It is a matter of size - Manipulating body size with virtual reality modulates reward sensitivity

It is a matter of size - Manipulating body size with virtual reality modulates reward sensitivity | 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 It is a matter of size - Manipulating body size with virtual reality modulates reward sensitivity Lorenzo Pia, Michael Freedberg, Maria Pyasik, Rebecca Boarini, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7151013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Obesity is a major concern in clinical practice given its impact on medical and psychiatric conditions, and the pervasive social stigma it carries. Reward processing has a key role in the development and maintenance of obesity, as evidenced by reduced brain activation within the reward pathway and concurrent decrease in desire to eat following bariatric surgery. Interestingly, the experimental paradigm known as the Full Body Illusion (FBI) has been effective in impacting body size on eating attitudes. However, while both medically and experimentally induced modulation of body size modify body size perception and attitudes toward food, it remains unclear whether such illusory manipulations can affect reward-based behavior in healthy adults. To address this question, we investigated whether FBI-induced changes in body size influence reward-based learning, implicit attitudes toward food and body weight. As expected, embodying a larger avatar enhanced reward-based learning and implicit attitudes toward high-calorie foods, while reductions of the implicit weight bias occurred independently of the avatar size. This is the first study to establish a link between reward-related learning and the perception of one's body size, emphasizing a critical role for one’s own perception of their weight beyond their physical weight, in shaping approach and avoidance behavior. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology feedback-based learning body size full body illusion obesity body ownership social stigma Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The terms overweight and obesity refer to conditions characterized by excessive fat deposits, which results from an imbalance of energy intake (diet) and energy expenditure (physical activity). However, they differ in severity and health impact, with the latter being a complex, multifactorial disease rooted in obesogenic environments, psycho-social factors, and genetic variants 1 . Obesity has become a major concern in clinical practice due to its growing prevalence, its significant impact on a wide range of medical and psychiatric conditions 2 – 4 along with the pervasive social stigma it generates - the weight bias 5 . Consistently, understanding this condition is of considerable scientific relevance even to manage clinical approaches. A growing body of evidence highlights the key role of reward processing in the development and maintenance of obesity. These altered reward processes that regulate appetite, weight management, and treatment response, are believed to be at the heart of disease 6 – 8 : Both obesity and weight gain are associated with altered striatal, 9 – 14 frontal, 15 and dopaminergic 15 responses, which are key components of the human reward system 9 – 14 . Among the few studies investigating reward processing in relation to non-food cues, Balodis and collaborators 16 measured responses to monetary rewards and found that obese individuals, compared to normal-weight controls, exhibited increased bilateral ventral striatum activation during the anticipatory phase of reward processing. Bariatric surgery is employed to help individuals with obesity normalize their metabolism and gain an ideal body shape by losing weight. Interestingly, these interventions reduced mesolimbic reward pathway brain activation and postsurgical desire to eat. Furthermore, both of these are more pronounced in response to high-calorie compared to low-calorie food cues 17 . The impact of body size on eating attitude has been reported in an experimental paradigm known as the Full Body Illusion (FBI) 18 , 19 . Here, a virtual avatar presented from the first-person perspective (i.e., visually substituting for the participant’s body) induces the illusion of inhabiting that virtual body. A larger avatar causes an overestimation of body size, whereas a slimmer avatar leads to an underestimation of one's body size 20 – 24 . Crucially for this study, the avatar size also modulates eating attitudes: larger virtual bodies increased the preference for unhealthy foods 25 , while slimmer avatars induced avoidance of high-calorie food 24 . The abovementioned evidence shows that both medically and experimentally (illusory) induced alterations in body size can modify body size perception and attitudes toward food. However, it remains unclear whether illusory manipulation of body size can modify reward learning in healthy adults. Therefore, we investigated whether the illusory changes in body size also influence reward-based learning, implicit attitudes towards food and body weight/size, and weight bias as a further implicit attitude related to body size 26 . To this aim, learning preferences in a probabilistic learning task (with reward and punishment feedback), implicit food approach/avoidance behavior, and implicit weight bias were quantified in normal-weight participants before and after embodying an avatar 15% larger (experimental condition) or slimmer (control condition) than their actual body. We hypothesized that embodying a larger avatar would change all measured processes coherently with the avatar’s size, enhancing reward-based learning, increasing approach tendencies toward high-calorie foods, and reducing implicit weight bias. RESULTS Thirty-seven right-handed, normo-weight females participated in the study, which consisted of three sessions on consecutive days. On Day 1 (baseline), participants underwent a series of behavioral tasks to quantify their implicit attitudes toward body size and food, along with feedback-based learning performance. On Day 2 and 3 participants experienced the FBI paradigm of larger (Large condition) and slimmer (Slim condition) avatar and then repeated the behavioral tasks administered at baseline. Before and after each FBI, participants performed the Body Size Estimation Task (BSET) to quantify the subjective perception of their body size and the Body Ownership Questionnaire (BOQ) to assess the effectiveness of the FBI itself. See Fig. 1 for a summary of the experimental setup and procedures. The final sample consisted of 35 females since two participants were excluded before statistical analysis due to data loss and the presence of an eating disorder. A. Experimental design. Three ~ 1.5-hour sessions on consecutive days. Each session included a series of behavioral tasks (panel B). Day 1 served as baseline; Days 2 and 3 measured changes induced by the Full Body Illusion (FBI; panel C). B. Behavioral Measurements. The Weight Bias Implicit Association Task (WB-IAT) assessed implicit attitudes toward body size. Participants categorized body silhouettes (slim or large) into groups labeled with positive (e.g., happy, pleasure) or negative (e.g., dirty, awful) words, following the standard IAT procedure. The Food Bias Approach-Avoidance Task (FB-AAT) assessed implicit attraction/repulsion toward food. Stimuli included hypercaloric (e.g., pizza), hypocaloric (e.g., fruit), and neutral numeric digits. Half of each appeared in circular frames, half in square ones. Participants categorized stimuli by shape and responded by pulling (attraction) or pushing (repulsion) the mouse. The Learning Task assessed feedback-based learning. Participants learned to categorize four abstract stimuli into two categories, receiving point feedback (reward or punishment) based on accuracy. Two stimuli were mostly rewarded, two mostly punished, each associated with Category A or B on 80% of trials. C. Virtual reality. The Body Size Estimation Task (BSET) was administered before and after the FBI to measure changes in perceived hip width. With eyes closed, participants estimated their hip size using their arms. After the FBI, the Embodiment Questionnaire (OBQ) assessed subjective experience using four illusion and four control statements across four embodiment dimensions. The FBI was induced once with an avatar 15% slimmer and once 15% larger than the participant’s body (order counterbalanced). To induce ownership, participants received 180 seconds of synchronous visuo-tactile stimulation: a green ball moved toward/away from the avatar’s abdomen, synchronized with vibrotactile feedback upon contact. Feedback-based Probabilistic Learning Task Participants performed a two-choice reaction time task where the reinforcement rate for each stimulus was 80%. As in other procedural learning studies, we used optimal responding rather than trial-wise accuracy 27 as our performance metric for analysis. Optimal responses were defined as the response most likely to produce a reward or avoid punishment. Two participants’ data were discarded because they performed below chance across the three sessions (baseline, large, and slim), leaving 33 participants for this analysis. Data from the three training blocks were divided into twelve sub-blocks (four sub-blocks per block) to more adequately characterize learning curves, as in our prior work (Schintu et al., 2018). Optimal responses were modeled across sub-blocks (1–12) using a linear mixed effects model in which the slope parameter indexed the rate of learning, and the quadratic slope indexed the acceleration or deceleration in learning rate. The model was fit to sub-block-specific performance using the lme4 package in R (Bates et al., 2015). Before modeling, the sub-block variable was centered so the intercept corresponded to the halfway point during task performance. The fixed effect of Feedback was effect-coded (”1” for reward and “-1” for punished). The model included dummy-coded contrasts between Baseline, Large, and Slim sessions. Finally, we included BMI scores as a continuous factor. Maximum likelihood was used to estimate all fixed and random effects simultaneously. Chi-square model comparisons indicated that the most complex model, including random intercepts and slopes of the linear and quadratic terms on participants, provided significantly better model fits than models where these random effects were removed: $$\:Performance\:\sim\:\left(SUBBLOCK*FEEDBACK*SESSION*BMI\right)$$ $$\:+{\:(SUBBLOCK}^{2}*FEEDBACK*SESSION*BMI)$$ $$\:+\:(1+SUBBLOCK+{SUBBLOCK}^{2}|\:SUBJECT)$$ Fixed effects for the best-fitting model were then interpreted as described in the results. R 2 values are reported for all significant effects and interactions. Missed responses occurred infrequently (2 times across all participants). Figure 2 shows the proportion of optimal responding for each session for all participants (Fig. 2 A), those with higher BMIs (Fig. 2 B), and those with lower BMI’s (Fig. 2 C) according to a median split. The analysis revealed a significant effect of Subblock [t(31.91) = 3.170, p = 0.003, R 2 = 0.239], indicating that optimal responding increased across blocks regardless of Session, BMI, and Feedback. A significant main effect of Feedback was also detected [t(2290) = 2.97, p < 0.001, R 2 = 0.004], indicating that optimal responding was higher for punished trials compared to rewarded trials. The interaction between Feedback and Session was also significant [t(2290) = -3.606, p < 0.001, R 2 = 0.006]. When we unpacked this interaction by session (baseline, large, slim), we found that performance on punishment trials was superior compared to rewarded trials at baseline [t(718.1) = 4.784, p < 0.001, R 2 = 0.031] as well as after the slim illusion [t(718.2) = 3.184, p = 0.002, R 2 = 0.014], but the opposite was true after the Large illusion [t(718.0) = -2.993, p = 0.003, R 2 = 0.012]. These results indicate that the Large illusion significantly improved performance on rewarded trials compared to punished trials. We also identified a significant Subblock by BMI interaction [t(31.91) = -2.329, p = 0.026, R 2 = 0.145]. To unpack this interaction, we performed a median split of participants based on BMI and performed separate analyses for these groups. We found that the effect of Subblock was highly significant for participants with higher [t(15.97) = 5.474, p < 0.001, R 2 = 0.652] and lower [t(15.11) = 7.988, p < 0.001, R 2 = 0.809] BMI. These results indicate that while both groups experienced significant learning, learning for participants with lower BMI was steeper. The interaction between Feedback and BMI also achieved significance [t(2290) = -3.127, p = 0.002, R 2 = 0.004]. To unpack this interaction, we performed a median split of participants based on BMI and analyzed these groups separately. We found that while participants with higher BMI showed a trend favoring better performance on reward trials [t(1181) = -1.873, p = 0.061, R 2 = 0.003], participants with lower BMI showed no significant difference [t(1110) = -0.437, p = 0.662]. These results indicate that participants with higher BMIs were more responsive to rewarded feedback than participants with lower BMIs. The interaction between Feedback, Session, and BMI was also significant [t(2290) = 3.817, p < 0.001, R 2 = 0.006]. When we unpacked this interaction by BMI, as described above, we found that the interaction between Feedback and Session was significant for participants with higher BMI [t(1181) = 3.888, p < 0.001, R 2 = 0.013], but not for participants with lower BMI [t(1110) = -0.924, p = 0.356]. These results indicate that participants with higher BMI were more responsive to rewarded feedback than to punishments, specifically after the large illusion. The interaction between Feedback, Session, AND Sub-block (quadratic) was also significant [t(2290) = 3.443, p < 0.001, R 2 = 0.005]. When we unpacked this interaction by session, as described above, we found that the Feedback by quadratic interaction was significant at Baseline [t(718.1) = -2.572, p = 0.010, R 2 = 0.009] and after the Slim illusion [t(718.2) = -3.005, p = 0.003, R 2 = 0.012], indicating significantly faster punishment learning compared to reward. However, we found that the opposite was true after the Large illusion [t(718.0) = 2.892, p = 0.004, R 2 = 0.012]. These results show that the FBI of Larger avatars significantly increased the rewarded learning rate relative to baseline and punished trials. Finally, the