Interoception Predicts Mental Imagery Vividness: Exploring a Key Relationship

Interoception Predicts Mental Imagery Vividness: Exploring a Key Relationship | 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 Interoception Predicts Mental Imagery Vividness: Exploring a Key Relationship Yoko Nagai, Sofia Arooj, Tamarin R Futeran-Blake, Ceryn Manders, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6715348/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Interoception, the sense of the internal state of the body, plays a fundamental role in emotional awareness and self-referential processing. Recently, interoception has been linked to the experience of mental imagery. However, this association has not been empirically characterized. We therefore tested how task-based and self-reported measures of cardiac interoception predict individual differences in task-based and self-reported measures of mental imagery. Participants (N = 104) completed two heartbeat detection tasks (heartbeat tracking and heartbeat discrimination; assessing objective interoceptive performance accuracy), and the Multidimensional Assessment of Interoceptive Awareness (MAIA) questionnaire, assessing subjective dimensions of interoceptive experience. Objective and subjective aspects of mental imagery were assessed respectively from performance of a mental rotation task and from scores on the Vividness of Visual Imagery Questionnaire (VVIQ). Results revealed that, across participants, interoceptive (heartbeat discrimination performance) accuracy predicted mental rotation ability. In contrast, both heartbeat tracking accuracy and self-reported interoceptive awareness (MAIA) predicted self-reported vividness of mental imagery (VVIQ). Interoceptive measures did not predict performance on a control task (2-back working memory task). Together, these findings suggest that distinct components of interoception underpin different aspects of mental imagery: Heightened (cardioceptive) physiological sensitivity facilitates the active deployment of imagery in mental rotation and enhances the vividness of imagery experience. Moreover, people reporting more subjective sensitivity to interoceptive state also perceive greater vividness of mental imagery. These new results underscore the influence of bodily representation in shaping conscious experience through both controlled and spontaneous expressions of mental simulation. Biological sciences/Neuroscience/Cognitive neuroscience Biological sciences/Neuroscience/Emotion Biological sciences/Neuroscience/Visual system Introduction Interoception is the ability to sense, interpret, and integrate internal bodily signals including heartbeats, respiration, and visceral sensations. Interoception acts as a crucial bridge between bodily physiology and conscious experience 1 , 2 . While interoception is proximally linked to motivational states, emotional feelings and bodily awareness, it also serves as a substrate for higher-order cognitive functions. The integration of bodily signals with cognitive processes informs decision-making and allows for adaptive responses to uncertainty 2 – 4 . Individuals with greater interoceptive accuracy typically demonstrate improved error detection and more effective decision-making, particularly in emotionally and socially significant contexts 4 . Furthermore, interoception underpins an integrated representation of a ‘biological self’ from which, arguably, is built a stable, unified sense of self as an active agent 2 , 5 , 6 . The integration of bodily states with both perception and cognition extend to mental imagery, i.e. the ability to generate and manipulate sensory representations in the absence of a direct sensory input 7 . Mental imagery is exemplified by visual representations, yet it is inherently multimodal, involving the integration of emotional and physiological components alongside representations in visual and other sensory modalities. For example, imagining a familiar person extends beyond the sensory recollection of visual features to evoke emotional feelings and bodily states associated with past interactions m. From this perspective, interoceptive signals may serve as a physiological anchor for imagery, ensuring that internally generated experiences remain owned and embodied rather than purely abstract (see Muraki et al., 2023 8 , for an embodied perspective on mental imagery). Consistent with this perspective, self-ratings of interoceptive ‘awareness’ are linked to mental imagery: Individuals who score high for traits of mindfulness (with more awareness of their internal experiences) report both more vivid imagery 9 , 10 . More recently, several self-reported interoceptive dimensions, including emotional awareness, self-regulation, and body listening, were found to be positively associated with imagery vividness 11 . Moreover, individuals with aphantasia, who are unable to experience mental imagery, report lower scores on these same interoceptive dimensions 12 . However, self-reported interoception is not a measure of actual interoceptive ability, i.e. the accurate perception of internal bodily signals. For example, adults with autism often report high subjective interoceptive sensitivity, yet on more objective measures, notably heartbeat detection tasks, show significant impairments in task performance 13 . Similarly, scores on the questionnaire ‘Interoceptive Accuracy Scale’, are linked to visual imagery and aphantasia 12 , 14 , yet there is a poor correlation between self-ratings this scale and behavioral measures of interoceptive task performance accuracy 15 . These discrepancies underscore the importance of including task-based assessments of interoception to characterize how dimensions of interoception, including differences in interoceptive ability, relate to mental imagery. To address this issue, we employed both task-based and self-reported interoceptive measures to investigate the relationship between interoception and mental imagery phenomenology. Specifically, participants completed two established cardioceptive tasks, the heartbeat tracking and heartbeat discrimination, to assess interoceptive performance accuracy (and associated confidence). In addition, participants also completed the self-rated Multidimensional Assessment of Interoceptive Awareness (MAIA) questionnaire to capture subjective, experiential aspects of interoceptive awareness. We then tested whether these interoceptive measures predicted objective and experiential aspects of mental imagery. Participants performed a widely used mental rotation task (Metzler & Shepard, 1974), which indexes the effective deployment of visuospatial imagery. To control for domain-general cognitive demands, we included an n-back working memory task as a non-imagery comparison condition. The subjective experiential aspect of mental imagery was quantified using the self-report Vividness of Visual Imagery Questionnaire-2 (VVIQ-2) 16 . Finally, to examine if any observed associations reflected a broader link between interoception and internal cognitive experiences, we included the self-rated Involuntary Autobiographical Memory Inventory (IAMI), which captures the spontaneous occurrence of imagery-based memories in everyday life. Methods Participants A total of 104 participants (77 females, mean age: 22, years, SD = 6.5 years) were recruited from the University of Surrey participant pool and received an Amazon voucher for their participation. All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the University of Surrey Research Ethics Committee. Informed consent was obtained from all participants and/or their legal guardian(s) prior to participation in the study. Heartbeat tracking and discrimination tasks These two cardioceptive tasks were delivered using established procedures 13 using a validated digital platform that combines a dedicated electrocardiogram (ECG) and R-wave detector 17 with a tablet computer running bespoke software to deliver instructions and task conditions ( HeartRater Clinical : https://www.heartrater.co.uk ). This platform is a refinement of methods used in two earlier clinical trials 18 , 19 of interoceptive training and was optimised for a third (ongoing) trial 20 . Here, ECG replaced optical (finger or ear pulse photoplethysmography) measures of heartbeat occurrence to mitigate signal/sensor problems common in people with darker skin or poor peripheral circulation. In the heartbeat tracking task (HBT; e.g. Pollatos et al, 2007 21 ), participants were instructed: “Without manually checking, can you silently count each heartbeat you feel in your body from the time you hear ‘start’ to when you hear ‘stop’?” This task was repeated six times, with time windows of 25, 30, 35, 40, 45, and 50 seconds, presented in a randomized order. After each trial, participants entered their responses on a tablet computer. In the heartbeat discrimination task (HBD) 22 , 23 , participants judged whether a series of ten auditory tones were synchronous with their heartbeat. Each participant received the following instructions: “You will hear ten tones. Please tell me if the tones are in or out of sync with your heartbeat.” This task consisted of 26 trials, with each trial presenting ten tones at 440 Hz with a 100 ms duration, triggered by the sequential heartbeats of the participant. In the synchronous condition, tones were delivered at 250 ms after detection of the ECG R-wave. For the asynchronous condition tones were delivered 550 ms after detection of the R-wave. At the end of each trial, participants responded by stating whether they perceived the tones to be synchronous or asynchronous with their heartbeat. Since tones were always presented at the participant’s own heart rate (either on the heartbeat or time-shifted), participants could not rely on tempo or knowledge of their heart rate to guide responses—only the phase synchrony between tones and heartbeats served as the relevant cue (Weins & Palmer 2001). In both tasks, after each trial, participants immediately rated their confidence in the accuracy of their response. This confidence rating was provided on a tablet-based visual analogue scale (VAS), with “Total guess” on one end and “Total confidence” on the other. Mental rotation task (MRT) This