Unhealthy Food Bias in Food Addiction: Behavioral Evidence from a Probabilistic Image Choice Task

Unhealthy Food Bias in Food Addiction: Behavioral Evidence from a Probabilistic Image Choice Task | 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 Research Article Unhealthy Food Bias in Food Addiction: Behavioral Evidence from a Probabilistic Image Choice Task Marcello Solinas, Sabrina Ingrand, Claudia Chauvet, Emilie Dugast, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7827708/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Journal of Eating Disorders → Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims: Food addiction (FA) shares behavioral and neurobiological similarities with substance use disorders, but objective behavioral measures of FA are lacking. This study aimed to adapt the Probabilistic Image Choice (PIC) task to assess behavioral bias toward unhealthy food, and to determine its association with FA and obesity. Design: Cross-sectional study with a one-year follow-up in a subset of participants. Setting: Online study with participants from the UK and Ireland. Participants: 415 adults (50% women) recruited via the Prolific platform, with 72 completing a follow-up assessment after 12 months. Measurements: Participants completed the Yale Food Addiction Scale (YFAS 2.0), reported height and weight for BMI calculation, and performed the adapted PIC task featuring four image categories: neutral, pleasant, healthy food, and unhealthy food. Primary outcome measures included unhealthy food bias (difference between unhealthy and healthy food image selections) and its relationship with food addiction and obesity status. Findings: Individuals with severe FA or obesity showed greater selection of unhealthy food images compared to those without food addiction or obesity. Gender analyses revealed that unhealthy food bias was larger in women than in men with food addiction Test-retest analysis showed high stability of the unhealthy food bias over a one-year period. Conclusions: The FA PIC task provides an operant behavioral measure of unhealthy food bias that is associated with food addiction, particularly in women. The stability of this bias over time suggests it may represent a trait-like characteristic with potential diagnostic and prognostic value for FA and related disorders. Food addiction obesity probabilistic image choice task gender differences unhealthy food bias Figures Figure 1 Figure 2 Figure 3 Plain English Summary People often struggle to regulate their eating behaviors, especially when it comes to foods high in sugar, fat, and salt. According to some researchers, this difficulty might be related to a behavioral process similar to substance use disorders. To test this idea, we used a computer task where people choose which pictures they want to see from different categories: everyday objects, pleasant scenes, healthy foods like fruits and vegetables, or unhealthy foods like pizza and cookies. We found that people showing signs of food addiction consistently chose to look more at pictures of unhealthy foods compared to healthy foods. This pattern was especially strong in women. When we tested some people again a year later, their choices remained remarkably similar, suggesting this preference is stable over time rather than just a temporary state. Importantly, not everyone who is obese shows this pattern, and not everyone with food addiction signs is obese, showing these are related but different processes. This behavioral task might help doctors and researchers better understand the behavioral and neurobiological mechanisms underlying food addiction. This understanding may eventually lead to more personalized approaches to supporting people struggling to control their eating behavior. INTRODUCTION The prevalence of obesity (defined as a body mass index, BMI ≥ 30) and overweight (defined as a 25≤ BMI<30) has dramatically increased in the world in the last decades (World Health Organization, www.who.int). Obesity is associated with increased mortality, morbidity and impaired quality of life (1) and, an analysis of societal costs has estimated that its economic impact is on average 1.8% of gross domestic product and may increase up to 3.6% in 2060 (2). The prevalence of eating disorders has also increased in the last decade with bulimia and binge eating disorders representing the most common disorders (3). Importantly, eating disorders are common among obese people, especially when no compensatory purging behaviors are used (4). Whereas these disorders have multiple causes, the inability to adapt their diet to health necessities and to refrain from consuming excessive amounts of food are common symptoms of these disorders. Due to similarities between maladaptive food consumption and substance use disorders, the term 'food addiction' has gained popularity among physicians and the general public (5). Importantly, although food addiction is not currently listed as a psychiatric disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), many researchers and clinicians believe that this condition exists and plays a role in behavioral disorders related to excessive food consumption (5–8). The Yale Food Addiction Scale (YFAS), a questionnaire based on the symptoms of substance use disorder, has been developed to assess food addiction in patients and healthy population (9,10) and has provided important insight into the epidemiology of food addiction, and its relevance to obesity and eating disorders. Indeed, although these disorders do not entirely overlap with (and therefore are not the same as) food addiction, people suffering from obesity and binge eating disorders consistently show higher YFAS score than the general population (6,7,9,10). Recent developments in the field have placed particular emphasis on ultra-processed foods which appear particularly important in the development of food addiction (11) and have led to the proposal of a condition named ultra-processed food addiction (UPFA) (12,13). Brain imaging and animal studies have started elucidating the neurobiological mechanisms of food addiction and have shown that palatable food addiction and substance use disorders share common behavioral and neurobiological mechanisms (14–16). In fact, both highly caloric food and drugs act on the same neurobiological substrates (although with different intensity), namely the mesolimbic dopamine system, leading to neuroadapations in the brain and consequent loss of control over food consumption (14–16). Importantly, cues predictive of these strong rewards, through repeated association, acquire motivational value and play a major role in maintaining addiction-related behavior (17–20). While it has been shown that individuals with high YFAS scores demonstrate differential brain reactivity to cues of highly processed (palatable) food versus minimally processed food (21) and that this cue reactivity predict eating and weight gain (22), the ability of these cues to guide and drive behavior and interfere with individual choices has not been investigated. The probabilistic image choice (PIC) task is a tool designed to assess the ability of drug-related visual cues to drive choice behavior in individuals with substance use disorder, and it has been successfully used for cocaine (23), methamphetamine (24), opiates (25), and tobacco (26) use disorders. Participants must choose the most attractive pictures from four decks of face-down cards, each containing pleasant, unpleasant, neutral and drug-related pictures. The image identity in the PIC task is uncertain (probabilistic) and varies as the task progresses and, typically, between 20 to 40% of people taking the test, are unaware of their actual choices (25,27). Importantly, the PIC task has been shown to have potential clinical utility since behavior in the PIC predict rates of relapse to cocaine (28). Thus, the PIC task provides an operant measure of the ability of strong rewards, which may be objects of addiction, to drive behavior and may help diagnosis, classification and prognosis of addiction-related disorders. In the present study, we aimed at adapting the PIC task to investigate processes associated with food addiction. Since individuals with high YFAS scores show differential responses to healthy versus unhealthy foods(21), we we used these two categories in the adapted PIC task. Thus, our food addiction (FA) PIC task had four categories of images: neutral, positive, healthy (low calories, unprocessed) food and unhealthy (high calories, highly processed) food. We then investigated in participants (n= 415) recruited online, whether selection of unhealthy food over healthy food or pleasant images was associated with food addiction measured by the YFAS and obesity. Finally, given that the prevalence of food addiction (7,9,29) and obesity (30–32) is higher among females compared to males, we performed exploratory investigation of gender differences in the PIC task. METHODS Participants Participants were 415 adults recruited via the research online platform Prolific (https://www.prolific.com/https://www.prolific.co/), (see Table 1 for sample demographics). Sample sizes varied across measures due to missing or invalid responses (see Table 1). All available data were included for each analysis. Participants were eligible for the study if they were residents from the UK or Ireland, and if they were native English speakers. Participants completed an initial session of tests between the March 8, 2024 and April 19, 2024 (Time 1). About one year later, between February 20 and February 22, 2025, the participants were recontacted via the platform Prolific and invited to respond to a follow-up survey (Time 2). A total of 72 participants completed the