interaction between Feedback, Session, BMI, and Sub-block (quadratic) was also significant [t(2290) = -3.542, p < 0.001, R 2 = 0.005]. When we unpacked the interaction by BMI, as described above, we observed a three-way interaction between Feedback, Session, and Sub-block (quadratic) for participants with higher BMI [t(1181) = -2.123, p < 0.034, R 2 = 0.004], but not for participants with lower BMI [t(1110) = 0.872, p = 0.383)]. We further unpacked the significant interaction for the higher BMI participants by Session and observed a significant interaction between Feedback and Sub-block (quadratic) after the Large illusion [t(370) = -2.923, p = 0.004, R 2 = 0.023], but not at Baseline [t(370.0) = 1.644, p = 0.101] or after the Slim illusion [t(370.1) = 0.674, p = 0.501]. These results confirm that the large illusion increased the rate of reward learning, specifically for participants with higher BMI. Food Preferences Approach-Avoidance Test (FP-AAT) The FP-AAT was administered at the baseline session and after the Large and Slim illusion to assess the change in the implicit food preferences. Five participants were excluded because their d-score was more than two standard deviations, leaving 30 participants for this analysis. The D-score was calculated on total standard deviation as avoidance (Push) minus approach (Pull), such that positive numbers mean a bias toward avoidance (Push) and negative numbers a bias toward approach (Pull). Delta d-score was calculated as session minus baseline, such that positive numbers represent an increase in avoidance (push away from the body) and negative numbers represent an increase in approach (pull toward the body). To test the presence of significant changes in implicit attitudes toward food, we first compared the delta d-score against zero. This analysis revealed that when participants embodied a larger avatar (Large condition), they displayed an increased approach to high-calorie food [t(29) = -3.207, p = 0.003, d = -0.586]. No significant change in approach/avoidance behavior was found for low-calorie food or any kind of stimulus in the Slim condition (all ps > .05; see Fig. 3 A). A 2x2 repeated-measures ANOVA for Embodiment condition (Large, Slim) x Calorie content (High, Low) performed for the delta d-scores showed no significant effects or interactions (all ps > .05). These results indicate that the embodiment of a larger avatar increased the bias of approaching high-calorie food. Weight Bias Implicit Association Task (WB-IAT) The weight Bias IAT was administered at baseline and after the Large and Slim illusion to assess the change in implicit weight bias. One participant was excluded because their d-score exceeded two standard deviations, leaving 34 participants for this analysis. Delta was calculated as a session minus baseline, such that positive numbers represent an increase and negative numbers represent a decrease. To test the presence of significant changes in the implicit attitudes toward weight, we first compared the delta d-score against zero. This analysis revealed that when a subject embodied a larger avatar (Large condition) weight bias significantly decreased [t(33) = -3.94, p 0.05; see Fig. 3 B). These results reveal that embodying an avatar, regardless of size, decreases the implicit weight bias. Full Body Illusion (FBI) Body Size Estimation Task (BSET) The BSET was administered before and after the Large and Slim FBI to estimate the subjective change in body size. The amount of change in body size estimation in each condition was calculated by subtracting the baseline measurements from each post-FBI measurement (delta-BSE), so that positive numbers indicate an increase and negative numbers indicate a decrease in body size estimation. The paired-sample t-test showed that the delta-BSE was positive and significantly larger in the Large as compared to the Slim condition [t(34) = 2.283, p = 0.029, d = 0.386; see Fig. 3 C). In each condition the change of the body size estimation was tested against 0 and resulted to be significant in the Large [t(34) = 4.20, p < 0.001, d = 0.710] but not Slim [t(34) = 0.498, p = 0.622, d = 0.084]. Furthermore, baseline body size estimation did not differ between sessions [t(34) -1.106, p = 0.277, d = -0.187], thus excluding a possible carry-over effect of the changes induced by the previous FBI session. The Pearson correlation between the delta-BSE in the large condition, which produced a significant change in BSE, and BMI revealed that the higher the BMI, the larger the body size estimation (r(35) 0.445, p = 0.007). This means that the higher the participant’s BMI, and consequently the size of the avatar in the Large condition, the greater the change in body size estimation. These results indicate that merely experiencing the illusion of owning a larger avatar body led to a subjective overestimation of participants’ body size and that the magnitude of this effect was related to their actual BMI. Body Ownership Questionnaire (BOQ) The BOQ was administered after the Large and Slim illusion to assess the subjective experience of the FBI. One participant was excluded because of missing data, leaving 34 participants for this analysis. To test whether the illusory experience took place for each FBI condition, we compared the average score of the four “illusion” questions to the averaged “control” statements (see Table 1 in method section for a list of questions). The two-tailed paired t-test revealed that the real questions mean differed from the control one for both the Large [t(33) = 9.231, p < 0.001, d = 1.583] and Slim illusion [t(33) = 6.933, p < 0.001, d = 1.195], meaning that both FBI conditions induced the subjective illusory experience (Fig. 3 D). Furthermore, no difference was found for the real questions between the Large and Slim illusion [t(33) 1.880, p = 0.069, d = 0.195], indicating that the illusory experience did not differ between avatar’s size. These results of the FBI paradigm show that the subjective illusory experience occurred in both conditions and did not differ between the two avatars’ sizes. DISCUSSION We investigated the effects of the illusory experience of owning a virtual body of a different size than one’s real body on cognition. After having confirmed that subjective embodiment occurs independently of the avatar size, we quantified the cognitive changes before and after the embodiment of a larger and slimmer avatar. Based on evidence implicating the reward system in obesity, especially in relation to high-calorie food 6 – 8 , 15 , we expected participants to increase reward-based sensitivity along with approach-behavior toward high-calorie food paired with a decrease implicit weight bias. In agreement with the prediction, embodying an avatar 15% larger increased reward-based learning compared to baseline and slim avatar, and increased the approach behavior toward high-calorie food. No changes in reward-based learning and food-related approach-avoidance behavior were observed in the slim avatar condition. This study also explored, for the first time, the effect of the embodiment of a larger body size avatar on implicit social weight bias. Findings showed a reduction of social stigma as a result of embodying a different body, regardless of its specific size. Feedback-based Probabilistic Learning In general, participants experienced greater performance on rewarded trials than on punishment trials. Interestingly, this effect was modulated by participants’ BMI, indicating that participants with higher BMIs were generally more responsive to rewarded feedback than participants with lower BMIs. When considering the effect of the FBI, results indicated that performance on punishment trials was superior compared to rewarded trials at baseline as well as after the Slim illusion, but the opposite was true after the Large illusion - Large illusion significantly improved performance on rewarded trials compared to punished trials. This effect was also found to interact with BMI, indicating that participants with higher BMI were more responsive to rewarded feedback than punishments, specifically after the large illusion. Interestingly, the large FBI modulated also the learning rate, as the acceleration in learning rate from reward feedback was significantly greater after the large and slim illusions compared to the baseline. Indeed, while faster punishment learning compared to reward was found at baseline and after the Slim illusion, the opposite was true after the Large illusion. The FBI of large avatars significantly increased the reward learning rate relative to the baseline. Also, the learning rate interacted with BMI, thus confirming that the Large FBI increased the rate of reward learning specifically for participants with higher BMI. The reason only higher BMI had a significant effect may lie in how avatar sizes were calculated – each was scaled individually to ± 15% of each participant’s actual body size. Although only participants with a BMI within the normal range were recruited, those on the lower end may have perceived the larger avatar as still not overweight. In contrast, for the group of participants on the higher end of the BMI spectrum, if their BMI is increased by 15%, it exceeds the normal range, making the larger avatar appear overweight. This discrepancy, indirectly supported by the finding that the higher the BMI, the larger the body size estimation following the large FBI, could explain the differential effects. To account for reward-based modulation induced by the FBI, we speculate that shared and interacting brain regions may be involved. Embodiment has been consistently associated with activating a brain network comprising the posterior parietal cortex, namely the inferior parietal sulcus (IPS), and premotor areas 28 . These areas are involved in the multisensory integration necessary for the experience of embodiment 29 – 31 . Among the key components of embodiment, self-location is primarily linked to activity in the intraparietal cortex, hippocampus, posterior cingulate, and retrosplenial cortex, whereas body ownership is supported by activation of premotor and intraparietal regions 32 . Notably, the posterior cingulate cortex mediates these two neural systems, suggesting its crucial role in integrating self-location and body ownership. The parietal cortex and the hippocampus, two core regions of the FBI network, are anatomically and functionally connected, as shown by the a seminal study by Wang et al. 33 in which excitatory TMS applied to the parietal cortex directly increased hippocampal activity. The hippocampus also strongly connects with the nucleus accumbens (NAcc), a central node in the mesocorticolimbic dopamine system responsible for reward processing 34 . In animal models, the strength of this connection has been shown to increase with the presentation of rewards, leading to dopamine (DA) release 35 . Additional support for the NAcc's role in reward sensitivity comes from recent findings showing that ultrasound stimulation of the human NAcc enhances reward-related behaviors, such as increased learning from positive feedback 36 . Building on this evidence, we propose that the FBI may influence reward-based learning by modulating connectivity among the parietal cortex, hippocampus, and NAcc. This modulation could, in turn, affect the functioning of the mesolimbic circuit and reward signal processing. However, this remains a hypothesis, and further research is needed to elucidate the neural mechanism underlying the observed behavioral outcomes. Implicit attitudes The implicit food preferences shifted after the embodiment of a large avatar toward a stronger preference for high-calorie food. While being in line with the previous findings 24 , this result is, more notably, coherent with food behaviors that are observed in overweight and obese individuals in real-world contexts. Indeed, these individuals present a stronger preference for high-calorie foods than low-calorie ones 37 , which might be rooted in the general tendency of higher reward sensitivity 38 , 39 . Observing such food-related behavior after a temporary embodiment of a larger body, without actual body-size changes, further demonstrates the crucial role of one's body perception on cognition 19 and the potential applications of the FBI-based protocols in various contexts, including clinical ones. Beyond the cognitive effect of the FBI, we also examined its possible impact on the weight bias- an implicit social bias known to affect well-being and quality of life of obese individuals 5 . Our results indicated a reduction in the implicit weight bias following the embodiment of an avatar compared to baseline. This finding only partially agrees with our initial hypothesis since it occurred following both the large and slim FBI. This size-independent decrease in weight bias cannot be directly compared to prior findings, as this is the first study to investigate the effects of the FBI specifically on the weight bias, rather than weight-related satisfaction. While earlier studies 21 , 24 used body-IAT in healthy individuals to assess embodiment effects, focusing on implicit attitudes towards one's body image in relation to body size, the present study examined the weight bias in the context of general social stereotypes that associate obesity with negative personality traits, such as laziness or lack of motivation 40 . The reduction of the weight bias after both FBI conditions might be attributed to the fact that the weight bias per se is not necessarily related to an individual’s body weight. Indeed, especially in societies with a high prevalence of obesity, even people with higher body weight and BMI present high levels of weight bias, similarly to people with lower BMI 41 . In the present study, it is possible that embodiment, regardless of avatar size, focused participants’ attention on “their” body more than at baseline and created the feeling that their body is different from the one they usually perceive as their own. This experience may have fostered increased body acceptance, reducing implicit weight bias. In addition, it has been suggested that negative implicit biases are reduced after the embodiment of out-group virtual bodies 19 . This effect occurs with in-group avatars resembling their real appearance, implying that owning a body different from one’s own can reduce prejudice toward the social group associated with that body. From this perspective, both large and slim avatars may have been considered an out-group because both