task was adapted from the classic Shepard and Metzler mental rotation experiment and used stimuli from the Mental Rotation Stimulus Library 24 . Each stimulus consisted of 10 white cubes connected in various orientations to form "arms," presented against a black background. A total of 138 stimuli were selected, all rotating around the x-axis with a full, unobstructed view. The rotation angles (40°, 85°, and 220°) and other stimulus parameters were adapted from Pounder et al. (2022) 25 . The task consisted of one block of 96 trials, with 32 trials per difficulty level. On each trial, participants judged whether two images depicted the same shape in different orientations (48 trials) or completely different shapes (24 trials). The stimuli remained on screen until participants responded, ensuring they had sufficient time to complete the comparison. Before the main task, they completed a practice block of 32 trials to familiarize themselves with the task. N-back task As a control task, participants completed a 2-back working memory task, which required continuous monitoring and updating of remembered information. In this task, they viewed a sequence of single-digit numbers (0–9), each presented for 500 ms, followed by a 1500 ms fixation cross. Their task was to detect when a number matched the one presented two trials earlier (25% of trials were targets). It was carried out in four blocks of 50 trials, with participants providing a confidence rating at the end of each block. Before the main task, participants completed two practice blocks to familiarize themselves with the procedure. They were instructed to respond as quickly and accurately as possible using their right index finger. Questionnaire Measures Self-reported aspects of interoception were measured using the 37-item Multidimensional Assessment of Interoceptive Awareness ( MAIA-2; Mehling et al., 2018), which captures how individuals perceive and relate to bodily sensations. The scale includes eight subscales: Noticing (awareness of bodily signals), Not-Distracting and Not-Worrying (responses to discomfort), Attention Regulation (ability to focus on bodily sensations), Emotional Awareness (recognizing bodily-emotional links), Self-Regulation (using body awareness to manage distress), Body Listening (seeking insight from bodily cues), and Trusting (feeling safe and at ease in the body). The phenomenology of participants' mental images was assessed using the Vividness of Visual Imagery Questionnaire-2 (VVIQ-2) 16 , a 32-item self-report measure. Participants were asked to visualize different scenes, such as a sun rising above the horizon into a hazy sky, and rate how vividly they could picture each one. Responses were given on a 5-point scale, ranging from 1 ("No image at all, only a conceptual understanding") to 5 ("Perfectly clear and as vivid as real life"), capturing individual differences in imagery strength. Involuntary autobiographical memories were measured using the Involuntary Autobiographical Memory Inventory (IAMI) 26 , a 20-item questionnaire designed to assess the frequency and nature of spontaneously occurring autobiographical memories (e.g., “Memories of personal events pop into my mind by themselves—without me consciously trying to remember them”). Each item was rated on a 0–4 scale: Never (0), Once a month or more (1), Once a week or more (2), Once a day or more (3), Once an hour or more (4). Procedure Upon arrival at the experimental testing room, participants' blood pressure and pulse rate were measured twice, the average of which was used in the analyses. The participants then completed the questionnaires (administered in a randomised order via a desktop computer using Qualtrics ( https://www.qualtrics.com ). For the performance of the cardiac interoceptive tasks, electrocardiogram (ECG) sensors were placed on the right and left clavicles and the lower left abdomen of each participant to monitor cardiac activity. The participants were seated at a table and instructed to rest their arms on their laps, refrain from crossing their legs, and minimize movement throughout the trials to optimize signal quality. A tablet computer was positioned in front of them, which they used to record responses using their dominant hand. For the performance of the mental rotation and n-back tasks, participants sat facing a computer screen (at a fixed distance of 57 cm). These tasks were presented using Psychopy 2024.2.4 (py3.10) ( https://www.psychopy.org/ ; Peirce et al, 2019). The interoceptive tasks were conducted first to minimize potential arousal effects of the cognitive tasks on heart-rate measurements otherwise task performance was counterbalanced. Data preprocessing Data were analyzed using the Jamovi software package. All variables were first standardized by converting scores to z-scores. Participants with missing data or chance-level performance on the mental rotation and N-back tasks were excluded on a per-variable basis. Outliers for each variable were removed using the interquartile range (IQR) method, excluding values more than 1.5 times the IQR above the third quartile or below the first quartile. Statistical analyses To investigate the contributions of interoceptive processes to mental imagery, we employed a hierarchical linear regression approach guided by theoretical considerations. Predictors were entered in three conceptually distinct blocks: Task-based interoceptive measures : In the first step, objective indices of interoceptive ability derived from heart rate tasks were included: HR tracking accuracy, HR tracking confidence, HR discrimination accuracy, and HR discrimination confidence. Autonomic physiological measures : In the second step, we added autonomic nervous system indices—systolic and diastolic blood pressure and resting pulse rate—to capture the influence of physiological arousal on cognition. These were entered after interoceptive accuracy to reflect their role as bottom-up bodily signals shaped by, and subordinate to, central interoceptive inference. Self-reported interoceptive awareness measures : Finally, we included self-reported interoceptive awareness based on the 8 subscales from the Multidimensional Assessment of Interoceptive Awareness (MAIA). This stepwise modeling strategy allowed us to examine the incremental contribution of each domain while controlling for shared variance. Results Descriptive statistics are shown in Table 1 . Table 2 summarizes the hierarchical regression results across six imagery-related outcomes. VVIQ was significantly predicted by both task-based interoceptive accuracy and self-reported interoceptive awareness, with both Step 1 and Step 3 contributing uniquely to the final model. In contrast, mental rotation accuracy and confidence were predicted only by task-based interoceptive measures (Step 1), with no added explanatory power from physiological or self-report variables. Involuntary memory was significantly predicted only by self-reported interoceptive awareness (Step 3), despite non-significant contributions from earlier steps. No significant effects were found for N-back accuracy or confidence, although the latter showed a trend toward improvement when self-report measures were added. These findings indicate that different forms of mental imagery are supported by distinct interoceptive components, with voluntary and involuntary imagery showing partially dissociable profiles. Table 1 Descriptive statistics. Measure Mean Standard Deviation VVIQ (questionnaire range 32/160) 120.0 19.7 Heartbeat Tracking (HBT) accuracy 0.593 0.235 Heartbeat Tracking (HBT) confidence (1–10 scale) 5.04 1.9 Systole (mmHg) 110.0 11.9 Dystole (mmHg) 70.6 9.05 Pulse rate (bpm) 76.4 11.0 Involuntary Memory (questionnaire range 0–80) 45.5 14.7 Heartbeat Discrimination (HBD) accuracy 0.546 0.121 Heartbeat Discrimination (HBD) confidence (1–10 scale) 5.98 1.2 Mental Rotation accuracy 0.766 0.128 Mental Rotation Confidence (1–5 scale) 4.27 0.507 N-back accuracy 0.803 0.123 N-back confidence (1–5 scale) 3.31 0.801 Table 2 Summary of Hierarchical Multiple Regression Analyses Predicting Imagery. Each outcome variable was regressed on three blocks of predictors: (1) Task-based Interoception (HBT and HBD accuracy and confidence), (2) Autonomic measures (systolic BP, diastolic BP, pulse rate), and (3) Self-reported interoceptive awareness (MAIA subscales). R² = proportion of variance explained. ΔR² = change in variance explained relative to the previous model. Bold p-values indicate statistically significant models (p < .05). VVIQ was significantly predicted by both task-based interoception and self-reported interoceptive awareness. Mental rotation accuracy and confidence were predicted only by task-based interoception whereas involuntary memory was predicted only by self-reported interoceptive awareness. No models significantly predicted N-back accuracy. Outcome Variable Step 1: Task-based Interoception Step 2: Autonomic measures Step 3: Self-reported Interoceptive Awareness (MAIA) Total Model: (Adj. R²) Significant Model Steps Mental Rotation accuracy R² = .177, p = .017 ΔR² = .042, p = .383 ΔR² = .121, p = .348 .143; p = 0.077 Step 1 only Mental Rotation confidence R² = .147, p = .043 ΔR² = .017, p = .756 ΔR² = .168, p = .158 .132; p = 0.092 Step 1 only Vividness VVIQ R² = .136, p = .039 ΔR² = .052, p = .257 ΔR² = .190, p p = .043 . 213; p = 0.012 Step 1 & Step 3; Involuntary Memory R² = .032, p = .664 ΔR² = .026, p = .594 ΔR² = .254, p = .010 . 