Characterizing the behavioral phenotypes of phobic avoidance

Characterizing the behavioral phenotypes of phobic avoidance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characterizing the behavioral phenotypes of phobic avoidance Sergio Frumento, Edoardo Magnavacca, Alessio Iannizzotto, Angelo Gemignani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7534187/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Avoidance is a core psychopathological symptom, yet its elective assessment remains overly simplistic. Here, we introduce a standardized Virtual Reality Behavioral Avoidance Test (vr-BAT) for a multidimensional, dynamic characterization of avoidance behavior. In 75 participants with varying arachnophobia levels, we validated the vr-BAT by demonstrating that key avoidance metrics ( Distance , Time , and Velocity ) correlated robustly with self-reported fear (SPQ). Based on the trajectories taken to approach the spider, we identified five distinct dimensions of avoidance through principal component analysis – capturing strategies of initial hesitation ( Initial freezing and Action latency ), coping mechanisms during the task ( Hesitant approach and Spatial distancing ), and final reluctance ( Pre-contact freezing ). A subsequent clustering analysis revealed two behavioral subtypes of avoidance, only partially overlapping with SPQ-based classifications, emphasizing that subjective fear and behavioral avoidance are complementary but distinct constructs. The present vr-BAT represents a robust and standardized framework for assessing avoidance, with translational potential across anxiety disorders. Social science/Psychology/Human behaviour Health sciences/Diseases/Psychiatric disorders/Anxiety Health sciences/Health care/Diagnosis avoidance phobia fear anxiety avoidant behavior Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights • Avoidance is a hallmark of most anxiety disorders, yet it is poorly assessed • we present and validate a virtual-reality behavioral avoidance test (vr-BAT) • 5 main dimensions characterize avoidance in different phases of the task • Subjective fear & avoidant behavior are complementary yet distinct facets of phobia • our vr-BAT has a translational potential across other species and anxiety disorders 1. Introduction Avoidance is defined as “the act of keeping away from stress-related circumstances” (American Psychiatric Association, 2013 ) through a “physical (spatial or temporal) or psychological distance between the agent and perceived or actual threat” (Arnaudova et al., 2017 ), and represents a constituent symptom of more than 20 mental disorders (Forbes et al., 2024 ; see Supplementary table 1 ), including anxiety disorders (the most common ones; Bandelow & Michaelis, 2015 ): nevertheless, avoidant behaviours have been poorly assessed to date. Their measurement is typically indirect, subjective (as based on self-reports), and cannot be translated between different mental disorders or animal species (Ball & Gunaydin, 2022 ). The few objective assessments are based on the measurement of single variables (e.g., the distance kept from a feared stimulus; Lang & Lazovik, 1963 ) which can hardly convey exhaustive information about the many strategies that can be adopted to pursue avoidance and about the multifaceted manifestations of this behavior (Supplementary table 1 ). On the contrary, a detailed characterization of maladaptive avoidance could help differentiating 1) fear-related from avoidance-related symptoms, 2) physical from psychological distancing, 3) healthy from subclinical subjects, 4) treatment-responsive from treatment-unresponsive patients, 5) disorder-specific from trans-diagnostic features (as well as human-specific from translational ones) thus fostering the development of exposure therapies tailored on each patient’s specificities. Why have these potentials been so far left unattended? Mainly because of technological and methodological limitations (LeDoux et al., 2017 ) that are paradigmatically represented by the measurement of phobic avoidance. Indeed, specific phobias are an anxiety disorder characterized by pathologically intense fear of specific animals or situations that are actively avoided or endured with disproportionate anxiety (American Psychiatric Association, 2013 ): addressing (or even just alleviating) avoidance induce a virtuous cycle that can prevent the worsening of symptomatic constellation or lead to complete recovery (Craske et al., 2014 ), whereas the maintenance of avoidant behaviours can feed maladaptive thoughts even in the absence of any phobic encounter (Mowrer, 1947 ; 1956 ) as well as of conscious awareness (LeDoux et al., 2017 ). Despite the significance of stimulus avoidance in the assessment of specific phobias and in the evaluation of its treatments, the existing instruments for measuring this behavior have notable limitations. Self-report questionnaires (e.g., Muris & Merckelbach, 1996 ) are mainly addressing fear (whose relationship with avoidance is not obvious, as phobics can feel intense fear and still find the courage to not avoid the phobic stimulus; Ball & Gunaydin, 2022 ); in addition, they can only measure avoidant behaviours indirectly, by prompting responders to rate extreme situations – e.g., “I wouldn't take a course in biology if I thought I might have to handle live spiders” (Klorman et al., 1974 ) – that may overlook the nuances of specific phobias (Ball & Gunaydin, 2022 ) and underestimate subclinical symptomatology constellations. Consequently, scientific papers typically refer to participants as “phobic” only when a self-report measurement is integrated with a direct behavioral assessment of phobic avoidance (Siegel et al., 2020 ). However, the direct assessment of phobic avoidance with a behavioral procedure can present meaningful challenges. The gold standard for this assessment is represented by the Behavioral Avoidance Task (BAT) first introduced by Lang and Lazovik in 1963 for snake phobia evaluation. Their procedure involved two steps: 1) participants were asked to enter a room where a snake was housed in a cage 4.5 meters from the entrance and 2) the minimum distance kept from the cage was measured (Lang & Lazovik, 1963 ). Subsequent studies adapted this procedure for different phobias and for the space available in the different laboratories, inevitably introducing so many changes that the outcomes of each paradigm are hardly comparable (Supplementary table 2). Consequently, although Behavioral Avoidance Tasks are often described as standardized procedures (e.g., Hansmeier et al., 2021 ), they demonstrate considerable variability in the assessment of phobic avoidance. Specifically, the spider used in these assessments can: A) belong to a plethora of spider specimens ranging from domestic little ones – such as Eratigena atrica (Healey et al., 2017 ) – to wild and large-bodied species – like Grammostola rosea (Michaliszyn et al., 2010 ); B) be initially covered by a blanket (e.g., Healey et al., 2017 ; Siegel et al., 2011 ), or not (e.g., Miloff et al., 2019 ); C) be placed in a fixed position that the participant has to approach (Siegel et al., 2011 ), or on a moving roller that the participant has to bring closer (Côté & Bouchard, 2009 ); D) be located at initial different distances – e.g., 4.5 (Lang & Lazovik, 1963 ), 3 (Meng et al., 2004 ), or 1.73 (Côté & Bouchard, 2009 ) meters – divided in various steps – e.g., 14 (Minns et al., 2018 ), or 9 (Healey et al., 2017 ) steps; E) exhibits sudden movements which make the participant’s performance hardly comparable to that of the others subjects, leading to the exclusion of its data from the analysis (Siegel & Gallagher, 2015 ). A potential solution to address the lack of standardization and replicability has been seen in the implementation of a BAT to be performed in virtual reality. This approach would have enabled researchers and clinicians to make assessments independent of laboratory settings, thereby allowing comparable, more rigorous, and cost-effective evaluations that can be adapted to each patient’s specificities: in fact, a “resurrection” of interest in avoidance has been recently claimed (LeDoux et al., 2017 ) – although for other authors this interest never faded, not even temporarily (Fernández-Teruel & Tobeña, 2018 ). Actually, at least nine studies (detailed in Supplementary table 2) have introduced BAT protocols using virtual reality with partially-comparable paradigms: unfortunately, each of them has limitations such as outdated graphics (Mühlberger et al., 2008 ), unrealistically big spiders (Reitmaier et al., 2022 ), or lack of validation and comparison with the traditional BAT (e.g., Binder et al., 2022 ). In addition, none of them integrated the parameter traditionally measured (i.e., the minimum distance kept from the phobic stimulus; Lang & Lazovik, 1963 ) with more informative measures made possible by virtual reality (Supplementary table 2; the only additional parameter – i.e., time – could be easily measured in real-world BAT too). The higher informativity, replicability and standardization of immersive virtual scenarios would allow an exhaustive characterization of behavioral avoidance – potentially applicable to any disorder centered on the physical avoidance of a feared stimulus (e.g., Social Anxiety Disorder; Reichenberger et al., 2019 ). To overcome the above-mentioned limitations of behavioral avoidance tasks carried out with real spiders (from now on, real-BAT) or in virtual scenarios (from now on, vr-BAT), in the present study we take advantage of the validation of a virtual scenario for BAT to address the complexity of avoidance by directly measuring its multiple facets. Specifically, the present paradigm addresses two main research objectives: a comparison between vr-BAT and real-BAT with respect to their relationship with self-reported levels of fear, as measured by the Spider Phobia Questionnaire (SPQ). The measures recorded in both paradigms were considered, including the minimum distance kept from the phobic stimulus (the feature originally used in real-BAT; Lang & Lazovik, 1963 ) and the time needed to reach it (preferable for vr-BAT; Dibbets & Fonteyne, 2015 ). The ratio of these measures – Velocity – was introduced. The relationship between each of these features and SPQ was then tested in both paradigms; an exploration of the complexity of avoidance behaviour to establish an exhaustive characterisation and modelling of this phenomenon. Beyond the improvement of the assessment of a specific phobia, the detailing of the spatio-temporal and psychological core features of avoidance (Arnaudova et al., 2017 ) would allow for a cross-diagnostic modelling (Ball & Gunaydin, 2022 ) of the constituent symptom described for more than 20 mental disorders (Forbes et al., 2024 ; see Supplementary table 1 ). Indeed, the larger amount and higher accuracy of data recordable in the vr-BAT allows an unprecedented characterization of avoidance based on its direct measurement and on a deep analysis of its behavioral correlates. The potential mismatch between these behavioral manifestations of avoidance and the subjective fear reported by patients can convey fundamental information to improve diagnoses and assessment of therapeutic outcomes (Ball & Gunaydin, 2022 ). Answering these questions resulted in 1) a tool – openly shared with the scientific and clinical communities – capable of assessing phobic avoidance with an accuracy at least comparable to that of its traditional alternative, and 2) the most comprehensive characterization of a behavior (i.e., avoidance) so far assessed through unreliable and simplistic measures (Ball & Gunaydin, 2022 ) despite its complexity and its centrality in the diagnosis of many mental disorders (Forbes et al., 2024 ; see Supplementary table 1 for details). While validated on arachnophobia, the presented paradigm could be easily adapted to several clinical or research needs involving the assessment of avoidance: the analytical approach implemented here will be applied on an increasing amount of data shared on a voluntary basis by the clinicians and researchers who will adopt the proposed vr-BAT. 2. Results The vr-BAT was programmed to reproduce the environment of the real-BAT, so that the two experimental settings were both consisting of a corridor with the same dimensions (11.97×1.80×3.00 m) and architectural properties (e.g., lateral doors). In both paradigms, the participant’s task was to get as close as possible to a caged spider placed on a pedestal at the end of a corridor (Fig. 4 ): however, volunteers were allowed to interrupt the approach whenever it was felt as not acceptable. All participants had been previously instructed to touch the spider’s cage (as long as they could get close enough to it): this movement, when enacted, was interrupted by the experimenter (in the real-BAT) or by the automatic exit from the virtual scene (in the vr-BAT) as soon as the sphere representing the participant’s hand collided with the virtual cage. This comparability allowed the vr-BAT to record all the parameters typically recorded in the real-BAT (i.e., in most cases, the minimum distance kept from the spider; see Supplementary table 2) plus the exact trajectory enacted moment-by-moment during the approaching. A total sample of 75 volunteers with different degrees of fear for spiders participated in the study, each having to complete two sessions (the order of which was randomized) in different days distanced by at least two weeks: one with the real-BAT and one with the vr-BAT. 2.1 vr-BAT validation through comparison with real-BAT In this section, we validate a standardized virtual reality BAT (vr-BAT) that preserves the core principles of traditional BAT (real-BAT) while offering higher standardization, reproducibility, accessibility, and control over experimental conditions. To this aim, we applied Linear Mixed Models (LMMs) to assess whether the key avoidance metrics exhibited comparable relationships with SPQ scores across BAT conditions (real-BAT vs. vr-BAT) – analogously to what done in previous studies (e.g., Grill et al., 2024 ). These metrics consisted of 1) the minimum distance kept from the spider ( Distance ), 2) the time taken to complete (or to interrupt) the task ( Time ), and the ratio of Distance and Time ( Velocity ). Figure 1 summarizes these three key avoidance parameters extracted from both real-BAT and vr-BAT. For each parameter, a comparison between real-BAT and vr-BAT is represented through boxplots (panels B, D, F) and post-hoc linear models (panels A, C, E), illustrating their relationship with SPQ scores. The comparison between real-BAT and vr-BAT was characterized by: shorter average Distance in vr-BAT (Fig. 1 B) longer average approaching Time in vr-BAT (Fig. 1 F) lower average Velocity in vr-BAT (Fig. 1 D) Despite these differences, the core relationship between avoidance behavior and SPQ scores remained consistent across BAT conditions, confirming the validity of vr-BAT as a standardized alternative to real-BAT. The following sections present these results in more detail. Minimum distance kept from the spider ( Distance ) The minimum distance maintained from the spider ( Distance ) is the most diffuse measure in BAT paradigms, as it would correspond to the balance between two motivational drives – i.e., avoiding the spider (to have a relief from fear) and approaching it (to complete the task as requested). Figure 1 A-B compares the Distance across BAT conditions and its relationship with SPQ scores. As expected, self-reported fear of spiders (SPQ scores) significantly predicted Distance in both BAT conditions ( p < .001), demonstrating that Distance remains a robust avoidance metric in both real and virtual environments. A significant difference was found between Distance values in the two conditions, with participants demonstrating higher compliance in approaching the spider in vr-BAT compared to real-BAT. Specifically, Fig. 1 B (violin plots) shows that while most participants completed the task (reaching a Distance of 0 meters) in both conditions, they maintained a significantly greater Distance in the real-BAT than in the vr-BAT. Despite this difference, SPQ scores consistently modulated Distance in both conditions ( p < .001). However, post hoc comparisons showed that the SPQ- Distance relationship was stronger in the real-BAT ( p < .0001) than in the vr-BAT ( p = .04), suggesting that while vr-BAT provides a comparable measure, real-BAT might offer a slightly higher resolution in capturing avoidance behavior. Session order had no significant effect on Distance ( p = .34). Total time spent to approach the spider ( Time ) Time spent completing the task ( Time ) is another avoidance measure (e.g., Dibbets & Fonteyne, 2015 ) as it reflects hesitation and decision-making latency in fear responses, partially related to Distance . Figure 1 E-F shows Time ’s comparison across BAT conditions and illustrates its correlation with SPQ scores in each condition. Similarly to Distance , Time was significantly predicted by SPQ scores across both BAT conditions. However, participants took significantly longer ( p < .001) to complete the task in the vr-BAT than in the real-BAT (Fig. 1 F). This increase in approach time in vr-BAT suggests that virtual environments may amplify hesitation and deliberation processes compared to real-world scenarios. Importantly, this Time metric is affected by the inclusion of participants who interrupted the task before reaching the spider, which naturally results in shorter completion times for the same distance traveled. Excluding these individuals would have selectively removed participants with the highest levels of fear, as they tended to interrupt the task more frequently than those with lower SPQ scores. In addition to the longer approaching times in vr-BAT, also the SPQ × BAT condition interaction was significant ( p = .01). Post-hoc comparisons showed that the SPQ- Time relationship was significant in both conditions and, interestingly, this relationship was stronger in the vr-BAT ( p < .0001) than in the real-BAT ( p = .01). This confirmed the hypothesis – already proven in the literature (Dibbets & Fonteyne, 2015 ) – that Time may serve as a particularly sensitive indicator of avoidance behavior in virtual environments. Session order had no significant effect on Time ( p = .38). Mean velocity in approaching the spider ( Velocity ) Velocity , defined as the ratio of Distance to Time , integrates information of avoidance behavior from these two constituent features. Although, to our knowledge, Velocity has not been used in BAT paradigms (see Supplementary table 2), it represents an intuitive metric that combines both spatial and temporal avoidance components, thus overcoming the interpretation issue related to those subjects who interrupted the task before reaching the spider. As expected from previous results, SPQ significantly predicted Velocity ( p < .001), confirming its sensitivity to individual differences in fear of spiders. Moreover, consistent with findings for Distance and Time , participants were faster in the real-BAT than in the vr-BAT, as shown in the violin plots in Fig. 1 D. The SPQ × BAT condition interaction was also significant (p < .001), indicating that despite the overall difference in task execution speed, vr-BAT still preserved a meaningful relationship between SPQ scores and Velocity. Post hoc comparisons confirmed that while the SPQ-Velocity relationship was significant in both BAT conditions, it was stronger in real-BAT (p < .0001) than in vr-BAT (p = .0004). Nevertheless, in both conditions, the relationship was strong, further validating vr-BAT as a reliable tool for avoidance assessment. Touching Behavior A key behavioral marker in BAT paradigms is whether participants physically engage with the feared stimulus. To assess consistency between real and virtual conditions, we analyzed Touch vs. No Touch behavior in both BAT paradigms (Supplementary table 4). The McNemar’s test (a non-parametric test applied to 2 x 2 frequency tables) yielded a totally non-significant result ( p = 1), indicating a perfect agreement between real-BAT and vr-BAT in participants' willingness (or unwillingness) to touch the spider. This indicates that vr-BAT can effectively replicate real-world avoidance behaviors, further supporting its validity as a standardized alternative to traditional BAT procedures. 