second time point of data collection (the other participants could not be reached, were not available, or did not respond to the survey). Of these, 5 participants were excluded because of missing prolific ID information to allow matching. All participants were debriefed and received 10£/hour as compensation. The study was approved by the local institutional ethical committee (Comité d’Ethique de la Recherche des Universités de Tours et Poitiers, CRTP2023.12) and conforms with the European regulations on data protection. Participants provided electronic informed consent after being informed in advance that the tasks included images of palatable food. Procedure After a few demographic questions (gender, age, nationality) and information about the height and weight, participants were asked to fill the YFAS questionnaire, and subsequently they completed the PIC task. Probabilistic Image Choice (PIC) Task for food We modified the original PIC task developed by Moeller et al. (23) for drugs to assess the behavioral tendency to choose unhealthy food over healthy food and compare to pleasant and neutral pictures. Participants were presented with four decks of flipped-over cards. Participants were informed that each deck contains neutral, pleasant, healthy, and unhealthy pictures, but that some decks contain more pictures of one type than the others. Participants were asked to find and select the deck(s) that they found most appealing. Upon selection, the picture was displayed to fill the entire screen and passively viewed for 2000 ms. Participants could then select again from the same deck or switch to another one. Each deck contained a total of 30 pictures, which unbeknownst to the participants were pseudorandomly sorted according to the following two constraints: (A) there were no picture repetitions between the four decks; and (B) each deck contained 26 pictures (87%) of one picture category (e.g., heathy food), two pictures (7%) of another category (e.g., unhealthy food), and one picture (3%) of each of the remaining two categories (e.g., pleasant, neutral). These percentages were selected to reduce awareness of deck identity, while still allowing for preference to be established. A run terminated when participants selected from a particular deck for a total of eight times. Participants completed four runs. To further reduce awareness of deck identity, and to overcome the potential impact on results of perseverative responding (e.g., repeatedly choosing from the same deck across the runs), the dominant picture categories were pseudorandomized across the decks between the runs (i.e., the deck location of the four picture categories did not repeat across the runs). The total number of cards selected per picture category (neutral, pleasant, healthy food, and unhealthy food) across the four runs was summed. The current version of the task, adapted for food addiction, included 30 pleasant and 30 neutral, 30 healthy food and 30 unhealthy food pictures. Images for natural- healthy food and transformed-unhealthy food were chosen from the food-pics database (https://www.eat.sbg.ac.at/resources/food-pics) (33). Our selection was based on objective nutritional criteria from food-pics database (34): healthy foods were fruits and vegetables 100 kcal/100g with low fiber. Images are shown in Fig.S1 and Fig.S2 and are freely accessible via the Open Science Framework link: https://osf.io/u73k4/. Nutritional and psychometric comparison based on food-pics database information are shown in supplementary Tables 1 and 2. Pleasant and neutral pictures selected from the International Affective Picture System (35), depicting pleasant scenes (e.g., smiling faces, baby images, natural landscapes) and neutral scenes (e.g., places, household objects) respectively. These pictures were the same used in our previous paper (26). The current version of the task was initially developed in Python and later converted to JavaScript to enable online functionality. Given that selection of neutral or pleasant images did not differ as a function of food addiction or obesity, to facilitate comparison among different measures, we calculated an unhealthy food bias (number of unhealthy images – number of healthy images). YFAS Questionnaire The Yale Food Addiction Scale 2.0 (YFAS 2.0) is a widely utilized self-report questionnaire designed to assess addiction-like eating behaviors based on the diagnostic criteria for substance use disorders as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The YFAS 2.0 consists of 35 items that evaluate symptoms experienced over the past 12 months, employing an eight-point Likert scale ranging from 0 (never) to 7 (every day) to quantify the frequency of these behaviors (9,36). The YFAS allows for the collection of subjective data regarding the frequency and severity of food addiction symptoms, which include criteria such as eating larger amounts than intended, persistent desire to cut down, and continued use despite negative consequences (9,36). YFAS 2.0 scores were dichotomized into severe food addiction versus no food addiction for analysis, following established criteria (9,36). Statistical analyses Prior to the main analyses, we screened for outliers using a ROUT test, setting the default value of Q (the threshold to determine the likelihood of being considered an outlier) at 1%; no outliers were detected. For the main analyses, at Time 1, we conducted 2×4 mixed ANOVA with repeated measures to examine whether FA or obesity are associated with a higher preference for unhealthy food images in the PIC task: Food addiction (Severe vs. no food addiction) or obesity (BMI ≥ 30 vs BMI < 30 kg/m 2 ) × 4 (picture type: Unhealthy food, Healthy food pleasant, and neutral). We also performed exploratory investigation of behavior in the PIC task as a function of gender. First, we simplified statistical comparison of image choice in the FA PIC task, focusing on the difference between choice of unhealthy – healthy images, which we called unhealthy food bias. Then, given the differences in sample size, we used the Levene test to check for homogeneity of variance. The test showed homogeneity of variance (Food Addiction: F(3,370) = 0.512, p = 0.674, Obesity: F(3,381) = 0.192, p = 0.126); finally, we performed 2×4 mixed ANOVA with Food Addiction (FA vs noFA) or Obesity (Obese vs NonObese) and gender (Male vs Female). We followed up significant interactions with post hoc t-tests. The follow-up data (Time 2) were used solely to assess test-retest reliability and stability of measures over time. Correlation coefficients were calculated between Time 1 and Time 2 scores for BMI, YFAS, and PIC task performance to examine the consistency of these measures. RESULTS General description of the population A total of 415 participants (50% women, 50% men, mean age = 41.8 years (SD = 13.8), ranging from 18 to 77) were included in the study (Table 1). Self-reported height and weight allowed to determine BMI which, in our population sample, was 27.15 (SD = 6.99) ranging from 15.82 to 72.40 kg/m 2 . Prevalence of Obesity and Food addiction in our population Prevalence of overweight (25 ≤ BMI < 30) and obesity (BMI ≥ 30) in our total sample was 28.28% and 25.96% respectively (Table 2). The average YFAS score among the 415 participants with valid data was 3.39 (SD = 3.38). Then, subgroup analyses were carried out using the YFAS diagnostic criteria for no, mild, moderate and severe FA (Gearhardt et al., 2016). Based on these scores, we determined that 344 had no food addiction (NoFA, 83.29%), 4 had mild FA (0.97%), 5 had moderate FA (1.21%) and 60 had severe FA (14.53%). We excluded the mild and moderate subgroups because there were too few observations in these groups for the analyses. Thus, the following analysis directly compared participants with NoFA (n = 344) and severe FA (n = 60). Food Addiction and Obesity When looking at the prevalence of obesity in people as a function of food addiction, we found that in participants with severe food addiction 57.89% were obese whereas in participants with no food addiction (NoFA) 20.87% were obese (Table 3). Conversely, when looking at the prevalence of food addiction as a function of obesity, we found that in obese participants severe food addiction was detected in 33% of the participants whereas in Non-obese participants, food addiction was detected in 8.63% of participants (Table 3). Using the FA categories, the BMI score was 31.60 (SD = 7.15) in severe FA participants and in NoFA 26.37 (SD = 6.77) participants. Statistical analysis by Mann-Whitney test revealed a significant difference between the two groups (p< 0.001, Cohen effect size 0.47). Within this sub-group, 33 participants (57.89%) were obese, 14 (24.56%) were overweight and 10 (17.54%) had normal weight. Thus, although the FA constituted only 14.53 % of the population, more than half of the obese participants were in this group. PIC task, Food addiction and Obesity We then investigated choice in the PIC task based on the FA vs NoFA classification. A 2 (FA status) x 4 (Picture type) repeated measures ANOVA on pictures choices after Huynh-Feldt correction, revealed no main effect of FA status ( F < 1), and a significant main effect of picture type ( F (2.53, 1017.05) = 74,45, p <.001, h 2 p = .156), indicating that participants in the PIC task selected more food pictures (both healthy and unhealthy) than any other images (all pair-wise comparison tests, p s < .001) (Fig. 1A). Interestingly, this main effect was qualified by the predicted interaction between FA status and picture type ( F (2.53, 1017.05) = 2.99, p = .038, h 2 p = .007). Simple main effects showed that whereas there were no significant differences between FA and NoFA in choosing neutral ( p = .597), positive ( p = .76) or healthy food-related picture ( p = .354), FA group selected significantly more unhealthy food-related images than NoFA group ( p = .015). Similarly, a 2 (Obesity status) x 4 (Picture type) mixed ANOVA showed no main effects of weight status ( F (1, 385) = 3.054, p = .081, h 2 p = .008) but a significant main effect of picture type ( F (2.56, 985.57) = 89.26, p <.001, h 2 p = .188) and a significant interaction between obesity status and picture type ( F (2.56, 985.57) = 2.99, p = .038, h 2 p = .008). Simple main effects revealed significant higher unhealthy food choices in obese people compared with non-obese people (Fig. 1B). Gender differences Then, we looked at gender differences in obesity and FA status in our population (Table 4) and perform exploratory analysis of behavior in the PIC task as a function of gender (Fig. 2). Prevalence of obesity was higher in female (29.79%) compared to male participants (22.05%). However, this difference did not reach significance (Wald test p = 0.075). Similarly, prevalence of FA was higher in female (19.39%) compared to male participants (10.66%). Logistic regression demonstrated a significant effect of gender (Wald test p = 0.019) with odds ratio of about 2 for FA in females. To simplify statistical comparison of image choice in the FA PIC task, we focused on the difference between choice of unhealthy – healthy images, which we called unhealthy food bias. We found that women with severe food addiction, but not men with severe food addiction, demonstrated higher unhealthy food bias compared to participants without food addiction (Fig. 2A). Statistical analysis revealed a significant effect of gender (F (1, 370) = 7.429, P=0.0067, h 2 p = .020) and a significant gender X Food addiction interaction (F (1, 370) = 9.374, P=0.0024, h 2 p = .025). In contrast, men and women did not differ in the behavior in the PIC task as a function of obesity (Fig. 2B). Test-retest stability of the PIC task, Food addiction Score and BMI Almost one year after the first test, we performed a retest in a subgroup of participants (n = 72) to determine the stability of the behavior in the PIC FA task and of Food addiction scores and BMI values. We found that the unhealthy food bias, the BMI and the food addiction scores at T1 and T2 were highly correlated (Pearson’s r values: Unhealthy food bias = 0.61; BMI = 0.91, FA scores = 0.79 Fig. 3) demonstrating a high stability of all these measures. Given the relative low number of responders to the second survey, we could not investigate performance in the PIC task as a function of FA or obesity status. DISCUSSION In this study, we adapted the Probabilistic Image Choice (PIC) task, previously used to assess drug-related behavioral choices ( 23 , 25 , 26 , 37 – 39 ), to investigate food choices associated with food addiction and obesity. Our findings reveal that individuals with severe food addiction and those with obesity demonstrate a significant bias toward unhealthy food images compared to participants without food addiction or obesity. In addition, we show significant gender differences in food addiction and behavior in the PIC, with women being more vulnerable than men. These results highlight the potential utility of the PIC task as a diagnostic tool for food addiction-related disorders, but also suggest that it may be more useful in women than in men. The prevalence of severe food addiction in our sample (about 15%) is consistent with previous epidemiological studies using the YFAS ( 7 , 9 ), confirming the existence of a minority of people that struggles with their behavior toward food. Furthermore, the significant association between food addiction and obesity in our study—with almost 58% of participants with severe food addiction being obese compared to only 21% of participants without food addiction —support existing literature suggesting that while food addiction and obesity are distinct conditions, they share substantial overlap ( 6 , 7 ). The PIC task was initially developed to investigate behavioral markers of substance use disorder that could help diagnosis and prognosis of this psychiatric condition ( 23 , 37 ). Because of the many similarities in the neurobiological effects of drugs and processed food ( 14 – 16 ), we hypothesized that the PIC task could be used to identify behavioral markers of food addiction. To do this, we modified the PIC task substituting pictures of drugs of abuse pictures with pictures of unhealthy food and introducing picture of healthy food (instead of unpleasant picture) for comparison. In agreement with our hypothesis, we found that similarly to what found for drugs of abuse, food addicted individuals showed increased selection of unhealthy food compared to non-addicted individuals. Importantly, the PIC task has been shown to predict relapse to cocaine ( 37 ), and more recently, it has been shown to be a better predictor of treatment adherence than self-reports in people suffering from opioid use disorder ( 39 ). Therefore, it will be interesting to test whether the FA PIC task could provide helpful information about people willing to control their eating habits. Our adapted FA PIC task successfully distinguished between individuals with and without severe food addiction, with food addiction participants showing a specific preference for unhealthy food images while demonstrating similar selection patterns for neutral, pleasant, and healthy food images compared to participants without food addiction. This selective bias suggests that the motivational value of unhealthy food cues may drive behavior in individuals with food addiction, which in turn may contribute to overweight and obesity ( 22 ). This finding is consistent with previous neuroimaging studies showing that individuals with high YFAS scores exhibit differential brain responses to highly processed versus minimally processed food cues ( 21 , 40 ), but extends this research by demonstrating how these cues may influence actual behavioral choices. An important finding of the exploratory part of this study is the marked gender difference in the PIC task performance of food addicted individuals. Indeed, whereas men with food addiction did not differ from men without food addiction in unhealthy food bias, women with food addiction clearly showed significantly higher bias than women without it. These results also suggest that pooling genders dilutes the robust effect observed in women. This pattern indicates that the mechanisms underlying food addiction may differ substantially between genders, with cue-driven behavioral biases playing a more central role in women's food addiction pathology. These findings are in agreement with epidemiological data showing that women have higher risks than men of developing food addiction ( 7 , 9 , 29 ) and obesity ( 30 – 32 ). In addition, women report more intense and more frequent craving for highly processed food, especially sweet food ( 32 ). In our population, although there were trends for gender differences in food addiction and BMI scores, these differences did not reach statistical difference. The stronger effect in women may also reflect differences in how food addiction manifests behaviorally: women may be more vulnerable to visual food cues, whereas men's food addiction may be driven by different mechanisms such as habitual responding or contextual triggers not captured by the PIC task. These findings have important implications for both research and clinical practice, suggesting that assessment tools and interventions for food addiction may need to be gender-specific. Future studies should investigate whether other implicit measures show similar gender patterns and explore the neurobiological basis of these differences. Another important aspect of this study is that we evaluated test-retest reliability of the FA PIC task after a long 11–12-month period. We found that unhealthy food bias at T1 and T2 was significantly correlated. A previous study conducted in cigarettes smokers demonstrated that the PIC task has good test-retest reliability ( 26 ). However, in that study, test and retest were performed at a one-month interval whereas in the present study, they were performed almost a one-year interval. These results suggest that behavioral bias toward unhealthy food measured by the PIC task is relatively stable over time allowing for the use of this measure in longitudinal studies. BMI and food addiction scores at T1 and T2 was also strongly correlated. The level of test retest reliability for food addiction was similar to what previously reported ( 41 ). This finding underscores the potential value of behavioral tasks like the PIC as complementary measures to self-report scales in the assessment of food addiction. Our findings can be interpreted within the framework of contemporary neurobiological models of addiction. According to these models, addiction involves neuroadaptations in reward and motivational systems, leading to heightened incentive salience of addiction-related cues, habitual responding, and diminished executive control ( 14 – 16 ). The specific bias toward unhealthy food images observed in our food addiction participants may reflect the enhanced incentive value assigned to these cues through repeated association with rewarding food consumption experiences. The practical and theoretical implications of our findings are substantial. First, the PIC task could serve as an operant behavioral measure complementing self-report assessments of food addiction. Given the stability of PIC performance over time, it might be particularly useful for tracking the efficacy of interventions targeting implicit food-related biases. Second, the identification of a specific