144; p = 0.048 Step 3 N-back accuracy R² = .011, p = .966 ΔR² = .088, p = .218 ΔR² = .121, p = .643 –.080; p = 0.736 No significant steps N-back confidence R² = .101, p = .171 ΔR² = .092, p = .107 ΔR² = .167, p = .159 .161; p = 0.062 No significant steps Significant predictors: Mental Rotation accuracy Across all models, HBD accuracy emerged as a consistent predictor of Mental Rotation accuracy. In the final model, it remained significant (B = 0.403, SE = 0.136, t = 2.97, p = .005, β = 0.386), indicating that individuals with greater ability to detect their own heartbeats also performed better on the spatial task. No other predictor was statistically significant. Model assumptions were met. Residuals were normally distributed (Shapiro–Wilk = 0.990, p = .890), independent (Durbin–Watson = 1.75, p = .270), and there were no influential outliers (Cook’s Distance Max = 0.157). Multicollinearity was acceptable (all VIFs < 2.36). Significant predictors: Mental Rotation confidence MAIA Worry emerged as a significant negative predictor of confidence on the Mental Rotation task (B = -0.286, SE = 0.0996, t = -2.87, p = .006, β = -0.375), suggesting that individuals who report greater worry about bodily sensations tend to be less confident in their spatial decisions. HBD confidence showed a positive but marginal effect (B = 0.312, SE = 0.161, t = 1.94, p = .058, β = 0.291). Assumption checks supported the model’s validity: residuals were approximately normally distributed (Shapiro–Wilk = 0.978, p = .292), with no strong evidence of autocorrelation (Durbin–Watson = 2.18, p = .494) or influential outliers (Cook’s Distance Max = 0.196). Multicollinearity was low to moderate (VIFs < 2). Significant individual predictors: Vividness of Visual Imagery (VVIQ) The significance level of the regression models is shown in Table 2 . In terms of the individual predictors, HBT accuracy was a significant positive predictor of VVIQ (B = 0.269, SE = 0.087, t = 3.08, p = .003, β = 0.352), indicating that individuals with better heartbeat tracking accuracy reported more vivid visual imagery. Individual differences in resting pulse rate also emerged as a significant predictor (B = 0.276, SE = 0.106, t = 2.61, p = .011, β = 0.303), suggesting that higher physiological arousal is associated with increased imagery vividness. Among the self-reported interoceptive awareness subscales, MAIA Noticing (B = 0.186, SE = 0.093, t = 1.99, p = .051, β = 0.250), MAIA Emotion (B = 0.208, SE = 0.106, t = 1.96, p = .055, β = 0.253), and MAIA Trust (B = 0.176, SE = 0.093, t = 1.88, p = .065, β = 0.243) each showed positive associations at trend level. Assumption checks supported model validity. Residuals were normally distributed (Shapiro–Wilk = 0.988, p = .725), independent (Durbin–Watson = 2.43, p = .076), and not influenced by outliers (Cook’s Distance Max = 0.172). Multicollinearity was acceptable, with all VIFs < 2.35. Significant individual predictors: Involuntary memory While Model 3 (see Table 2 ) was statistically significant, none of the individual predictors reached conventional levels of significance, although MAIA Noticing showed a positive trend (B = 0.266, SE = 0.138, t = 1.92, p = .059, β = 0.245), suggesting individuals more attuned to bodily sensations may experience more involuntary memories. Assumption checks indicated normally distributed residuals (Shapiro–Wilk = 0.991, p = .892), low autocorrelation (Durbin–Watson = 2.22, p = .368), and acceptable multicollinearity (all VIFs ≤ 2.20). No influential outliers were detected (Cook’s Distance Max = 0.083). This suggests that while individual subscale effects were below statistical threshold, the MAIA scales contributed cumulatively to involuntary memory performance. Discussion The aim of this study was to characterize the relationship between (objective and subjective) measures of interoceptive sensitivity and the experience of mental imagery. First, regression analyses indicated that accuracy and confidence in performing a mental rotation task (indexing the capacity to mobilise mental imagery) were linked exclusively to task-based interoceptive measures (HBD performance accuracy and confidence, respectively). In contrast, the reported vividness of visual imagery (VVIQ) was significantly predicted by both task-based interoceptive measures (HBT performance accuracy) as well as self-reported interoceptive awareness (as assessed by MAIA questionnaire). Involuntary autobiographical memory was also significantly predicted only by self-reported interoceptive awareness. For n-back task, none of the models incorporating interoceptive and autonomic measures reached statistical significance. Distinct effects of heartbeat tracking (HBT) and heartbeat discrimination (HBD) A key finding was that the two task-based interoceptive measures (HBT and HBD) were linked to distinct aspects of mental imagery. Better HBT performance accuracy was predictive of increasing vividness of mental imagery whereas HBD predicted performance on a mental rotation task dependent upon the flexible deployment of mental imagery. This dissociation suggests that while interoceptive ability is linked to mental imagery, performance of the HBT and HBD tasks engage different cognitive processes that align respectively with dissociable perceptual (VVIQ) and executive (mental rotation) aspects of mental imagery. The HBT task and rating the d vividness of mental imagery both rely on attention to, and perceptual judgement of, internal representations in the absence of an external reference. In contrast, both the ‘cross-modal’ HBD task and the mental rotation task involve the comparison of an internally generated representation to external information: The HBD task requires individuals to judge whether their heartbeats are synchronous to external stimuli (auditory tones). Similarly, in the mental rotation task, individuals must mentally transform an internal representation of an object and compare its imagined orientation to a target stimulus on the screen. Relationship between pulse rate and VVIQ Pulse rate was also a significant predictor of VVIQ, suggesting that autonomic states may influence imagery vividness, potentially reflecting a broader connection between physiological arousal and internally generated experiences. Pulse rate serves not only as as a direct index of autonomic nervous system (ANS) activity, reflecting the balance between sympathetic and parasympathetic responses, but also as a measure of the feedback (via e.g. baroreceptor activation) of cardiovascular arousal states on brain function. Higher pulse rates are typically associated with greater sympathetic activation, which typically accompanies states of mental alertness and emotional/behavioural engagement. This bidirectional link raises the possibility that the vividness of mental imagery may be amplified by heightened psychophysiological arousal. One explanation is that, though both sympathetic efferent drive and afferent interoceptive pathways, increased autonomic engagement enhances the salience of internally generated representations increasing feelings of reality and immersion. This aligns with research showing that emotional and physiological responses are encoded alongside perceptual experiences, strengthening both recall and imagery 27 . If interoceptive signals, including heart rate fluctuations, provide an embodied foundation for mental imagery, then autonomic activation may amplify the sensory and emotional intensity of internally generated experiences, reinforcing the link between bodily states and cognitive simulations. Distinct Contributions of task-based and self-reported interoception While mental rotation performance was solely predicted by task-based interoceptive measures (i.e. Step 1 in regression), the vividness of visual imagery (VVIQ) was additionally predicted by self-reported interoceptive awareness (Step 3 in regression), as assessed by the MAIA questionnaire. This highlights the convergence of two levels of interoception representation towards the construction of the subjective experience of mental imagery. The heartbeat tracking task arguably measures the precision of detecting internal bodily signals, whereas the MAIA focuses on how one pays attention to, emotionally respond to, and regulates one’s bodily experiences. These two measures tap into dissociable processes: HBT performance accuracy, although not immune to long acknowledged top-down influences that constrain over-interpretation, encompasses basic online sensory signal detection, while the MAIA subscales reflect an elaborated integration of interoceptive sensations with past emotional and cognitive experiences. Together, the findings suggest that vivid mental imagery depends on both access to bodily signals and the way in which these signals are integrated sensorially into intentional mental processes. Dissociation between voluntary imagery and involuntary autobiographical memory Notably, the regression analysis indicated that both VVIQ and involuntary autobiographical memory (IAMI) were associated self-reported interoceptive awareness. As both VVIQ and IAMI quantify experiential aspects of mental imagery, this finding supports the view that conscious interoceptive representation provides a foundational substrate for imagery more broadly, whether spontaneously occurring or deliberately constructed. However, only voluntary imagery vividness (VVIQ) was also significantly linked to task-based measures of interoceptive accuracy. This pattern suggests that while involuntary imagery may be driven predominantly by bottom-up processes such as spontaneous activation of sensorimotor and interoceptive representations and/or an increased attention devoted to them, voluntary imagery additionally recruits top-down mechanisms that depend on the accurate detection and integration of internal bodily signals to support intentional and vivid mental simulation. Taken together, these findings indicate that subjective interoceptive awareness is a core feature of imagery experiences, yet the volitional construction of imagery relies also on interoceptive precision, These new observations support the idea that heightened sensitivity to internal bodily signals, such as fluctuations in heart rate, strengthens the connection between physiological states and mental simulations, leading to more vivid mental imagery 7 . Mental imagery, by nature, engages both physiological and emotional responses as it reconstructs past experiences and envisions future events. This mind-body connection is mediated by autonomic responses and interoceptive processes. When encountering a new stimulus, the body undergoes stereotypical changes, including shifts in heart rate and electrodermal activity, which are encoded in memory 27 , 28 . Later, these physiological responses can be reactivated during recall or imagination, enhancing the immersive quality of imagery. This reactivation is especially evident in emotional imagery, where imagining fear-related scenes can trigger measurable