2.2 Characterizing avoidance with the behavioral features derivable from the vr-BAT Validating the vr-BAT was the first step. Given its capacity to extract a novel range of behavioral features, we moved beyond the traditional avoidance metrics to gain a more comprehensive understanding of avoidant behavior. Accordingly, unlike previous studies that relied solely on single-variable endpoints, our approach allows a multidimensional characterization of avoidance. This shift is critical for capturing the complexity of defensive behaviors, moving beyond "how far" and "how long" to explore "how" avoidance unfolds dynamically. We first explored the characteristics and relationships among these features using a correlational analysis, followed by a Principal Component Analysis (PCA) to identify key dimensions of avoidance. Finally, we performed a clustering analysis to determine whether avoidance behaviors could be grouped into meaningful subtypes, independent of self-reported fear levels. Path trajectories while getting closer to the spider in the vr-BAT scenario Traditional BAT paradigms primarily assess avoidance based on static, endpoint measures – such as the minimum distance kept from the spider or the time spent to complete the task (Lang & Lazovik, 1963 ). However, avoidance behavior is inherently dynamic, integrating a range of micro-strategies that evolve throughout the approach. For the first time, the vr-BAT allows us to extract a rich, high-resolution dataset easily capturing the entire behavioral trajectory of participants during the task without expensive and complex motion tracking systems. This new set of features – detailed in Table 2 – provides an unprecedented level of granularity in quantifying all strategies participants adopt to regulate their proximity to the spider across the different phases of the approach. For instance, the vr-BAT enables the measurement of: initiation delay → the time taken to make the first movement toward the spider (First Step in Fig. 4 ); hesitation dynamics → the number and duration of pauses while advancing; pre-contact latency → the time elapsed between reaching the minimum distance and attempting to touch the spider. To illustrate these behavioral patterns, Fig. 4 G showcases three exemplary avoidance trajectories, corresponding to low, intermediate, and high levels of spider fear (SPQ scores = 3, 15, and 22, respectively). Each trajectory can be decomposed into three major macro-phases: initial hesitation phase (top-horizontal line in Fig. 4 G) → participants remain static while orienting themselves toward the spider, culminating in their First Step (i.e., first forward movement); Approach phase (diagonal trajectory in Fig. 4 G) → the inclination of this trajectory reflects the smoothness of the approach, with frequent pauses and hesitation episodes manifesting as deviations from a linear path; Final hesitation before contact (bottom-horizontal line labeled as Touch in Fig. 4 G) → the time spent leaning toward the spider before executing the final touch movement. Additionally, in some cases, a sharp vertical segment at the end of the trajectory indicates a successful task completion, meaning that the participant reached and touched the spider’s cage (labeled as conclusion in Fig. 4 G). These behavioral patterns are systematically analyzed in Fig. 2 , which presents a correlation matrix summarizing the mutual relationships among: self-reported fear measures (SPQ and its behavioral avoidance subscale, SPQ BA ); traditional real-BAT avoidance metrics ( Distance , Time , and Velocity ); the newly extracted vr-BAT features, capturing the fine-grained structure of avoidance behaviors. Principal Component Analysis (PCA) The rich, high-dimensional dataset extracted from the vr-BAT provides a new, easily accessible level of detail compared to traditional BAT paradigms. While this granularity is an advantage, it also necessitates a data-driven dimensionality reduction approach to extract meaningful underlying patterns of avoidance behavior. To achieve this, we applied PCA to the behavioral features identified in Table 2 , revealing a set of key avoidance dimensions that summarize distinct strategies of defensive behavior. The first five principal components (PCs) accounted for 92.9% of the total variance (Fig. 4 A-B), allowing us to distill avoidance behaviors into a small number of interpretable dimensions. Each principal component captures a unique dimension of avoidance behavior: PC1 – Spatial distancing (36.1% variance) → this component is primarily driven by Distance and its variability, Time , and the standard deviation of instantaneous velocity during the Approach phase. It reflects a strategy in which individuals maintain a large physical distance from the spider, take longer to complete the task, but move at a steady, controlled velocity; PC2 – Hesitant approach (25.9% variance) → this component is defined by mean instantaneous velocity, the number of pauses , total Time , and mean pause duration during the Approach phase. It describes a pattern of slow progression toward the spider, characterized by frequent stops and prolonged hesitation periods; PC3 – Action latency (18.0% variance) → this component captures the time required to initiate the First Step and the time needed to execute the final Touch , as well as the mean stillness duration during the Approach phase. It represents a behavioral profile where participants exhibit a behavioural resistance at key transition points (initiation, intermediate progression, and final contact); PC4 – Initial freezing (7.1% variance) → this component is primarily driven by First Step latency and extended stillness at the beginning of the task. It highlights a strong initial inhibition, where participants take a prolonged time before engaging in the approach; PC5 – Pre-contact freezing (5.8% variance) → this component is defined by the duration of stillness during the final phase of the task. It describes a pattern where participants successfully reach the minimum Distance but experience marked hesitation before touching the spider. The identification of these five core avoidance dimensions represents a major step forward in the characterization of fear-driven behaviors: avoidance is not a singular construct but consists of multiple, dissociable strategies – ranging from maintaining physical distance (PC1) to hesitating through frequent pauses (PC2) or freezing at critical moments (PC3-PC5); traditional measures – i.e., Distance (and, to a lesser extent, Time ) – fail to capture the full variability in avoidance strategies. These PCs provide a more nuanced, interpretable framework for understanding individual differences in fear responses; PC3-PC5 reveal previously unaccounted patterns of motor inhibition, suggesting that anticipatory and pre-contact freezing behaviors play a critical role in avoidance regulation. Together, these findings expand our conceptualization of avoidance, providing a data-driven foundation for more precise assessments of phobic behaviors. Regression Models using PCA Once the main dimensions of avoidance behaviour had been extracted by PCA, we examined the extent to which each of these components explained the level of fear/avoidance of spiders assessed by the traditional measures, both self-report (i.e., the SPQ and its subset of items directly assessing avoidance, the SPQ BA ) and behavioural ( Distance , Time and Velocity ). The linear models involving PCA components, SPQ, SPQ BA , and real-BAT parameters (Distance, Time and Velocity) – whose results are summarized in Table 1 – indicated that PC1 ( Spatial distancing ) has the strongest predictive power across multiple outcomes, further confirming its role as a core determinant of avoidance behavior. PC2 ( Hesitant approach ) significantly correlates with Time and Velocity , reinforcing its link with movement hesitation dynamics. Interestingly, PC3 ( Action latency ) shows no significant relationship with standard measures, suggesting that it captures a novel behavioral dimension that is not accounted by traditional avoidance indices. Notably, PC4 ( Initial freezing ) is the only component significantly associated with SPQ BA , the subscale measuring behavioral avoidance tendencies. This suggests that early hesitation behaviors may be more strongly linked to subjective fear of avoidance than to overall spider phobia scores. Table 1 Relationships between principal components, self-report and real-BAT measures with beta (β) and p-values. Significant p-values are highlighted by one (< 0.05), two (< 0.005), or three (< 0.001) asterisks real-BAT’s Principal components SPQ SPQ BA Distance Time Velocity PC1 p < 0.001*** β = 2.48 p = 0.001** β = 0.34 p < 0.001*** β = 1.12 p = 0.052 β = 5.07 p < 0.001*** β = -0.09 PC2 p = 0.09 β = 0.97 p = 0.48 β = 0.08 p = 0.24 β = 0.26 p = 0.001** β = 10.55 p = 0.002** β = -0.06 PC3 p = 0.34 β = 0.65 p = 0.94 β = 0.01 p = 0.82 β = -0.05 p = 0.23 β = 4.31 p = 0.08 β = -0.04 PC4 p = 0.18 β = 1.47 p = 0.03* β = 0.50 p = 0.19 β = -0.56 p = 0.41 β = 4.72 p = 0.48 β = -0.02 Clusterization of avoidance behavioral patterns Having identified key avoidance dimensions through PCA, we next tested whether these behavioral patterns form distinct, naturally emerging clusters. Specifically, we applied k-means clustering on PCs. This analysis aimed at identifying subgroups of participants based on their behavioral avoidance patterns, independent of self-reported fear scores. Indeed, based on the scientific literature (Ball & Gunaydin, 2022 ; Frumento et al., 2021 ; Landová et al., 2023 ) we assume self-reported fear of spiders and the avoidance behavior can be complementary – but different – information to be considered for a better assessment and an exhaustive characterization of specific phobias. The results, visualized in Fig. 3 C considering only the first 2 PCs for convenience, reveal two distinct clusters: Cluster 1 (orange) includes participants displaying the strongest behavioral avoidance, characterized by high PC1 and PC2 values (i.e., maintaining large distances from the spider, and slow hesitant approaches fragmented by frequent pauses); Cluster 2 (green) includes participants exhibiting minimal or no avoidance behaviors, moving more directly and fluidly toward the spider. When comparing these behavioral clusters with self-reported fear levels (SPQ-based clustering), a crucial discrepancy emerges. While participants with low fear (SPQ ≤ 10; squares in Fig. 3 C) consistently fall into the low-avoidance cluster, some individuals with intermediate (SPQ > 10 20; circles in Fig. 3 C) self-reported fear of spiders appear in both clusters, suggesting that fear does not always translate into a comparable level of avoidance. This challenges the assumption that subjective fear and behavioral avoidance are interchangeable, highlighting that avoidance responses involve additional cognitive and motoric processes that are not fully captured by self-reports alone. 3. Discussion The need to develop a vr-BAT arises from the criticisms raised for real-BAT (Ball & Gunaydin, 2022 ; Pittig et al., 2018 ), which, however, remains the gold standard for measuring avoidance behavior. Indeed, despite its lack of standardization and the reductionism of the parameters recorded (in most cases, Distance only; see Supplementary table 2), this paradigm has been considered so far as an objective measure of phobic behavior (Lang & Lazovik, 1963 ) and thus as an assessment necessary to integrate self-reports while diagnosing specific phobias (e.g., Siegel et al., 2021 ; Siegel & Peterson, 2022 ). Further confirming the limited capability of questionnaires to measure phobic avoidance, a selection of SPQ items meant to explicitly assess the self-reported avoidant behaviors towards spiders (SPQ BA ) showed a correlation with real-BAT and vr-BAT parameters weaker than that shown by the SPQ as a whole (Fig. 2 ). This evidence comes with two meaningful implications: 1) SPQ failed to reliably assess phobic avoidance through its dedicated items, confirming the necessity to integrate assessments based on self-report with behavioral measures when formulating a diagnosis; 2) avoidance is too complex to be reliably measured by merely asking participants to estimate it. This complexity was addressed by the vr-BAT proposed in the present study, which demonstrated a capability to discriminate more-or-less phobic behaviors comparable – and, in many aspects, superior – to that of real-BAT. Indeed, all parameters that can be extracted from the real-BAT – the minimum distance kept from the phobic stimulus (Distance), the time spent to do it (Time), and their relationship (Velocity) – significantly predicted the self-reported level of fear of spiders (i.e., SPQ scores) in the vr-BAT too, even if with some differences: the real-BAT has a more significant impact on the overall variance of Distance, while Time showed a better resolution in the vr-BAT (coherently with previous studies reviewed in Supplementary table 2). However, the point is not just about the replication of real-world measures in a more rigorous and standardized virtual scenario, but about the possibility to objectively describe unprecedented shades of avoidant behavior. Indeed, traditional BATs typically limit their characterization of avoidance to Distance only (see Supplementary table 2), despite this measure alone would not distinguish between the avoidant behavior represented by the blue and the orange lines of Fig. 4 G (enacted by participants whose self-reported fear of spiders is very different, 3/30 and 15/30 respectively) since both touched the virtual spider. Our vr-BAT allowed the extraction of additional features that add nuance to the characterization of phobic avoidance, by individuating various forms of avoidance through a data-driven reduction of approaching patterns: Spatial distancing (based on keeping a long distance from the phobic stimulus), Hesitant approach (manifesting as a slowdown – but not a full stop – of approach), and a resistance to action occurring before (Initial freezing), during (Action latency) and after (Pre-contact freezing) reaching the virtual spider. Importantly, these strategies have a different weight on the totality of avoidant behavior: Spatial distancing and Hesitant approach explain respectively 36.1% and 25.9% of variance, while the three remaining strategies overall explain 30.9% of variance. The relationships reported in Table 1 show that the metrics based on self-reports or on the real-BAT account for Spatial distancing and Hesitant approach strategies, explaining more than half (~ 62%) of the approaching behaviour; the selection of questionnaire’s items supposed to directly assess avoidance (SPQ BA ) was in fact accounting for the avoidance strategy consisting of an Initial freezing, explaining only 7.1% of variance. This means that the assessments of avoidance currently available are scotomizing ~ 1/3 of the total avoidant behavior. Coherently with this evidence, the clusterization based on principal components and that based on SPQ are only partially overlapped (Fig. 3 C), suggesting that the self-reported fear of spiders and the avoidant behaviour enacted in the vr-BAT are complementary assessments of specific phobias. These various forms of avoidance could underlie different decisional processes and rely on different emotion-regulation strategies. In particular, Spatial distancing, Action latency and Initial freezing (echoing the spatio-temporal distancing described by Arnaudova et al., 2017 ) could represent the manifestations of phobic avoidance mostly preventing patients from undergoing exposure therapy: on the other hand, Hesitant approach and Pre-contact freezing (echoing the psychological distancing described by Arnaudova et al., 2017 ) do not necessarily impede an encounter with the phobic stimulus – as long as this can be approached slowly and finally removed. The level of detail in the assessment of avoidant behavior reached by the present vr-BAT could 1) assist clinicians in preliminary assessing the proneness of patients to exposure to tailor the therapy on their specificities, and 2) help patients to become aware of each little improvement achieved during the therapeutic path (e.g., shifting from the red path to the orange one in Fig. 4 ). Finally, recognizing avoidance patterns could help treating extinction-resistance avoidance (Ball & Gunaydin, 2022 ) and explaining the possible dissociations between fear and avoidance (Krypotos et al., 2015 ). To summarize, the present results revealed that avoidance behavior is way too complex to be effectively reduced to a single parameter like the minimum distance kept from the phobic stimulus (the only measurement typically recorded in real-BAT since its introduction by Lang & Lazovik in 1963). On the contrary, beyond validating our vr-BAT as a more rigorous and standardized alternative to traditional BATs, the present results allowed the distinction and weighting of various strategies enacted in different phases of the approaching task. The exhaustive analytical approach to behavioral data underlying the present results about arachnophobic participants can be profitably applied to other psychopathologies and even to animal species other than humans, thus meeting the “need for objective behavioral measurement of avoidance, outside the context of any one disorder” which “would facilitate comparison across individuals, across anxiety pathology, and across species” (Ball & Gunaydin, 2022 ). Being based on a standardized virtual setting made freely available, it will be possible to corroborate the current analysis with the data voluntarily shared by all researchers and clinicians who will adopt the same tool. To maximize the vr-BAT's potential and ensure its broad applicability, we strategically designed it to be easily implemented in both research and clinical environments, even those with limited physical space or reduced participant mobility. Accordingly, the system operates via joystick while participants remain seated – an intentional choice aimed at maximizing accessibility and standardization across settings. As a result, the comparison between vr-BAT and real-BAT inevitably involves procedural differences. In our implementation of the real-BAT, which aligns with common laboratory and clinical protocols, participants dropped a salt bag to mark the closest distance they could reach from the spider: while this method is less precise than continuous motion tracking, it was essential to avoid prolonged exposure to the phobic stimulus and ensure the task remained both ethically and practically feasible. In contrast, the virtual setup enables real-time, high-resolution tracking of approach behavior within a fully controlled and replicable environment, offering both fine-grained measurement and broad applicability. Despite these procedural differences, the robust and consistent correlations between behavioral and self-report measures observed across both conditions suggest that the constructs assessed are not confounded by the differences in motor execution modality. While the present study provides the most comprehensive and data-rich characterization of phobic avoidance to date, it also opens valuable avenues for future research aimed at further improving ecological validity – such as enhancing graphical realism, testing hybrid systems (e.g., enabling walking in VR via omnidirectional treadmills), or integrating physiological signals to complement behavioral indices. However, it is worth noting that the absence of hyper-realistic graphics in the current vr-BAT was a deliberate design choice to ensure high tolerability among phobic participants: in clinical contexts, excessive realism may increase dropout rates or induce distress levels that compromise diagnostic reliability. Our findings show that even a moderately realistic virtual environment can elicit robust and behaviorally meaningful avoidance responses, supporting its applicability across both research and therapeutic settings. Looking ahead, future studies could evaluate the sensitivity of the vr-BAT to therapeutic change over time and explore its potential as a longitudinal tool for monitoring patient progress and tailoring exposure interventions to individual avoidance profiles. 