bias toward unhealthy food images suggests that interventions focused on modifying automatic responses to food cues might be beneficial for individuals with food addiction. Such approaches could include attention bias modification ( 42 ), cognitive bias modification ( 43 ), or mindfulness-based techniques ( 44 ) that increase awareness of automatic responses to food cues. Beyond the practical and clinical implications, these findings have important theoretical significance, confirming that the same implicit biases observed in substance use disorders are also found in food addiction. An important consideration about this research is that our aim was to focus on behavioral bias towards drugs belonging to different pharmacological classes, and to investigate whether similar biases could be observed with food, with a longer-term aim of testing this approach in clinical populations. Accordingly, our focus was on behavioral processes, and not on the "substance" aspects of food addiction, i.e., which foods are more likely to be associated to food addiction as successfully done by other groups ( 11 – 13 ). This is in line with the original idea of this paper to use the PIC task, which has been shown to reveal generalized motivational and attentional biases toward reward-related stimuli. Thus, we chose images that 1) could be easily recognized as highly palatable and highly caloric for people living in France; 2) had low levels of fibers; 3) were selected from the food-pics database ( 33 ). Whereas our results show that our choice allowed identifying bias in the specific population in this study, it is possible that a different selection, for example oversampling ultra-processed foods ( 11 – 13 ), would have yielded stronger effects, and images in the PIC task may be adapted to specific aims and to specific populations to amplify the targeted effects. For example, in our panel of unhealthy food, we included 2/3 of images of salty/fat food and 1/3 of images of sweet food. Since women have been shown to be particularly sensitive to sweet food ( 32 ), a higher proportion of sweet food images may have further increased unhealthy food bias in this population. This study has several limitations. First, although we use the term food addicted individuals in our manuscript, this definition is not related to a medical diagnosis but to classification based on responses in the YFAS questionnaire. However, it should be noted that food addiction is not yet considered a mental disorder in the DSM-5 and, for the time being, the YFAS self-questionnaire represents the standard in the field to determine the presence and the severity of food addiction. A related limitation in terms of clinical usefulness of this study is that the participants in the study were not seeking treatment. Therefore, it is important to investigate whether similar results would be obtained in a clinical population and whether results in the PIC may actually help diagnosis or prognosis. Similarly, it may be interesting to investigate whether PIC task performance correlates with and predict real-world eating behaviors using ecological momentary assessment approaches ( 45 ). In addition, we did not control hunger levels of participants which could represent a confounding factor for the results. However, given the large population sampled, it is unlikely that this has considerably affected the results. On the other hand, in laboratory study, it may be important to control the time from the last meal and even investigate how hunger levels influence behavior in the PIC task. Third, the population sampled lived in Ireland or the UK and therefore, cultural factors may have influenced our results. In conclusion, this study show that PIC task provides an operant measure of the ability of visual cues related to unhealthy food to attract the attention and drive the behavior of people with symptoms of severe food addiction and obesity. Importantly, women with food addiction were particularly sensitive to the incentive value of unhealthy food and this, independently from obesity. Therefore, the PIC task may be useful tool to investigate the behavioral and neurobiological mechanisms underlying food addiction. Future studies in clinical settings should be performed to determine whether the PIC task may also be useful in the classification, diagnosis, and prognosis of food addiction and related pathological conditions. Declarations Funding statement This work was supported by the Centre National pour la Recherche Scientifique, the Institut National de la Santé et de la Recherche Médicale, the University of Poitiers, the IRESP and the Aviesan Alliance (IRESP-19-ADDICTIONS-20, to MS). Declarations of competing interest: The authors declare no competing interests. Data Availability: Data and images are freely accessible via the Open Science Framework link: https://osf.io/u73k4/. Authors' contributions : MS: Conceptualization & Design, Data Collection & Curation, Analysis, Writing original draft, Funding acquisition SI: Data Collection & Curation, Analysis, Writing original draft CC: Conceptualization, Writing reviewing and editing ED: Data Collection & Curation, Writing reviewing and editing BM: Writing reviewing and editing CLC: Writing reviewing and editing NJ: Writing reviewing and editing AC: Conceptualization & Design, Data Collection & Curation, Writing reviewing and editing Declaration of Generative AI and AI-assisted technologies in the writing process During the preparation of this work, the authors used Claude Sonnet 4.5 in order to improve readability, and grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Abdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Ann Transl Med. 2017 Apr;5(7):161. Okunogbe A, Nugent R, Spencer G, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for eight countries. BMJ Global Health. 2021 Oct 1;6(10):e006351. Galmiche M, Déchelotte P, Lambert G, Tavolacci MP. Prevalence of eating disorders over the 2000–2018 period: a systematic literature review. The American Journal of Clinical Nutrition. 2019 May;109(5):1402–13. de Zwaan M. Binge eating disorder and obesity. Int J Obes. 2001 May;25(1):S51–5. Meule A. Back by Popular Demand: A Narrative Review on the History of Food Addiction Research. YALE JOURNAL OF BIOLOGY AND MEDICINE. 2015;88. Meule A, Gearhardt A. Food Addiction in the Light of DSM-5. Nutrients. 2014 Sept 16;6(9):3653–71. Pursey KM, Stanwell P, Gearhardt AN, Collins CE, Burrows TL. The Prevalence of Food Addiction as Assessed by the Yale Food Addiction Scale: A Systematic Review. Nutrients. 2014 Oct;6(10):4552–90. Leary M, Pursey KM, Verdejo-Garcia A, Burrows TL. Current Intervention Treatments for Food Addiction: A Systematic Review. Behavioral Sciences. 2021 May 23;11(6):80. Gearhardt AN, Corbin WR, Brownell KD. Development of the Yale Food Addiction Scale Version 2.0. Psychology of Addictive Behaviors. 2016 Feb;30(1):113–21. Gearhardt AN, Corbin WR, Brownell KD. Preliminary validation of the Yale Food Addiction Scale. Appetite. 2009 Apr;52(2):430–6. Skinner JA, Garg ML, Dayas CV, Burrows TL. Using participant ratings to construct food image paradigms for use in the Australian population – A pilot study. Food Quality and Preference. 2020 June 1;82:103885. LaFata EM, Allison KC, Audrain-McGovern J, Forman EM. Ultra-Processed Food Addiction: A Research Update. Current Obesity Reports. 2024 May 18;13(2):214. McCausland HC, Gearhardt AN, Peralta JM, LaFata EM. A Critical Evaluation of the Terms Used To Describe Foods Implicated in Addictive-Like Eating. Curr Addict Rep. 2025 Oct 7;12(1):71. Kenny PJ. Common cellular and molecular mechanisms in obesity and drug addiction. Nat Rev Neurosci. 2011 Nov;12(11):638–51. Smith DG, Robbins TW. The neurobiological underpinnings of obesity and binge eating: a rationale for adopting the food addiction model. Biol Psychiatry. 2013 May 1;73(9):804–10. Volkow ND, Wise RA, Baler R. The dopamine motive system: implications for drug and food addiction. Nat Rev Neurosci. 2017 16;18(12):741–52. Berridge KC. ‘Liking’ and ‘wanting’ food rewards: Brain substrates and roles in eating disorders. Physiology & Behavior. 2009 July;97(5):537–50. Berridge KC, Robinson TE. Liking, wanting, and the incentive-sensitization theory of addiction. Am Psychol. 2016 Nov;71(8):670–9. Hyman SE. Addiction: A Disease of Learning and Memory. AJP. 2005 Aug 1;162(8):1414–22. Di Chiara G. Drug addiction as dopamine-dependent associative learning disorder. Eur J Pharmacol. 1999 June 30;375(1–3):13–30. Schulte EM, Yokum S, Jahn A, Gearhardt AN. Food cue reactivity in food addiction: A functional magnetic resonance imaging study. Physiology & Behavior. 2019 Sept;208:112574. Boswell RG, Kober H. Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obesity Reviews. 2016;17(2):159–77. Moeller SJ, Maloney T, Parvaz MA, Dunning JP, Alia-Klein N, Woicik PA, et al. Enhanced Choice for Viewing Cocaine Pictures in Cocaine Addiction. Biol Psychiatry. 2009 July;66(2):169–76. Moeller SJ, Okita K, Robertson CL, Ballard ME, Konova AB, Goldstein RZ, et al. Low Striatal Dopamine D2-type Receptor Availability is Linked to Simulated Drug Choice in Methamphetamine Users. Neuropsychopharmacology. 2018 Mar;43(4):751–60. Moeller SJ, Hanley AW, Garland EL. Behavioral preference for viewing drug v. pleasant images predicts current and future opioid misuse among chronic pain patients. Psychol Med. 2020 Mar;50(4):644–52. Solinas M, Chauvet C, Lafay-Chebassier C, Vanderkam P, Barillot L, Moeller SJ, et al. Tobacco Images Choice and its Association With Craving and Dependence in People Who Smoke Cigarettes. Nicotine and Tobacco Research. 2024 Nov 22;26(12):1636–45. Moeller SJ, Maloney T, Parvaz MA, Alia-Klein N, Woicik PA, Telang F, et al. Impaired insight in cocaine addiction: laboratory evidence and effects on cocaine-seeking behaviour. Brain. 