae, recorded bilaterally 13–15. The end of the auditory stimulus (trigger o3set) was 513 determined from the analogue trigger signal recorded during playback. After DC o3set 514 correction (mean of the first 200 samples), the signal was scanned to detect the global 515 peak, followed by the signal descent below a dynamic threshold set at 10% of the peak 516 amplitude. Instead, erector spinae onset was identified from EMG signals first low-pass 517 filtered at 200 Hz (4th-order Butterworth) and then rectified. The envelope was computed 518 via a secondary low -pass filter at 0.02 Hz (5th -order Butterworth). A dynamic threshold 519 for activation was set as the mean baseline activity plus three standard deviations, 520 calculated over a 100-sample window (i.e., 50 ms at 2000 Hz sampling rate) immediately 521 preceding movement onset14,51. 522 Task 2: Distance estimation 523 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint To estimate perceived sound location, we computed the Euclidean 3D distance between 524 the participant’s pointing index finger and the ipsilateral shoulder, based on VICON 525 motion capture data recorded during the final posture of the distance estimation task. 526 The analysis window began 3 seconds after the sound o3set , capturing the response 527 phase. Within this window, a 15-sample moving average filter was applied to reduce 528 measurement noise, and finger stabilisation was identified as periods where the finger’s 529 3D velocity remained below 0.01 m/s for at least 40 ms13. The final distance estimate was 530 calculated as the Euclidean distance between the mean position of the stabilised 531 pointing finger and the dominant-side shoulder. 532 Task 3: A*ective ratings 533 For each participant, the valence and arousal ratings collected by employing the SAM 534 mannequin were averaged over repetitions, yielding one valence and one arousal score 535 per stimulus type and distance pair. 536 Task 4: PHR score 537 Participants’ raw PHR scores were obtained by summing the 13 items (each rated 1 to 5), 538 which were used as a continuous predictor in the Bayesian models for Tasks 1 and 2. 539 3. Statistical analysis 540 The statistical inference workflow combined frequentist and Bayesian approaches to 541 evaluate the e3ects of stimulus type and sound distance on behavioural and 542 physiological responses. The section begins with an analysis of group -level di3erences 543 using repeated-measures ANOVA. The analysis of di3erences is followed by Bayesian 544 linear and generalised regression models tailored to the distributional characteristics of 545 each dependent variable, allowing us to quantify uncertainty and test directed 546 hypotheses on the relationships between measured quantities and experimental factors. 547 Then, we explore how suggestibility traits, as captured by the MISS questionnaire, 548 modulate perceptual outcomes. 549 Analysis of the di*erences 550 Prior to statistical analysis, measurements were averaged over repetitions to guarantee 551 stable estimates of individual performance and reduce the influence of trial -level noise 552 or outliers. When reporting m eans and standard errors for each stimulus type , we 553 computed them using a correction method for within-subjects52. 554 For the measurements of valence and arousal as well as for perceived distance metric, 555 given the lack of normality on the residuals, we run a two-way aligned rank transform 556 (ART) ANOVA31 on with the within-subject factors: stimulus type (Applause, Dentist Drill, 557 Pink Noise) and distance (.3,.4,.5,.6,.7 m). E3ect sizes (generalised eta squared, η²) were 558 computed for all significant e3ects. For significant main e3ects or interactions, post hoc 559 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint pairwise comparisons were conducted using sum -to-zero contrasts with Tukey 560 correction for multiple comparisons. Post hoc contrasts were performed using the ART 561 procedure for contrasts (ART -C)32. This method allows for valid nonparametric 562 comparisons of factor levels following an ART ANOVA by applying linear contrasts to 563 aligned-and-ranked data, while preserving the structure of factorial designs. 564 Statistical evaluation for the pm-RT metric was performed following a two-way ANOVA 565 with two within-group factors, with five levels of distance and three levels for stimulus 566 type (as for the previously described metrics). The data were processed and normalised 567 per participant . Sphericity corrections ( i.e., Greenhouse-Geisser adjustment) were 568 introduced, and linear model residuals followed the normality assumption (p > .05). 569 E3ect sizes (generalised eta squared, η²) were computed for all significant e3ects. Post 570 hoc comparisons were conducted using the estimated marginal means (EMMs) 571 framework to follow up significant main e3ects and interactions identified in the ANOVA. 572 Pairwise contrasts were applied to the EMMs for both stimulus type and distance, with 573 adjustments for multiple comparisons using the Tukey method. Only contrasts with 574 statistically significant adjusted p-values (p ≤ 0.05) were considered in the interpretation 575 of results. 576 Bayesian Statistical Analysis 577 With the aim of quantifying the relationships between experimental factors and 578 measurements, we adopted a fully probabilistic modelling approach for inference based 579 on Bayesian statistics27. For this analysis, we focused on the relationships between 580 perceived distance, pm-RT, sound type, and the PHR score. 