4. Conclusions The present study aimed 1) at validating a standardized behavioral avoidance test based on virtual reality (vr-BAT) by comparing it with its traditional alternative (real-BAT) for what concerns the capability to stratify more-or-less spiderfearful participants (as assessed through SPQ), and 2) at exhaustively characterizing avoidant behavior (a fundamental diagnostic marker of many anxiety disorders). The validation was successful, as the parameters extractable from vr-BAT (which exceed in quantity and accuracy those extractable from real-BAT) showed a correlation with self-reported arachnophobia equal or stronger than that between SPQ and the few parameters (i.e., Distance, Time, and Velocity) extractable from real-BAT. With regard to the second point, the complexity of avoidant behavior was addressed with an unprecedented exhaustiveness by characterizing and weighting various avoidant strategies. In addition to outperforming real-BAT in both the quantity and reliability of the recorded metrics, the vr-BAT implies many advantages making it preferable to previous paradigms for its methodological robustness and for the variety of its potential applications: indeed, 1) it allows an exact reproducibility in any experimental or clinical setting; 2) the outcomes of each person can be directly compared to those of all the other people previously tested, as based on the same virtual setting; 3) the phobic stimulus can be easily modified to address the needs of each individual, experiment, or clinical purpose (e.g., its acceptability can be manipulated by making the animal more or less realistic); 4) the habituation induced by stimulus exposure can be generalized by showing multiple versions of the same phobic animal. Concluding, the present vr-BAT currently represents the most complete and versatile tool to objectively assess phobic avoidance for both experimental and clinical purposes. Future research should characterize vr-BAT’s psychophysiological correlates and test its sensitivity to improvements induced by the exposure to phobic stimuli. Beyond the current application to spider phobia, the present virtual scenario and the analytical approach of its data comes with transdiagnostic and translational potential: the original version (made openly available for the sake of replicability, usability, and customization) can be easily adapted to unveil previously overlooked details of avoidant behavior in other psychopathologies centered on avoidance as a diagnostic marker, as well as in animal species other than humans. Thanks to the innovative paradigm presented herein, an unexplored level of detail in the characterization of the key symptom of anxiety disorders – behavioural avoidance – can be finally achieved. 5. Methods 5.1 Experimental design This study aimed at characterizing the complex nature of phobic avoidance up to an unprecedented level, while also validating a virtual-reality version of the Behavioral Avoidance Test / Behavioral Approach Task (vr-BAT). To do so, a within-subjects design was adopted comparing the metrics of more-or-less spiderfearful participants: the analysis validating the vr-BAT compared the metrics shared with its traditional version (real-BAT), while those characterizing the avoidance dimensions considered the path-related metrics extracted from the vr-BAT only. 5.2 Participants Volunteers were recruited through post requests on notice boards and other informational materials spread in the University halls and online, accordingly with what approved by the local Ethical Committee with protocol 0025068/2019. A total of 75 participants (female = 65%, male = 35%; mean age = 25.3, standard deviation = 3.9) were recruited, based on the phobic symptoms self-reported filling the Spider Phobia Questionnaire (SPQ; Klorman et al., 1974 ), in order to obtain a sample equally composed by participants with low (SPQ < 10), intermediate (SPQ ≥ 10 < 19), and high (SPQ ≥ 20) fear of spiders (detailed anagraphics for each group are detailed in Supplementary table 3). The higher rate of females is coherent with the greater incidence of arachnophobia among women (Eaton et al., 2018 ; Fredrikson et al., 1996 ; Kiejna et al., 2015 ; Ajdacic-Gross et al., 2016 ; Zsido, 2017 ), and is typically accepted in the research line concerning specific phobias as more representative of the phobic population (Frumento et al., 2024 ; Siegel et al., 2020 ). Each participant was also preliminary screened for possible confounding factors – i.e., the presence of psychopathological symptoms other than phobia above clinical thresholds– through the Symptom Check-List 90 Revised (SCL-90 R; Derogatis & Unger, 2010 ), the State-Trait Anxiety Inventory form Y2 (Spielberger et al., 1983 ), as well as by a clinical interview conducted by a senior Psychologist (SF). Experimental data are made openly available at the Open Science Framework repository https://osf.io/yn29t/ indicating each participant’s pseudonymized code (adopted in all phases of the study to preserve privacy and confidentiality of all recorded information). 5.3 Experimental materials and settings The following hardwares and softwares were used during the experiments: a VR headset (Oculus Quest 2, Meta), including the related controllers and characterized by 2 liquid crystal displays (resolution of 1832x1920 pixel; refresh rate of 90 Hz); the Meta Quest Link desktop app for Windows OS; a PC with Intel(R) Core(TM) i7-10700 CPU at 290 GHz, 16 GB of RAM installed, mounting Windows 10 Pro (version 22H2) working at 64 bit; a 5 Gbps 5 meters cable connecting the VR headset to the PC; Unity used to run the VR–BAT. Unity and Blender softwares were used to design, create or eventually modify 3D assets downloaded from Unity Asset Store. The two possible BATs (real or virtual) were carried out in the following experimental settings: the experimental setting for the real-BAT session consisted of a 12 x 1,80 x 3,00 m corridor (Fig. 4 B) located in a university hospital building, furnished with various doors – three on the sides and one at the end – that were always closed during the tasks. At the end of the corridor, a taxidermy of a real spider ( Eurypelma spinicrus ; Fig. 4 D) was placed in a cage on a pedestal. Each participant started in the same position (marked with a sign on the floor) facing a further door, initially giving the back to the corridor; the (virtual) experimental setting for the vr-BAT consisted of a scenario reliably reproducing the architectural properties of its real counterpart with minimal differences – same corridor of 12 x 1,80 x 3,00 m dimensions, same number and position of the doors (Fig. 4 F, 1 E) – and using more soft-toned colors and lights known to result neutral (Costa et al., 2018 ; Frumento et al., 2023 ). Differently from the setting used in the real-BAT (which placed the starting point in front of a closed door), in the vr-BAT 1) the participant’s starting point was in front of a window facing a garden (Fig. 4 B) to avoid inducing claustrophobia, and 2) the spider’s cage (Fig. 4 C) was placed in front of a wall. 5.4 Procedures The following protocol has been published on the protocol.io platform ( https://www.protocols.io/view/behavioral-avoidance-test-in-virtual-reality-vr-ba-d54q88vw ) after its completion, for the sake of description’s standardization and clarity. Volunteers were asked to participate in two experimental sessions separated by at least two weeks. In one session they were asked to undergo the vr-BAT, and in the other one the traditional version with a real spider (real-BAT): the order of the sessions was randomized. During each stage of the task, volunteers – even if invited to get as close as possible to the spider – were explicitly allowed to interrupt the task whenever they would have feel that it was intolerable: in that case, in the real-BAT they had to drop a bag of coarse salt that they had to hold in the dominant hand; in the vr-BAT they had to simultaneously press the two buttons (primary button of the Meta Controller held by the participant's dominant hand) of the VR controller, which caused an immediate shutdown of the virtual scenario. After these instructions, in the real-BAT the experimenter asked the participant to approach a real spider placed in a cage on a pedestal at the end of a corridor (Fig. 4 F). Unbeknownst to volunteers, the caged spider (Fig. 4 D) was a taxidermy spider: however, the experimenter described it as a living one if the participant asked for details. Participants were first asked to leave the lab room and enter the corridor from a perspective that did not immediately allow to see the spider; if they agreed, they were then asked to turn by 180° (thus being able to see the pedestal at the end of the corridor) and to approach the spider as close as possible; if they reached the cage, they were asked to touch it and immediately stopped if they were actually going to do it. Of note, the beginning of the task was determined through a countdown – “one, two, three, go!” – by the experimenter: simultaneously with the command “go!”, the experimenter also started a timer to measure the completion time of the task, which was stopped when the salt bag was dropped or when the participant reached the spider’s cage. Analogously, after the instructions, in the vr-BAT participants were asked to wear the VR headset and to hold the related controller with the dominant hand: that controller was represented in the virtual scenario as a sphere whose movements mirrored those of the real hand holding it. The task was carried out sitting on a rotating stool (Fig. 4 A) where the participant was instructed to rotate at the beginning of the task to turn the virtual body towards the pedestal with the spider cage on top (Fig. 4 B). By moving the controller’s stick, the virtual avatar approached the cage at a maximum velocity of 0.6 m/s circa: if the participant could not tolerate the spider’s closeness, the task could be stopped by pressing the two controller’s buttons simultaneously. The approaching movement was allowed only perpendicularly to the spider’s cage (Fig. 4 F), and orienting the VR controller diagonally slowed the avatar's velocity depending on the angle. Once reached a distance of 0.94 m from the spider’s cage (Fig. 4 C), any further approach was impeded: the participant was then instructed to reach the cage with the sphere representing the hand. As soon as the sphere collided with the spider’s cage (Fig. 4 B), the scenario was turned off and the participant exited the virtual immersion. Figure 4 B summarizes the vr-BAT procedure (and, analogously, the real-BAT one), highlighting the main phases – First step , Approach , and Touch – measurable in the vr-BAT only (as exemplified in Fig. 4 G). The video available at the OSF repository https://osf.io/yn29t/files/osfstorage/680a49470aa52afa1125b0c4 shows examples of the two procedures enacted by low-spiderfearful and high-spiderfearful participants. 5.5 Data extraction Analysis was conducted using MATLAB and R (R Core Team, 2021 ). Data and scripts are publicly available at the Open Science Framework repository https://osf.io/yn29t/ . Psychometric data retrieved from self-report questionnaires were scored accordingly with each questionnaire’s rules and used to screen candidates for inclusion and exclusion criteria. Our real-BAT only allowed measuring the minimum distance kept from the spider (from now on, Distance ) and the total amount of time needed to conclude or interrupt the task (from now on, Time ), with the consequent estimate of the mean velocity (i.e., Distance divided by Time ; from now on, Velocity ): Distance is the metric originally and mostly used in the scientific literature, but Time occasionally occurs, and from their ratio is possible to measure the Velocity (however, to our knowledge this is the first study assessing it; see Supplementary table 2 for details). It is worth specifying that the recording of path metrics could be theoretically achieved through motion-tracking systems, but in fact this possibility to our knowledge has been never put into practice – probably due to the costs of implementing such a system, which would nevertheless yield less reliable measurements than a cheaper virtual setting. The vr-BAT, on the other hand, allowed estimating these same parameters but also recording the virtual avatar’s position at each sampling time, from which it was possible to derive additional features to characterize the participant’s dynamics in moving towards the spider’s cage. Based on the trajectory, it was possible to identify three stages: the initial orienting phase “First step”, the following approaching phase “Approach” and the final phase “Touch” (Fig. 4 G). Thus, for each phase, specific features characterizing it were defined as detailed in Table 2 . Three of them – i.e., the minimum distance kept from the spider ( Distance ), the total time needed to complete or interrupt the task ( Time ), and the Distance/Time ratio ( Velocity ) – could be extracted from both the real-BAT and the vr-BAT. The eight remaining could be extracted from vr-BAT only, and consist of Distance ’s standard deviation (StD distance ), mean and standard deviation of the instantaneous velocity during Approach ( ist−vel_ A mean and ist−vel_ A StD respectively), the mean time passed before the participant made the First step (FirstStep), the number of stops during the task (n°pauses), the mean time spent staying still during the Approach phase (MstillT_ A ), and the time spent since the last movement to the spider’s touch (touchingT). Both the raw data and their preprocessed version containing all these features are openly available at the repository https://osf.io/yn29t/ . Table 2 features extracted from the data concerning participants’ path during both the real-BAT and the vr-BAT, with respect to the path phases represented in Fig. 4 G Feature Description path phase real-BAT vr-BAT Distance minimum distance kept from the spider whole path ✓ ✓ Time total time needed to complete the task whole path ✓ ✓ Velocity Distance / Time whole path ✓ ✓ StD distance Distance ’s standard deviation whole path ✘ ✓ ist-vel_ A instantaneous velocity during Approach (Fig. 4 G) Approach ✘ ✓ ist−vel_A mean ist-vel_A mean Approach ✘ ✓ ist−vel_A StD ist-vel_A standard deviation Approach ✘ ✓ FirstStep mean time passed before the First step (Fig. 4 G) First step ✘ ✓ n°pauses number of stops during the task First step ✘ ✓ MstillT_ A mean time spent still during Approach (Fig. 4 G) Approach ✘ ✓ touchingT time spent to touch the spider (Fig. 4 G) Touch ✘ ✓ 5.5 Statistical analysis To first evaluate the vr-BAT as a reliable alternative to the real-BAT, we assessed whether key behavioral avoidance measures exhibit comparable relationships with self-reported fear across both conditions. Establishing this validity is a crucial prerequisite to exploit the advantages of virtual reality and allow for the extraction of a broader set of behavioral features, enabling a more detailed characterization of avoidance behavior beyond the traditional measures examined in this phase. Particularly, we employed a series of linear mixed models (LMMs) to analyze three key dependent variables extracted from both real-BAT and vr-BAT: minimum distance maintained from the spider ( Distance ), total time spent approaching the spider ( Time ), and velocity of approach ( Velocity ). Each of these measures was modeled as a function of SPQ scores (self-reported fear of spiders), BAT condition (real-BAT vs. vr-BAT), and their interaction (SPQ * BAT condition), controlling for session order (Order). Given the within-subjects design, subject ID was included as a random intercept to account for interindividual variability. The full model specification was: DV ∼ SPQ ∗ BAT condition + Order + (1∣Subject) where DV represents each of the three dependent variables ( Distance , Time , and Velocity ). The models were fitted using Restricted Maximum Likelihood (REML), and significance testing was performed using Satterthwaite’s approximation for degrees of freedom. The first model considered Distance as the dependent variable. Previous research has validated minimum distance from the spider in real-BAT as a robust measure of avoidance, demonstrating a significant correlation with SPQ scores (e.g., Grill et al., 2024 ; Kindt et al., 1996 ). Thus, we used this model to examine whether a similar relationship holds in vr-BAT and whether the strength of this relationship differs between the two conditions. A significant main effect of SPQ would indicate that greater fear is associated with increased avoidance across conditions, whereas a significant SPQ * BAT condition interaction would suggest that the relationship between fear and avoidance differs between real-BAT and vr-BAT. Post hoc comparisons were conducted to determine whether SPQ significantly predicted Distance within each condition and whether one condition exhibited a stronger relationship than the other. In a second model, we replaced Distance with Time , defined as the total duration spent approaching the spider. This alternative measure was motivated by both theoretical and practical considerations. Although previous studies and our own validation confirm that Distance is a reliable index of avoidance, it does not fully capture individual differences in the dynamic process of approaching the feared stimulus. In particular, it has been observed that, while vr-BAT facilitated a higher number of participants completing the task (i.e., reaching the final position), the time required to do so varied considerably (Dibbets & Fonteyne, 2015 ). Consequently, analyzing Time as a dependent variable allows us to assess whether avoidance manifests not only as an increased stopping distance but also as a prolonged hesitation in approaching the stimulus. The same model specification was used, with post hoc analyses examining whether the relationship between SPQ and Time differed between conditions. Finally, we introduced Velocity as a dependent variable, defined as the ratio between Distance and Time . This measure captures a more nuanced aspect of avoidance, reflecting not only how far participants stop from the spider ( Distance ) but also how quickly they reach that point ( Time ). Given that Velocity inherently integrates both Distance and Time , it provides an additional perspective on the behavioral manifestations of fear. A significant relationship between SPQ and Velocity would suggest that avoidance is characterized