2010 May;133(5):1484–93. Moeller SJ, Zilverstand A, Konova AB, Kundu P, Parvaz MA, Preston-Campbell R, et al. Neural Correlates of Drug-Biased Choice in Currently Using and Abstinent Individuals With Cocaine Use Disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018 May;3(5):485–94. Pedram P, Wadden D, Amini P, Gulliver W, Randell E, Cahill F, et al. Food Addiction: Its Prevalence and Significant Association with Obesity in the General Population. PLOS ONE. 2013 Sept 4;8(9):e74832. Kanter R, Caballero B. Global Gender Disparities in Obesity: A Review. Advances in Nutrition. 2012 July;3(4):491–8. Cooper AJ, Gupta SR, Moustafa AF, Chao AM. Sex/Gender Differences in Obesity Prevalence, Comorbidities, and Treatment. Curr Obes Rep. 2021 Dec 1;10(4):458–66. Hallam J, Boswell RG, DeVito EE, Kober H. Gender-related Differences in Food Craving and Obesity. YALE JOURNAL OF BIOLOGY AND MEDICINE. 2016;89:161–73. Blechert J, Lender A, Polk S, Busch NA, Ohla K. Food-Pics_Extended—An Image Database for Experimental Research on Eating and Appetite: Additional Images, Normative Ratings and an Updated Review. Front Psychol [Internet]. 2019 Mar 7 [cited 2025 May 26];10. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.00307/full Medawar E, Thieleking R, Witte AV. Dietary Fiber and WHO Food Categories Extension for the Food-Pics_Extended Database. Front Psychol [Internet]. 2022 June 10 [cited 2025 Dec 16];13. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.818471/full Bradley MM, Lang PJ. International Affective Picture System. In: Zeigler-Hill V, Shackelford TK, editors. Encyclopedia of Personality and Individual Differences [Internet]. Cham: Springer International Publishing; 2017 [cited 2025 May 26]. p. 1–4. Available from: https://doi.org/10.1007/978-3-319-28099-8_42-1 Schulte EM, Gearhardt AN. Development of the Modified Yale Food Addiction Scale Version 2.0. European Eating Disorders Review. 2017;25(4):302–8. Moeller SJ, Beebe-Wang N, Woicik PA, Konova AB, Maloney T, Goldstein RZ. Choice to view cocaine images predicts concurrent and prospective drug use in cocaine addiction. Drug Alcohol Depend. 2013 June;130(1–3):178–85. Parikh A, Moeller SJ, Garland EL. Simulated opioid choice linked to opioid use disorder severity among veterans with chronic pain: initial validation of a novel paradigm. Am J Drug Alcohol Abuse. 2022 July 4;48(4):403–12. McClain N, Ceceli AO, Kronberg G, Alia-Klein N, Goldstein RZ. Moving beyond self-report in characterizing drug addiction: Using drug-biased behavior to prospectively inform treatment adherence in opioid use disorder [Internet]. 2025 [cited 2025 Apr 27]. Available from: http://medrxiv.org/lookup/doi/10.1101/2025.01.01.25319860 Delgado-Rodríguez R, Moreno-Padilla M, Moreno-Domínguez S, Cepeda-Benito A. Food addiction correlates with emotional and craving reactivity to industrially prepared (ultra-processed) and home-cooked (processed) foods but not unprocessed or minimally processed foods. Food Quality and Preference. 2023 Aug 1;110:104961. Pursey KM, Collins CE, Stanwell P, Burrows TL. The stability of ‘food addiction’ as assessed by the Yale Food Addiction Scale in a non-clinical population over 18-months. Appetite. 2016 Jan;96:533–8. Heitmann J, Bennik EC, Van Hemel-Ruiter ME, De Jong PJ. The effectiveness of attentional bias modification for substance use disorder symptoms in adults: a systematic review. Syst Rev. 2018 Dec;7(1):160. Boffo M, Zerhouni O, Gronau QF, van Beek RJJ, Nikolaou K, Marsman M, et al. Cognitive Bias Modification for Behavior Change in Alcohol and Smoking Addiction: Bayesian Meta-Analysis of Individual Participant Data. Neuropsychology Review [Internet]. 2019 Jan 14 [cited 2019 May 3]; Available from: http://link.springer.com/10.1007/s11065-018-9386-4 Larsen JK, Hollands GJ, Garland EL, Evers AWM, Wiers RW. Be more mindful: Targeting addictive responses by integrating mindfulness with cognitive bias modification or cue exposure interventions. Neuroscience & Biobehavioral Reviews. 2023 Oct 1;153:105408. Varnado A, Smith A, Mason TB, Smith KE. The Ecological Validity of the Yale Food Addiction Scale 2.0 and Momentary Food Addiction Symptoms. Psychol Addict Behav. 2024 Aug;38(5):628–36. Tables TABLE 1 Demographic characteristics and weight-related variables. Valid Mean (SD) Range Sex Age BMI Female Male Not disclosed/Other Total Female Male Not disclosed/Other Total Female Male Not disclosed/Other Total 202 202 11 415 200 201 6 401 188 195 6 389 - - - 42.3 (13.6) 41.2 (14.0) 45.3 (13.9) 41.8 (13.8) 27.80 (7.98) 26.38 (5.26) 31.92 (16.3) 27.15 (6.99) - - - 18-74 20-77 27-64 18-77 15.82-72.40 16.62-52.57 17.93-58.59 15.82-72.40 Abbreviation: BMI, body mass index; SD, standard deviation. Note that sample sizes vary due to missing or invalid data. TABLE 2 Prevalence of Obesity and Food addiction in our population. N Mean (SD) % BMI YFAS score FA diagnosis Underweight Normal Weight Overweight Obesity Total NoFA Mild Moderate Severe Total 7 171 110 101 389 415 344 4 5 60 413 3.39 (3.38) 28.28 25.96 43.96 1.80 83.29 0.97 1.21 14.53 Abbreviation: BMI, body mass index; YFAS, Yale Food Addiction Scale; FA, food addiction; SD, standard deviation. Note that sample sizes vary due to missing or invalid data. TABLE 3. Prevalence of obesity in people as a function of food addiction and prevalence of food addiction in people as a function of obesity. N BMI Mean (SD) BMI Range % NoFA Severe FA Obese NonObese Underweight Normal weight Overweight Obesity Total Underweight Normal weight Overweight Obesity Total NoFA Severe FA Total NoFA Severe FA Total 7 158 89 67 321 - 10 14 33 57 67 33 100 254 24 278 17.31 (0.81) 22.33 (1.70) 27.2 (1.4) 36.02 (8.34) 26.37 (6.77) 21.79 (1.99) 27.01 (1.43) 36.24 (5.33) 31.60 (7.15) 15.82-17.96 18.52-25.00 25.00-29.90 30.06-72.40 15.82-72.40 19.11-24.80 25.05-29.94 30.04-48.19 19.11-48.19 2.18 49.22 27.72 20.87 17.54 24.56 57.89 67 33 91.37 8.63 Abbreviation: BMI, body mass index; FA, food addiction; SD, standard deviation. Note that sample sizes vary due to missing or invalid data. TABLE 4 Gender differences in obesity and FA status. N % Female Male Female Male NonObese Obese Total NonObese Obese Total FA NoFA Total FA NoFA Total 132 56 188 152 43 195 38 158 196 21 176 197 70.21 29.79 77.95 22.05 19.39 80.61 10.66 89.34 Abbreviation: FA, food addiction Note that sample sizes vary due to missing or invalid data. Additional Declarations No competing interests reported. Supplementary Files PICFASupplementaryMaterialsV2.docx Cite Share Download PDF Status: Published Journal Publication published 03 Apr, 2026 Read the published version in Journal of Eating Disorders → Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 18 Dec, 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. 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1","display":"","copyAsset":false,"role":"figure","size":1342287,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Performance in the PIC task in A) FA and NoFA and B) Obese and NonObese people. \u003c/strong\u003eNumber of selection of images in each category in the PIC task for A)\u003cstrong\u003e \u003c/strong\u003eNoFA- (empty circles) and FA-groups (full circles) and B) Obese people (full circles) and NonObese- (empty circles). Data are means ±SD; FA, n = 60, NoFA, n = 344; Obese, n = 101, NonObese, n = 288. * p \u0026lt; .05 FA \u003cem\u003evs\u003c/em\u003e NoFA and Obese \u003cem\u003evs\u003c/em\u003e NonObese.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7827708/v1/da12c8dddfead998500c8bd3.png"},{"id":99186955,"identity":"1159634e-48c0-46c5-9ef9-8e23cd28cf40","added_by":"auto","created_at":"2025-12-30 00:08:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":879562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnhealthy food bias in men and women as a function of A) food addiction and B) obesity. \u003c/strong\u003eData are means ± SD; Males FA, n = 21, Females FA, n = 38, Males NoFA, n = 176, Females NoFA, n = 158; Males Obese, n = 43, Females Obese, n = 56, Males NonObese, n = 152, Females NonObese, n = 132. * and **, p \u0026lt; .05 and p \u0026lt; .01.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7827708/v1/3be47b9d420b2c98657f8b1d.png"},{"id":99186965,"identity":"aed73eec-ec14-402c-9f19-ca7a9ebaf86b","added_by":"auto","created_at":"2025-12-30 00:08:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTest-Retest correlation for unhealthy food bias, BMI and Food Addiction scores.\u003c/strong\u003e n = 72.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7827708/v1/a64715c5f4b0d3690b834d34.png"},{"id":106343501,"identity":"0e297485-e905-4215-a628-af3053697674","added_by":"auto","created_at":"2026-04-07 16:06:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3146592,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7827708/v1/ab4b5b82-29c8-4726-97c2-c42f6e86e4d9.pdf"},{"id":99186972,"identity":"aaac7c94-0a9a-47a4-9b94-00de0bbf083e","added_by":"auto","created_at":"2025-12-30 00:08:43","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1234666,"visible":true,"origin":"","legend":"","description":"","filename":"PICFASupplementaryMaterialsV2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7827708/v1/41de8b29fc2c1810e754923e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unhealthy Food Bias in Food Addiction: Behavioral Evidence from a Probabilistic Image Choice Task","fulltext":[{"header":"Plain English Summary","content":"\u003cp\u003ePeople often struggle to regulate their eating behaviors, especially when it comes to foods high in sugar, fat, and salt. According to some researchers, this difficulty might be related to a behavioral process similar to substance use disorders. To test this idea, we used a computer task where people choose which pictures they want to see from different categories: everyday objects, pleasant scenes, healthy foods like fruits and vegetables, or unhealthy foods like pizza and cookies. We found that people showing signs of food addiction consistently chose to look more at pictures of unhealthy foods compared to healthy foods. This pattern was especially strong in women. When we tested some people again a year later, their choices remained remarkably similar, suggesting this preference is stable over time rather than just a temporary state. Importantly, not everyone who is obese shows this pattern, and not everyone with food addiction signs is obese, showing these are related but different processes. This behavioral task might help doctors and researchers better understand the behavioral and neurobiological mechanisms underlying food addiction. This understanding may eventually lead to more personalized approaches to supporting people struggling to control their eating behavior.