physiological arousal, similar to firsthand experience 29 . Additionally, these results show that the vividness of mental imagery depends not only on the ability to access internal bodily signals but also on how well individuals integrate these signals with their emotional and cognitive states. Our findings thus support the view that mental imagery is an integrative process, where current and past sensory, emotional, and physiological information dynamically interact, to construct a coherent conscious experience. The identified associations between interoception and imagery suggest that bodily signals provide a foundation for grounding mental simulations in self-referential bodily states. Rather than operating independently, imagery-based cognition is embedded within ongoing physiological processes. Our findings highlight the importance of considering the body within cognitive models of imagery and underscores the need for future research to explore how different types of interoceptive signals—such as heartbeat, respiration, and digestive cues—affect specific expressions of mental imagery and affective processing. Data availability Statement The experimental data of this study are available in https://osf.io/hbdxv/ . Declarations Data availability Statement The experimental data of this study are available in https://osf.io/hbdxv/. Funding JS was funded by ESRC. Author Contribution YN and JS were responsible for the overall study process, including conceptualization, methodology, and execution. YN, JS, and HC contributed to writing the main manuscript text. SA, TF, and CM were involved in data collection. All authors reviewed and approved the final manuscript. 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New directions for mental imagery research. J. Ment. Imagery 19 , 153–167. (1995). Möller T, G. Y., Voss M, Kaltwasser L. in EEE Signal Processing in Medicine and Biology Symposium Dec 3 (pp. 1-3) (IEEE, 2022). Quadt, L. et al. Interoceptive training to target anxiety in autistic adults (ADIE): A single-center, superiority randomized controlled trial. EClinicalMedicine 39 , 101042 (2021). https://doi.org/10.1016/j.eclinm.2021.101042 Davies, G. et al. Altering Dynamics of Autonomic Processing Therapy (ADAPT) trial: a novel, targeted treatment for reducing anxiety in joint hypermobility. Trials 22 , 645 (2021). https://doi.org/10.1186/s13063-021-05555-4 Critchley, H., & Quadt, L. . (Medical Research Council. , 2023). Pollatos O, T.-M. E., Schroeder H, Schandry R. . Interoceptive awareness mediates the relationship between anxiety and the intensity of unpleasant feelings. Journal of anxiety disorders. Journal of anxiety disorders. 21 , 931-943. (2007 ). Katkin ES, R. S., Deroo C. . A methodological analysis of 3 techniques for the assessment of individual-differences in heartbeat detection. . Psychophysiology 20 , 452-452 (1983 ). Wiens, S. & Palmer, S. N. Quadratic trend analysis and heartbeat detection. Biol Psychol 58 , 159-175 (2001). https://doi.org/10.1016/s0301-0511(01)00110-7 Petersen, S. E. & Posner, M. I. The attention system of the human brain: 20 years after. Annu Rev Neurosci 35 , 73-89 (2012). https://doi.org/10.1146/annurev-neuro-062111-150525 Pounder Z, J. J., Evans S, Loveday C, Eardley AF, Silvanto J Only minimal differences between individuals with congenital aphantasia and those with typical imagery on neuropsychological tasks that involve imagery. Cortex 148 , 180-192. (2022). https://doi.org/10.1016/j.cortex.2021.12.010 Berntsen, D., Rubin, D. C. & Salgado, S. The frequency of involuntary autobiographical memories and future thoughts in relation to daydreaming, emotional distress, and age. Conscious Cogn 36 , 352-372 (2015). https://doi.org/10.1016/j.concog.2015.07.007 van der Kolk, B. A. The body keeps the score: memory and the evolving psychobiology of posttraumatic stress. Harv Rev Psychiatry 1 , 253-265 (1994). https://doi.org/10.3109/10673229409017088 Sokolov, E. N. Higher nervous functions; the orienting reflex. Annu Rev Physiol 25 , 545-580 (1963). https://doi.org/10.1146/annurev.ph.25.030163.002553 Lang, P. J. A bio‐informational theory of emotional imagery. Psychophysiology 16 , 495-512 (1979). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6715348","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482459475,"identity":"eb547123-85f6-4373-b95a-7057d483513e","order_by":0,"name":"Yoko Nagai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYPACCTkDOJuHSC3GJGthSNxAtBaDA7wHP36psUjfzn86+QNDjR2DwZkDhLTwJUvLHJPI3Tkjd5sEw7FkBoOzDYS08BhIS7BJ5G64wbuNgYHtAIPBeYIO4zH+LfFPIt3g/NnNHxj+EafFTPJjm0SCwYHcDRKMbQcIO0zyMI+ZNWOfhCHYL4l9yTyShLzPd7zH+OaPb3Xy5vxAh334ZifHdyYBvxaFwwwMzPCYSCAmIuWBDmf8QVDZKBgFo2AUjGgAAAQzQiz1/wP6AAAAAElFTkSuQmCC","orcid":"","institution":"University of Sussex","correspondingAuthor":true,"prefix":"","firstName":"Yoko","middleName":"","lastName":"Nagai","suffix":""},{"id":482459477,"identity":"b5d327ca-0d66-4137-bafb-fc8a9cc1b580","order_by":1,"name":"Sofia Arooj","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Arooj","suffix":""},{"id":482459478,"identity":"bc519a00-1121-490a-9001-80254826c86e","order_by":2,"name":"Tamarin R Futeran-Blake","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Tamarin","middleName":"R","lastName":"Futeran-Blake","suffix":""},{"id":482459479,"identity":"ecdfbaf0-b2a6-4cf4-a97c-9e1c59bf5652","order_by":3,"name":"Ceryn Manders","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Ceryn","middleName":"","lastName":"Manders","suffix":""},{"id":482459480,"identity":"2f1bc674-295f-4a89-b775-3ab21ca92bbe","order_by":4,"name":"Hugo Critchley","email":"","orcid":"","institution":"University of Sussex","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"","lastName":"Critchley","suffix":""},{"id":482459481,"identity":"ee0fab61-51bb-4613-9134-871a8218bfbc","order_by":5,"name":"Juha Silvanto","email":"","orcid":"","institution":"University of Macau","correspondingAuthor":false,"prefix":"","firstName":"Juha","middleName":"","lastName":"Silvanto","suffix":""}],"badges":[],"createdAt":"2025-05-21 10:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6715348/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6715348/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-43805-0","type":"published","date":"2026-03-19T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105223259,"identity":"d0e84861-447f-4b81-a77e-6ddf91d622ae","added_by":"auto","created_at":"2026-03-23 16:01:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1308945,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6715348/v1/48921844-5e05-4b4b-ac55-e10cf41902c7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interoception Predicts Mental Imagery Vividness: Exploring a Key Relationship","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInteroception is the ability to sense, interpret, and integrate internal bodily signals including heartbeats, respiration, and visceral sensations. Interoception acts as a crucial bridge between bodily physiology and conscious experience \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While interoception is proximally linked to motivational states, emotional feelings and bodily awareness, it also serves as a substrate for higher-order cognitive functions. The integration of bodily signals with cognitive processes informs decision-making and allows for adaptive responses to uncertainty\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Individuals with greater interoceptive accuracy typically demonstrate improved error detection and more effective decision-making, particularly in emotionally and socially significant contexts\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Furthermore, interoception underpins an integrated representation of a \u0026lsquo;biological self\u0026rsquo; from which, arguably, is built a stable, unified sense of self as an active agent\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe integration of bodily states with both perception and cognition extend to mental imagery, i.e. the ability to generate and manipulate sensory representations in the absence of a direct sensory input \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Mental imagery is exemplified by visual representations, yet it is inherently multimodal, involving the integration of emotional and physiological components alongside representations in visual and other sensory modalities. For example, imagining a familiar person extends beyond the sensory recollection of visual features to evoke emotional feelings and bodily states associated with past interactions m. From this perspective, interoceptive signals may serve as a physiological anchor for imagery, ensuring that internally generated experiences remain owned and embodied rather than purely abstract (see Muraki et al., 2023\u003csup\u003e8\u003c/sup\u003e, for an embodied perspective on mental imagery).\u003c/p\u003e\u003cp\u003eConsistent with this perspective, self-ratings of interoceptive \u0026lsquo;awareness\u0026rsquo; are linked to mental imagery: Individuals who score high for traits of mindfulness (with more awareness of their internal experiences) report both more vivid imagery\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. More recently, several self-reported interoceptive dimensions, including emotional awareness, self-regulation, and body listening, were found to be positively associated with imagery vividness\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Moreover, individuals with aphantasia, who are unable to experience mental imagery, report lower scores on these same interoceptive dimensions\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, self-reported interoception is not a measure of actual interoceptive ability, i.e. the accurate perception of internal bodily signals. For example, adults with autism often report high subjective interoceptive sensitivity, yet on more objective measures, notably heartbeat detection tasks, show significant impairments in task performance\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Similarly, scores on the questionnaire \u0026lsquo;Interoceptive Accuracy Scale\u0026rsquo;, are linked to visual imagery and aphantasia\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, yet there is a poor correlation between self-ratings this scale and behavioral measures of interoceptive task performance accuracy\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. These discrepancies underscore the importance of including task-based assessments of interoception to characterize how dimensions of interoception, including differences in interoceptive ability, relate to mental imagery.