by both the final stopping point and the progressive approaching behavior toward the spider. The same interaction model was applied to determine whether SPQ predicted Velocity and whether this relationship differed between real-BAT and vr-BAT. For all models, session order (Order) was included as a control variable to account for potential carryover effects between the two BAT conditions. Μoreover, the accordance between real-BAT and vr-BAT was checked also for what concerns the participants’ compliance in addressing the final instruction asking to touch the spider: McNemar’s test was used to test possible differences in this behavior. Dimensionality Reduction and Clustering Analysis on vr-BAT features To reach a comprehensive characterization of avoidance behavior, beyond traditional metrics, we analyzed a set of behavioral features extracted from the vr-BAT (detailed in Table 2 ). These features were examined through multiple analytical steps to assess their interrelationships, their potential to reveal distinct avoidance profiles and their alignment with established avoidance measures from real-BAT. Correlation Analysis First, we computed Pearson correlation coefficients to assess the relationships among vr-BAT features and their cross-correlations with standard avoidance measures from real-BAT, namely Distance , Time , and Velocity , as well as with SPQ scores (self-reported fear of spiders). This step allowed us to identify highly intercorrelated features and to evaluate how vr-BAT metrics aligned with traditional indices of avoidance and subjective fear assessments. Principal Component Analysis (PCA) To reduce dimensionality while retaining the most informative components, we applied Principal Component Analysis (PCA) to the extracted vr-BAT features. Principal components (PCs) were retained if they explained at least 90% of the total variance. This selection threshold ensured that the retained PCs captured the majority of the variance while minimizing redundant or noise-driven dimensions. Following PCA, a qualitative analysis of the principal components was conducted to interpret their behavioral significance. Specifically, we examined the loading patterns of individual features on each PC to infer their potential meaning in terms of distinct avoidance behaviors. This allowed us to identify which components predominantly reflected spatial distancing (e.g., distance-maintaining behaviors), temporal hesitation (e.g., prolonged approach durations), or other dynamic aspects of movement patterns during the task. Correlation of Principal Components with Standard Avoidance Measures To evaluate the alignment of these extracted dimensions with established avoidance indicators, we build several linear models between the selected PCs and the real-BAT measures ( Distance , Time , and Velocity ), as well as SPQ scores. This analysis assessed the degree to which vr-BAT-derived components corresponded to traditional measures of avoidance and subjective fear ratings, offering insights into whether these newly derived components could serve as robust proxies for avoidance behavior. Clustering Analysis for Avoidance Profiling Finally, we performed a k-means clustering analysis on the extracted behavioral features to determine whether distinct high-avoidance and low-avoidance profiles could be identified. The optimal number of clusters (k) was determined using the elbow method and the silhouette coefficient, ensuring that the selected clusters provided the best balance between compactness and separation. Once the clusters were identified, we compared them with participant labels based on SPQ scores (i.e., high- vs. low-spiderfear), acknowledging that avoidance – although highly correlated with fear – does not measure exactly the same construct as self-reported phobia severity (Landová et al., 2023 ). This comparison allowed us to assess the degree of overlap between behavioral avoidance clusters and subjective fear classifications. This step aimed to explore whether vr-BAT behavioral patterns could independently differentiate individuals with varying levels of avoidance tendencies, potentially revealing behavioral subtypes that may not be fully captured by self-report measures alone. Declarations Data Availability In accordance with the principles of open science, all data are openly shared at the OSF (Open Science Framework) repository https://osf.io/yn29t/ (DOI 10.17605/OSF.IO/YN29T), containing both their raw and their pre-processed version. 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Psychiatry Research , 257 , 61–66. https://doi.org/10.1016/j.psychres.2017.07.024 Additional Declarations There is NO Competing Interest. Supplementary Files RealVsVRdataset.csv Data Set 1 VRpathFeatureDataset.csv Data Set 2 SupplementaryMaterial.docx Supplementary materials LessThan50mb.mov Video-demo of the vr-BAT – light version lighterfile.mp4 Video-demo of the vr-BAT – heavy version Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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17:08:46","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206069,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/8afbcfb4b6b0fab24798dcb4.html"},{"id":93616521,"identity":"45aecc56-8fa5-4c84-a3b9-c82044cf45c3","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":197638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between real-BAT and VR-BAT.\u003c/strong\u003e The three parameters that can be extracted from both real-BAT and vr-BAT have been compared, i.e., the minimum distance kept from the spider (\u003cem\u003eDistance\u003c/em\u003e; Figure 1A shows estimated data and not raw values, and thus for low SPQ scores it estimates negative values of \u003cem\u003eDistance\u003c/em\u003ethat were actually impossible in both the real-BAT and the vr-BAT), the total time spent to reach it (\u003cem\u003eTime\u003c/em\u003e), and the resulting mean velocity during the task (\u003cem\u003eVelocity\u003c/em\u003e). For each parameter, a comparison between the real-BAT and the vr-BAT is represented through violin plots (panels B, D, F) and through a linear model (panels A, C, E) conveying also the relationship of each parameter with the level of fear of spiders expressed as SPQ scores. Compared to the real-BAT, the vr-BAT was characterized by a shorter \u003cem\u003eDistance\u003c/em\u003e (panel B), a longer \u003cem\u003eTime \u003c/em\u003e(panel F), and a faster \u003cem\u003eVelocity\u003c/em\u003e(panel D); the association of these parameters with the self-reported level of fear of spiders (SPQ; panels A, C, E) was stronger in the vr-BAT for what concerns \u003cem\u003eTime\u003c/em\u003e, and in the real-BAT for what concerns \u003cem\u003eDistance\u003c/em\u003e and \u003cem\u003eVelocity\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/06f1fe9dbe859615ef731706.png"},{"id":93616520,"identity":"b30fe48d-3281-4e59-9936-88edb1e1b260","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":360643,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation matrix.\u003c/strong\u003e The listed parameters are 1) the self-report questionnaire for the assessment of arachnophobia (Spider Phobia Questionnaire; SPQ) and its subset of items assessing phobic avoidance (SPQ items specifically related to Behavioral Avoidance; SPQ\u003csup\u003eBA\u003c/sup\u003e), 2) the parameters that can be extracted from the real-BAT and 3) those that can be extracted from the vr-BAT. The colorbar on the right specifies the strength and the positive or negative direction of the correlation: the same colors are used to represent the Pearson’s correlation coefficient through the circles reported in the top-right half. P-values are reported in the bottom-left half, with significant relationships graphically highlighted by one, two, or three asterisks.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/004dee15b12e33d40b8ba0ba.png"},{"id":93616523,"identity":"11943bc4-eaf1-4396-8bd4-cbd8c482deec","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults from Principal Component Analysis (PCA) and clustering. \u003c/strong\u003ePanel A represents the correlation loadings of the parameters derivable from the vr-BAT and of the principal components (PC); panel B represents the scree plot of principal components; panel C shows the clusterization based on principal components 1 and 2, compared with that based on the subjective fear self-reported through the Spider Phobia Questionnaire (SPQ)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/3643b28090f7a90f3c5348b3.png"},{"id":93617586,"identity":"40b52cc3-63a5-4ae8-ba24-539aaabb723d","added_by":"auto","created_at":"2025-10-15 17:08:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":405704,"visible":true,"origin":"","legend":"\u003cp\u003eexperimental settings of both vr-BAT and real-BAT and three examples of the path recordable in the vr-BAT detailed with the parameters that can be extracted from it\u003c/p\u003e\n\u003cp\u003ePanel A shows the VR headset and controller worn by participants during the vr-BAT. The vr-BAT scenario is shown from an isometric perspective (B) showing the main phases of the task (also represented in panel G), from the participant’s point of view (E), and from a close-up to the virtual spider (C). The real-BAT scenario is shown from the participant’s point of view (F) and from a close-up to the real spider (D). In both the real-BAT and the vr-BAT the task was to get as close as possible to the spider: each participant, regardless of the level of fear of spiders, was instructed about the possibility to interrupt the task in any moment in case of intolerable fear (by simultaneously pressing the two circular buttons on the VR controller, during the vr-BAT; by dropping a sack to later measure the closest distance reached and then receding, in the real-BAT). The only differences between the real-BAT and the vr-BAT were that in the latter participants did wear the VR headset and were sitting instead of standing.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/151b747d7fc7bfebd5d18db1.png"},{"id":93619130,"identity":"d78fca52-ab8b-4572-ba78-6d7618ef77fb","added_by":"auto","created_at":"2025-10-15 17:32:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2128024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/2e943151-8d74-4055-bbe3-9cbbc86764ca.pdf"},{"id":93616519,"identity":"d1c411ac-6479-45f5-aa4d-cdb41656ec72","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10123,"visible":true,"origin":"","legend":"Data Set 1","description":"","filename":"RealVsVRdataset.csv","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/632dd43979a849f09e9a2a27.csv"},{"id":93616518,"identity":"c472ee92-1e68-4462-8cc7-9073b69b8dba","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19749,"visible":true,"origin":"","legend":"Data Set 2","description":"","filename":"VRpathFeatureDataset.csv","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/dbe49d51f603140cac7abb63.csv"},{"id":93616525,"identity":"068c66db-1dda-4c29-85a7-5202395fcac3","added_by":"auto","created_at":"2025-10-15 17:00:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5354663,"visible":true,"origin":"","legend":"Supplementary materials","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/a42bd622f682729bade579dc.docx"},{"id":93618680,"identity":"cc123338-58af-4467-8126-642f87328281","added_by":"auto","created_at":"2025-10-15 17:24:46","extension":"mov","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":8253585,"visible":true,"origin":"","legend":"Video-demo of the vr-BAT \u0026#x2013; light version","description":"","filename":"LessThan50mb.mov","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/22790c06e428a49171cf43ae.mov"},{"id":93616578,"identity":"c24d2f6d-cf02-4d7e-b70c-bd5ec9ac9aed","added_by":"auto","created_at":"2025-10-15 17:00:55","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":577716083,"visible":true,"origin":"","legend":"Video-demo of the vr-BAT \u0026#x2013;\u0026#x00A0;heavy version","description":"","filename":"lighterfile.mp4","url":"https://assets-eu.researchsquare.com/files/rs-7534187/v1/cbf28f216059f69f5d10e404.mp4"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Characterizing the behavioral phenotypes of phobic avoidance","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Avoidance is a hallmark of most anxiety disorders, yet it is poorly assessed\u003c/p\u003e\u003cp\u003e\u0026bull; we present and validate a virtual-reality behavioral avoidance test (vr-BAT)\u003c/p\u003e\u003cp\u003e\u0026bull; 5 main dimensions characterize avoidance in different phases of the task\u003c/p\u003e\u003cp\u003e\u0026bull; Subjective fear \u0026amp; avoidant behavior are complementary yet distinct facets of phobia\u003c/p\u003e\u003cp\u003e\u0026bull; our vr-BAT has a translational potential across other species and anxiety disorders\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAvoidance is defined as \u0026ldquo;the act of keeping away from stress-related circumstances\u0026rdquo; (American Psychiatric Association, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) through a \u0026ldquo;physical (spatial or temporal) or psychological distance between the agent and perceived or actual threat\u0026rdquo; (Arnaudova et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and represents a constituent symptom of more than 20 mental disorders (Forbes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; see Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including anxiety disorders (the most common ones; Bandelow \u0026amp; Michaelis, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e): nevertheless, avoidant behaviours have been poorly assessed to date. Their measurement is typically indirect, subjective (as based on self-reports), and cannot be translated between different mental disorders or animal species (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The few objective assessments are based on the measurement of single variables (e.g., the distance kept from a feared stimulus; Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) which can hardly convey exhaustive information about the many strategies that can be adopted to pursue avoidance and about the multifaceted manifestations of this behavior (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On the contrary, a detailed characterization of maladaptive avoidance could help differentiating 1) fear-related from avoidance-related symptoms, 2) physical from psychological distancing, 3) healthy from subclinical subjects, 4) treatment-responsive from treatment-unresponsive patients, 5) disorder-specific from trans-diagnostic features (as well as human-specific from translational ones) thus fostering the development of exposure therapies tailored on each patient\u0026rsquo;s specificities.\u003c/p\u003e\u003cp\u003eWhy have these potentials been so far left unattended? Mainly because of technological and methodological limitations (LeDoux et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that are paradigmatically represented by the measurement of phobic avoidance. Indeed, specific phobias are an anxiety disorder characterized by pathologically intense fear of specific animals or situations that are actively avoided or endured with disproportionate anxiety (American Psychiatric Association, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e): addressing (or even just alleviating) avoidance induce a virtuous cycle that can prevent the worsening of symptomatic constellation or lead to complete recovery (Craske et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), whereas the maintenance of avoidant behaviours can feed maladaptive thoughts even in the absence of any phobic encounter (Mowrer, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1947\u003c/span\u003e; \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1956\u003c/span\u003e) as well as of conscious awareness (LeDoux et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Despite the significance of stimulus avoidance in the assessment of specific phobias and in the evaluation of its treatments, the existing instruments for measuring this behavior have notable limitations. Self-report questionnaires (e.g., Muris \u0026amp; Merckelbach, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) are mainly addressing fear (whose relationship with avoidance is not obvious, as phobics can feel intense fear and still find the courage to not avoid the phobic stimulus; Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); in addition, they can only measure avoidant behaviours indirectly, by prompting responders to rate extreme situations \u0026ndash; e.g., \u0026ldquo;I wouldn't take a course in biology if I thought I might have to handle live spiders\u0026rdquo; (Klorman et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) \u0026ndash; that may overlook the nuances of specific phobias (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and underestimate subclinical symptomatology constellations. Consequently, scientific papers typically refer to participants as \u0026ldquo;phobic\u0026rdquo; only when a self-report measurement is integrated with a direct behavioral assessment of phobic avoidance (Siegel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the direct assessment of phobic avoidance with a behavioral procedure can present meaningful challenges. The gold standard for this assessment is represented by the Behavioral Avoidance Task (BAT) first introduced by Lang and Lazovik in 1963 for snake phobia evaluation. Their procedure involved two steps: 1) participants were asked to enter a room where a snake was housed in a cage 4.5 meters from the entrance and 2) the minimum distance kept from the cage was measured (Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). Subsequent studies adapted this procedure for different phobias and for the space available in the different laboratories, inevitably introducing so many changes that the outcomes of each paradigm are hardly comparable (Supplementary table 2). Consequently, although Behavioral Avoidance Tasks are often described as standardized procedures (e.g., Hansmeier et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), they demonstrate considerable variability in the assessment of phobic avoidance.