581 Model design 582 Bayesian models were fitted for each behavioural metric, mirroring the full factorial 583 design used in the ANOVA. This allowed for direct comparability between frequentist and 584 Bayesian approaches and ensured that all models captured the key experimental 585 manipulations: stimulus type, distance, and their interaction, along with random 586 intercepts for participant ID to account for repeated measures. 587 The analysis of perceived distance employed a Bayesian beta regression, which 588 appropriately accounted for the bounded and non -Gaussian nature of this measure 53. 589 Perceived distances were expressed by pointing on the dominant arm, resulting in values 590 between 0 and 1 m. Modelling both the mean and precision as a function of distance, 591 stimulus type, and their interaction enabled the assessment of how both perceived 592 proximity and response variability changed across conditions. 593 Instead, a Bayesian linear regression with a Gaussian likelihood described the pm-RT 594 variability across experimental conditions . As with the distance model, this model 595 included the full factorial fixed e3ects and participant -level random intercepts and 596 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint modelled the residual standard deviation as a function of the same predictors to account 597 for possible heteroscedasticity. 598 As a last step, w e incorporated the normalised PHR score alongside the existing fixed 599 e3ects (stimulus type, distance, and their interaction), as well as all two - and three-way 600 interactions involving PHR. This extended structure was applied to both the mean 𝜇 and 601 precision 𝜙 (or residual 𝜎) components of the models, allowing the assessment of how 602 individual di3erences in suggestibility influenced not only response magnitude but also 603 response variability. Using the Wilkison notation and defining 𝑓(∙) and 𝑔(∙) as linking 604 functions (i.e. identity for the Gaussian family, or 𝑙𝑜𝑔𝑖𝑡 and 𝑙𝑜𝑔 respectively for a Beta 605 regression53), the models followed this structure: 606 𝑓(𝜇) = (𝑆𝑡𝑖𝑚 ∗ 𝐷𝑖𝑠𝑡 ∗ 𝑃𝐻𝑅) + (1|𝐼𝐷), 607 𝑔(𝜙) = (𝑆𝑡𝑖𝑚 ∗ 𝐷𝑖𝑠𝑡 ∗ 𝑃𝐻𝑅) + (1|𝐼𝐷), 608 incorporating stimulus type (𝑆𝑡𝑖𝑚), distance (𝐷𝑖𝑠𝑡), and normalised PHR score (𝑃𝐻𝑅) as 609 fixed e3ects. Participant identifiers 𝐼𝐷 were included as a random intercept. 610 Parameter estimation and model evaluation 611 Model’s parameters were estimated using four Markov Chain Monte Carlo (MCMC) 612 chains, with 3000 iterations per chain and 1000 warm-up steps. We increased iterations 613 to 4000 and 2000 warm-up steps for models including PHR scores to reach convergence. 614 Sampling employed Stan, a probabilistic programming language for Bayesian inference 615 and statistical modelling54 and its default sampling scheme , No-U-Turn (NUTS), an 616 adaptive form of Hamiltonian Monte Carlo designed to e3iciently explore complex 617 posterior distributions without manual tuning 55. Default weakly informative priors 618 (normal distribution, mean = 0, SD = 1). Convergence diagnostics (R̂ < 1.01) and e3ective 619 sample sizes were verified for all parameters27. 620 Model adequacy was evaluated using posterior predictive checks, drawing between 2000 621 samples from the fitted posterior distributions to compare model predictions with 622 observed data across key experimental conditions. We compared the model’s average 623 predictions and how much they varied from the real data. This approach enables the 624 possibility to assess if the model reproduced the patterns observed in the data by visually 625 inspecting summary plots and by employing aggregated statistics. 626 Probabilistic queries 627 Experimental e3ects were assessed through probabilistic queries of the posterior 628 distributions of fixed e3ects. Directional and comparative hypotheses were tested with 629 posterior probabilities for targeted hypotheses (e.g., whether the e3ect of stimulus Pink 630 Noise was reliably smaller than Dentist Drill in proximity estimation). Posterior 631 distributions were summarised using means and 95% credible intervals (95%-CI), giving 632 a clear range of plausible e3ect values 27. To further quantify certainty regarding the 633 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint direction of these e3ects, we computed the probability of direction, representing the 634 proportion of posterior samples consistent with the sign of the posterior mean. These 635 metrics allowed us to interpret e3ect magnitudes and robustness without relying on 636 binary significance thresholds. 637 To facilitate meaningful comparison between models using di3erent likelihood families 638 (i.e., Gaussian versus Beta regression), we standardised measures of variability. 639 Specifically, for the beta regression, we computed the posterior standard deviation from 640 the precision parameter to allow consistent interpretation of variability across di3erent 641 distributional families53. 642 We computed marginal e3ects to quantify how changes in predictors influenced 643 outcome variables across their posterior distributions. For continuous predictors (e.g., 644 distance), we estimated average marginal slopes, reflecting the instantaneous rate of 645 change in the outcome per unit increase in the predictor. For categorical predictors (e.g., 646 stimulus type), we computed average contrasts between categories, summarising 647 di3erences in predicted outcomes across experimental conditions. Marginal e3ect 648 estimates were summarised as posterior means and 95%-CI, providing robust 649 population-level inferences integrated over observed covariate distributions. 