differed from the participants’ actual body size. While this does not directly explain why the weight bias was reduced even in the slim condition, it further supports the idea that inhabiting a body different from one’s increases the openness toward diverse body types. Finally, it is important to note that the observed reduction in weight bias was due to repeated exposure to IAT (i.e., learning effect), as the order of conditions was counterbalanced across participants. Full Body illusion The subjective feeling of embodiment, quantified by the embodiment questionnaire, was successful for both larger and slimmer avatars, indicating that it occurs regardless of the virtual body size. This is consistent with evidence suggesting that embodiment is independent of the body appearance 19 and size 21 , 24 . The sense of body ownership is indeed malleable and can extend to bodies that look different from one’s own, but solely if the key condition for embodiment illusions are met, namely, first-person perspective, anatomical plausibility, synchronous multisensory stimulation 18 , 42 . The same flexibility/malleability does not hold for changes in perceived body size, since only the large FBI condition shifted body size estimation in the direction of the avatar’s size. Participants judged the size of their hips to be larger than the actual size after the embodiment of a large avatar, and the opposite did not happen following the embodiment of a slim avatar. While some studies reported changes across both embodiment conditions 24 , others have found a reduction in the body size estimation only in the slim condition 21 , and still others suggested that embodying larger limbs produced stronger effects 43 – 45 . Our finding aligns with the latter evidence, which further confirms the idea that body representation might adapt more easily to the increase in body size 44 . In addition, discrepancies with the previous findings might be due to differences in the embodiment paradigm. In both previous studies, only the hip size of the virtual body or mannequin was modified, while here we adjusted the entire virtual body based on each participant’s body measurements. As a result, the slim avatar might have appeared too slim and unrealistic (as noted in some participants’ feedback), which could have affected the adaptation of the perceived body size. However, it is necessary to point out that the subjective embodiment was equally present in both conditions, as described above. To summarize, this study shows that embodying a larger virtual body can enhance reward-based learning and implicit attitudes toward high-calorie food, while reductions in weight bias occur independently of body size. This is the first study to disentangle reward sensitivity from food-related behavior, providing a more nuanced understanding of how body representation influences cognitive processing. Notably, although both avatars were equally embodied at a subjective level, only the larger avatar affected body perception and cognitive responses, the personalized scaling of avatars may have contributed to differential effects based on participants’ BMI. Overall, our findings underscore the profound interplay between body and mind. Given that the body is a constant and inseparable part of human experience, changes in its representation can shape cognitive processes. These results support the view that physical and psychological states are mutually influential - a principle rooted in the idea that a healthy mind in a healthy body reflects a bidirectional relationship rather than a one-way effect. MATERIALS AND METHODS Participants Based on similar previous studies 24 , 46 that reported medium-to-high effect sizes, we conducted an a priori power analysis in G*Power 47 for a two-tailed paired t-test with a medium effect size ( d = 0.50) and alpha level 0.05. This analysis revealed that a sample size of 34 subjects is necessary to obtain power > 0.8. Thirty-seven right-handed volunteers were recruited for the study. Since body size stereotyping is more prevalent among young women 48 , 49 , we recruited healthy females between 20 and 30 years of age, with normal weight as quantified by the Body Mass Index (BMI) and no eating disorder symptoms, as assessed by the Eating Disorder Inventory (EDI) 50 . Before statistical analysis, one participant was excluded because of missing data, and one because the EDI score was above the cut-off (> 50). The final sample consisted of 35 females (mean age = 23.21, SEM = 0.5; mean EDI = 13.97, SEM = 1.87; mean BMI = 20.73, SEM = ± 0.31). All participants gave informed consent and were compensated for participation. The study was approved by the Human Experimental Ethics Committee of the University of Trento (protocol 2019-011) and was conducted in accordance with the ethical standards of the Helsinki Declaration 51 . Procedure The within-subjects study consisted of three sessions on consecutive days. Each session lasted about 1.5 hours and started, for each participant, at approximately the same time of day. As shown in Fig. 1 , Day 1 included consenting, EDI 50 , and weight and height measurements to calculate their BMI. The experimenter then took participants’ body measurements (i.e., neck circumference, arm, wrist, upper bust, waist, hips, thigh, knee, ankle, and neck-shoulder distance) to create the avatars for the full body illusion (FBI). Once this preliminary phase was completed, participants underwent a series of behavioral tasks to quantify the implicit attitudes toward body size and food, feedback-based learning performance (Fig. 1 B). In the second and third sessions, participants experienced the virtual FBI paradigm; all participants were exposed to the larger avatar (Large condition) and the slimmer one (Slim condition) in a counterbalanced manner. Before and after each FBI, participants performed the Body Size Estimation Task (BSET) to quantify the subjective perception of their body size and the Body Ownership Questionnaire (BOQ) to assess the effectiveness of the FBI itself. Following each FBI, participants repeated the behavioral tasks administered at baseline. Since the FBI duration is unknown, the order of the behavioral tasks was kept stable across sessions, and task administration started, for all participants, ~ 5 minutes after the FBI. Participants were debriefed at the end of the experiment, and their impressions and thoughts about the FBI were recorded. Assessment of eating and body representation disorders Body Mass Index (BMI) The Body Mass Index (BMI) was computed by dividing the weight (kilograms) by the square of the height (meters). Only Participants with a BMI within the normal range (18.5–24.9) participated in the study. Eating Disorder Inventory (EDI) The EDI (Vetrone et al., 2006) was used to assess the presence of eating disorders. It comprises 64 items divided into eight subscales (i.e., drive for slimness, bulimia, body dissatisfaction, ineffectiveness, perfectionism, interpersonal distrust, interoceptive awareness, and maturity fears). Participants were asked to rate their level of agreement on a 6-point Likert scale from 0 (never) to 5 (always). As in the Tambone et al. 24 study, the EDI cut-off scored 50 50 , and individuals who exceeded this score were excluded. Full Body Illusion (FBI) The FBI timeline and a view of the virtual scene in the two conditions are presented in Fig. 1 C. Participants wore a Head Mounted Display, HMD [Oculus Quest 2 2021 equipped with RGB LCDs 1832x1920 pixels, refresh rate at 120 Hz, the field of view of 110° (diagonal FOV) and 6 degrees of freedom]. The scenario was created in 3D Studio Max 2023 (Autodesk, Inc.) and implemented in Unity 2017 game software environment ( http://unity.com ). It included a simple room with a chair, a table, and a window. A dressed female avatar was placed sitting on the chair. The avatars were created using MakeHuman software ( www.makehumancommunity.org ) and animated with MotionBuilder 2023 (Autodesk, Inc.). The avatars’ body size was individually calculated by subtracting (Slim condition) or adding (Large condition) 15% of each participant’s body measurements. At the beginning of the FBI procedure, once the participants put on the HMD, the virtual scene was occluded, and participants saw only a blue background color surrounding them. During this phase, the pre-embodiment BSET was administered (see details below). Once the BSET was completed, the virtual scene and one of the avatars (slim/large, depending on the condition) were activated and became visible. Participants, who were also sitting on a chair, were asked to position themselves as the avatar, adjusting the position of their body and the chair until they felt to be located exactly in the position of the avatar they were seeing from a first-person perspective. Their position was further adjusted by shifting the virtual camera upwards or downwards until participants reported feeling as if they were looking at the virtual scene and body precisely from the avatar’s perspective. Participants familiarized themselves with the virtual environment for 30 seconds by moving their heads to look around, including at themselves (i.e., avatars). To induce the feeling of ownership of a virtual body, we employed synchronous visuo-tactile stimulation. An Arduino-controlled vibrotactile stimulator (diameter x height = 8 mm × 3mm, 3 V, rated speed – 12,000 RPM) was located on the participants’ abdomens and was used during the FBI to deliver the tactile stimulation. After the 30-second familiarization phase, the three-minute synchronous visuo-tactile stimulation started. A green ball moved towards and away from the avatar’s abdomen synchronously with the vibrotactile stimulation that occurred when the ball touched the avatar’s body. Frequency and duration of each set of touches were 6 Hz and 500 ms, respectively; a set of touches was delivered every 4–5 s. After three minutes, the visuo-tactile stimulation stopped, the blue background occluded the virtual environment and the avatar as in the beginning of the procedure, and the BSET and BOQ were administered. Body Size Estimation Task (BSET) The BSET was used to quantify the subjective estimation of the body size before and after each FBI [adapted from 52 ]. Participants stood up and, while still wearing the HMD and keeping their eyes closed, were asked to extend their arms along their body (without touching it), then raise their arms in front of them with their palms facing each other and adjust the distance between their palms to the perceived size of their hips. This procedure was repeated three times; each time, the distance between their palms was measured with a measuring tape (accuracy of 0.5 cm), and the mean of the three measurements was considered. Body Ownership Questionnaire (BOQ) The BOQ was adapted from questionnaires used in previous studies 46 , 53 , 54 to assess the subjective experience of the FBI. It consisted of eight statements, four directly related to the illusion and four control items, each related to one of the four dimensions of embodiment - location, ownership, appearance, and touch (see Table 1 ). Participants answered the questions asked in random order by specifying their level of agreement on a Likert scale varying from − 3 (total disagreement) to + 3 (total agreement), with 0 meaning “I don’t know”. The questionnaire was administered right after the BSET while participants were still standing, wearing the head-mounted display, and keeping their eyes closed. Table 1 Body Ownership Questionnaire. Four statements and respective controls. N. Domain Type Statements Content 1 Location Real At times, it seemed to me that my body was where the virtual body was Control At times, it felt like I was out of my body 2 Ownership Real At times, it seemed to me that the virtual body that I saw looking down Control At times, it felt like the virtual body belonged to someone else 3 Appearance Real At times, it seemed to me that the virtual body resembled my body (e.g., shape, skin color, or other characteristics) Control At times, it felt like my body disappeared 4 Touch Real At times, it seemed to me that I felt the touch in the same place where the virtual body was being touched Control At times, it seemed to me that my body was touched in a different part from where the virtual body was touched Experimental Tasks Weight Bias Implicit Association Task (WB-IAT) The weight bias IAT ( https://implicit.harvard.edu/ ) was used to quantify illusion-induced changes in weight bias and implicit attitude towards large people, 55 following the embodiment of a slim and large avatar (Fig. 1 C, left panel). Participants were asked to categorize stimuli (i.e., concepts) into two categories (i.e., attributes). The concepts “slim people” and “large people” were represented by pictures of black “slim” or “large” silhouettes, and the “good” and “bad” attributes by words in the “good” (e.g., happy, glorious, pleasure) and “bad” (e.g., awful, dirty, negative) category. Twenty pictures (10 in each category) and 16 attributes (8 in each category) were presented. Black silhouettes and the concept words were presented at the center of the screen. At the same time, categories were at the top left or top right of the screen and were aligned with the response key side (e.g., to categorize the concept in the category on the left or the right, respectively, E or I response keys on a QWERTY keyboard were used). Participants were asked to categorize items into groups as fast and accurately as possible by pressing the corresponding key on the keyboard. Throughout the task, the stimuli and the category labels were presented until a response was given; If participants made a mistake, a red cross appeared on the screen, and they had to press the appropriate key to continue the task. The procedure followed the standard IAT structure 56 with 7 task blocks that included 3 practice blocks (blocks 1, 2 and 5) and 4 test blocks, 2 for congruent and 2 for incongruent mapping of category pairs (blocks 3–4 and 6–7, respectively). The categories were mapped as congruent when the response key was the same for the concept “slim people” and the attribute “good”, and the other response key corresponded to the concept “large people” and the attribute “bad”. These pairs were switched in the incongruent blocks, thus mapping “slim people” and “bad” to one response key, and “large people” and “good” to the other. The order of congruent/incongruent test blocks was counterbalanced across participants, so