\u003c/p\u003e\u003cp\u003eSpecifically, the spider used in these assessments can: A) belong to a plethora of spider specimens ranging from domestic little ones \u0026ndash; such as Eratigena atrica (Healey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) \u0026ndash; to wild and large-bodied species \u0026ndash; like Grammostola rosea (Michaliszyn et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e); B) be initially covered by a blanket (e.g., Healey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Siegel et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), or not (e.g., Miloff et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); C) be placed in a fixed position that the participant has to approach (Siegel et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), or on a moving roller that the participant has to bring closer (C\u0026ocirc;t\u0026eacute; \u0026amp; Bouchard, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); D) be located at initial different distances \u0026ndash; e.g., 4.5 (Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e), 3 (Meng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), or 1.73 (C\u0026ocirc;t\u0026eacute; \u0026amp; Bouchard, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) meters \u0026ndash; divided in various steps \u0026ndash; e.g., 14 (Minns et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), or 9 (Healey et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) steps; E) exhibits sudden movements which make the participant\u0026rsquo;s performance hardly comparable to that of the others subjects, leading to the exclusion of its data from the analysis (Siegel \u0026amp; Gallagher, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA potential solution to address the lack of standardization and replicability has been seen in the implementation of a BAT to be performed in virtual reality. This approach would have enabled researchers and clinicians to make assessments independent of laboratory settings, thereby allowing comparable, more rigorous, and cost-effective evaluations that can be adapted to each patient\u0026rsquo;s specificities: in fact, a \u0026ldquo;resurrection\u0026rdquo; of interest in avoidance has been recently claimed (LeDoux et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) \u0026ndash; although for other authors this interest never faded, not even temporarily (Fern\u0026aacute;ndez-Teruel \u0026amp; Tobe\u0026ntilde;a, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Actually, at least nine studies (detailed in Supplementary table 2) have introduced BAT protocols using virtual reality with partially-comparable paradigms: unfortunately, each of them has limitations such as outdated graphics (M\u0026uuml;hlberger et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), unrealistically big spiders (Reitmaier et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or lack of validation and comparison with the traditional BAT (e.g., Binder et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, none of them integrated the parameter traditionally measured (i.e., the minimum distance kept from the phobic stimulus; Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) with more informative measures made possible by virtual reality (Supplementary table 2; the only additional parameter \u0026ndash; i.e., time \u0026ndash; could be easily measured in real-world BAT too). The higher informativity, replicability and standardization of immersive virtual scenarios would allow an exhaustive characterization of behavioral avoidance \u0026ndash; potentially applicable to any disorder centered on the physical avoidance of a feared stimulus (e.g., Social Anxiety Disorder; Reichenberger et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo overcome the above-mentioned limitations of behavioral avoidance tasks carried out with real spiders (from now on, real-BAT) or in virtual scenarios (from now on, vr-BAT), in the present study we take advantage of the validation of a virtual scenario for BAT to address the complexity of avoidance by directly measuring its multiple facets.\u003c/p\u003e\u003cp\u003eSpecifically, the present paradigm addresses two main research objectives:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ea comparison between vr-BAT and real-BAT with respect to their relationship with self-reported levels of fear, as measured by the Spider Phobia Questionnaire (SPQ). The measures recorded in both paradigms were considered, including the minimum distance kept from the phobic stimulus (the feature originally used in real-BAT; Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) and the time needed to reach it (preferable for vr-BAT; Dibbets \u0026amp; Fonteyne, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The ratio of these measures \u0026ndash; Velocity \u0026ndash; was introduced. The relationship between each of these features and SPQ was then tested in both paradigms;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ean exploration of the complexity of avoidance behaviour to establish an exhaustive characterisation and modelling of this phenomenon. Beyond the improvement of the assessment of a specific phobia, the detailing of the spatio-temporal and psychological core features of avoidance (Arnaudova et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) would allow for a cross-diagnostic modelling (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) of the constituent symptom described for more than 20 mental disorders (Forbes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; see Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Indeed, the larger amount and higher accuracy of data recordable in the vr-BAT allows an unprecedented characterization of avoidance based on its direct measurement and on a deep analysis of its behavioral correlates. The potential mismatch between these behavioral manifestations of avoidance and the subjective fear reported by patients can convey fundamental information to improve diagnoses and assessment of therapeutic outcomes (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAnswering these questions resulted in 1) a tool \u0026ndash; openly shared with the scientific and clinical communities \u0026ndash; capable of assessing phobic avoidance with an accuracy at least comparable to that of its traditional alternative, and 2) the most comprehensive characterization of a behavior (i.e., avoidance) so far assessed through unreliable and simplistic measures (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) despite its complexity and its centrality in the diagnosis of many mental disorders (Forbes et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; see Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details).\u003c/p\u003e\u003cp\u003eWhile validated on arachnophobia, the presented paradigm could be easily adapted to several clinical or research needs involving the assessment of avoidance: the analytical approach implemented here will be applied on an increasing amount of data shared on a voluntary basis by the clinicians and researchers who will adopt the proposed vr-BAT.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eThe vr-BAT was programmed to reproduce the environment of the real-BAT, so that the two experimental settings were both consisting of a corridor with the same dimensions (11.97\u0026times;1.80\u0026times;3.00 m) and architectural properties (e.g., lateral doors). In both paradigms, the participant\u0026rsquo;s task was to get as close as possible to a caged spider placed on a pedestal at the end of a corridor (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): however, volunteers were allowed to interrupt the approach whenever it was felt as not acceptable. All participants had been previously instructed to touch the spider\u0026rsquo;s cage (as long as they could get close enough to it): this movement, when enacted, was interrupted by the experimenter (in the real-BAT) or by the automatic exit from the virtual scene (in the vr-BAT) as soon as the sphere representing the participant\u0026rsquo;s hand collided with the virtual cage. This comparability allowed the vr-BAT to record all the parameters typically recorded in the real-BAT (i.e., in most cases, the minimum distance kept from the spider; see Supplementary table 2) plus the exact trajectory enacted moment-by-moment during the approaching.\u003c/p\u003e\u003cp\u003eA total sample of 75 volunteers with different degrees of fear for spiders participated in the study, each having to complete two sessions (the order of which was randomized) in different days distanced by at least two weeks: one with the real-BAT and one with the vr-BAT.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 vr-BAT validation through comparison with real-BAT\u003c/h2\u003e\u003cp\u003eIn this section, we validate a standardized virtual reality BAT (vr-BAT) that preserves the core principles of traditional BAT (real-BAT) while offering higher standardization, reproducibility, accessibility, and control over experimental conditions.\u003c/p\u003e\u003cp\u003eTo this aim, we applied Linear Mixed Models (LMMs) to assess whether the key avoidance metrics exhibited comparable relationships with SPQ scores across BAT conditions (real-BAT vs. vr-BAT) \u0026ndash; analogously to what done in previous studies (e.g., Grill et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These metrics consisted of 1) the minimum distance kept from the spider (\u003cem\u003eDistance\u003c/em\u003e), 2) the time taken to complete (or to interrupt) the task (\u003cem\u003eTime\u003c/em\u003e), and the ratio of \u003cem\u003eDistance\u003c/em\u003e and \u003cem\u003eTime\u003c/em\u003e (\u003cem\u003eVelocity\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes these three key avoidance parameters extracted from both real-BAT and vr-BAT. For each parameter, a comparison between real-BAT and vr-BAT is represented through boxplots (panels B, D, F) and post-hoc linear models (panels A, C, E), illustrating their relationship with SPQ scores. The comparison between real-BAT and vr-BAT was characterized by:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eshorter average \u003cem\u003eDistance\u003c/em\u003e in vr-BAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003elonger average approaching \u003cem\u003eTime\u003c/em\u003e in vr-BAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003elower average \u003cem\u003eVelocity\u003c/em\u003e in vr-BAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eDespite these differences, the core relationship between avoidance behavior and SPQ scores remained consistent across BAT conditions, confirming the validity of vr-BAT as a standardized alternative to real-BAT. The following sections present these results in more detail.\u003c/p\u003e\u003cp\u003eMinimum distance kept from the spider (\u003cem\u003eDistance\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eThe minimum distance maintained from the spider (\u003cem\u003eDistance\u003c/em\u003e) is the most diffuse measure in BAT paradigms, as it would correspond to the balance between two motivational drives \u0026ndash; i.e., avoiding the spider (to have a relief from fear) and approaching it (to complete the task as requested).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B compares the \u003cem\u003eDistance\u003c/em\u003e across BAT conditions and its relationship with SPQ scores. As expected, self-reported fear of spiders (SPQ scores) significantly predicted \u003cem\u003eDistance\u003c/em\u003e in both BAT conditions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), demonstrating that \u003cem\u003eDistance\u003c/em\u003e remains a robust avoidance metric in both real and virtual environments.\u003c/p\u003e\u003cp\u003eA significant difference was found between \u003cem\u003eDistance\u003c/em\u003e values in the two conditions, with participants demonstrating higher compliance in approaching the spider in vr-BAT compared to real-BAT. Specifically, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB (violin plots) shows that while most participants completed the task (reaching a \u003cem\u003eDistance\u003c/em\u003e of 0 meters) in both conditions, they maintained a significantly greater \u003cem\u003eDistance\u003c/em\u003e in the real-BAT than in the vr-BAT.\u003c/p\u003e\u003cp\u003eDespite this difference, SPQ scores consistently modulated \u003cem\u003eDistance\u003c/em\u003e in both conditions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). However, post hoc comparisons showed that the SPQ-\u003cem\u003eDistance\u003c/em\u003e relationship was stronger in the real-BAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) than in the vr-BAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04), suggesting that while vr-BAT provides a comparable measure, real-BAT might offer a slightly higher resolution in capturing avoidance behavior. Session order had no significant effect on \u003cem\u003eDistance\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.34).\u003c/p\u003e\u003cp\u003eTotal time spent to approach the spider (\u003cem\u003eTime\u003c/em\u003e)\u003c/p\u003e\u003cp\u003eTime spent completing the task (\u003cem\u003eTime\u003c/em\u003e) is another avoidance measure (e.g., Dibbets \u0026amp; Fonteyne, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) as it reflects hesitation and decision-making latency in fear responses, partially related to \u003cem\u003eDistance\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F shows \u003cem\u003eTime\u003c/em\u003e\u0026rsquo;s comparison across BAT conditions and illustrates its correlation with SPQ scores in each condition.\u003c/p\u003e\u003cp\u003eSimilarly to \u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e was significantly predicted by SPQ scores across both BAT conditions. However, participants took significantly longer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) to complete the task in the vr-BAT than in the real-BAT (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). This increase in approach time in vr-BAT suggests that virtual environments may amplify hesitation and deliberation processes compared to real-world scenarios.\u003c/p\u003e\u003cp\u003eImportantly, this \u003cem\u003eTime\u003c/em\u003e metric is affected by the inclusion of participants who interrupted the task before reaching the spider, which naturally results in shorter completion times for the same distance traveled. Excluding these individuals would have selectively removed participants with the highest levels of fear, as they tended to interrupt the task more frequently than those with lower SPQ scores.\u003c/p\u003e\u003cp\u003eIn addition to the longer approaching times in vr-BAT, also the SPQ \u0026times; BAT condition interaction was significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01). Post-hoc comparisons showed that the SPQ-\u003cem\u003eTime\u003c/em\u003e relationship was significant in both conditions and, interestingly, this relationship was stronger in the vr-BAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001) than in the real-BAT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.01). This confirmed the hypothesis \u0026ndash; already proven in the literature (Dibbets \u0026amp; Fonteyne, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) \u0026ndash; that \u003cem\u003eTime\u003c/em\u003e may serve as a particularly sensitive indicator of avoidance behavior in virtual environments.\u003c/p\u003e\u003cp\u003eSession order had no significant effect on \u003cem\u003eTime\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38).\u003c/p\u003e\u003cp\u003eMean velocity in approaching the spider (\u003cem\u003eVelocity\u003c/em\u003e)\u003c/p\u003e\u003cp\u003e\u003cem\u003eVelocity\u003c/em\u003e, defined as the ratio of \u003cem\u003eDistance\u003c/em\u003e to \u003cem\u003eTime\u003c/em\u003e, integrates information of avoidance behavior from these two constituent features. Although, to our knowledge, \u003cem\u003eVelocity\u003c/em\u003e has not been used in BAT paradigms (see Supplementary table 2), it represents an intuitive metric that combines both spatial and temporal avoidance components, thus overcoming the interpretation issue related to those subjects who interrupted the task before reaching the spider.\u003c/p\u003e\u003cp\u003eAs expected from previous results, SPQ significantly predicted \u003cem\u003eVelocity\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming its sensitivity to individual differences in fear of spiders. Moreover, consistent with findings for \u003cem\u003eDistance\u003c/em\u003e and \u003cem\u003eTime\u003c/em\u003e, participants were faster in the real-BAT than in the vr-BAT, as shown in the violin plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD.\u003c/p\u003e\u003cp\u003eThe SPQ \u0026times; BAT condition interaction was also significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that despite the overall difference in task execution speed, vr-BAT still preserved a meaningful relationship between SPQ scores and Velocity. Post hoc comparisons confirmed that while the SPQ-Velocity relationship was significant in both BAT conditions, it was stronger in real-BAT (p\u0026thinsp;\u0026lt;\u0026thinsp;.0001) than in vr-BAT (p\u0026thinsp;=\u0026thinsp;.0004). Nevertheless, in both conditions, the relationship was strong, further validating vr-BAT as a reliable tool for avoidance assessment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTouching Behavior\u003c/p\u003e\u003cp\u003eA key behavioral marker in BAT paradigms is whether participants physically engage with the feared stimulus. To assess consistency between real and virtual conditions, we analyzed Touch vs. No Touch behavior in both BAT paradigms (Supplementary table 4).\u003c/p\u003e\u003cp\u003eThe McNemar\u0026rsquo;s test (a non-parametric test applied to 2 x 2 frequency tables) yielded a totally non-significant result (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1), indicating a perfect agreement between real-BAT and vr-BAT in participants' willingness (or unwillingness) to touch the spider. This indicates that vr-BAT can effectively replicate real-world avoidance behaviors, further supporting its validity as a standardized alternative to traditional BAT procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Characterizing avoidance with the behavioral features derivable from the vr-BAT\u003c/h2\u003e\u003cp\u003eValidating the vr-BAT was the first step. Given its capacity to extract a novel range of behavioral features, we moved beyond the traditional avoidance metrics to gain a more comprehensive understanding of avoidant behavior. Accordingly, unlike previous studies that relied solely on single-variable endpoints, our approach allows a multidimensional characterization of avoidance. This shift is critical for capturing the complexity of defensive behaviors, moving beyond \"how far\" and \"how long\" to explore \"how\" avoidance unfolds dynamically.\u003c/p\u003e\u003cp\u003eWe first explored the characteristics and relationships among these features using a correlational analysis, followed by a Principal Component Analysis (PCA) to identify key dimensions of avoidance. Finally, we performed a clustering analysis to determine whether avoidance behaviors could be grouped into meaningful subtypes, independent of self-reported fear levels.\u003c/p\u003e\u003cp\u003ePath trajectories while getting closer to the spider in the vr-BAT scenario\u003c/p\u003e\u003cp\u003eTraditional BAT paradigms primarily assess avoidance based on static, endpoint measures \u0026ndash; such as the minimum distance kept from the spider or the time spent to complete the task (Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). However, avoidance behavior is inherently dynamic, integrating a range of micro-strategies that evolve throughout the approach. For the first time, the vr-BAT allows us to extract a rich, high-resolution dataset easily capturing the entire behavioral trajectory of participants during the task without expensive and complex motion tracking systems.\u003c/p\u003e\u003cp\u003eThis new set of features \u0026ndash; detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026ndash; provides an unprecedented level of granularity in quantifying all strategies participants adopt to regulate their proximity to the spider across the different phases of the approach. For instance, the vr-BAT enables the measurement of:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003einitiation delay\u003c/em\u003e \u0026rarr; the time taken to make the first movement toward the spider (First Step in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003ehesitation dynamics\u003c/em\u003e \u0026rarr; the number and duration of pauses while advancing;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003epre-contact latency\u003c/em\u003e \u0026rarr; the time elapsed between reaching the minimum distance and attempting to touch the spider.