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eThe prevalence of obesity (defined as a body mass index, BMI\u0026nbsp;\u0026ge;\u0026nbsp;30) and overweight (defined as a 25\u0026le; BMI\u0026lt;30) has dramatically increased in the world in the last decades (World Health Organization, www.who.int). Obesity is associated with increased mortality, morbidity and impaired quality of life (1) and, an analysis of societal costs has estimated that its economic impact is on average 1.8% of gross domestic product and may increase up to 3.6% in 2060 (2). The prevalence of eating disorders has also increased in the last decade with bulimia and binge eating disorders representing the most common disorders\u0026nbsp;(3). Importantly, eating disorders are common among obese people, especially when no compensatory purging behaviors are used\u0026nbsp;(4). Whereas these disorders\u0026nbsp;have multiple causes, the inability to adapt their diet to health necessities and to refrain from consuming excessive amounts of food are common symptoms of these disorders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to similarities between maladaptive food consumption and substance use disorders, the term \u0026apos;food addiction\u0026apos; has gained popularity among physicians and the general public (5). Importantly, although food addiction is not currently listed as a psychiatric disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), many researchers and clinicians believe that this condition exists and plays a role in behavioral disorders related to excessive food consumption (5\u0026ndash;8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Yale Food Addiction Scale (YFAS), a questionnaire based on the symptoms of substance use disorder, has been developed to assess food addiction in patients and healthy population (9,10) and has provided important insight into the epidemiology of food addiction, and its relevance to obesity and eating disorders. Indeed, although these disorders do not entirely overlap with (and therefore are not the same as) food addiction, people suffering from obesity and binge eating disorders consistently show higher YFAS score than the general population (6,7,9,10).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent developments in the field have placed particular emphasis on ultra-processed foods which appear particularly important in the development of food addiction (11) and have led to the proposal of a condition named ultra-processed food addiction (UPFA) (12,13). Brain imaging and animal studies have started elucidating the neurobiological mechanisms of food addiction and have shown that palatable food addiction and substance use disorders share common behavioral and neurobiological mechanisms (14\u0026ndash;16). In fact, both highly caloric food and drugs act on the same neurobiological substrates (although with different intensity), namely the mesolimbic dopamine system, leading to neuroadapations in the brain and consequent loss of control over food consumption (14\u0026ndash;16). Importantly, cues predictive of these strong rewards, through repeated association, acquire motivational value and play a major role in maintaining addiction-related behavior (17\u0026ndash;20). \u0026nbsp;While it has been shown that individuals with high YFAS scores demonstrate differential brain reactivity to cues of highly processed (palatable) food versus minimally processed food (21) and that this cue reactivity predict eating and weight gain (22), the ability of these cues to guide and drive behavior and interfere with individual choices has not been investigated.\u003c/p\u003e\n\u003cp\u003eThe probabilistic image choice (PIC) task is a tool designed to assess the ability of drug-related visual cues to drive choice behavior in individuals with substance use disorder, and it has been successfully used for cocaine (23), methamphetamine (24), opiates (25), and tobacco (26) use disorders. Participants must choose the most attractive pictures from four decks of face-down cards, each containing pleasant, unpleasant, neutral and drug-related pictures. The image identity in the PIC task is uncertain (probabilistic) and varies as the task progresses and, typically, between 20 to 40% of people taking the test, are unaware of their actual choices (25,27). Importantly, the PIC task has been shown to have potential clinical utility since behavior in the PIC predict rates of relapse to cocaine (28). Thus, the PIC task provides an operant measure of the ability of strong rewards, which may be objects of addiction, to drive behavior and may help diagnosis, classification and prognosis of addiction-related disorders.\u003c/p\u003e\n\u003cp\u003eIn the present study, we aimed at adapting the PIC task to investigate processes associated with food addiction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince individuals with high YFAS scores show differential responses to healthy versus unhealthy foods(21), \u0026nbsp;we we used these two categories in the adapted PIC task. Thus, our food addiction (FA) PIC task had four categories of images: neutral, positive, healthy (low calories, unprocessed) food and unhealthy (high calories, highly processed) food. We then investigated in participants (n= 415) recruited online, whether selection of unhealthy food over healthy food or pleasant images was associated with food addiction measured by the YFAS and obesity. Finally, given that the \u0026nbsp;prevalence of food addiction (7,9,29) and obesity (30\u0026ndash;32) is higher among females compared to males, we performed exploratory investigation of gender differences in the PIC task.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were 415 adults recruited via the research online platform Prolific (https://www.prolific.com/https://www.prolific.co/), (see Table 1 for sample demographics). Sample sizes varied across measures due to missing or invalid responses (see Table 1). All available data were included for each analysis. Participants were eligible for the study if they were residents from the UK or Ireland, and if they were native English speakers. Participants completed an initial session of tests between the March 8, 2024 and April 19, 2024 (Time 1). About one year later, between February 20 and February 22, 2025, the participants were recontacted via the platform Prolific and invited to respond to a follow-up survey (Time 2). A total of 72 participants completed the second time point of data collection (the other participants could not be reached, were not available, or did not respond to the survey). Of these, 5 participants were excluded because of missing prolific ID information to allow matching. All participants were debriefed and received 10\u0026pound;/hour as compensation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local institutional ethical committee (Comit\u0026eacute; d\u0026rsquo;Ethique de la Recherche des Universit\u0026eacute;s de Tours et Poitiers, CRTP2023.12) and conforms with the European regulations on data protection. Participants provided electronic informed consent after being informed in advance that the tasks included images of palatable food.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter a few demographic questions (gender, age, nationality) and information about the height and weight, participants were asked to fill the YFAS questionnaire, and subsequently they completed the PIC task.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProbabilistic Image Choice (PIC) Task for food\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe modified the original PIC task developed by Moeller et al. (23) for drugs to assess the behavioral tendency to choose unhealthy food over healthy food and compare to pleasant and neutral pictures. Participants were presented with four decks of flipped-over cards. Participants were informed that each deck contains neutral, pleasant, healthy, and unhealthy pictures, but that some decks contain more pictures of one type than the others. Participants were asked to find and select the deck(s) that they found most appealing. Upon selection, the picture was\u0026nbsp;displayed to fill the entire screen and passively viewed for 2000 ms. Participants could then select again from the same deck or switch to another one. Each deck contained a total of 30 pictures, which unbeknownst to the participants were pseudorandomly sorted according to the following two constraints: (A) there were no picture repetitions between the four decks; and (B) each deck contained 26 pictures (87%) of one picture category (e.g., heathy food), two pictures (7%) of another category (e.g., unhealthy food), and one picture (3%) of each of the remaining two categories (e.g., pleasant, neutral). These percentages were selected to reduce awareness of deck identity, while still allowing for preference to be established. A run terminated when participants selected from a particular deck for a total of eight times. Participants completed four runs. To further reduce awareness of deck identity, and to overcome the potential impact on results of perseverative responding (e.g., repeatedly choosing from the same deck across the runs), the dominant picture categories were pseudorandomized across the decks between the runs (i.e., the deck location of the four picture categories did not repeat across the runs). The total number of cards selected per picture category (neutral, pleasant, healthy food, and unhealthy food) across the four runs was summed.