that for half of the participants, blocks 1, 3, and 4 were switched with blocks 4, 5, and 7. Blocks 1, 2, 3, and 6 included 20 trials; blocks 4, 5, and 7 included 40 trials. The order of the association blocks was counterbalanced across participants. The task lasted about five minutes. Food Preferences Approach-Avoidance Test (FP-AAT) The FP-AAT task was used to analyze the implicit attraction or repulsion towards food 24 , 57 (see Fig. 1 C, middle panel). The stimuli used, subserving implicit emotional valence (i.e., attraction or repulsion), were eight images of hypercaloric foods (e.g., pizza, French fries, chocolate), eight images of hypocaloric foods (e.g., fruits and white meat), and eight numerical digits served as neutral stimuli. For each category, half of the stimuli were presented within a round frame and the other half within a square frame. Participants had to categorize stimuli according to their shape, while the stimulus category (hyper- or hypo-caloric foods and numbers) was task-irrelevant. They were asked to pull the mouse towards them (i.e., attraction) or push it away from them (i.e., repulsion) based on the stimulus shape (i.e., circle or square). The stimulus's semantic (e.g., calorie content) is associated with a specific valence (attraction or avoidance). Thus, it affects the participant's response time according to whether the answer modality (i.e., pulling/pushing) to the frame shape feature (i.e., circle or square) was congruent or incongruent with the stimulus's semantic (e.g., calorie content). For example, if participants had an implicit preference for hypercaloric foods, they should have been faster when asked to pull the mouse than if they were to push it. To induce the perception of pulling, simultaneously with the action executed by the participant, the picture progressively grew bigger until it occupied the whole screen. Similarly, when pulling the picture, it progressively shrank until it became a dot. A red X would show up in the middle of the screen once an incorrect response was made, and the stimulus would stay on until the participant gave the correct response. The association between the action (pulling and pushing) and stimulus shape (circle and square) was counterbalanced across participants. The task lasted about five minutes. Feedback-based Probabilistic Classification Learning Task As in Schintu et al. 58 , the feedback-based learning task was used to quantify learning based on punishment and reward feedback (see Fig. 1 C, right panel). The task asked participants to learn to identify four abstracted stimuli as belonging to one of two categories. After reading the instructions on the screen, participants were guided through an example of correct and incorrect responses and performed a practice session of 20 trials. The stimuli of the practice session differed from those employed in the actual task and were not analyzed. Participants categorized one stimulus per trial by pressing the masked keys of a standard keyboard corresponding to the two categories. Participants could win (rewarded trials) or lose (punished trials) points based on the selected category. Feedback, in the form of points added (rewarded trials) or subtracted (punished trials), appeared under the stimulus, and the point total was always visible for each trial. Two stimuli were chosen to be rewarding, and two to be punishing. Rewarded and punished stimuli were chosen so that one of each pair belonged to Category A on 80% of trials and the other to Category B on 80% of trials. The task consisted of three blocks of 40 trials each, totaling 120 trials. Twelve monochrome abstract figures were used as stimuli. The interval between each trial was 2 seconds, and each stimulus remained on the screen for up to 2 seconds or until a response was made. Five hundred points were the starting tally. For each correctly predicted category on rewarded trials, 25 points were awarded, while failed predictions resulted in no point change. On punished trials, guessing incorrectly meant losing 25 points, while guessing correctly resulted in no point change. To avoid stimulus and order effects, stimulus assignment to rewarding and punishing trials was counterbalanced within sessions and across participants. The task lasted about 15 minutes. Statistical analyses were performed using JASP (Version 0.19.3) and R (R Development, 2023.06.0 + 421) with alpha set at .05 (two-tailed). Bonferroni correction was used to correct for multiple comparisons. All data are presented as means with the Standard Error of the Mean (SEM). Effect sizes are indicated for significant effects. Declarations COMPETING INTERESTS The authors declare no competing interests. FUNDING This work was supported by PRIN PNRR P2022SAPYZ Grant (neuRocognitive effects oF Embodiment on Implicit aTtitudes – NextGenerationEU – missione 4, componente 2, investimento 1.1 CUP D53D23020800001) to LP and SS Author Contribution LP, MZ, and SS designed the study, RB, ANH, CML, and SS performed the experiments, LP, MF, MP, and SS analyzed the data, and LP, MF, MP, and SS wrote the paper. All authors reviewed the manuscript. Data Availability Data presented in this paper will be available upon request to the corresponding author ( [email protected] ). References Obesity. preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ. Tech. Rep. Ser. 894 , i–xii (2000). Must, A. et al. The disease burden associated with overweight and obesity. JAMA 282 , 1523–1529 (1999). Lykouras, L. & Michopoulos, J. 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Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 30 Jul, 2025 Editor invited by journal 24 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 22 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Experimental design.\u003c/strong\u003eThree ~1.5-hour sessions on consecutive days. Each session included a series of behavioral tasks (panel B). Day 1 served as baseline; Days 2 and 3 measured changes induced by the Full Body Illusion (FBI; panel C). \u003cstrong\u003eB. Behavioral Measurements.\u003c/strong\u003eThe Weight Bias Implicit Association Task (WB-IAT) assessed implicit attitudes toward body size. Participants categorized body silhouettes (slim or large) into groups labeled with positive (e.g., happy, pleasure) or negative (e.g., dirty, awful) words, following the standard IAT procedure. The Food Bias Approach-Avoidance Task (FB-AAT) assessed implicit attraction/repulsion toward food. Stimuli included hypercaloric (e.g., pizza), hypocaloric (e.g., fruit), and neutral numeric digits. Half of each appeared in circular frames, half in square ones. Participants categorized stimuli by shape and responded by pulling (attraction) or pushing (repulsion) the mouse. The Learning Task assessed feedback-based learning. Participants learned to categorize four abstract stimuli into two categories, receiving point feedback (reward or punishment) based on accuracy. Two stimuli were mostly rewarded, two mostly punished, each associated with Category A or B on 80% of trials. \u003cstrong\u003eC. Virtual reality.\u003c/strong\u003e The Body Size Estimation Task (BSET) was administered before and after the FBI to measure changes in perceived hip width. With eyes closed, participants estimated their hip size using their arms. After the FBI, the Embodiment Questionnaire (OBQ) assessed subjective experience using four illusion and four control statements across four embodiment dimensions. The FBI was induced once with an avatar 15% slimmer and once 15% larger than the participant’s body (order counterbalanced). To induce ownership, participants received 180 seconds of synchronous visuo-tactile stimulation: a green ball moved toward/away from the avatar’s abdomen, synchronized with vibrotactile feedback upon contact.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7151013/v1/57feba1858516d78bdad6615.jpg"},{"id":91191526,"identity":"fd64fca5-59ed-457d-aa14-9818214a8623","added_by":"auto","created_at":"2025-09-12 14:38:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the probabilistic reward learning task\u003c/strong\u003e. Average proportion correct scores are modeled for 12 sub-blocks for all participants (\u003cstrong\u003eA)\u003c/strong\u003e, participants above the median BMI (\u003cstrong\u003eB\u003c/strong\u003e), and participants at or below the median BMI (C). Lines, dots, and shading represent modeled learning curves, raw data, and SEM, respectively, for rewarded (green) and punished (red) trials.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7151013/v1/597c7f8fcc6009e40614622d.jpg"},{"id":91189557,"identity":"e23ba483-702e-47c3-ae2e-9f0f6b20db7c","added_by":"auto","created_at":"2025-09-12 14:30:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the FBI, weight, and food biases tasks.\u003c/strong\u003e \u003cstrong\u003eA. \u003c/strong\u003eFood bias calculated for the FP-AAT as the difference in the D scores in each of the FBI conditions (large, slim) and the baseline; \u003cstrong\u003eB. \u003c/strong\u003eWeight bias calculated for the WB-IAT as the difference in the D scores in each of the FBI conditions (large, slim) and the baseline; \u003cstrong\u003eC. \u003c/strong\u003eBSET calculated as the difference in BSE post-embodiment and BSE pre-embodiment in the large and slim FBI conditions; \u003cstrong\u003eD. \u003c/strong\u003eSubjective embodiment quantified as the mean ratings in the illusion and control statements of the Body ownership questionnaire (BOQ) in the large and slim FBI conditions. In all panels, bar plot show means with the standard error, the dots represent individual values, and the side distributions illustrate data variability for each condition. * = significant differences. HF= high calorie food; LF = Low calorie food; BSE= body size estimation.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7151013/v1/8b91b2af9021bf96c73f8d2a.jpg"},{"id":105756095,"identity":"41c0de9b-f55a-4ac4-8fd3-034cad4b8b13","added_by":"auto","created_at":"2026-03-30 16:35:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1039223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7151013/v1/53198415-823a-4f8b-b50e-512ac2f9e0a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"It is a matter of size - Manipulating body size with virtual reality modulates reward sensitivity","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe terms \u003cem\u003eoverweight\u003c/em\u003e and \u003cem\u003eobesity\u003c/em\u003e refer to conditions characterized by excessive fat deposits, which results from an imbalance of energy intake (diet) and energy expenditure (physical activity). However, they differ in severity and health impact, with the latter being a complex, multifactorial disease rooted in obesogenic environments, psycho-social factors, and genetic variants\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Obesity has become a major concern in clinical practice due to its growing prevalence, its significant impact on a wide range of medical and psychiatric conditions\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 along with the pervasive social stigma it generates - the weight bias\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Consistently, understanding this condition is of considerable scientific relevance even to manage clinical approaches.\u003c/p\u003e\u003cp\u003eA growing body of evidence highlights the key role of reward processing in the development and maintenance of obesity. These altered reward processes that regulate appetite, weight management, and treatment response, are believed to be at the heart of disease\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e: Both obesity and weight gain are associated with altered striatal,\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e frontal,\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and dopaminergic\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e responses, which are key components of the human reward system\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Among the few studies investigating reward processing in relation to non-food cues, Balodis and collaborators\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e measured responses to monetary rewards and found that obese individuals, compared to normal-weight controls, exhibited increased bilateral ventral striatum activation during the anticipatory phase of reward processing.\u003c/p\u003e\u003cp\u003eBariatric surgery is employed to help individuals with obesity normalize their metabolism and gain an ideal body shape by losing weight. Interestingly, these interventions reduced mesolimbic reward pathway brain activation and postsurgical desire to eat. Furthermore, both of these are more pronounced in response to high-calorie compared to low-calorie food cues\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The impact of body size on eating attitude has been reported in an experimental paradigm known as the Full Body Illusion (FBI)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Here, a virtual avatar presented from the first-person perspective (i.e., visually substituting for the participant\u0026rsquo;s body) induces the illusion of inhabiting that virtual body. A larger avatar causes an overestimation of body size, whereas a slimmer avatar leads to an underestimation of one's body size\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Crucially for this study, the avatar size also modulates eating attitudes: larger virtual bodies increased the preference for unhealthy foods\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, while slimmer avatars induced avoidance of high-calorie food\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe abovementioned evidence shows that both medically and experimentally (illusory) induced alterations in body size can modify body size perception and attitudes toward food. However, it remains unclear whether illusory manipulation of body size can modify reward learning in healthy adults. Therefore, we investigated whether the illusory changes in body size also influence reward-based learning, implicit attitudes towards food and body weight/size, and weight bias as a further implicit attitude related to body size\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo this aim, learning preferences in a probabilistic learning task (with reward and punishment feedback), implicit food approach/avoidance behavior, and implicit weight bias were quantified in normal-weight participants before and after embodying an avatar 15% larger (experimental condition) or slimmer (control condition) than their actual body. We hypothesized that embodying a larger avatar would change all measured processes coherently with the avatar\u0026rsquo;s size, enhancing reward-based learning, increasing approach tendencies toward high-calorie foods, and reducing implicit weight bias.