\u003c/p\u003e\u003cp\u003eTo address this issue, we employed both task-based and self-reported interoceptive measures to investigate the relationship between interoception and mental imagery phenomenology. Specifically, participants completed two established cardioceptive tasks, the heartbeat tracking and heartbeat discrimination, to assess interoceptive performance accuracy (and associated confidence). In addition, participants also completed the self-rated Multidimensional Assessment of Interoceptive Awareness (MAIA) questionnaire to capture subjective, experiential aspects of interoceptive awareness. We then tested whether these interoceptive measures predicted objective and experiential aspects of mental imagery. Participants performed a widely used mental rotation task (Metzler \u0026amp; Shepard, 1974), which indexes the effective deployment of visuospatial imagery. To control for domain-general cognitive demands, we included an n-back working memory task as a non-imagery comparison condition. The subjective experiential aspect of mental imagery was quantified using the self-report Vividness of Visual Imagery Questionnaire-2 (VVIQ-2)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Finally, to examine if any observed associations reflected a broader link between interoception and internal cognitive experiences, we included the self-rated Involuntary Autobiographical Memory Inventory (IAMI), which captures the spontaneous occurrence of imagery-based memories in everyday life.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eA total of 104 participants (77 females, mean age: 22, years, SD\u0026thinsp;=\u0026thinsp;6.5 years) were recruited from the University of Surrey participant pool and received an Amazon voucher for their participation. \u003cb\u003e All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the University of Surrey Research Ethics Committee. Informed consent was obtained from all participants and/or their legal guardian(s) prior to participation in the study.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eHeartbeat tracking and discrimination tasks\u003c/h3\u003e\n\u003cp\u003eThese two cardioceptive tasks were delivered using established procedures\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e using a validated digital platform that combines a dedicated electrocardiogram (ECG) and R-wave detector\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e with a tablet computer running bespoke software to deliver instructions and task conditions (\u003cb\u003eHeartRater Clinical\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.heartrater.co.uk\u003c/span\u003e\u003cspan address=\"https://www.heartrater.co.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e This platform is a refinement of methods used in two earlier clinical trials\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e of interoceptive training and was optimised for a third (ongoing) trial\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Here, ECG replaced optical (finger or ear pulse photoplethysmography) measures of heartbeat occurrence to mitigate signal/sensor problems common in people with darker skin or poor peripheral circulation. In the heartbeat tracking task (HBT; e.g. Pollatos et al, 2007\u003csup\u003e21\u003c/sup\u003e), participants were instructed: \u0026ldquo;Without manually checking, can you silently count each heartbeat you feel in your body from the time you hear \u0026lsquo;start\u0026rsquo; to when you hear \u0026lsquo;stop\u0026rsquo;?\u0026rdquo; This task was repeated six times, with time windows of 25, 30, 35, 40, 45, and 50 seconds, presented in a randomized order. After each trial, participants entered their responses on a tablet computer. In the heartbeat discrimination task (HBD)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, participants judged whether a series of ten auditory tones were synchronous with their heartbeat. Each participant received the following instructions: \u0026ldquo;You will hear ten tones. Please tell me if the tones are in or out of sync with your heartbeat.\u0026rdquo; This task consisted of 26 trials, with each trial presenting ten tones at 440 Hz with a 100 ms duration, triggered by the sequential heartbeats of the participant. In the synchronous condition, tones were delivered at 250 ms after detection of the ECG R-wave. For the asynchronous condition tones were delivered 550 ms after detection of the R-wave. At the end of each trial, participants responded by stating whether they perceived the tones to be synchronous or asynchronous with their heartbeat. Since tones were always presented at the participant\u0026rsquo;s own heart rate (either on the heartbeat or time-shifted), participants could not rely on tempo or knowledge of their heart rate to guide responses\u0026mdash;only the phase synchrony between tones and heartbeats served as the relevant cue (Weins \u0026amp; Palmer 2001). In both tasks, after each trial, participants immediately rated their confidence in the accuracy of their response. This confidence rating was provided on a tablet-based visual analogue scale (VAS), with \u0026ldquo;Total guess\u0026rdquo; on one end and \u0026ldquo;Total confidence\u0026rdquo; on the other.\u003c/p\u003e\n\u003ch3\u003eMental rotation task (MRT)\u003c/h3\u003e\n\u003cp\u003eThis task was adapted from the classic Shepard and Metzler mental rotation experiment and used stimuli from the Mental Rotation Stimulus Library \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Each stimulus consisted of 10 white cubes connected in various orientations to form \"arms,\" presented against a black background. A total of 138 stimuli were selected, all rotating around the x-axis with a full, unobstructed view. The rotation angles (40\u0026deg;, 85\u0026deg;, and 220\u0026deg;) and other stimulus parameters were adapted from Pounder et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The task consisted of one block of 96 trials, with 32 trials per difficulty level. On each trial, participants judged whether two images depicted the same shape in different orientations (48 trials) or completely different shapes (24 trials). The stimuli remained on screen until participants responded, ensuring they had sufficient time to complete the comparison. Before the main task, they completed a practice block of 32 trials to familiarize themselves with the task.\u003c/p\u003e\n\u003ch3\u003eN-back task\u003c/h3\u003e\n\u003cp\u003eAs a control task, participants completed a 2-back working memory task, which required continuous monitoring and updating of remembered information. In this task, they viewed a sequence of single-digit numbers (0\u0026ndash;9), each presented for 500 ms, followed by a 1500 ms fixation cross. Their task was to detect when a number matched the one presented two trials earlier (25% of trials were targets). It was carried out in four blocks of 50 trials, with participants providing a confidence rating at the end of each block. Before the main task, participants completed two practice blocks to familiarize themselves with the procedure. They were instructed to respond as quickly and accurately as possible using their right index finger.\u003c/p\u003e\n\u003ch3\u003eQuestionnaire Measures\u003c/h3\u003e\n\u003cp\u003eSelf-reported aspects of interoception were measured using the 37-item \u003cem\u003eMultidimensional Assessment of Interoceptive Awareness (\u003c/em\u003eMAIA-2; Mehling et al., 2018), which captures how individuals perceive and relate to bodily sensations. The scale includes eight subscales: Noticing (awareness of bodily signals), Not-Distracting and Not-Worrying (responses to discomfort), Attention Regulation (ability to focus on bodily sensations), Emotional Awareness (recognizing bodily-emotional links), Self-Regulation (using body awareness to manage distress), Body Listening (seeking insight from bodily cues), and Trusting (feeling safe and at ease in the body).\u003c/p\u003e\u003cp\u003eThe phenomenology of participants' mental images was assessed using the \u003cem\u003eVividness of Visual Imagery Questionnaire-2\u003c/em\u003e (VVIQ-2)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, a 32-item self-report measure. Participants were asked to visualize different scenes, such as a sun rising above the horizon into a hazy sky, and rate how vividly they could picture each one. Responses were given on a 5-point scale, ranging from 1 (\"No image at all, only a conceptual understanding\") to 5 (\"Perfectly clear and as vivid as real life\"), capturing individual differences in imagery strength.\u003c/p\u003e\u003cp\u003eInvoluntary autobiographical memories were measured using the \u003cem\u003eInvoluntary Autobiographical Memory Inventory (IAMI)\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, a 20-item questionnaire designed to assess the frequency and nature of spontaneously occurring autobiographical memories (e.g., \u0026ldquo;Memories of personal events pop into my mind by themselves\u0026mdash;without me consciously trying to remember them\u0026rdquo;). Each item was rated on a 0\u0026ndash;4 scale: Never (0), Once a month or more (1), Once a week or more (2), Once a day or more (3), Once an hour or more (4).