650 Finally, we conducted post hoc exploratory analyses on model -derived parameters not 651 directly estimated within the primary Bayesian models. Specifically, we extracted 652 participant-level model estimates or their dispersion from posterior draws and then 653 analysed their relationship with individual -level covariates (e.g., standardised PHR 654 scores). For this procedure, we sampled 1000 draws and summarised them with mean 655 and standard deviation over levels. These analyses allowed us to explore whether 656 participant di3erences in suggestibility explained systematic variation in model 657 uncertainty or sensitivity to experimental manipulations. 658 4. Software tools 659 Data acquisition and preprocessing were conducted using custom -written scripts in 660 MATLAB. All subsequent statistical analyses were performed in R (version 4.4.2). For data 661 manipulation and visualisation, we used packages including data.table, ggplot2, car, and 662 Rmisc. Frequentist analyses (e.g., repeated -measures ANOVA, post hoc contrasts, and 663 e3ect size estimation) were implemented using afex, ARTool, emmeans, and e>ectsize. 664 Bayesian analyses relied on Stan via the interfaces provided by brms and rstan, 665 complemented by marginale>ects, tidybayes, and posterior for marginal e3ects 666 estimation, posterior exploration, model checking, and hypothesis evaluation. 667 668 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint Funding declaration 669 This study was financially supported by the European Community Program 670 NextGenerationEU in the form of a grant (PNRR M4-C2.1.1 PRIN 2022 project “S-TWIN” - 671 533 2022F9FWZ8 003 - CUP G53D23002840006) received by MG and PC. 672

673 Figures 1a, 2a, and 3a include icons designed using resources from Flaticon.com. 674 Author Contributions 675 Roberto Barumerli: Formal analysis; Visuali sation; Writing – original draft; Writing – 676 review & editing. 677 Michele Geronazzo: Funding acquisition; Conceptuali sation; Methodology; Data 678 Acquisition; Writing – review & editing. 679 Paola Cesari: Funding acquisition; Supervision; Conceptuali sation; Methodology; 680 Writing – review & editing. 681 All authors have read and approved the submitted version of the manuscript and agree 682 to be personally accountable for their own contributions and for ensuring the integrity of 683 any part of the work. 684 Conflict of Interest 685 The authors declare no competing financial or non-financial interests. 686 Additional Information 687 The authors declare that they have no competing financial or non -financial interests in 688 relation to this work. 689 Data availability 690 The datasets for the current study, as well as the analysis scripts, are available from the 691 corresponding author on request. 692 693 694 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint

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Carpenter, B. et al. Stan: A Probabilistic Programming Language. Journal of 826 Statistical Software 76, 1–32 (2017). 827 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint 55. Hoffman, M. D. & Gelman, A. The No -U-Turn Sampler: Adaptively Setting Path 828 Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research 15, 1593–829 1623 (2014). 830 831 832 Legends 833 Figure 1: characteristics of auditory stimuli and their a3ective evaluation. (a) Intensity 834 curve of the experimental stimulus (vertical bars represent the five virtual stopping 835 distances) along both time and distance axes. The participant’s ear canal was positioned 836 at zero distance. (b) Scatterplot of mean arousal ratings as a function of mean valence 837 ratings for three auditory stimuli. Error bars depict the standard error of the mean for both 838 valence and arousal over participants. 839 Figure 2 : d istance estimation task and perceived distance of looming sounds . (a) 840 Visualisation of a participant using her arm to provide the perceived final distance of the 841 presented stimulus. (b) Estimated response of distance as a function of simulated ending 842 distance for three auditory stimuli (Pink Noise, Applause, and Dentist Drill). Shaded areas 843 represent 95% credible intervals of Bayesian beta regression while error bars report 844 standard errors computed over participants. (c) Response dispersion as a function of 845 distance, displayed with credible intervals at 50, 80 and 95% levels. 846 Figure 3: premotor reaction time (pmRT) and its timing variability for looming sounds. (a) 847 Visualisation of the participant raising her arm after the stimulus presentation . (b) 848 Premotor reaction times as a function of simulated ending distance for three auditory 849 stimuli (Pink Noise, Applause, and Dentist Drill). Shaded areas represent 95% credible 850 intervals of Bayesian regression and error bars representing standard errors over 851 participants. (c) Timing dispersion (i.e. standard deviation) as a function of distance, 852 displayed with credible intervals at 95% levels. 853 Figure 4: influence of suggestibility on premotor reaction time and its dispersion. a) The 854 plot shows the linear relationship between normalised PHR score and pm-RT response 855 for three stimulus categories. b) The plot reports the e3ect of the normalised PHR score 856 on response dispersion is shown separately for three categories. Lines represent linear 857 regressions, and shaded areas indicate the standard deviation of slope estimates . PHR 858 score is reported on a normalised scale (arbitray unit – a.u.). 859 .CC-BY 4.0 International licensemade available under a (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 The copyright holder for this preprintthis version posted July 4, 2025. ; https://doi.org/10.1101/2025.06.30.662313doi: bioRxiv preprint