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThirty-seven right-handed, normo-weight females participated in the study, which consisted of three sessions on consecutive days. On Day 1 (baseline), participants underwent a series of behavioral tasks to quantify their implicit attitudes toward body size and food, along with feedback-based learning performance. On Day 2 and 3 participants experienced the FBI paradigm of larger (Large condition) and slimmer (Slim condition) avatar and then repeated the behavioral tasks administered at baseline. Before and after each FBI, participants performed the Body Size Estimation Task (BSET) to quantify the subjective perception of their body size and the Body Ownership Questionnaire (BOQ) to assess the effectiveness of the FBI itself. See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a summary of the experimental setup and procedures. The final sample consisted of 35 females since two participants were excluded before statistical analysis due to data loss and the presence of an eating disorder.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eA. Experimental design.\u003c/b\u003e Three\u0026thinsp;~\u0026thinsp;1.5-hour sessions on consecutive days. Each session included a series of behavioral tasks (panel B). Day 1 served as baseline; Days 2 and 3 measured changes induced by the Full Body Illusion (FBI; panel C). \u003cb\u003eB. Behavioral Measurements.\u003c/b\u003e The Weight Bias Implicit Association Task (WB-IAT) assessed implicit attitudes toward body size. Participants categorized body silhouettes (slim or large) into groups labeled with positive (e.g., happy, pleasure) or negative (e.g., dirty, awful) words, following the standard IAT procedure. The Food Bias Approach-Avoidance Task (FB-AAT) assessed implicit attraction/repulsion toward food. Stimuli included hypercaloric (e.g., pizza), hypocaloric (e.g., fruit), and neutral numeric digits. Half of each appeared in circular frames, half in square ones. Participants categorized stimuli by shape and responded by pulling (attraction) or pushing (repulsion) the mouse. The Learning Task assessed feedback-based learning. Participants learned to categorize four abstract stimuli into two categories, receiving point feedback (reward or punishment) based on accuracy. Two stimuli were mostly rewarded, two mostly punished, each associated with Category A or B on 80% of trials. \u003cb\u003eC. Virtual reality.\u003c/b\u003e The Body Size Estimation Task (BSET) was administered before and after the FBI to measure changes in perceived hip width. With eyes closed, participants estimated their hip size using their arms. After the FBI, the Embodiment Questionnaire (OBQ) assessed subjective experience using four illusion and four control statements across four embodiment dimensions. The FBI was induced once with an avatar 15% slimmer and once 15% larger than the participant\u0026rsquo;s body (order counterbalanced). To induce ownership, participants received 180 seconds of synchronous visuo-tactile stimulation: a green ball moved toward/away from the avatar\u0026rsquo;s abdomen, synchronized with vibrotactile feedback upon contact.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFeedback-based Probabilistic Learning Task\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants performed a two-choice reaction time task where the reinforcement rate for each stimulus was 80%. As in other procedural learning studies, we used optimal responding rather than trial-wise accuracy\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e as our performance metric for analysis. Optimal responses were defined as the response most likely to produce a reward or avoid punishment. Two participants\u0026rsquo; data were discarded because they performed below chance across the three sessions (baseline, large, and slim), leaving 33 participants for this analysis. Data from the three training blocks were divided into twelve sub-blocks (four sub-blocks per block) to more adequately characterize learning curves, as in our prior work (Schintu et al., 2018). Optimal responses were modeled across sub-blocks (1\u0026ndash;12) using a linear mixed effects model in which the slope parameter indexed the rate of learning, and the quadratic slope indexed the acceleration or deceleration in learning rate. The model was fit to sub-block-specific performance using the lme4 package in R (Bates et al., 2015). Before modeling, the sub-block variable was centered so the intercept corresponded to the halfway point during task performance. The fixed effect of Feedback was effect-coded (\u0026rdquo;1\u0026rdquo; for reward and \u0026ldquo;-1\u0026rdquo; for punished). The model included dummy-coded contrasts between Baseline, Large, and Slim sessions. Finally, we included BMI scores as a continuous factor. Maximum likelihood was used to estimate all fixed and random effects simultaneously. Chi-square model comparisons indicated that the most complex model, including random intercepts and slopes of the linear and quadratic terms on participants, provided significantly better model fits than models where these random effects were removed:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Performance\\:\\sim\\:\\left(SUBBLOCK*FEEDBACK*SESSION*BMI\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:+{\\:(SUBBLOCK}^{2}*FEEDBACK*SESSION*BMI)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:+\\:(1+SUBBLOCK+{SUBBLOCK}^{2}|\\:SUBJECT)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFixed effects for the best-fitting model were then interpreted as described in the results. R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e values are reported for all significant effects and interactions. Missed responses occurred infrequently (2 times across all participants). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the proportion of optimal responding for each session for all participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), those with higher BMIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and those with lower BMI\u0026rsquo;s (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) according to a median split.\u003c/p\u003e\u003cp\u003eThe analysis revealed a significant effect of Subblock [t(31.91)\u0026thinsp;=\u0026thinsp;3.170, p\u0026thinsp;=\u0026thinsp;0.003, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.239], indicating that optimal responding increased across blocks regardless of Session, BMI, and Feedback. A significant main effect of Feedback was also detected [t(2290)\u0026thinsp;=\u0026thinsp;2.97, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004], indicating that optimal responding was higher for punished trials compared to rewarded trials.\u003c/p\u003e\u003cp\u003eThe interaction between Feedback and Session was also significant [t(2290) = -3.606, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.006]. When we unpacked this interaction by session (baseline, large, slim), we found that performance on punishment trials was superior compared to rewarded trials at baseline [t(718.1)\u0026thinsp;=\u0026thinsp;4.784, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.031] as well as after the slim illusion [t(718.2)\u0026thinsp;=\u0026thinsp;3.184, p\u0026thinsp;=\u0026thinsp;0.002, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.014], but the opposite was true after the Large illusion [t(718.0) = -2.993, p\u0026thinsp;=\u0026thinsp;0.003, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.012]. These results indicate that the Large illusion significantly improved performance on rewarded trials compared to punished trials.\u003c/p\u003e\u003cp\u003eWe also identified a significant Subblock by BMI interaction [t(31.91) = -2.329, p\u0026thinsp;=\u0026thinsp;0.026, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.145]. To unpack this interaction, we performed a median split of participants based on BMI and performed separate analyses for these groups. We found that the effect of Subblock was highly significant for participants with higher [t(15.97)\u0026thinsp;=\u0026thinsp;5.474, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.652] and lower [t(15.11)\u0026thinsp;=\u0026thinsp;7.988, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.809] BMI. These results indicate that while both groups experienced significant learning, learning for participants with lower BMI was steeper.\u003c/p\u003e\u003cp\u003eThe interaction between Feedback and BMI also achieved significance [t(2290) = -3.127, p\u0026thinsp;=\u0026thinsp;0.002, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004]. To unpack this interaction, we performed a median split of participants based on BMI and analyzed these groups separately. We found that while participants with higher BMI showed a trend favoring better performance on reward trials [t(1181) = -1.873, p\u0026thinsp;=\u0026thinsp;0.061, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.003], participants with lower BMI showed no significant difference [t(1110) = -0.437, p\u0026thinsp;=\u0026thinsp;0.662]. These results indicate that participants with higher BMIs were more responsive to rewarded feedback than participants with lower BMIs.\u003c/p\u003e\u003cp\u003eThe interaction between Feedback, Session, and BMI was also significant [t(2290)\u0026thinsp;=\u0026thinsp;3.817, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.006]. When we unpacked this interaction by BMI, as described above, we found that the interaction between Feedback and Session was significant for participants with higher BMI [t(1181)\u0026thinsp;=\u0026thinsp;3.888, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.013], but not for participants with lower BMI [t(1110) = -0.924, p\u0026thinsp;=\u0026thinsp;0.356]. These results indicate that participants with higher BMI were more responsive to rewarded feedback than to punishments, specifically after the large illusion.\u003c/p\u003e\u003cp\u003eThe interaction between Feedback, Session, AND Sub-block (quadratic) was also significant [t(2290)\u0026thinsp;=\u0026thinsp;3.443, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.005]. When we unpacked this interaction by session, as described above, we found that the Feedback by quadratic interaction was significant at Baseline [t(718.1) = -2.572, p\u0026thinsp;=\u0026thinsp;0.010, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.009] and after the Slim illusion [t(718.2) = -3.005, p\u0026thinsp;=\u0026thinsp;0.003, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.012], indicating significantly faster punishment learning compared to reward. However, we found that the opposite was true after the Large illusion [t(718.0)\u0026thinsp;=\u0026thinsp;2.892, p\u0026thinsp;=\u0026thinsp;0.004, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.012]. These results show that the FBI of Larger avatars significantly increased the rewarded learning rate relative to baseline and punished trials.\u003c/p\u003e\u003cp\u003eFinally, the interaction between Feedback, Session, BMI, and Sub-block (quadratic) was also significant [t(2290) = -3.542, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.005]. When we unpacked the interaction by BMI, as described above, we observed a three-way interaction between Feedback, Session, and Sub-block (quadratic) for participants with higher BMI [t(1181) = -2.123, p\u0026thinsp;\u0026lt;\u0026thinsp;0.034, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004], but not for participants with lower BMI [t(1110)\u0026thinsp;=\u0026thinsp;0.872, p\u0026thinsp;=\u0026thinsp;0.383)]. We further unpacked the significant interaction for the higher BMI participants by Session and observed a significant interaction between Feedback and Sub-block (quadratic) after the Large illusion [t(370) = -2.923, p\u0026thinsp;=\u0026thinsp;0.004, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.023], but not at Baseline [t(370.0)\u0026thinsp;=\u0026thinsp;1.644, p\u0026thinsp;=\u0026thinsp;0.101] or after the Slim illusion [t(370.1)\u0026thinsp;=\u0026thinsp;0.674, p\u0026thinsp;=\u0026thinsp;0.501]. These results confirm that the large illusion increased the rate of reward learning, specifically for participants with higher BMI.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFood Preferences Approach-Avoidance Test (FP-AAT)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe FP-AAT was administered at the baseline session and after the Large and Slim illusion to assess the change in the implicit food preferences. Five participants were excluded because their d-score was more than two standard deviations, leaving 30 participants for this analysis.\u003c/p\u003e\u003cp\u003eThe D-score was calculated on total standard deviation as avoidance (Push) minus approach (Pull), such that positive numbers mean a bias toward avoidance (Push) and negative numbers a bias toward approach (Pull). Delta d-score was calculated as session minus baseline, such that positive numbers represent an increase in avoidance (push away from the body) and negative numbers represent an increase in approach (pull toward the body). To test the presence of significant changes in implicit attitudes toward food, we first compared the delta d-score against zero. This analysis revealed that when participants embodied a larger avatar (Large condition), they displayed an increased approach to high-calorie food [t(29) = -3.207, p\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ed\u003c/em\u003e = -0.586]. No significant change in approach/avoidance behavior was found for low-calorie food or any kind of stimulus in the Slim condition (all ps\u0026thinsp;\u0026gt;\u0026thinsp;.05; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). A 2x2 repeated-measures ANOVA for Embodiment condition (Large, Slim) x Calorie content (High, Low) performed for the delta d-scores showed no significant effects or interactions (all ps\u0026thinsp;\u0026gt;\u0026thinsp;.05).\u003c/p\u003e\u003cp\u003eThese results indicate that the embodiment of a larger avatar increased the bias of approaching high-calorie food.