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eUpon arrival at the experimental testing room, participants' blood pressure and pulse rate were measured twice, the average of which was used in the analyses. The participants then completed the questionnaires (administered in a randomised order via a desktop computer using Qualtrics \u003cb\u003e(\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.qualtrics.com\u003c/span\u003e\u003cspan address=\"https://www.qualtrics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e For the performance of the cardiac interoceptive tasks, electrocardiogram (ECG) sensors were placed on the right and left clavicles and the lower left abdomen of each participant to monitor cardiac activity. The participants were seated at a table and instructed to rest their arms on their laps, refrain from crossing their legs, and minimize movement throughout the trials to optimize signal quality. A tablet computer was positioned in front of them, which they used to record responses using their dominant hand. For the performance of the mental rotation and n-back tasks, participants sat facing a computer screen (at a fixed distance of 57 cm). \u003cb\u003eThese tasks were presented using Psychopy 2024.2.4 (py3.10) (\u003c/b\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.psychopy.org/\u003c/span\u003e\u003cspan address=\"https://www.psychopy.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; \u003cb\u003ePeirce et al, 2019).\u003c/b\u003e The interoceptive tasks were conducted first to minimize potential arousal effects of the cognitive tasks on heart-rate measurements otherwise task performance was counterbalanced.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData preprocessing\u003c/h3\u003e\n\u003cp\u003eData were analyzed using the Jamovi software package. All variables were first standardized by converting scores to z-scores. Participants with missing data or chance-level performance on the mental rotation and N-back tasks were excluded on a per-variable basis. Outliers for each variable were removed using the interquartile range (IQR) method, excluding values more than 1.5 times the IQR above the third quartile or below the first quartile.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eTo investigate the contributions of interoceptive processes to mental imagery, we employed a hierarchical linear regression approach guided by theoretical considerations. Predictors were entered in three conceptually distinct blocks:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eTask-based interoceptive measures\u003c/em\u003e: In the first step, objective indices of interoceptive ability derived from heart rate tasks were included: HR tracking accuracy, HR tracking confidence, HR discrimination accuracy, and HR discrimination confidence.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAutonomic physiological measures\u003c/em\u003e: In the second step, we added autonomic nervous system indices\u0026mdash;systolic and diastolic blood pressure and resting pulse rate\u0026mdash;to capture the influence of physiological arousal on cognition. These were entered after interoceptive accuracy to reflect their role as bottom-up bodily signals shaped by, and subordinate to, central interoceptive inference.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eSelf-reported interoceptive awareness measures\u003c/em\u003e: Finally, we included self-reported interoceptive awareness based on the 8 subscales from the Multidimensional Assessment of Interoceptive Awareness (MAIA).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThis stepwise modeling strategy allowed us to examine the incremental contribution of each domain while controlling for shared variance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the hierarchical regression results across six imagery-related outcomes. VVIQ was significantly predicted by both task-based interoceptive accuracy and self-reported interoceptive awareness, with both Step 1 and Step 3 contributing uniquely to the final model. In contrast, mental rotation accuracy and confidence were predicted only by task-based interoceptive measures (Step 1), with no added explanatory power from physiological or self-report variables. Involuntary memory was significantly predicted only by self-reported interoceptive awareness (Step 3), despite non-significant contributions from earlier steps. No significant effects were found for N-back accuracy or confidence, although the latter showed a trend toward improvement when self-report measures were added. These findings indicate that different forms of mental imagery are supported by distinct interoceptive components, with voluntary and involuntary imagery showing partially dissociable profiles.\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\u003eDescriptive statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Deviation\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\u003eVVIQ (questionnaire range 32/160)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeartbeat Tracking (HBT) accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeartbeat Tracking (HBT) confidence (1\u0026ndash;10 scale)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystole (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDystole (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePulse rate (bpm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvoluntary Memory (questionnaire range 0\u0026ndash;80)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeartbeat Discrimination (HBD) accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeartbeat Discrimination (HBD) confidence (1\u0026ndash;10 scale)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMental Rotation accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMental Rotation Confidence (1\u0026ndash;5 scale)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN-back accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN-back confidence (1\u0026ndash;5 scale)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Hierarchical Multiple Regression Analyses Predicting Imagery. \u003cem\u003eEach outcome variable was regressed on three blocks of predictors: (1) Task-based Interoception (HBT and HBD accuracy and confidence), (2) Autonomic measures (systolic BP, diastolic BP, pulse rate), and (3) Self-reported interoceptive awareness (MAIA subscales). R\u0026sup2; = proportion of variance explained. ΔR\u0026sup2; = change in variance explained relative to the previous model. Bold p-values indicate statistically significant models (p\u0026thinsp;\u0026lt;\u0026thinsp;.05). VVIQ was significantly predicted by both task-based interoception and self-reported interoceptive awareness. Mental rotation accuracy and confidence were predicted only by task-based interoception whereas involuntary memory was predicted only by self-reported interoceptive awareness. No models significantly predicted N-back accuracy.\u003c/em\u003e\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\u003cp\u003e\u003cem\u003eOutcome Variable\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStep 1:\u003c/p\u003e\u003cp\u003eTask-based Interoception\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStep 2: Autonomic measures\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStep 3:\u003c/p\u003e\u003cp\u003eSelf-reported Interoceptive Awareness (MAIA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Model:\u003c/p\u003e\u003cp\u003e(Adj. R\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSignificant Model Steps\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\u003eMental Rotation accuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u0026sup2; = .177, p\u0026thinsp;=\u0026thinsp;.017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .042, p\u0026thinsp;=\u0026thinsp;.383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔR\u0026sup2; = .121, p\u0026thinsp;=\u0026thinsp;.348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.143; p\u0026thinsp;=\u0026thinsp;0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStep 1 only\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMental Rotation confidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u0026sup2; = .147, p\u0026thinsp;=\u0026thinsp;.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .017, p\u0026thinsp;=\u0026thinsp;.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔR\u0026sup2; = .168, p\u0026thinsp;=\u0026thinsp;.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.132; p\u0026thinsp;=\u0026thinsp;0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStep 1 only\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVividness\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVVIQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u0026sup2; = .136, p\u0026thinsp;=\u0026thinsp;.039\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .052, p\u0026thinsp;=\u0026thinsp;.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eΔR\u0026sup2; = .190, p p\u0026thinsp;=\u0026thinsp;.043\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003cb\u003e213; p\u0026thinsp;=\u0026thinsp;0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStep 1 \u0026amp;\u003c/p\u003e\u003cp\u003eStep 3;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInvoluntary Memory\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u0026sup2; = .032, p\u0026thinsp;=\u0026thinsp;.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .026, p\u0026thinsp;=\u0026thinsp;.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eΔR\u0026sup2; = .254, p\u0026thinsp;=\u0026thinsp;.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003cb\u003e144; p\u0026thinsp;=\u0026thinsp;0.048\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStep 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN-back\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eaccuracy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u0026sup2; = .011, p\u0026thinsp;=\u0026thinsp;.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .088, p\u0026thinsp;=\u0026thinsp;.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔR\u0026sup2; = .121, p\u0026thinsp;=\u0026thinsp;.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;.080; p\u0026thinsp;=\u0026thinsp;0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo significant steps\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN-back confidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u0026sup2; = .101, p\u0026thinsp;=\u0026thinsp;.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔR\u0026sup2; = .092, p\u0026thinsp;=\u0026thinsp;.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔR\u0026sup2; = .167, p\u0026thinsp;=\u0026thinsp;.