\u003c/p\u003e\n\u003cp\u003eThe current version of the task, adapted for food addiction, included 30 pleasant and 30 neutral, 30 healthy food and 30 unhealthy food pictures. Images for natural- healthy food and transformed-unhealthy food were chosen from the\u0026nbsp;food-pics database (https://www.eat.sbg.ac.at/resources/food-pics)\u0026nbsp;(33).\u0026nbsp;Our selection was based on objective nutritional criteria from food-pics database\u0026nbsp;(34): healthy foods were fruits and vegetables \u0026lt;80 kcal/100g with high fiber; unhealthy foods were processed items \u0026gt;100 kcal/100g with low fiber. Images are shown in Fig.S1 and Fig.S2 and are freely accessible via the Open Science Framework link:\u0026nbsp;https://osf.io/u73k4/.\u0026nbsp;Nutritional and psychometric comparison based on food-pics database information are shown in supplementary Tables 1 and 2.\u0026nbsp;Pleasant and neutral pictures selected from the International Affective Picture System\u0026nbsp;(35), depicting pleasant scenes (e.g., smiling faces, baby images, natural landscapes) and neutral scenes (e.g., places, household objects) respectively.\u0026nbsp;These pictures were the same used in our previous paper\u0026nbsp;(26).\u0026nbsp;The current version of the task was initially developed in Python and later converted to JavaScript to enable online functionality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven that selection of neutral or pleasant images did not differ as a function of food addiction or obesity, to facilitate comparison among different measures, we calculated an unhealthy food bias (number of unhealthy images \u0026ndash; number of healthy images).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYFAS Questionnaire\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Yale Food Addiction Scale 2.0 (YFAS 2.0) is a widely utilized self-report questionnaire designed to assess addiction-like eating behaviors based on the diagnostic criteria for substance use disorders as outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The YFAS 2.0 consists of 35 items that evaluate symptoms experienced over the past 12 months, employing an eight-point Likert scale ranging from 0 (never) to 7 (every day) to quantify the frequency of these behaviors (9,36). The YFAS allows for the collection of subjective data regarding the frequency and severity of food addiction symptoms, which include criteria such as eating larger amounts than intended, persistent desire to cut down, and continued use despite negative consequences (9,36). YFAS 2.0 scores were dichotomized into severe food addiction versus no food addiction for analysis, following established criteria (9,36).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to the main analyses, we screened for outliers using a ROUT test, setting the default value of Q (the threshold to determine the likelihood of being considered an outlier) at 1%; no outliers were detected. For the main analyses, at Time 1, we conducted 2\u0026times;4 mixed ANOVA with repeated measures to examine whether FA or obesity are associated with a higher preference for unhealthy food images in the PIC task: Food addiction (Severe vs. no food addiction) or obesity (BMI\u0026nbsp;\u0026ge; 30 vs BMI \u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e) \u0026times; 4 (picture type: Unhealthy food, Healthy food pleasant, and neutral).\u003c/p\u003e\n\u003cp\u003eWe also performed exploratory investigation of behavior in the PIC task as a function of gender. First, we simplified statistical comparison of image choice in the FA PIC task, focusing on the difference between choice of unhealthy \u0026ndash; healthy images, which we called unhealthy food bias. Then, given the differences in sample size, we used the Levene test to check for homogeneity of variance. The test showed homogeneity of variance (Food Addiction: F(3,370) = 0.512, p = 0.674, Obesity: F(3,381) = 0.192, p = 0.126); finally, we performed 2\u0026times;4 mixed ANOVA with Food Addiction (FA vs noFA) or Obesity (Obese vs NonObese) and gender (Male vs Female). We followed up significant interactions with post hoc t-tests. The follow-up data (Time 2) were used solely to assess test-retest reliability and stability of measures over time. Correlation coefficients were calculated between Time 1 and Time 2 scores for BMI, YFAS, and PIC task performance to examine the consistency of these measures.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eGeneral description of the population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 415 participants (50% women, 50% men, mean age = 41.8 years (SD = 13.8), ranging from 18 to 77) were included in the study (Table 1). Self-reported height and weight allowed to determine BMI which, in our population sample, was 27.15 (SD = 6.99) ranging from 15.82 to 72.40 kg/m\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of Obesity and Food addiction in our population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevalence of overweight (25 \u0026le; BMI \u0026lt; 30) and obesity (BMI \u0026ge; 30) in our total sample was 28.28% and 25.96% respectively (Table 2).\u003c/p\u003e\n\u003cp\u003eThe average YFAS score among the 415 participants with valid data was 3.39 (SD = 3.38). Then, subgroup analyses were carried out using the YFAS diagnostic criteria for no, mild, moderate and severe FA (Gearhardt et al., 2016). Based on these scores, we determined that 344 had no food addiction (NoFA, 83.29%), 4 had mild FA (0.97%), 5 had moderate FA (1.21%) and 60 had severe FA (14.53%). We excluded the mild and moderate subgroups because there were too few observations in these groups for the analyses. Thus, the following analysis directly compared participants with NoFA (n = 344) and severe FA (n = 60).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFood Addiction and Obesity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen looking at the prevalence of obesity in people as a function of food addiction, we found that in participants with severe food addiction 57.89% were obese whereas in participants with no food addiction (NoFA) 20.87% were obese (Table 3).\u003c/p\u003e\n\u003cp\u003eConversely, when looking at the prevalence of food addiction as a function of obesity, we found that in obese participants severe food addiction was detected in 33% of the participants whereas in Non-obese participants, food addiction was detected in\u0026nbsp;8.63% of participants (Table 3).\u003c/p\u003e\n\u003cp\u003eUsing the FA categories, the BMI score was 31.60 (SD = 7.15) in severe FA participants and in NoFA 26.37 (SD = 6.77) participants. Statistical analysis by Mann-Whitney test revealed a significant difference between the two groups (p\u0026lt; 0.001, Cohen effect size 0.47). Within this sub-group, 33 participants (57.89%) were obese, 14 (24.56%) were overweight and 10 (17.54%) had normal weight. Thus, although the FA constituted only 14.53 % of the population, more than half of the obese participants were in this group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePIC task, Food addiction and Obesity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe then investigated choice in the PIC task based on the FA vs NoFA classification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA 2 (FA status) x 4 (Picture type) repeated measures ANOVA on pictures choices after Huynh-Feldt correction, revealed no main effect of FA status (\u003cem\u003eF\u003c/em\u003e \u0026lt; 1), and a significant main effect of picture type (\u003cem\u003eF\u003c/em\u003e(2.53, 1017.05) = 74,45, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001,\u0026nbsp;h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .156), indicating that participants in the PIC task selected more food pictures (both healthy and unhealthy) than any other images (all pair-wise comparison tests, \u003cem\u003ep\u003c/em\u003es \u0026lt; .001) (Fig. 1A). Interestingly, this main effect was qualified by the predicted interaction between FA status and picture type (\u003cem\u003eF\u003c/em\u003e(2.53, 1017.05) = 2.99, \u003cem\u003ep\u003c/em\u003e = .038, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .007). Simple main effects showed that whereas there were no significant differences between FA and NoFA in choosing neutral (\u003cem\u003ep\u003c/em\u003e = .597), positive (\u003cem\u003ep\u003c/em\u003e = .76) or healthy food-related picture (\u003cem\u003ep\u003c/em\u003e = .354), FA group selected significantly more unhealthy food-related images than NoFA group (\u003cem\u003ep\u003c/em\u003e = .015).