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWeight Bias Implicit Association Task (WB-IAT)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe weight Bias IAT was administered at baseline and after the Large and Slim illusion to assess the change in implicit weight bias. One participant was excluded because their d-score exceeded two standard deviations, leaving 34 participants for this analysis.\u003c/p\u003e\u003cp\u003eDelta was calculated as a session minus baseline, such that positive numbers represent an increase and negative numbers represent a decrease. To test the presence of significant changes in the implicit attitudes toward weight, we first compared the delta d-score against zero. This analysis revealed that when a subject embodied a larger avatar (Large condition) weight bias significantly decreased [t(33) = -3.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e = -0.676] and the same happened when the was the slim avatar to be embodied t(33) = -2.939, p\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003ed\u003c/em\u003e = -0.504]. The two deltas did not differ (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eThese results reveal that embodying an avatar, regardless of size, decreases the implicit weight bias.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFull Body Illusion (FBI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody Size Estimation Task (BSET)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe BSET was administered before and after the Large and Slim FBI to estimate the subjective change in body size. The amount of change in body size estimation in each condition was calculated by subtracting the baseline measurements from each post-FBI measurement (delta-BSE), so that positive numbers indicate an increase and negative numbers indicate a decrease in body size estimation. The paired-sample t-test showed that the delta-BSE was positive and significantly larger in the Large as compared to the Slim condition [t(34)\u0026thinsp;=\u0026thinsp;2.283, p\u0026thinsp;=\u0026thinsp;0.029, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.386; see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In each condition the change of the body size estimation was tested against 0 and resulted to be significant in the Large [t(34)\u0026thinsp;=\u0026thinsp;4.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.710] but not Slim [t(34)\u0026thinsp;=\u0026thinsp;0.498, p\u0026thinsp;=\u0026thinsp;0.622, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.084]. Furthermore, baseline body size estimation did not differ between sessions [t(34) -1.106, p\u0026thinsp;=\u0026thinsp;0.277, d = -0.187], thus excluding a possible carry-over effect of the changes induced by the previous FBI session.\u003c/p\u003e\u003cp\u003eThe Pearson correlation between the delta-BSE in the large condition, which produced a significant change in BSE, and BMI revealed that the higher the BMI, the larger the body size estimation (r(35) 0.445, p\u0026thinsp;=\u0026thinsp;0.007). This means that the higher the participant\u0026rsquo;s BMI, and consequently the size of the avatar in the Large condition, the greater the change in body size estimation.\u003c/p\u003e\u003cp\u003eThese results indicate that merely experiencing the illusion of owning a larger avatar body led to a subjective overestimation of participants\u0026rsquo; body size and that the magnitude of this effect was related to their actual BMI.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody Ownership Questionnaire (BOQ)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe BOQ was administered after the Large and Slim illusion to assess the subjective experience of the FBI. One participant was excluded because of missing data, leaving 34 participants for this analysis.\u003c/p\u003e\u003cp\u003eTo test whether the illusory experience took place for each FBI condition, we compared the average score of the four \u0026ldquo;illusion\u0026rdquo; questions to the averaged \u0026ldquo;control\u0026rdquo; statements (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in method section for a list of questions). The two-tailed paired t-test revealed that the real questions mean differed from the control one for both the Large [t(33)\u0026thinsp;=\u0026thinsp;9.231, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.583] and Slim illusion [t(33)\u0026thinsp;=\u0026thinsp;6.933, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.195], meaning that both FBI conditions induced the subjective illusory experience (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, no difference was found for the real questions between the Large and Slim illusion [t(33) 1.880, p\u0026thinsp;=\u0026thinsp;0.069, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.195], indicating that the illusory experience did not differ between avatar\u0026rsquo;s size.\u003c/p\u003e\u003cp\u003eThese results of the FBI paradigm show that the subjective illusory experience occurred in both conditions and did not differ between the two avatars\u0026rsquo; sizes.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe investigated the effects of the illusory experience of owning a virtual body of a different size than one\u0026rsquo;s real body on cognition. After having confirmed that subjective embodiment occurs independently of the avatar size, we quantified the cognitive changes before and after the embodiment of a larger and slimmer avatar. Based on evidence implicating the reward system in obesity, especially in relation to high-calorie food\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, we expected participants to increase reward-based sensitivity along with approach-behavior toward high-calorie food paired with a decrease implicit weight bias. In agreement with the prediction, embodying an avatar 15% larger increased reward-based learning compared to baseline and slim avatar, and increased the approach behavior toward high-calorie food. No changes in reward-based learning and food-related approach-avoidance behavior were observed in the slim avatar condition. This study also explored, for the first time, the effect of the embodiment of a larger body size avatar on implicit social weight bias. Findings showed a reduction of social stigma as a result of embodying a different body, regardless of its specific size.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFeedback-based Probabilistic Learning\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn general, participants experienced greater performance on rewarded trials than on punishment trials. Interestingly, this effect was modulated by participants\u0026rsquo; BMI, indicating that participants with higher BMIs were generally more responsive to rewarded feedback than participants with lower BMIs. When considering the effect of the FBI, results indicated that performance on punishment trials was superior compared to rewarded trials at baseline as well as after the Slim illusion, but the opposite was true after the Large illusion - Large illusion significantly improved performance on rewarded trials compared to punished trials. This effect was also found to interact with BMI, indicating that participants with higher BMI were more responsive to rewarded feedback than punishments, specifically after the large illusion. Interestingly, the large FBI modulated also the learning rate, as the acceleration in learning rate from reward feedback was significantly greater after the large and slim illusions compared to the baseline. Indeed, while faster punishment learning compared to reward was found at baseline and after the Slim illusion, the opposite was true after the Large illusion. The FBI of large avatars significantly increased the reward learning rate relative to the baseline. Also, the learning rate interacted with BMI, thus confirming that the Large FBI increased the rate of reward learning specifically for participants with higher BMI. The reason only higher BMI had a significant effect may lie in how avatar sizes were calculated \u0026ndash; each was scaled individually to \u0026plusmn; 15% of each participant\u0026rsquo;s actual body size. Although only participants with a BMI within the normal range were recruited, those on the lower end may have perceived the larger avatar as still not overweight. In contrast, for the group of participants on the higher end of the BMI spectrum, if their BMI is increased by 15%, it exceeds the normal range, making the larger avatar appear overweight. This discrepancy, indirectly supported by the finding that the higher the BMI, the larger the body size estimation following the large FBI, could explain the differential effects.\u003c/p\u003e\u003cp\u003eTo account for reward-based modulation induced by the FBI, we speculate that shared and interacting brain regions may be involved. Embodiment has been consistently associated with activating a brain network comprising the posterior parietal cortex, namely the inferior parietal sulcus (IPS), and premotor areas\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These areas are involved in the multisensory integration necessary for the experience of embodiment\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Among the key components of embodiment, \u003cem\u003eself-location\u003c/em\u003e is primarily linked to activity in the intraparietal cortex, hippocampus, posterior cingulate, and retrosplenial cortex, whereas \u003cem\u003ebody ownership\u003c/em\u003e is supported by activation of premotor and intraparietal regions\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Notably, the posterior cingulate cortex mediates these two neural systems, suggesting its crucial role in integrating self-location and body ownership. The parietal cortex and the hippocampus, two core regions of the FBI network, are anatomically and functionally connected, as shown by the a seminal study by Wang et al.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e in which excitatory TMS applied to the parietal cortex directly increased hippocampal activity. The hippocampus also strongly connects with the nucleus accumbens (NAcc), a central node in the mesocorticolimbic dopamine system responsible for reward processing\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In animal models, the strength of this connection has been shown to increase with the presentation of rewards, leading to dopamine (DA) release\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additional support for the NAcc's role in reward sensitivity comes from recent findings showing that ultrasound stimulation of the human NAcc enhances reward-related behaviors, such as increased learning from positive feedback\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Building on this evidence, we propose that the FBI may influence reward-based learning by modulating connectivity among the parietal cortex, hippocampus, and NAcc. This modulation could, in turn, affect the functioning of the mesolimbic circuit and reward signal processing. However, this remains a hypothesis, and further research is needed to elucidate the neural mechanism underlying the observed behavioral outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplicit attitudes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe implicit food preferences shifted after the embodiment of a large avatar toward a stronger preference for high-calorie food. While being in line with the previous findings\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, this result is, more notably, coherent with food behaviors that are observed in overweight and obese individuals in real-world contexts. Indeed, these individuals present a stronger preference for high-calorie foods than low-calorie ones\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, which might be rooted in the general tendency of higher reward sensitivity\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eObserving such food-related behavior after a temporary embodiment of a larger body, without actual body-size changes, further demonstrates the crucial role of one's body perception on cognition\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and the potential applications of the FBI-based protocols in various contexts, including clinical ones.\u003c/p\u003e\u003cp\u003eBeyond the cognitive effect of the FBI, we also examined its possible impact on the weight bias- an implicit social bias known to affect well-being and quality of life of obese individuals\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Our results indicated a reduction in the implicit weight bias following the embodiment of an avatar compared to baseline. This finding only partially agrees with our initial hypothesis since it occurred following both the large and slim FBI. This size-independent decrease in weight bias cannot be directly compared to prior findings, as this is the first study to investigate the effects of the FBI specifically on the weight bias, rather than weight-related satisfaction. While earlier studies\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e used body-IAT in healthy individuals to assess embodiment effects, focusing on implicit attitudes towards one's body image in relation to body size, the present study examined the weight bias in the context of general social stereotypes that associate obesity with negative personality traits, such as laziness or lack of motivation\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The reduction of the weight bias after both FBI conditions might be attributed to the fact that the weight bias per se is not necessarily related to an individual\u0026rsquo;s body weight. Indeed, especially in societies with a high prevalence of obesity, even people with higher body weight and BMI present high levels of weight bias, similarly to people with lower BMI\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In the present study, it is possible that embodiment, regardless of avatar size, focused participants\u0026rsquo; attention on \u0026ldquo;their\u0026rdquo; body more than at baseline and created the feeling that their body is different from the one they usually perceive as their own. This experience may have fostered increased body acceptance, reducing implicit weight bias. In addition, it has been suggested that negative implicit biases are reduced after the embodiment of out-group virtual bodies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This effect occurs with in-group avatars resembling their real appearance, implying that owning a body different from one\u0026rsquo;s