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.161; p\u0026thinsp;=\u0026thinsp;0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo significant steps\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSignificant predictors: Mental Rotation accuracy\u003c/h2\u003e\u003cp\u003eAcross all models, HBD accuracy emerged as a consistent predictor of Mental Rotation accuracy. In the final model, it remained significant (B\u0026thinsp;=\u0026thinsp;0.403, SE\u0026thinsp;=\u0026thinsp;0.136, t\u0026thinsp;=\u0026thinsp;2.97, p\u0026thinsp;=\u0026thinsp;.005, β\u0026thinsp;=\u0026thinsp;0.386), indicating that individuals with greater ability to detect their own heartbeats also performed better on the spatial task. No other predictor was statistically significant. Model assumptions were met. Residuals were normally distributed (Shapiro\u0026ndash;Wilk\u0026thinsp;=\u0026thinsp;0.990, p\u0026thinsp;=\u0026thinsp;.890), independent (Durbin\u0026ndash;Watson\u0026thinsp;=\u0026thinsp;1.75, p\u0026thinsp;=\u0026thinsp;.270), and there were no influential outliers (Cook\u0026rsquo;s Distance Max\u0026thinsp;=\u0026thinsp;0.157). Multicollinearity was acceptable (all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;2.36).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSignificant predictors: Mental Rotation confidence\u003c/h2\u003e\u003cp\u003eMAIA Worry emerged as a significant negative predictor of confidence on the Mental Rotation task (B = -0.286, SE\u0026thinsp;=\u0026thinsp;0.0996, t = -2.87, p\u0026thinsp;=\u0026thinsp;.006, β = -0.375), suggesting that individuals who report greater worry about bodily sensations tend to be less confident in their spatial decisions. HBD confidence showed a positive but marginal effect (B\u0026thinsp;=\u0026thinsp;0.312, SE\u0026thinsp;=\u0026thinsp;0.161, t\u0026thinsp;=\u0026thinsp;1.94, p\u0026thinsp;=\u0026thinsp;.058, β\u0026thinsp;=\u0026thinsp;0.291). Assumption checks supported the model\u0026rsquo;s validity: residuals were approximately normally distributed (Shapiro\u0026ndash;Wilk\u0026thinsp;=\u0026thinsp;0.978, p\u0026thinsp;=\u0026thinsp;.292), with no strong evidence of autocorrelation (Durbin\u0026ndash;Watson\u0026thinsp;=\u0026thinsp;2.18, p\u0026thinsp;=\u0026thinsp;.494) or influential outliers (Cook\u0026rsquo;s Distance Max\u0026thinsp;=\u0026thinsp;0.196). Multicollinearity was low to moderate (VIFs\u0026thinsp;\u0026lt;\u0026thinsp;2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSignificant individual predictors: Vividness of Visual Imagery (VVIQ)\u003c/h2\u003e\u003cp\u003eThe significance level of the regression models is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In terms of the individual predictors, HBT accuracy was a significant positive predictor of VVIQ (B\u0026thinsp;=\u0026thinsp;0.269, SE\u0026thinsp;=\u0026thinsp;0.087, t\u0026thinsp;=\u0026thinsp;3.08, p\u0026thinsp;=\u0026thinsp;.003, β\u0026thinsp;=\u0026thinsp;0.352), indicating that individuals with better heartbeat tracking accuracy reported more vivid visual imagery. Individual differences in resting pulse rate also emerged as a significant predictor (B\u0026thinsp;=\u0026thinsp;0.276, SE\u0026thinsp;=\u0026thinsp;0.106, t\u0026thinsp;=\u0026thinsp;2.61, p\u0026thinsp;=\u0026thinsp;.011, β\u0026thinsp;=\u0026thinsp;0.303), suggesting that higher physiological arousal is associated with increased imagery vividness. Among the self-reported interoceptive awareness subscales, MAIA Noticing (B\u0026thinsp;=\u0026thinsp;0.186, SE\u0026thinsp;=\u0026thinsp;0.093, t\u0026thinsp;=\u0026thinsp;1.99, p\u0026thinsp;=\u0026thinsp;.051, β\u0026thinsp;=\u0026thinsp;0.250), MAIA Emotion (B\u0026thinsp;=\u0026thinsp;0.208, SE\u0026thinsp;=\u0026thinsp;0.106, t\u0026thinsp;=\u0026thinsp;1.96, p\u0026thinsp;=\u0026thinsp;.055, β\u0026thinsp;=\u0026thinsp;0.253), and MAIA Trust (B\u0026thinsp;=\u0026thinsp;0.176, SE\u0026thinsp;=\u0026thinsp;0.093, t\u0026thinsp;=\u0026thinsp;1.88, p\u0026thinsp;=\u0026thinsp;.065, β\u0026thinsp;=\u0026thinsp;0.243) each showed positive associations at trend level. Assumption checks supported model validity. Residuals were normally distributed (Shapiro\u0026ndash;Wilk\u0026thinsp;=\u0026thinsp;0.988, p\u0026thinsp;=\u0026thinsp;.725), independent (Durbin\u0026ndash;Watson\u0026thinsp;=\u0026thinsp;2.43, p\u0026thinsp;=\u0026thinsp;.076), and not influenced by outliers (Cook\u0026rsquo;s Distance Max\u0026thinsp;=\u0026thinsp;0.172). Multicollinearity was acceptable, with all VIFs\u0026thinsp;\u0026lt;\u0026thinsp;2.35.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSignificant individual predictors: Involuntary memory\u003c/h2\u003e\u003cp\u003eWhile Model 3 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was statistically significant, none of the individual predictors reached conventional levels of significance, although MAIA Noticing showed a positive trend (B\u0026thinsp;=\u0026thinsp;0.266, SE\u0026thinsp;=\u0026thinsp;0.138, t\u0026thinsp;=\u0026thinsp;1.92, p\u0026thinsp;=\u0026thinsp;.059, β\u0026thinsp;=\u0026thinsp;0.245), suggesting individuals more attuned to bodily sensations may experience more involuntary memories. Assumption checks indicated normally distributed residuals (Shapiro\u0026ndash;Wilk\u0026thinsp;=\u0026thinsp;0.991, p\u0026thinsp;=\u0026thinsp;.892), low autocorrelation (Durbin\u0026ndash;Watson\u0026thinsp;=\u0026thinsp;2.22, p\u0026thinsp;=\u0026thinsp;.368), and acceptable multicollinearity (all VIFs\u0026thinsp;\u0026le;\u0026thinsp;2.20). No influential outliers were detected (Cook\u0026rsquo;s Distance Max\u0026thinsp;=\u0026thinsp;0.083). This suggests that while individual subscale effects were below statistical threshold, the MAIA scales contributed cumulatively to involuntary memory performance.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of this study was to characterize the relationship between (objective and subjective) measures of interoceptive sensitivity and the experience of mental imagery. First, regression analyses indicated that accuracy and confidence in performing a mental rotation task (indexing the capacity to mobilise mental imagery) were linked exclusively to task-based interoceptive measures (HBD performance accuracy and confidence, respectively). In contrast, the reported vividness of visual imagery (VVIQ) was significantly predicted by both task-based interoceptive measures (HBT performance accuracy) as well as self-reported interoceptive awareness (as assessed by MAIA questionnaire). Involuntary autobiographical memory was also significantly predicted only by self-reported interoceptive awareness. For n-back task, none of the models incorporating interoceptive and autonomic measures reached statistical significance.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDistinct effects of heartbeat tracking (HBT) and heartbeat discrimination (HBD)\u003c/h2\u003e\u003cp\u003eA key finding was that the two task-based interoceptive measures (HBT and HBD) were linked to distinct aspects of mental imagery. Better HBT performance accuracy was predictive of increasing vividness of mental imagery whereas HBD predicted performance on a mental rotation task dependent upon the flexible deployment of mental imagery. This dissociation suggests that while interoceptive ability is linked to mental imagery, performance of the HBT and HBD tasks engage different cognitive processes that align respectively with dissociable perceptual (VVIQ) and executive (mental rotation) aspects of mental imagery. The HBT task and rating the d vividness of mental imagery both rely on attention to, and perceptual judgement of, internal representations in the absence of an external reference. In contrast, both the \u0026lsquo;cross-modal\u0026rsquo; HBD task and the mental rotation task involve the comparison of an internally generated representation to external information: The HBD task requires individuals to judge whether their heartbeats are synchronous to external stimuli (auditory tones). Similarly, in the mental rotation task, individuals must mentally transform an internal representation of an object and compare its imagined orientation to a target stimulus on the screen.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eRelationship between pulse rate and VVIQ\u003c/h2\u003e\u003cp\u003ePulse rate was also a significant predictor of VVIQ, suggesting that autonomic states may influence imagery vividness, potentially reflecting a broader connection between physiological arousal and internally generated experiences. Pulse rate serves not only as as a direct index of autonomic nervous system (ANS) activity, reflecting the balance between sympathetic and parasympathetic responses, but also as a measure of the feedback (via e.g. baroreceptor activation) of cardiovascular arousal states on brain function. Higher pulse rates are typically associated with greater sympathetic activation, which typically accompanies states of mental alertness and emotional/behavioural engagement. This bidirectional link raises the possibility that the vividness of mental imagery may be amplified by heightened psychophysiological arousal. One explanation is that, though both sympathetic efferent drive and afferent interoceptive pathways, increased autonomic engagement enhances the salience of internally generated representations increasing feelings of reality and immersion. This aligns with research showing that emotional and physiological responses are encoded alongside perceptual experiences, strengthening both recall and imagery\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. If interoceptive signals, including heart rate fluctuations, provide an embodied foundation for mental imagery, then autonomic activation may amplify the sensory and emotional intensity of internally generated experiences, reinforcing the link between bodily states and cognitive simulations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eDistinct Contributions of task-based and self-reported interoception\u003c/h2\u003e\u003cp\u003eWhile mental rotation performance was solely predicted by task-based interoceptive measures (i.e. Step 1 in regression), the vividness of visual imagery (VVIQ) was additionally predicted by self-reported interoceptive awareness (Step 3 in regression), as assessed by the MAIA questionnaire. This highlights the convergence of two levels of interoception