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo illustrate these behavioral patterns, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG showcases three exemplary avoidance trajectories, corresponding to low, intermediate, and high levels of spider fear (SPQ scores\u0026thinsp;=\u0026thinsp;3, 15, and 22, respectively). Each trajectory can be decomposed into three major macro-phases:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003einitial hesitation phase (top-horizontal line in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) \u0026rarr; participants remain static while orienting themselves toward the spider, culminating in their \u003cem\u003eFirst Step\u003c/em\u003e (i.e., first forward movement);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eApproach phase\u003c/em\u003e (diagonal trajectory in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) \u0026rarr; the inclination of this trajectory reflects the smoothness of the approach, with frequent pauses and hesitation episodes manifesting as deviations from a linear path;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFinal hesitation before contact (bottom-horizontal line labeled as \u003cem\u003eTouch\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG) \u0026rarr; the time spent leaning toward the spider before executing the final touch movement.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAdditionally, in some cases, a sharp vertical segment at the end of the trajectory indicates a successful task completion, meaning that the participant reached and touched the spider\u0026rsquo;s cage (labeled as \u003cem\u003econclusion\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eThese behavioral patterns are systematically analyzed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which presents a correlation matrix summarizing the mutual relationships among:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eself-reported fear measures (SPQ and its behavioral avoidance subscale, SPQ\u003csup\u003eBA\u003c/sup\u003e);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003etraditional real-BAT avoidance metrics (\u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e, and \u003cem\u003eVelocity\u003c/em\u003e);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ethe newly extracted vr-BAT features, capturing the fine-grained structure of avoidance behaviors.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\u003cp\u003eThe rich, high-dimensional dataset extracted from the vr-BAT provides a new, easily accessible level of detail compared to traditional BAT paradigms. While this granularity is an advantage, it also necessitates a data-driven dimensionality reduction approach to extract meaningful underlying patterns of avoidance behavior.\u003c/p\u003e\u003cp\u003eTo achieve this, we applied PCA to the behavioral features identified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, revealing a set of key avoidance dimensions that summarize distinct strategies of defensive behavior. The first five principal components (PCs) accounted for 92.9% of the total variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B), allowing us to distill avoidance behaviors into a small number of interpretable dimensions.\u003c/p\u003e\u003cp\u003eEach principal component captures a unique dimension of avoidance behavior:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePC1 \u0026ndash; \u003cb\u003eSpatial distancing\u003c/b\u003e (36.1% variance) \u0026rarr; this component is primarily driven by \u003cem\u003eDistance\u003c/em\u003e and its variability, \u003cem\u003eTime\u003c/em\u003e, and the standard deviation of instantaneous velocity during the \u003cem\u003eApproach\u003c/em\u003e phase. It reflects a strategy in which individuals maintain a large physical distance from the spider, take longer to complete the task, but move at a steady, controlled velocity;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePC2 \u0026ndash; \u003cb\u003eHesitant approach\u003c/b\u003e (25.9% variance) \u0026rarr; this component is defined by mean instantaneous velocity, the number of \u003cem\u003epauses\u003c/em\u003e, total \u003cem\u003eTime\u003c/em\u003e, and mean pause duration during the \u003cem\u003eApproach\u003c/em\u003e phase. It describes a pattern of slow progression toward the spider, characterized by frequent stops and prolonged hesitation periods;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePC3 \u0026ndash; \u003cb\u003eAction latency\u003c/b\u003e (18.0% variance) \u0026rarr; this component captures the time required to initiate the \u003cem\u003eFirst Step\u003c/em\u003e and the time needed to execute the final \u003cem\u003eTouch\u003c/em\u003e, as well as the mean stillness duration during the \u003cem\u003eApproach\u003c/em\u003e phase. It represents a behavioral profile where participants exhibit a behavioural resistance at key transition points (initiation, intermediate progression, and final contact);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePC4 \u0026ndash; \u003cb\u003eInitial freezing\u003c/b\u003e (7.1% variance) \u0026rarr; this component is primarily driven by \u003cem\u003eFirst Step\u003c/em\u003e latency and extended stillness at the beginning of the task. It highlights a strong initial inhibition, where participants take a prolonged time before engaging in the approach;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePC5 \u0026ndash; \u003cb\u003ePre-contact freezing\u003c/b\u003e (5.8% variance) \u0026rarr; this component is defined by the duration of stillness during the final phase of the task. It describes a pattern where participants successfully reach the minimum \u003cem\u003eDistance\u003c/em\u003e but experience marked hesitation before touching the spider.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe identification of these five core avoidance dimensions represents a major step forward in the characterization of fear-driven behaviors:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eavoidance is not a singular construct but consists of multiple, dissociable strategies \u0026ndash; ranging from maintaining physical distance (PC1) to hesitating through frequent pauses (PC2) or freezing at critical moments (PC3-PC5);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003etraditional measures \u0026ndash; i.e., \u003cem\u003eDistance\u003c/em\u003e (and, to a lesser extent, \u003cem\u003eTime\u003c/em\u003e) \u0026ndash; fail to capture the full variability in avoidance strategies. These PCs provide a more nuanced, interpretable framework for understanding individual differences in fear responses;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePC3-PC5 reveal previously unaccounted patterns of motor inhibition, suggesting that anticipatory and pre-contact freezing behaviors play a critical role in avoidance regulation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTogether, these findings expand our conceptualization of avoidance, providing a data-driven foundation for more precise assessments of phobic behaviors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eRegression Models using PCA\u003c/p\u003e\u003cp\u003eOnce the main dimensions of avoidance behaviour had been extracted by PCA, we examined the extent to which each of these components explained the level of fear/avoidance of spiders assessed by the traditional measures, both self-report (i.e., the SPQ and its subset of items directly assessing avoidance, the SPQ\u003csup\u003eBA\u003c/sup\u003e) and behavioural (\u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e and \u003cem\u003eVelocity\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eThe linear models involving PCA components, SPQ, SPQ\u003csup\u003eBA\u003c/sup\u003e, and real-BAT parameters (Distance, Time and Velocity) \u0026ndash; whose results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026ndash; indicated that PC1 (\u003cem\u003eSpatial distancing\u003c/em\u003e) has the strongest predictive power across multiple outcomes, further confirming its role as a core determinant of avoidance behavior. PC2 (\u003cem\u003eHesitant approach\u003c/em\u003e) significantly correlates with \u003cem\u003eTime\u003c/em\u003e and \u003cem\u003eVelocity\u003c/em\u003e, reinforcing its link with movement hesitation dynamics. Interestingly, PC3 (\u003cem\u003eAction latency\u003c/em\u003e) shows no significant relationship with standard measures, suggesting that it captures a novel behavioral dimension that is not accounted by traditional avoidance indices. Notably, PC4 (\u003cem\u003eInitial freezing\u003c/em\u003e) is the only component significantly associated with SPQ\u003csup\u003eBA\u003c/sup\u003e, the subscale measuring behavioral avoidance tendencies. This suggests that early hesitation behaviors may be more strongly linked to subjective fear of avoidance than to overall spider phobia scores.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eRelationships between principal components, self-report and real-BAT measures with beta (β) and p-values.\u003c/b\u003e Significant p-values are highlighted by one (\u0026lt;\u0026thinsp;0.05), two (\u0026lt;\u0026thinsp;0.005), or three (\u0026lt;\u0026thinsp;0.001) asterisks\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003ereal-BAT\u0026rsquo;s\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrincipal components\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSPQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSPQ\u003csup\u003eBA\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eDistance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eTime\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eVelocity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001**\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.052\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ = -0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.09\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.24\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.001**\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;10.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.002**\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ = -0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.34\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.94\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.82\u003c/p\u003e\u003cp\u003eβ = -0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.23\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;4.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.08\u003c/p\u003e\u003cp\u003eβ = -0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePC4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ep\u0026thinsp;=\u0026thinsp;0.03*\u003c/b\u003e\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.19\u003c/p\u003e\u003cp\u003eβ = -0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.41\u003c/p\u003e\u003cp\u003eβ\u0026thinsp;=\u0026thinsp;4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e\u003cp\u003eβ = -0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eClusterization of avoidance behavioral patterns\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHaving identified key avoidance dimensions through PCA, we next tested whether these behavioral patterns form distinct, naturally emerging clusters. Specifically, we applied k-means clustering on PCs. This analysis aimed at identifying subgroups of participants based on their behavioral avoidance patterns, independent of self-reported fear scores. Indeed, based on the scientific literature (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Frumento et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Landov\u0026aacute; et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) we assume self-reported fear of spiders and the avoidance behavior can be complementary \u0026ndash; but different \u0026ndash; information to be considered for a better assessment and an exhaustive characterization of specific phobias.\u003c/p\u003e\u003cp\u003eThe results, visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC considering only the first 2 PCs for convenience, reveal two distinct clusters:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCluster 1 (orange) includes participants displaying the strongest behavioral avoidance, characterized by high PC1 and PC2 values (i.e., maintaining large distances from the spider, and slow hesitant approaches fragmented by frequent pauses);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCluster 2 (green) includes participants exhibiting minimal or no avoidance behaviors, moving more directly and fluidly toward the spider.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhen comparing these behavioral clusters with self-reported fear levels (SPQ-based clustering), a crucial discrepancy emerges. While participants with low fear (SPQ\u0026thinsp;\u0026le;\u0026thinsp;10; squares in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) consistently fall into the low-avoidance cluster, some individuals with intermediate (SPQ\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026thinsp;\u0026lt;\u0026thinsp;20; diamonds in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) or high (SPQ\u0026thinsp;\u0026gt;\u0026thinsp;20; circles in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC) self-reported fear of spiders appear in both clusters, suggesting that fear does not always translate into a comparable level of avoidance. This challenges the assumption that subjective fear and behavioral avoidance are interchangeable, highlighting that avoidance responses involve additional cognitive and motoric processes that are not fully captured by self-reports alone.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe need to develop a vr-BAT arises from the criticisms raised for real-BAT (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pittig et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which, however, remains the gold standard for measuring avoidance behavior. Indeed, despite its lack of standardization and the reductionism of the parameters recorded (in most cases, Distance only; see Supplementary table 2), this paradigm has been considered so far as an objective measure of phobic behavior (Lang \u0026amp; Lazovik, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) and thus as an assessment necessary to integrate self-reports while diagnosing specific phobias (e.g., Siegel et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Siegel \u0026amp; Peterson, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Further confirming the limited capability of questionnaires to measure phobic avoidance, a selection of SPQ items meant to explicitly assess the self-reported avoidant behaviors towards spiders (SPQ\u003csup\u003eBA\u003c/sup\u003e) showed a correlation with real-BAT and vr-BAT parameters weaker than that shown by the SPQ as a whole (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This evidence comes with two meaningful implications: 1) SPQ failed to reliably assess phobic avoidance through its dedicated items, confirming the necessity to integrate assessments based on self-report with behavioral measures when formulating a diagnosis; 2) avoidance is too complex to be reliably measured by merely asking participants to estimate it.\u003c/p\u003e\u003cp\u003eThis complexity was addressed by the vr-BAT proposed in the present study, which demonstrated a capability to discriminate more-or-less phobic behaviors comparable \u0026ndash; and, in many aspects, superior \u0026ndash; to that of real-BAT. Indeed, all parameters that can be extracted from the real-BAT \u0026ndash; the minimum distance kept from the phobic stimulus (Distance), the time spent to do it (Time), and their relationship (Velocity) \u0026ndash; significantly predicted the self-reported level of fear of spiders (i.e., SPQ scores) in the vr-BAT too, even if with some differences: the real-BAT has a more significant impact on the overall variance of Distance, while Time showed a better resolution in the vr-BAT (coherently with previous studies reviewed in Supplementary table 2). However, the point is not just about the replication of real-world measures in a more rigorous and standardized virtual scenario, but about the possibility to objectively describe unprecedented shades of avoidant behavior.\u003c/p\u003e\u003cp\u003eIndeed, traditional BATs typically limit their characterization of avoidance to Distance only (see Supplementary table 2), despite this measure alone would not distinguish between the avoidant behavior represented by the blue and the orange lines of Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG (enacted by participants whose self-reported fear of spiders is very different, 3/30 and 15/30 respectively) since both touched the virtual spider. Our vr-BAT allowed the extraction of additional features that add nuance to the characterization of phobic avoidance, by individuating various forms of avoidance through a data-driven reduction of approaching patterns: Spatial distancing (based on keeping a long distance from the phobic stimulus), Hesitant approach (manifesting as a slowdown \u0026ndash; but not a full stop \u0026ndash; of approach), and a resistance to action occurring before (Initial freezing), during (Action latency) and after (Pre-contact freezing) reaching the virtual spider.\u003c/p\u003e\u003cp\u003eImportantly, these strategies have a different weight on the totality of avoidant behavior: Spatial distancing and Hesitant approach explain respectively 36.1% and 25.9% of variance, while the three remaining strategies overall explain 30.9% of variance. The relationships reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show that the metrics based on self-reports or on the real-BAT account for Spatial distancing and Hesitant approach strategies, explaining more than half (~\u0026thinsp;62%) of the approaching behaviour; the selection of questionnaire\u0026rsquo;s items supposed to directly assess avoidance (SPQ\u003csup\u003eBA\u003c/sup\u003e) was in fact accounting for the avoidance strategy consisting of an Initial freezing, explaining only 7.1% of variance. This means that the assessments of avoidance currently available are scotomizing\u0026thinsp;~\u0026thinsp;1/3 of the total avoidant behavior. Coherently with this evidence, the clusterization based on principal components and that based on SPQ are only partially overlapped (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), suggesting that the self-reported fear of spiders and the avoidant behaviour enacted in the vr-BAT are complementary assessments of specific phobias.\u003c/p\u003e\u003cp\u003eThese various forms of avoidance could underlie different decisional processes and rely on different emotion-regulation strategies. In particular, Spatial distancing, Action latency and Initial freezing (echoing the spatio-temporal distancing described by Arnaudova et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) could represent the manifestations of phobic avoidance mostly preventing patients from undergoing exposure therapy: on the other hand, Hesitant approach and Pre-contact freezing (echoing the psychological distancing described by Arnaudova et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) do not necessarily impede an encounter with the phobic stimulus \u0026ndash; as long as this can be approached slowly and finally removed. The level of detail in the assessment of avoidant behavior reached by the present vr-BAT could 1) assist clinicians in preliminary assessing the proneness of patients to exposure to tailor the therapy on their specificities, and 2) help patients to become aware of each little improvement achieved during the therapeutic path (e.g., shifting from the red path to the orange one in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Finally, recognizing avoidance patterns could help treating extinction-resistance avoidance (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and explaining the possible dissociations between fear and avoidance (Krypotos et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo summarize, the present results revealed that avoidance behavior is way too complex to be effectively reduced to a single parameter like the minimum distance kept from the phobic stimulus (the only measurement typically recorded in real-BAT since its introduction by Lang \u0026amp; Lazovik in 1963). On the contrary, beyond validating our vr-BAT as a more rigorous and standardized alternative to traditional BATs, the present results allowed the distinction and weighting of various strategies enacted in different phases of the approaching task.