own can reduce prejudice toward the social group associated with that body. From this perspective, both large and slim avatars may have been considered an out-group because both differed from the participants\u0026rsquo; actual body size. While this does not directly explain why the weight bias was reduced even in the slim condition, it further supports the idea that inhabiting a body different from one\u0026rsquo;s increases the openness toward diverse body types. Finally, it is important to note that the observed reduction in weight bias was due to repeated exposure to IAT (i.e., learning effect), as the order of conditions was counterbalanced across participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFull Body illusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe subjective feeling of embodiment, quantified by the embodiment questionnaire, was successful for both larger and slimmer avatars, indicating that it occurs regardless of the virtual body size. This is consistent with evidence suggesting that embodiment is independent of the body appearance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and size\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The sense of body ownership is indeed malleable and can extend to bodies that look different from one\u0026rsquo;s own, but solely if the key condition for embodiment illusions are met, namely, first-person perspective, anatomical plausibility, synchronous multisensory stimulation\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe same flexibility/malleability does not hold for changes in perceived body size, since only the large FBI condition shifted body size estimation in the direction of the avatar\u0026rsquo;s size. Participants judged the size of their hips to be larger than the actual size after the embodiment of a large avatar, and the opposite did not happen following the embodiment of a slim avatar. While some studies reported changes across both embodiment conditions\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, others have found a reduction in the body size estimation only in the slim condition\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and still others suggested that embodying larger limbs produced stronger effects\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Our finding aligns with the latter evidence, which further confirms the idea that body representation might adapt more easily to the increase in body size\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In addition, discrepancies with the previous findings might be due to differences in the embodiment paradigm. In both previous studies, only the hip size of the virtual body or mannequin was modified, while here we adjusted the entire virtual body based on each participant\u0026rsquo;s body measurements. As a result, the slim avatar might have appeared too slim and unrealistic (as noted in some participants\u0026rsquo; feedback), which could have affected the adaptation of the perceived body size. However, it is necessary to point out that the subjective embodiment was equally present in both conditions, as described above.\u003c/p\u003e\u003cp\u003eTo summarize, this study shows that embodying a larger virtual body can enhance reward-based learning and implicit attitudes toward high-calorie food, while reductions in weight bias occur independently of body size. This is the first study to disentangle reward sensitivity from food-related behavior, providing a more nuanced understanding of how body representation influences cognitive processing. Notably, although both avatars were equally embodied at a subjective level, only the larger avatar affected body perception and cognitive responses, the personalized scaling of avatars may have contributed to differential effects based on participants\u0026rsquo; BMI.\u003c/p\u003e\u003cp\u003eOverall, our findings underscore the profound interplay between body and mind. Given that the body is a constant and inseparable part of human experience, changes in its representation can shape cognitive processes. These results support the view that physical and psychological states are mutually influential - a principle rooted in the idea that \u003cem\u003ea healthy mind in a healthy body\u003c/em\u003e reflects a bidirectional relationship rather than a one-way effect.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on similar previous studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e that reported medium-to-high effect sizes, we conducted an \u003cem\u003ea priori\u003c/em\u003e power analysis in G*Power\u003csup\u003e47\u003c/sup\u003e for a two-tailed paired t-test with a medium effect size (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.50) and alpha level 0.05. This analysis revealed that a sample size of 34 subjects is necessary to obtain power\u0026thinsp;\u0026gt;\u0026thinsp;0.8. Thirty-seven right-handed volunteers were recruited for the study. Since body size stereotyping is more prevalent among young women\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, we recruited healthy females between 20 and 30 years of age, with normal weight as quantified by the Body Mass Index (BMI) and no eating disorder symptoms, as assessed by the Eating Disorder Inventory (EDI)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Before statistical analysis, one participant was excluded because of missing data, and one because the EDI score was above the cut-off (\u0026gt;\u0026thinsp;50). The final sample consisted of 35 females (mean age\u0026thinsp;=\u0026thinsp;23.21, SEM\u0026thinsp;=\u0026thinsp;0.5; mean EDI\u0026thinsp;=\u0026thinsp;13.97, SEM\u0026thinsp;=\u0026thinsp;1.87; mean BMI\u0026thinsp;=\u0026thinsp;20.73, SEM\u0026thinsp;=\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31). All participants gave informed consent and were compensated for participation. The study was approved by the Human Experimental Ethics Committee of the University of Trento (protocol 2019-011) and was conducted in accordance with the ethical standards of the Helsinki Declaration\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProcedure\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe within-subjects study consisted of three sessions on consecutive days. Each session lasted about 1.5 hours and started, for each participant, at approximately the same time of day.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Day 1 included consenting, EDI\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and weight and height measurements to calculate their BMI. The experimenter then took participants\u0026rsquo; body measurements (i.e., neck circumference, arm, wrist, upper bust, waist, hips, thigh, knee, ankle, and neck-shoulder distance) to create the avatars for the full body illusion (FBI). Once this preliminary phase was completed, participants underwent a series of behavioral tasks to quantify the implicit attitudes toward body size and food, feedback-based learning performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eIn the second and third sessions, participants experienced the virtual FBI paradigm; all participants were exposed to the larger avatar (Large condition) and the slimmer one (Slim condition) in a counterbalanced manner. Before and after each FBI, participants performed the Body Size Estimation Task (BSET) to quantify the subjective perception of their body size and the Body Ownership Questionnaire (BOQ) to assess the effectiveness of the FBI itself. Following each FBI, participants repeated the behavioral tasks administered at baseline. Since the FBI duration is unknown, the order of the behavioral tasks was kept stable across sessions, and task administration started, for all participants, ~ 5 minutes after the FBI. Participants were debriefed at the end of the experiment, and their impressions and thoughts about the FBI were recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of eating and body representation disorders\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody Mass Index (BMI)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe Body Mass Index (BMI) was computed by dividing the weight (kilograms) by the square of the height (meters). Only Participants with a BMI within the normal range (18.5\u0026ndash;24.9) participated in the study.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEating Disorder Inventory (EDI)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe EDI (Vetrone et al., 2006) was used to assess the presence of eating disorders. It comprises 64 items divided into eight subscales (i.e., drive for slimness, bulimia, body dissatisfaction, ineffectiveness, perfectionism, interpersonal distrust, interoceptive awareness, and maturity fears). Participants were asked to rate their level of agreement on a 6-point Likert scale from 0 (never) to 5 (always). As in the Tambone et al.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e study, the EDI cut-off scored 50\u003csup\u003e50\u003c/sup\u003e, and individuals who exceeded this score were excluded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFull Body Illusion (FBI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe FBI timeline and a view of the virtual scene in the two conditions are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. Participants wore a Head Mounted Display, HMD [Oculus Quest 2 2021 equipped with RGB LCDs 1832x1920 pixels, refresh rate at 120 Hz, the field of view of 110\u0026deg; (diagonal FOV) and 6 degrees of freedom]. The scenario was created in 3D Studio Max 2023 (Autodesk, Inc.) and implemented in Unity 2017 game software environment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://unity.com\u003c/span\u003e\u003cspan address=\"http://unity.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e It included a simple room with a chair, a table, and a window. A dressed female avatar was placed sitting on the chair. The avatars were created using MakeHuman software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.makehumancommunity.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.makehumancommunity.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and animated with MotionBuilder 2023 (Autodesk, Inc.). The avatars\u0026rsquo; body size was individually calculated by subtracting (Slim condition) or adding (Large condition) 15% of each participant\u0026rsquo;s body measurements.\u003c/p\u003e\u003cp\u003eAt the beginning of the FBI procedure, once the participants put on the HMD, the virtual scene was occluded, and participants saw only a blue background color surrounding them. During this phase, the pre-embodiment BSET was administered (see details below). Once the BSET was completed, the virtual scene and one of the avatars (slim/large, depending on the condition) were activated and became visible. Participants, who were also sitting on a chair, were asked to position themselves as the avatar, adjusting the position of their body and the chair until they felt to be located exactly in the position of the avatar they were seeing from a first-person perspective. Their position was further adjusted by shifting the virtual camera upwards or downwards until participants reported feeling as if they were looking at the virtual scene and body precisely from the avatar\u0026rsquo;s perspective.\u003c/p\u003e\u003cp\u003eParticipants familiarized themselves with the virtual environment for 30 seconds by moving their heads to look around, including at themselves (i.e., avatars). To induce the feeling of ownership of a virtual body, we employed synchronous visuo-tactile stimulation. An Arduino-controlled vibrotactile stimulator (diameter x height\u0026thinsp;=\u0026thinsp;8 mm \u0026times; 3mm, 3 V, rated speed \u0026ndash; 12,000 RPM) was located on the participants\u0026rsquo; abdomens and was used during the FBI to deliver the tactile stimulation. After the 30-second familiarization phase, the three-minute synchronous visuo-tactile stimulation started. A green ball moved towards and away from the avatar\u0026rsquo;s abdomen synchronously with the vibrotactile stimulation that occurred when the ball touched the avatar\u0026rsquo;s body. Frequency and duration of each set of touches were 6 Hz and 500 ms, respectively; a set of touches was delivered every 4\u0026ndash;5 s. After three minutes, the visuo-tactile stimulation stopped, the blue background occluded the virtual environment and the avatar as in the beginning of the procedure, and the BSET and BOQ were administered.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody Size Estimation Task (BSET)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe BSET was used to quantify the subjective estimation of the body size before and after each FBI [adapted from\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e]. Participants stood up and, while still wearing the HMD and keeping their eyes closed, were asked to extend their arms along their body (without touching it), then raise their arms in front of them with their palms facing each other and adjust the distance between their palms to the perceived size of their hips. This procedure was repeated three times; each time, the distance between their palms was measured with a measuring tape (accuracy of 0.5 cm), and the mean of the three measurements was considered.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBody Ownership Questionnaire (BOQ)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe BOQ was adapted from questionnaires used in previous studies\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e to assess the subjective experience of the FBI. It consisted of eight statements, four directly related to the illusion and four control items, each related to one of the four dimensions of embodiment - location, ownership, appearance, and touch (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants answered the questions asked in random order by specifying their level of agreement on a Likert scale varying from \u0026minus;\u0026thinsp;3 (total disagreement) to +\u0026thinsp;3 (total agreement), with 0 meaning \u0026ldquo;I don\u0026rsquo;t know\u0026rdquo;. The questionnaire was administered right after the BSET while participants were still standing, wearing the head-mounted display, and keeping their eyes closed.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eBody Ownership Questionnaire.\u003c/b\u003e Four statements and respective controls.