representation towards the construction of the subjective experience of mental imagery. The heartbeat tracking task arguably measures the precision of detecting internal bodily signals, whereas the MAIA focuses on how one pays attention to, emotionally respond to, and regulates one\u0026rsquo;s bodily experiences. These two measures tap into dissociable processes: HBT performance accuracy, although not immune to long acknowledged top-down influences that constrain over-interpretation, encompasses basic online sensory signal detection, while the MAIA subscales reflect an elaborated integration of interoceptive sensations with past emotional and cognitive experiences. Together, the findings suggest that vivid mental imagery depends on both access to bodily signals and the way in which these signals are integrated sensorially into intentional mental processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDissociation between voluntary imagery and involuntary autobiographical memory\u003c/h2\u003e\u003cp\u003eNotably, the regression analysis indicated that both VVIQ and involuntary autobiographical memory (IAMI) were associated self-reported interoceptive awareness. As both VVIQ and IAMI quantify experiential aspects of mental imagery, this finding supports the view that conscious interoceptive representation provides a foundational substrate for imagery more broadly, whether spontaneously occurring or deliberately constructed. However, only \u003cem\u003evoluntary\u003c/em\u003e imagery vividness (VVIQ) was also significantly linked to task-based measures of interoceptive accuracy. This pattern suggests that while involuntary imagery may be driven predominantly by bottom-up processes such as spontaneous activation of sensorimotor and interoceptive representations and/or an increased attention devoted to them, voluntary imagery additionally recruits top-down mechanisms that depend on the accurate detection and integration of internal bodily signals to support intentional and vivid mental simulation. Taken together, these findings indicate that subjective interoceptive awareness is a core feature of imagery experiences, yet the volitional construction of imagery relies also on interoceptive precision,\u003c/p\u003e\u003cp\u003eThese new observations support the idea that heightened sensitivity to internal bodily signals, such as fluctuations in heart rate, strengthens the connection between physiological states and mental simulations, leading to more vivid mental imagery\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Mental imagery, by nature, engages both physiological and emotional responses as it reconstructs past experiences and envisions future events. This mind-body connection is mediated by autonomic responses and interoceptive processes. When encountering a new stimulus, the body undergoes stereotypical changes, including shifts in heart rate and electrodermal activity, which are encoded in memory\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Later, these physiological responses can be reactivated during recall or imagination, enhancing the immersive quality of imagery. This reactivation is especially evident in emotional imagery, where imagining fear-related scenes can trigger measurable physiological arousal, similar to firsthand experience\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdditionally, these results show that the vividness of mental imagery depends not only on the ability to access internal bodily signals but also on how well individuals integrate these signals with their emotional and cognitive states. Our findings thus support the view that mental imagery is an integrative process, where current and past sensory, emotional, and physiological information dynamically interact, to construct a coherent conscious experience. The identified associations between interoception and imagery suggest that bodily signals provide a foundation for grounding mental simulations in self-referential bodily states. Rather than operating independently, imagery-based cognition is embedded within ongoing physiological processes. Our findings highlight the importance of considering the body within cognitive models of imagery and underscores the need for future research to explore how different types of interoceptive signals\u0026mdash;such as heartbeat, respiration, and digestive cues\u0026mdash;affect specific expressions of mental imagery and affective processing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eData availability Statement\u003c/h2\u003e\u003cp\u003e\u003cb\u003eThe experimental data of this study are available in\u003c/b\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/hbdxv/\u003c/span\u003e\u003cspan address=\"https://osf.io/hbdxv/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability Statement\u003c/h2\u003e\n\u003cp\u003eThe experimental data of this study are available in https://osf.io/hbdxv/.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eJS was funded by ESRC.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYN and JS were responsible for the overall study process, including conceptualization, methodology, and execution. YN, JS, and HC contributed to writing the main manuscript text. SA, TF, and CM were involved in data collection. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors acknowledge and thank Dr. Philip Dean for his technical assistance during the course of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCritchley, H. D. \u0026amp; Garfinkel, S. N. Interoception and emotion. \u003cem\u003eCurr Opin Psychol\u003c/em\u003e \u003cstrong\u003e17\u003c/strong\u003e, 7-14 (2017). https://doi.org/10.1016/j.copsyc.2017.04.020\u003c/li\u003e\n\u003cli\u003eCraig, A. D. How do you feel--now? The anterior insula and human awareness. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 59-70 (2009). https://doi.org/10.1038/nrn2555\u003c/li\u003e\n\u003cli\u003eAzevedo, R. T., Garfinkel, S. N., Critchley, H. D. \u0026amp; Tsakiris, M. 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A bio‐informational theory of emotional imagery. \u003cem\u003ePsychophysiology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 495-512 (1979). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6715348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6715348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInteroception, the sense of the internal state of the body, plays a fundamental role in emotional awareness and self-referential processing. Recently, interoception has been linked to the experience of mental imagery. However, this association has not been empirically characterized. We therefore tested how task-based and self-reported measures of cardiac interoception predict individual differences in task-based and self-reported measures of mental imagery. Participants (N\u0026thinsp;=\u0026thinsp;104) completed two heartbeat detection tasks (heartbeat tracking and heartbeat discrimination; assessing objective interoceptive performance accuracy), and the Multidimensional Assessment of Interoceptive Awareness (MAIA) questionnaire, assessing subjective dimensions of interoceptive experience. Objective and subjective aspects of mental imagery were assessed respectively from performance of a mental rotation task and from scores on the Vividness of Visual Imagery Questionnaire (VVIQ). Results revealed that, across participants, interoceptive (heartbeat discrimination performance) accuracy predicted mental rotation ability. In contrast, both heartbeat tracking accuracy and self-reported interoceptive awareness (MAIA) predicted self-reported vividness of mental imagery (VVIQ). Interoceptive measures did not predict performance on a control task (2-back working memory task). Together, these findings suggest that distinct components of interoception underpin different aspects of mental imagery: Heightened (cardioceptive) physiological sensitivity facilitates the active deployment of imagery in mental rotation and enhances the vividness of imagery experience. Moreover, people reporting more subjective sensitivity to interoceptive state also perceive greater vividness of mental imagery. These new results underscore the influence of bodily representation in shaping conscious experience through both controlled and spontaneous expressions of mental simulation.\u003c/p\u003e","manuscriptTitle":"Interoception Predicts Mental Imagery Vividness: Exploring a Key Relationship","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-09 09:10:11","doi":"10.21203/rs.3.rs-6715348/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-13T19:20:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T23:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285634410644746192980116166864491088621","date":"2025-12-21T11:20:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T13:22:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114044217611691099052424254961800569268","date":"2025-07-08T14:46:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-07T11:05:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-25T16:41:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-19T11:22:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T15:05:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-06T15:02:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2fec881f-7ba2-4512-87b0-b750606fa45d","owner":[],"postedDate":"July 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":51226493,"name":"Biological sciences/Neuroscience/Cognitive neuroscience"},{"id":51226494,"name":"Biological sciences/Neuroscience/Emotion"},{"id":51226495,"name":"Biological sciences/Neuroscience/Visual system"}],"tags":[],"updatedAt":"2026-03-23T16:00:09+00:00","versionOfRecord":{"articleIdentity":"rs-6715348","link":"https://doi.org/10.1038/s41598-026-43805-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-19 15:57:31","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-07-09 09:10:11","video":"","vorDoi":"10.1038/s41598-026-43805-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43805-0","workflowStages":[]},"version":"v1","identity":"rs-6715348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6715348","identity":"rs-6715348","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}