\u003c/p\u003e\u003cp\u003eThe exhaustive analytical approach to behavioral data underlying the present results about arachnophobic participants can be profitably applied to other psychopathologies and even to animal species other than humans, thus meeting the \u0026ldquo;need for objective behavioral measurement of avoidance, outside the context of any one disorder\u0026rdquo; which \u0026ldquo;would facilitate comparison across individuals, across anxiety pathology, and across species\u0026rdquo; (Ball \u0026amp; Gunaydin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Being based on a standardized virtual setting made freely available, it will be possible to corroborate the current analysis with the data voluntarily shared by all researchers and clinicians who will adopt the same tool.\u003c/p\u003e\u003cp\u003eTo maximize the vr-BAT's potential and ensure its broad applicability, we strategically designed it to be easily implemented in both research and clinical environments, even those with limited physical space or reduced participant mobility. Accordingly, the system operates via joystick while participants remain seated \u0026ndash; an intentional choice aimed at maximizing accessibility and standardization across settings. As a result, the comparison between vr-BAT and real-BAT inevitably involves procedural differences. In our implementation of the real-BAT, which aligns with common laboratory and clinical protocols, participants dropped a salt bag to mark the closest distance they could reach from the spider: while this method is less precise than continuous motion tracking, it was essential to avoid prolonged exposure to the phobic stimulus and ensure the task remained both ethically and practically feasible. In contrast, the virtual setup enables real-time, high-resolution tracking of approach behavior within a fully controlled and replicable environment, offering both fine-grained measurement and broad applicability.\u003c/p\u003e\u003cp\u003eDespite these procedural differences, the robust and consistent correlations between behavioral and self-report measures observed across both conditions suggest that the constructs assessed are not confounded by the differences in motor execution modality.\u003c/p\u003e\u003cp\u003eWhile the present study provides the most comprehensive and data-rich characterization of phobic avoidance to date, it also opens valuable avenues for future research aimed at further improving ecological validity \u0026ndash; such as enhancing graphical realism, testing hybrid systems (e.g., enabling walking in VR via omnidirectional treadmills), or integrating physiological signals to complement behavioral indices. However, it is worth noting that the absence of hyper-realistic graphics in the current vr-BAT was a deliberate design choice to ensure high tolerability among phobic participants: in clinical contexts, excessive realism may increase dropout rates or induce distress levels that compromise diagnostic reliability. Our findings show that even a moderately realistic virtual environment can elicit robust and behaviorally meaningful avoidance responses, supporting its applicability across both research and therapeutic settings.\u003c/p\u003e\u003cp\u003eLooking ahead, future studies could evaluate the sensitivity of the vr-BAT to therapeutic change over time and explore its potential as a longitudinal tool for monitoring patient progress and tailoring exposure interventions to individual avoidance profiles.\u003c/p\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe present study aimed 1) at validating a standardized behavioral avoidance test based on virtual reality (vr-BAT) by comparing it with its traditional alternative (real-BAT) for what concerns the capability to stratify more-or-less spiderfearful participants (as assessed through SPQ), and 2) at exhaustively characterizing avoidant behavior (a fundamental diagnostic marker of many anxiety disorders).\u003c/p\u003e\u003cp\u003eThe validation was successful, as the parameters extractable from vr-BAT (which exceed in quantity and accuracy those extractable from real-BAT) showed a correlation with self-reported arachnophobia equal or stronger than that between SPQ and the few parameters (i.e., Distance, Time, and Velocity) extractable from real-BAT. With regard to the second point, the complexity of avoidant behavior was addressed with an unprecedented exhaustiveness by characterizing and weighting various avoidant strategies.\u003c/p\u003e\u003cp\u003eIn addition to outperforming real-BAT in both the quantity and reliability of the recorded metrics, the vr-BAT implies many advantages making it preferable to previous paradigms for its methodological robustness and for the variety of its potential applications: indeed, 1) it allows an exact reproducibility in any experimental or clinical setting; 2) the outcomes of each person can be directly compared to those of all the other people previously tested, as based on the same virtual setting; 3) the phobic stimulus can be easily modified to address the needs of each individual, experiment, or clinical purpose (e.g., its acceptability can be manipulated by making the animal more or less realistic); 4) the habituation induced by stimulus exposure can be generalized by showing multiple versions of the same phobic animal.\u003c/p\u003e\u003cp\u003eConcluding, the present vr-BAT currently represents the most complete and versatile tool to objectively assess phobic avoidance for both experimental and clinical purposes. Future research should characterize vr-BAT\u0026rsquo;s psychophysiological correlates and test its sensitivity to improvements induced by the exposure to phobic stimuli. Beyond the current application to spider phobia, the present virtual scenario and the analytical approach of its data comes with transdiagnostic and translational potential: the original version (made openly available for the sake of replicability, usability, and customization) can be easily adapted to unveil previously overlooked details of avoidant behavior in other psychopathologies centered on avoidance as a diagnostic marker, as well as in animal species other than humans.\u003c/p\u003e\u003cp\u003eThanks to the innovative paradigm presented herein, an unexplored level of detail in the characterization of the key symptom of anxiety disorders \u0026ndash; behavioural avoidance \u0026ndash; can be finally achieved.\u003c/p\u003e"},{"header":"5. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Experimental design\u003c/h2\u003e\n \u003cp\u003eThis study aimed at characterizing the complex nature of phobic avoidance up to an unprecedented level, while also validating a virtual-reality version of the Behavioral Avoidance Test / Behavioral Approach Task (vr-BAT).\u003c/p\u003e\n \u003cp\u003eTo do so, a within-subjects design was adopted comparing the metrics of more-or-less spiderfearful participants: the analysis validating the vr-BAT compared the metrics shared with its traditional version (real-BAT), while those characterizing the avoidance dimensions considered the path-related metrics extracted from the vr-BAT only.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Participants\u003c/h2\u003e\n \u003cp\u003eVolunteers were recruited through post requests on notice boards and other informational materials spread in the University halls and online, accordingly with what approved by the local Ethical Committee with protocol 0025068/2019.\u003c/p\u003e\n \u003cp\u003eA total of 75 participants (female\u0026thinsp;=\u0026thinsp;65%, male\u0026thinsp;=\u0026thinsp;35%; mean age\u0026thinsp;=\u0026thinsp;25.3, standard deviation\u0026thinsp;=\u0026thinsp;3.9) were recruited, based on the phobic symptoms self-reported filling the \u003cem\u003eSpider Phobia Questionnaire\u003c/em\u003e (SPQ; Klorman et al., \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e), in order to obtain a sample equally composed by participants with low (SPQ\u0026thinsp;\u0026lt;\u0026thinsp;10), intermediate (SPQ\u0026thinsp;\u0026ge;\u0026thinsp;10\u0026thinsp;\u0026lt;\u0026thinsp;19), and high (SPQ\u0026thinsp;\u0026ge;\u0026thinsp;20) fear of spiders (detailed anagraphics for each group are detailed in Supplementary table 3). The higher rate of females is coherent with the greater incidence of arachnophobia among women (Eaton et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fredrikson et al., \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kiejna et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ajdacic-Gross et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zsido, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and is typically accepted in the research line concerning specific phobias as more representative of the phobic population (Frumento et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Siegel et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eEach participant was also preliminary screened for possible confounding factors \u0026ndash; i.e., the presence of psychopathological symptoms other than phobia above clinical thresholds\u0026ndash; through the \u003cem\u003eSymptom Check-List 90 Revised\u003c/em\u003e (SCL-90 R; Derogatis \u0026amp; Unger, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), the State-Trait Anxiety Inventory form Y2 (Spielberger et al., \u003cspan class=\"CitationRef\"\u003e1983\u003c/span\u003e), as well as by a clinical interview conducted by a senior Psychologist (SF). Experimental data are made openly available at the Open Science Framework repository \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/yn29t/\u003c/span\u003e\u003c/span\u003e indicating each participant\u0026rsquo;s pseudonymized code (adopted in all phases of the study to preserve privacy and confidentiality of all recorded information).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Experimental materials and settings\u003c/h2\u003e\n \u003cp\u003eThe following hardwares and softwares were used during the experiments:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ea VR headset (Oculus Quest 2, Meta), including the related controllers and characterized by 2 liquid crystal displays (resolution of 1832x1920 pixel; refresh rate of 90 Hz);\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ethe Meta Quest Link desktop app for Windows OS;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ea PC with Intel(R) Core(TM) i7-10700 CPU at 290 GHz, 16 GB of RAM installed, mounting Windows 10 Pro (version 22H2) working at 64 bit;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ea 5 Gbps 5 meters cable connecting the VR headset to the PC;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eUnity used to run the VR\u0026ndash;BAT.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003eUnity and Blender softwares were used to design, create or eventually modify 3D assets downloaded from Unity Asset Store.\u003c/p\u003e\n \u003cp\u003eThe two possible BATs (real or virtual) were carried out in the following experimental settings:\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003ethe experimental setting for the real-BAT session consisted of a 12 x 1,80 x 3,00 m corridor (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) located in a university hospital building, furnished with various doors \u0026ndash; three on the sides and one at the end \u0026ndash; that were always closed during the tasks. At the end of the corridor, a taxidermy of a real spider (\u003cem\u003eEurypelma spinicrus\u003c/em\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD) was placed in a cage on a pedestal. Each participant started in the same position (marked with a sign on the floor) facing a further door, initially giving the back to the corridor;\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ethe (virtual) experimental setting for the vr-BAT consisted of a scenario reliably reproducing the architectural properties of its real counterpart with minimal differences \u0026ndash; same corridor of 12 x 1,80 x 3,00 m dimensions, same number and position of the doors (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF, \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE) \u0026ndash; and using more soft-toned colors and lights known to result neutral (Costa et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Frumento et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Differently from the setting used in the real-BAT (which placed the starting point in front of a closed door), in the vr-BAT 1) the participant\u0026rsquo;s starting point was in front of a window facing a garden (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) to avoid inducing claustrophobia, and 2) the spider\u0026rsquo;s cage (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC) was placed in front of a wall.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Procedures\u003c/h2\u003e\n \u003cp\u003eThe following protocol has been published on the protocol.io platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.protocols.io/view/behavioral-avoidance-test-in-virtual-reality-vr-ba-d54q88vw\u003c/span\u003e\u003c/span\u003e) after its completion, for the sake of description\u0026rsquo;s standardization and clarity.\u003c/p\u003e\n \u003cp\u003eVolunteers were asked to participate in two experimental sessions separated by at least two weeks. In one session they were asked to undergo the vr-BAT, and in the other one the traditional version with a real spider (real-BAT): the order of the sessions was randomized.\u003c/p\u003e\n \u003cp\u003eDuring each stage of the task, volunteers \u0026ndash; even if invited to get as close as possible to the spider \u0026ndash; were explicitly allowed to interrupt the task whenever they would have feel that it was intolerable: in that case, in the real-BAT they had to drop a bag of coarse salt that they had to hold in the dominant hand; in the vr-BAT they had to simultaneously press the two buttons (primary button of the Meta Controller held by the participant\u0026apos;s dominant hand) of the VR controller, which caused an immediate shutdown of the virtual scenario.\u003c/p\u003e\n \u003cp\u003eAfter these instructions, in the real-BAT the experimenter asked the participant to approach a real spider placed in a cage on a pedestal at the end of a corridor (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF). Unbeknownst to volunteers, the caged spider (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD) was a taxidermy spider: however, the experimenter described it as a living one if the participant asked for details. Participants were first asked to leave the lab room and enter the corridor from a perspective that did not immediately allow to see the spider; if they agreed, they were then asked to turn by 180\u0026deg; (thus being able to see the pedestal at the end of the corridor) and to approach the spider as close as possible; if they reached the cage, they were asked to touch it and immediately stopped if they were actually going to do it. Of note, the beginning of the task was determined through a countdown \u0026ndash; \u0026ldquo;one, two, three, go!\u0026rdquo; \u0026ndash; by the experimenter: simultaneously with the command \u0026ldquo;go!\u0026rdquo;, the experimenter also started a timer to measure the completion time of the task, which was stopped when the salt bag was dropped or when the participant reached the spider\u0026rsquo;s cage.\u003c/p\u003e\n \u003cp\u003eAnalogously, after the instructions, in the vr-BAT participants were asked to wear the VR headset and to hold the related controller with the dominant hand: that controller was represented in the virtual scenario as a sphere whose movements mirrored those of the real hand holding it. The task was carried out sitting on a rotating stool (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) where the participant was instructed to rotate at the beginning of the task to turn the virtual body towards the pedestal with the spider cage on top (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). By moving the controller\u0026rsquo;s stick, the virtual avatar approached the cage at a maximum velocity of 0.6 m/s circa: if the participant could not tolerate the spider\u0026rsquo;s closeness, the task could be stopped by pressing the two controller\u0026rsquo;s buttons simultaneously. The approaching movement was allowed only perpendicularly to the spider\u0026rsquo;s cage (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF), and orienting the VR controller diagonally slowed the avatar\u0026apos;s velocity depending on the angle. Once reached a distance of 0.94 m from the spider\u0026rsquo;s cage (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), any further approach was impeded: the participant was then instructed to reach the cage with the sphere representing the hand. As soon as the sphere collided with the spider\u0026rsquo;s cage (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), the scenario was turned off and the participant exited the virtual immersion.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB summarizes the vr-BAT procedure (and, analogously, the real-BAT one), highlighting the main phases \u0026ndash; \u003cem\u003eFirst step\u003c/em\u003e, \u003cem\u003eApproach\u003c/em\u003e, and \u003cem\u003eTouch\u003c/em\u003e \u0026ndash; measurable in the vr-BAT only (as exemplified in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG). The video available at the OSF repository \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/yn29t/files/osfstorage/680a49470aa52afa1125b0c4\u003c/span\u003e\u003c/span\u003e shows examples of the two procedures enacted by low-spiderfearful and high-spiderfearful participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5 Data extraction\u003c/h2\u003e\n \u003cp\u003eAnalysis was conducted using MATLAB and R (R Core Team, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data and scripts are publicly available at the Open Science Framework repository \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/yn29t/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003ePsychometric data retrieved from self-report questionnaires were scored accordingly with each questionnaire\u0026rsquo;s rules and used to screen candidates for inclusion and exclusion criteria.\u003c/p\u003e\n \u003cp\u003eOur real-BAT only allowed measuring the minimum distance kept from the spider (from now on, \u003cem\u003eDistance\u003c/em\u003e) and the total amount of time needed to conclude or interrupt the task (from now on, \u003cem\u003eTime\u003c/em\u003e), with the consequent estimate of the mean velocity (i.e., \u003cem\u003eDistance\u003c/em\u003e divided by \u003cem\u003eTime\u003c/em\u003e; from now on, \u003cem\u003eVelocity\u003c/em\u003e): \u003cem\u003eDistance\u003c/em\u003e is the metric originally and mostly used in the scientific literature, but \u003cem\u003eTime\u003c/em\u003e occasionally occurs, and from their ratio is possible to measure the \u003cem\u003eVelocity\u003c/em\u003e (however, to our knowledge this is the first study assessing it; see Supplementary table 2 for details). It is worth specifying that the recording of path metrics could be theoretically achieved through motion-tracking systems, but in fact this possibility to our knowledge has been never put into practice \u0026ndash; probably due to the costs of implementing such a system, which would nevertheless yield less reliable measurements than a cheaper virtual setting.