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomain\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatements Content\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it seemed to me that my body was where the virtual body was\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it felt like I was out of my body\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eOwnership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it seemed to me that the virtual body that I saw looking down\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it felt like the virtual body belonged to someone else\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAppearance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it seemed to me that the virtual body resembled my body\u003c/p\u003e\u003cp\u003e(e.g., shape, skin color, or other characteristics)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it felt like my body disappeared\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTouch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it seemed to me that I felt the touch in the same place where the virtual body was being touched\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAt times, it seemed to me that my body was touched in a different part from where the virtual body was touched\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\u003cb\u003eExperimental Tasks\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eWeight Bias Implicit Association Task (WB-IAT)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe weight bias IAT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://implicit.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://implicit.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used to quantify illusion-induced changes in weight bias and implicit attitude towards large people, \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e following the embodiment of a slim and large avatar (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, left panel). Participants were asked to categorize stimuli (i.e., concepts) into two categories (i.e., attributes). The concepts \u0026ldquo;slim people\u0026rdquo; and \u0026ldquo;large people\u0026rdquo; were represented by pictures of black \u0026ldquo;slim\u0026rdquo; or \u0026ldquo;large\u0026rdquo; silhouettes, and the \u0026ldquo;good\u0026rdquo; and \u0026ldquo;bad\u0026rdquo; attributes by words in the \u0026ldquo;good\u0026rdquo; (e.g., happy, glorious, pleasure) and \u0026ldquo;bad\u0026rdquo; (e.g., awful, dirty, negative) category. Twenty pictures (10 in each category) and 16 attributes (8 in each category) were presented. Black silhouettes and the concept words were presented at the center of the screen. At the same time, categories were at the top left or top right of the screen and were aligned with the response key side (e.g., to categorize the concept in the category on the left or the right, respectively, E or I response keys on a QWERTY keyboard were used). Participants were asked to categorize items into groups as fast and accurately as possible by pressing the corresponding key on the keyboard. Throughout the task, the stimuli and the category labels were presented until a response was given; If participants made a mistake, a red cross appeared on the screen, and they had to press the appropriate key to continue the task. The procedure followed the standard IAT structure \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e with 7 task blocks that included 3 practice blocks (blocks 1, 2 and 5) and 4 test blocks, 2 for congruent and 2 for incongruent mapping of category pairs (blocks 3\u0026ndash;4 and 6\u0026ndash;7, respectively). The categories were mapped as congruent when the response key was the same for the concept \u0026ldquo;slim people\u0026rdquo; and the attribute \u0026ldquo;good\u0026rdquo;, and the other response key corresponded to the concept \u0026ldquo;large people\u0026rdquo; and the attribute \u0026ldquo;bad\u0026rdquo;. These pairs were switched in the incongruent blocks, thus mapping \u0026ldquo;slim people\u0026rdquo; and \u0026ldquo;bad\u0026rdquo; to one response key, and \u0026ldquo;large people\u0026rdquo; and \u0026ldquo;good\u0026rdquo; to the other. The order of congruent/incongruent test blocks was counterbalanced across participants, so that for half of the participants, blocks 1, 3, and 4 were switched with blocks 4, 5, and 7. Blocks 1, 2, 3, and 6 included 20 trials; blocks 4, 5, and 7 included 40 trials. The order of the association blocks was counterbalanced across participants. The task lasted about five minutes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFood Preferences Approach-Avoidance Test (FP-AAT)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe FP-AAT task was used to analyze the implicit attraction or repulsion towards food \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, middle panel). The stimuli used, subserving implicit emotional valence (i.e., attraction or repulsion), were eight images of hypercaloric foods (e.g., pizza, French fries, chocolate), eight images of hypocaloric foods (e.g., fruits and white meat), and eight numerical digits served as neutral stimuli. For each category, half of the stimuli were presented within a round frame and the other half within a square frame. Participants had to categorize stimuli according to their shape, while the stimulus category (hyper- or hypo-caloric foods and numbers) was task-irrelevant. They were asked to pull the mouse towards them (i.e., attraction) or push it away from them (i.e., repulsion) based on the stimulus shape (i.e., circle or square). The stimulus's semantic (e.g., calorie content) is associated with a specific valence (attraction or avoidance). Thus, it affects the participant's response time according to whether the answer modality (i.e., pulling/pushing) to the frame shape feature (i.e., circle or square) was congruent or incongruent with the stimulus's semantic (e.g., calorie content). For example, if participants had an implicit preference for hypercaloric foods, they should have been faster when asked to pull the mouse than if they were to push it. To induce the perception of pulling, simultaneously with the action executed by the participant, the picture progressively grew bigger until it occupied the whole screen. Similarly, when pulling the picture, it progressively shrank until it became a dot. A red X would show up in the middle of the screen once an incorrect response was made, and the stimulus would stay on until the participant gave the correct response. The association between the action (pulling and pushing) and stimulus shape (circle and square) was counterbalanced across participants. The task lasted about five minutes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFeedback-based Probabilistic Classification Learning Task\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs in Schintu et al. \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, the feedback-based learning task was used to quantify learning based on punishment and reward feedback (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, right panel). The task asked participants to learn to identify four abstracted stimuli as belonging to one of two categories. After reading the instructions on the screen, participants were guided through an example of correct and incorrect responses and performed a practice session of 20 trials. The stimuli of the practice session differed from those employed in the actual task and were not analyzed.\u003c/p\u003e\u003cp\u003eParticipants categorized one stimulus per trial by pressing the masked keys of a standard keyboard corresponding to the two categories. Participants could win (rewarded trials) or lose (punished trials) points based on the selected category. Feedback, in the form of points added (rewarded trials) or subtracted (punished trials), appeared under the stimulus, and the point total was always visible for each trial. Two stimuli were chosen to be rewarding, and two to be punishing. Rewarded and punished stimuli were chosen so that one of each pair belonged to Category A on 80% of trials and the other to Category B on 80% of trials.\u003c/p\u003e\u003cp\u003eThe task consisted of three blocks of 40 trials each, totaling 120 trials. Twelve monochrome abstract figures were used as stimuli. The interval between each trial was 2 seconds, and each stimulus remained on the screen for up to 2 seconds or until a response was made. Five hundred points were the starting tally. For each correctly predicted category on rewarded trials, 25 points were awarded, while failed predictions resulted in no point change. On punished trials, guessing incorrectly meant losing 25 points, while guessing correctly resulted in no point change.\u003c/p\u003e\u003cp\u003e To avoid stimulus and order effects, stimulus assignment to rewarding and punishing trials was counterbalanced within sessions and across participants. The task lasted about 15 minutes. Statistical analyses were performed using JASP (Version 0.19.3) and R (R Development, 2023.06.0\u0026thinsp;+\u0026thinsp;421) with alpha set at .05 (two-tailed). Bonferroni correction was used to correct for multiple comparisons. All data are presented as means with the Standard Error of the Mean (SEM). Effect sizes are indicated for significant effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eThis work was supported by PRIN PNRR P2022SAPYZ Grant (neuRocognitive effects oF Embodiment on Implicit aTtitudes \u0026ndash; NextGenerationEU \u0026ndash; missione 4, componente 2, investimento 1.1 CUP D53D23020800001) to LP and SS\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLP, MZ, and SS designed the study, RB, ANH, CML, and SS performed the experiments, LP, MF, MP, and SS analyzed the data, and LP, MF, MP, and SS wrote the paper. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData presented in this paper will be available upon request to the corresponding author ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eObesity. preventing and managing the global epidemic. Report of a WHO consultation. \u003cem\u003eWorld Health Organ. Tech. Rep. Ser.\u003c/em\u003e \u003cb\u003e894\u003c/b\u003e, i\u0026ndash;xii (2000).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMust, A. et al. The disease burden associated with overweight and obesity. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e282\u003c/b\u003e, 1523\u0026ndash;1529 (1999).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLykouras, L. \u0026amp; Michopoulos, J. 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Left-shifting prism adaptation boosts reward-based learning. \u003cem\u003eCortex\u003c/em\u003e \u003cb\u003e109\u003c/b\u003e, 279\u0026ndash;286 (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"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":"feedback-based learning, body size, full body illusion, obesity, body ownership, social stigma","lastPublishedDoi":"10.21203/rs.3.rs-7151013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7151013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObesity is a major concern in clinical practice given its impact on medical and psychiatric conditions, and the pervasive social stigma it carries. Reward processing has a key role in the development and maintenance of obesity, as evidenced by reduced brain activation within the reward pathway and concurrent decrease in desire to eat following bariatric surgery. Interestingly, the experimental paradigm known as the Full Body Illusion (FBI) has been effective in impacting body size on eating attitudes. However, while both medically and experimentally induced modulation of body size modify body size perception and attitudes toward food, it remains unclear whether such illusory manipulations can affect reward-based behavior in healthy adults. To address this question, we investigated whether FBI-induced changes in body size influence reward-based learning, implicit attitudes toward food and body weight. As expected, embodying a larger avatar enhanced reward-based learning and implicit attitudes toward high-calorie foods, while reductions of the implicit weight bias occurred independently of the avatar size. This is the first study to establish a link between reward-related learning and the perception of one's body size, emphasizing a critical role for one\u0026rsquo;s own perception of their weight beyond their physical weight, in shaping approach and avoidance behavior.\u003c/p\u003e","manuscriptTitle":"It is a matter of size - Manipulating body size with virtual reality modulates reward sensitivity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 14:30:53","doi":"10.21203/rs.3.rs-7151013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-20T11:05:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T11:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178561467290360796149192241550197520414","date":"2025-09-21T18:50:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265938866826141745205984043591041018129","date":"2025-09-21T16:54:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43404452800897819058212826099412600660","date":"2025-09-21T13:55:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T20:39:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51101035166260018685308272428488621043","date":"2025-08-14T14:20:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T11:55:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T07:32:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T07:31:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-22T12:46:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-22T12:42:52+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":"15c7ab10-1aaa-4f6f-9b51-edef736face0","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54253155,"name":"Biological sciences/Neuroscience"},{"id":54253156,"name":"Biological sciences/Psychology"},{"id":54253157,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-30T16:32:29+00:00","versionOfRecord":{"articleIdentity":"rs-7151013","link":"https://doi.org/10.1038/s41598-026-45811-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-27 16:10:03","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2025-09-12 14:30:53","video":"","vorDoi":"10.1038/s41598-026-45811-8","vorDoiUrl":"https://doi.org/10.1038/s41598-026-45811-8","workflowStages":[]},"version":"v1","identity":"rs-7151013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7151013","identity":"rs-7151013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}