\u003c/p\u003e\n \u003cp\u003eThe vr-BAT, on the other hand, allowed estimating these same parameters but also recording the virtual avatar\u0026rsquo;s position at each sampling time, from which it was possible to derive additional features to characterize the participant\u0026rsquo;s dynamics in moving towards the spider\u0026rsquo;s cage.\u003c/p\u003e\n \u003cp\u003eBased on the trajectory, it was possible to identify three stages: the initial orienting phase \u0026ldquo;First step\u0026rdquo;, the following approaching phase \u0026ldquo;Approach\u0026rdquo; and the final phase \u0026ldquo;Touch\u0026rdquo; (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG). Thus, for each phase, specific features characterizing it were defined as detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Three of them \u0026ndash; i.e., the minimum distance kept from the spider (\u003cem\u003eDistance\u003c/em\u003e), the total time needed to complete or interrupt the task (\u003cem\u003eTime\u003c/em\u003e), and the \u003cem\u003eDistance/Time\u003c/em\u003e ratio (\u003cem\u003eVelocity\u003c/em\u003e) \u0026ndash; could be extracted from both the real-BAT and the vr-BAT. The eight remaining could be extracted from vr-BAT only, and consist of \u003cem\u003eDistance\u003c/em\u003e\u0026rsquo;s standard deviation (StD\u003csup\u003e\u003cem\u003edistance\u003c/em\u003e\u003c/sup\u003e), mean and standard deviation of the instantaneous velocity during \u003cem\u003eApproach\u003c/em\u003e (\u003csup\u003eist\u0026minus;vel_\u003cem\u003eA\u003c/em\u003e\u003c/sup\u003emean and \u003csup\u003eist\u0026minus;vel_\u003cem\u003eA\u003c/em\u003e\u003c/sup\u003eStD respectively), the mean time passed before the participant made the \u003cem\u003eFirst step\u003c/em\u003e (FirstStep), the number of stops during the task (n\u0026deg;pauses), the mean time spent staying still during the \u003cem\u003eApproach\u003c/em\u003e phase (MstillT_\u003cem\u003eA\u003c/em\u003e), and the time spent since the last movement to the spider\u0026rsquo;s \u003cem\u003etouch\u003c/em\u003e (touchingT). Both the raw data and their preprocessed version containing all these features are openly available at the repository\u0026nbsp;\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/yn29t/\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003efeatures extracted from the data concerning participants\u0026rsquo; path during both the real-BAT and the vr-BAT, with respect to the path phases represented in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epath phase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ereal-BAT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003evr-BAT\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDistance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eminimum distance kept from the spider\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewhole path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTime\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etotal time needed to complete the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewhole path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVelocity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDistance\u003c/em\u003e/\u003cem\u003eTime\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewhole path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStD\u003csup\u003e\u003cem\u003edistance\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDistance\u003c/em\u003e\u0026rsquo;s standard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ewhole path\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eist-vel_\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003einstantaneous velocity during \u003cem\u003eApproach\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eApproach\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003eist\u0026minus;vel_A\u003c/em\u003e\u003c/sup\u003emean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eist-vel_A\u003c/em\u003e mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eApproach\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cem\u003eist\u0026minus;vel_A\u003c/em\u003e\u003c/sup\u003eStD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eist-vel_A\u003c/em\u003e standard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eApproach\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirstStep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean time passed before the \u003cem\u003eFirst step\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFirst step\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026deg;pauses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enumber of stops during the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFirst step\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMstillT_\u003cem\u003eA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emean time spent still during \u003cem\u003eApproach\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eApproach\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etouchingT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003etime spent to \u003cem\u003etouch\u003c/em\u003e the spider (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTouch\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e✘\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e✓\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eTo first evaluate the vr-BAT as a reliable alternative to the real-BAT, we assessed whether key behavioral avoidance measures exhibit comparable relationships with self-reported fear across both conditions. Establishing this validity is a crucial prerequisite to exploit the advantages of virtual reality and allow for the extraction of a broader set of behavioral features, enabling a more detailed characterization of avoidance behavior beyond the traditional measures examined in this phase. Particularly, we employed a series of linear mixed models (LMMs) to analyze three key dependent variables extracted from both real-BAT and vr-BAT: minimum distance maintained from the spider (\u003cem\u003eDistance\u003c/em\u003e), total time spent approaching the spider (\u003cem\u003eTime\u003c/em\u003e), and velocity of approach (\u003cem\u003eVelocity\u003c/em\u003e). Each of these measures was modeled as a function of SPQ scores (self-reported fear of spiders), BAT condition (real-BAT vs. vr-BAT), and their interaction (SPQ * BAT condition), controlling for session order (Order). Given the within-subjects design, subject ID was included as a random intercept to account for interindividual variability. The full model specification was:\u003c/p\u003e\n \u003cp\u003eDV \u0026sim; SPQ \u0026lowast; BAT condition\u0026thinsp;+\u0026thinsp;Order + (1∣Subject)\u003c/p\u003e\n \u003cp\u003ewhere DV represents each of the three dependent variables (\u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e, and \u003cem\u003eVelocity\u003c/em\u003e). The models were fitted using Restricted Maximum Likelihood (REML), and significance testing was performed using Satterthwaite\u0026rsquo;s approximation for degrees of freedom.\u003c/p\u003e\n \u003cp\u003eThe first model considered \u003cem\u003eDistance\u003c/em\u003e as the dependent variable. Previous research has validated minimum distance from the spider in real-BAT as a robust measure of avoidance, demonstrating a significant correlation with SPQ scores (e.g., Grill et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kindt et al., \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e). Thus, we used this model to examine whether a similar relationship holds in vr-BAT and whether the strength of this relationship differs between the two conditions. A significant main effect of SPQ would indicate that greater fear is associated with increased avoidance across conditions, whereas a significant SPQ * BAT condition interaction would suggest that the relationship between fear and avoidance differs between real-BAT and vr-BAT. Post hoc comparisons were conducted to determine whether SPQ significantly predicted \u003cem\u003eDistance\u003c/em\u003e within each condition and whether one condition exhibited a stronger relationship than the other.\u003c/p\u003e\n \u003cp\u003eIn a second model, we replaced \u003cem\u003eDistance\u003c/em\u003e with \u003cem\u003eTime\u003c/em\u003e, defined as the total duration spent approaching the spider. This alternative measure was motivated by both theoretical and practical considerations. Although previous studies and our own validation confirm that \u003cem\u003eDistance\u003c/em\u003e is a reliable index of avoidance, it does not fully capture individual differences in the dynamic process of approaching the feared stimulus. In particular, it has been observed that, while vr-BAT facilitated a higher number of participants completing the task (i.e., reaching the final position), the time required to do so varied considerably (Dibbets \u0026amp; Fonteyne, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Consequently, analyzing \u003cem\u003eTime\u003c/em\u003e as a dependent variable allows us to assess whether avoidance manifests not only as an increased stopping distance but also as a prolonged hesitation in approaching the stimulus. The same model specification was used, with post hoc analyses examining whether the relationship between SPQ and \u003cem\u003eTime\u003c/em\u003e differed between conditions.\u003c/p\u003e\n \u003cp\u003eFinally, we introduced \u003cem\u003eVelocity\u003c/em\u003e as a dependent variable, defined as the ratio between \u003cem\u003eDistance\u003c/em\u003e and \u003cem\u003eTime\u003c/em\u003e. This measure captures a more nuanced aspect of avoidance, reflecting not only how far participants stop from the spider (\u003cem\u003eDistance\u003c/em\u003e) but also how quickly they reach that point (\u003cem\u003eTime\u003c/em\u003e). Given that \u003cem\u003eVelocity\u003c/em\u003e inherently integrates both \u003cem\u003eDistance\u003c/em\u003e and \u003cem\u003eTime\u003c/em\u003e, it provides an additional perspective on the behavioral manifestations of fear. A significant relationship between SPQ and \u003cem\u003eVelocity\u003c/em\u003e would suggest that avoidance is characterized by both the final stopping point and the progressive approaching behavior toward the spider. The same interaction model was applied to determine whether SPQ predicted \u003cem\u003eVelocity\u003c/em\u003e and whether this relationship differed between real-BAT and vr-BAT.\u003c/p\u003e\n \u003cp\u003eFor all models, session order (Order) was included as a control variable to account for potential carryover effects between the two BAT conditions.\u003c/p\u003e\n \u003cp\u003e\u0026Mu;oreover, the accordance between real-BAT and vr-BAT was checked also for what concerns the participants\u0026rsquo; compliance in addressing the final instruction asking to touch the spider: McNemar\u0026rsquo;s test was used to test possible differences in this behavior.\u003c/p\u003e\n \u003cp\u003eDimensionality Reduction and Clustering Analysis on vr-BAT features\u003c/p\u003e\n \u003cp\u003eTo reach a comprehensive characterization of avoidance behavior, beyond traditional metrics, we analyzed a set of behavioral features extracted from the vr-BAT (detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These features were examined through multiple analytical steps to assess their interrelationships, their potential to reveal distinct avoidance profiles and their alignment with established avoidance measures from real-BAT.\u003c/p\u003e\n \u003cp\u003eCorrelation Analysis\u003c/p\u003e\n \u003cp\u003eFirst, we computed Pearson correlation coefficients to assess the relationships among vr-BAT features and their cross-correlations with standard avoidance measures from real-BAT, namely \u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e, and \u003cem\u003eVelocity\u003c/em\u003e, as well as with SPQ scores (self-reported fear of spiders). This step allowed us to identify highly intercorrelated features and to evaluate how vr-BAT metrics aligned with traditional indices of avoidance and subjective fear assessments.\u003c/p\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA)\u003c/p\u003e\n \u003cp\u003eTo reduce dimensionality while retaining the most informative components, we applied Principal Component Analysis (PCA) to the extracted vr-BAT features. Principal components (PCs) were retained if they explained at least 90% of the total variance. This selection threshold ensured that the retained PCs captured the majority of the variance while minimizing redundant or noise-driven dimensions.\u003c/p\u003e\n \u003cp\u003eFollowing PCA, a qualitative analysis of the principal components was conducted to interpret their behavioral significance. Specifically, we examined the loading patterns of individual features on each PC to infer their potential meaning in terms of distinct avoidance behaviors. This allowed us to identify which components predominantly reflected spatial distancing (e.g., distance-maintaining behaviors), temporal hesitation (e.g., prolonged approach durations), or other dynamic aspects of movement patterns during the task.\u003c/p\u003e\n \u003cp\u003eCorrelation of Principal Components with Standard Avoidance Measures\u003c/p\u003e\n \u003cp\u003eTo evaluate the alignment of these extracted dimensions with established avoidance indicators, we build several linear models between the selected PCs and the real-BAT measures (\u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e, and \u003cem\u003eVelocity\u003c/em\u003e), as well as SPQ scores. This analysis assessed the degree to which vr-BAT-derived components corresponded to traditional measures of avoidance and subjective fear ratings, offering insights into whether these newly derived components could serve as robust proxies for avoidance behavior.\u003c/p\u003e\n \u003cp\u003eClustering Analysis for Avoidance Profiling\u003c/p\u003e\n \u003cp\u003eFinally, we performed a k-means clustering analysis on the extracted behavioral features to determine whether distinct high-avoidance and low-avoidance profiles could be identified. The optimal number of clusters (k) was determined using the elbow method and the silhouette coefficient, ensuring that the selected clusters provided the best balance between compactness and separation.\u003c/p\u003e\n \u003cp\u003eOnce the clusters were identified, we compared them with participant labels based on SPQ scores (i.e., high- vs. low-spiderfear), acknowledging that avoidance \u0026ndash; although highly correlated with fear \u0026ndash; does not measure exactly the same construct as self-reported phobia severity (Landov\u0026aacute; et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). This comparison allowed us to assess the degree of overlap between behavioral avoidance clusters and subjective fear classifications.\u003c/p\u003e\n \u003cp\u003eThis step aimed to explore whether vr-BAT behavioral patterns could independently differentiate individuals with varying levels of avoidance tendencies, potentially revealing behavioral subtypes that may not be fully captured by self-report measures alone.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eIn accordance with the principles of open science, all data are openly shared at the OSF (Open Science Framework) repository https://osf.io/yn29t/ (DOI 10.17605/OSF.IO/YN29T), containing both their raw and their pre-processed version.\u003c/p\u003e\n\u003cp\u003eThis repository also contains the Unity scenario to replicate the experiment, so that other researchers and clinicians will be able to reproduce the assessment autonomously.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjdacic-Gross, V., Rodgers, S., M\u0026uuml;ller, M., Hengartner, M. 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The spider and the snake \u0026ndash; A psychometric study of two phobias and insights from the Hungarian validation. \u003cem\u003ePsychiatry Research\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e, 61\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.psychres.2017.07.024\u003c/span\u003e\u003cspan address=\"10.1016/j.psychres.2017.07.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"avoidance, phobia, fear, anxiety, avoidant behavior","lastPublishedDoi":"10.21203/rs.3.rs-7534187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7534187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAvoidance is a core psychopathological symptom, yet its elective assessment remains overly simplistic. Here, we introduce a standardized Virtual Reality Behavioral Avoidance Test (vr-BAT) for a multidimensional, dynamic characterization of avoidance behavior.\u003c/p\u003e\u003cp\u003eIn 75 participants with varying arachnophobia levels, we validated the vr-BAT by demonstrating that key avoidance metrics (\u003cem\u003eDistance\u003c/em\u003e, \u003cem\u003eTime\u003c/em\u003e, and \u003cem\u003eVelocity\u003c/em\u003e) correlated robustly with self-reported fear (SPQ).\u003c/p\u003e\u003cp\u003eBased on the trajectories taken to approach the spider, we identified five distinct dimensions of avoidance through principal component analysis \u0026ndash; capturing strategies of initial hesitation (\u003cem\u003eInitial freezing\u003c/em\u003e and \u003cem\u003eAction latency\u003c/em\u003e), coping mechanisms during the task (\u003cem\u003eHesitant approach\u003c/em\u003e and \u003cem\u003eSpatial distancing\u003c/em\u003e), and final reluctance (\u003cem\u003ePre-contact freezing\u003c/em\u003e).\u003c/p\u003e\u003cp\u003eA subsequent clustering analysis revealed two behavioral subtypes of avoidance, only partially overlapping with SPQ-based classifications, emphasizing that subjective fear and behavioral avoidance are complementary but distinct constructs.\u003c/p\u003e\u003cp\u003eThe present vr-BAT represents a robust and standardized framework for assessing avoidance, with translational potential across anxiety disorders.\u003c/p\u003e","manuscriptTitle":"Characterizing the behavioral phenotypes of phobic avoidance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:00:41","doi":"10.21203/rs.3.rs-7534187/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-psychology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commspsychol","sideBox":"Learn more about [Communications Psychology](http://www.nature.com/commspsychol/)","snPcode":"44271","submissionUrl":"https://mts-commspsychol.nature.com/cgi-bin/main.plex","title":"Communications Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb6a6c27-76c3-4343-aaec-514cd55dc212","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55662082,"name":"Social science/Psychology/Human behaviour"},{"id":55662083,"name":"Health sciences/Diseases/Psychiatric disorders/Anxiety"},{"id":55662084,"name":"Health sciences/Health care/Diagnosis"}],"tags":[],"updatedAt":"2026-05-13T15:21:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 17:00:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7534187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7534187","identity":"rs-7534187","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}