\u003c/p\u003e\n\u003cp\u003eSimilarly, a 2 (Obesity status) x 4 (Picture type) mixed ANOVA showed no main effects of weight status (\u003cem\u003eF\u003c/em\u003e(1, 385) = 3.054, \u003cem\u003ep\u003c/em\u003e = .081, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .008) but a significant main effect of picture type (\u003cem\u003eF\u003c/em\u003e(2.56, 985.57) = 89.26, \u003cem\u003ep\u003c/em\u003e\u0026lt;.001,\u0026nbsp;h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .188) and a significant interaction between obesity status and picture type (\u003cem\u003eF\u003c/em\u003e(2.56, 985.57) = 2.99, \u003cem\u003ep\u003c/em\u003e = .038, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .008). Simple main effects revealed significant higher unhealthy food choices in obese people compared with non-obese people (Fig. 1B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThen, we looked at gender differences in obesity and FA status in our population (Table 4) and perform exploratory analysis of behavior in the PIC task as a function of gender (Fig. 2).\u003c/p\u003e\n\u003cp\u003ePrevalence of obesity was higher in female (29.79%) compared to male participants (22.05%). However, this difference did not reach significance (Wald test p = 0.075). \u0026nbsp;Similarly, prevalence of FA was higher in female (19.39%) compared to male participants (10.66%). Logistic regression demonstrated a significant effect of gender (Wald test p = 0.019) with odds ratio of about 2 for FA in females.\u003c/p\u003e\n\u003cp\u003eTo simplify statistical comparison of image choice in the FA PIC task, we focused on the difference between choice of unhealthy \u0026ndash; healthy images, which we called unhealthy food bias. We found that women with severe food addiction, but not men with severe food addiction, demonstrated higher unhealthy food bias\u0026nbsp;compared to participants without food addiction (Fig. 2A). Statistical analysis revealed a significant effect of gender (F (1, 370) = 7.429, P=0.0067,\u0026nbsp;h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .020) and a significant gender X Food addiction interaction (F (1, 370) = 9.374, P=0.0024, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ep\u003c/sub\u003e = .025). In contrast, men and women did not differ in the behavior in the PIC task as a function of obesity (Fig. 2B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTest-retest stability of the PIC task, Food addiction Score and BMI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlmost one year after the first test, we performed a retest in a subgroup of participants (n = 72) to determine the stability of the behavior in the PIC FA task and of Food addiction scores and BMI values. We found that the unhealthy food bias, the BMI and the food addiction scores at T1 and T2 were highly correlated (Pearson\u0026rsquo;s r values: Unhealthy food bias = 0.61; BMI = 0.91, FA scores = 0.79 Fig. 3) demonstrating a high stability of all these measures. Given the relative low number of responders to the second survey, we could not investigate performance in the PIC task as a function of FA or obesity status.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we adapted the Probabilistic Image Choice (PIC) task, previously used to assess drug-related behavioral choices (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), to investigate food choices associated with food addiction and obesity. Our findings reveal that individuals with severe food addiction and those with obesity demonstrate a significant bias toward unhealthy food images compared to participants without food addiction or obesity. In addition, we show significant gender differences in food addiction and behavior in the PIC, with women being more vulnerable than men. These results highlight the potential utility of the PIC task as a diagnostic tool for food addiction-related disorders, but also suggest that it may be more useful in women than in men.\u003c/p\u003e \u003cp\u003eThe prevalence of severe food addiction in our sample (about 15%) is consistent with previous epidemiological studies using the YFAS (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), confirming the existence of a minority of people that struggles with their behavior toward food. Furthermore, the significant association between food addiction and obesity in our study\u0026mdash;with almost 58% of participants with severe food addiction being obese compared to only 21% of participants without food addiction \u0026mdash;support existing literature suggesting that while food addiction and obesity are distinct conditions, they share substantial overlap (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PIC task was initially developed to investigate behavioral markers of substance use disorder that could help diagnosis and prognosis of this psychiatric condition (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Because of the many similarities in the neurobiological effects of drugs and processed food (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), we hypothesized that the PIC task could be used to identify behavioral markers of food addiction. To do this, we modified the PIC task substituting pictures of drugs of abuse pictures with pictures of unhealthy food and introducing picture of healthy food (instead of unpleasant picture) for comparison. In agreement with our hypothesis, we found that similarly to what found for drugs of abuse, food addicted individuals showed increased selection of unhealthy food compared to non-addicted individuals. Importantly, the PIC task has been shown to predict relapse to cocaine (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), and more recently, it has been shown to be a better predictor of treatment adherence than self-reports in people suffering from opioid use disorder (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, it will be interesting to test whether the FA PIC task could provide helpful information about people willing to control their eating habits.\u003c/p\u003e \u003cp\u003eOur adapted FA PIC task successfully distinguished between individuals with and without severe food addiction, with food addiction participants showing a specific preference for unhealthy food images while demonstrating similar selection patterns for neutral, pleasant, and healthy food images compared to participants without food addiction. This selective bias suggests that the motivational value of unhealthy food cues may drive behavior in individuals with food addiction, which in turn may contribute to overweight and obesity (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This finding is consistent with previous neuroimaging studies showing that individuals with high YFAS scores exhibit differential brain responses to highly processed versus minimally processed food cues (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), but extends this research by demonstrating how these cues may influence actual behavioral choices.\u003c/p\u003e \u003cp\u003eAn important finding of the exploratory part of this study is the marked gender difference in the PIC task performance of food addicted individuals. Indeed, whereas men with food addiction did not differ from men without food addiction in unhealthy food bias, women with food addiction clearly showed significantly higher bias than women without it. These results also suggest that pooling genders dilutes the robust effect observed in women. This pattern indicates that the mechanisms underlying food addiction may differ substantially between genders, with cue-driven behavioral biases playing a more central role in women's food addiction pathology. These findings are in agreement with epidemiological data showing that women have higher risks than men of developing food addiction (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and obesity (\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In addition, women report more intense and more frequent craving for highly processed food, especially sweet food (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In our population, although there were trends for gender differences in food addiction and BMI scores, these differences did not reach statistical difference. The stronger effect in women may also reflect differences in how food addiction manifests behaviorally: women may be more vulnerable to visual food cues, whereas men's food addiction may be driven by different mechanisms such as habitual responding or contextual triggers not captured by the PIC task. These findings have important implications for both research and clinical practice, suggesting that assessment tools and interventions for food addiction may need to be gender-specific. Future studies should investigate whether other implicit measures show similar gender patterns and explore the neurobiological basis of these differences.\u003c/p\u003e \u003cp\u003eAnother important aspect of this study is that we evaluated test-retest reliability of the FA PIC task after a long 11\u0026ndash;12-month period. We found that unhealthy food bias at T1 and T2 was significantly correlated. A previous study conducted in cigarettes smokers demonstrated that the PIC task has good test-retest reliability (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, in that study, test and retest were performed at a one-month interval whereas in the present study, they were performed almost a one-year interval. These results suggest that behavioral bias toward unhealthy food measured by the PIC task is relatively stable over time allowing for the use of this measure in longitudinal studies. BMI and food addiction scores at T1 and T2 was also strongly correlated. The level of test retest reliability for food addiction was similar to what previously reported (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). This finding underscores the potential value of behavioral tasks like the PIC as complementary measures to self-report scales in the assessment of food addiction.\u003c/p\u003e \u003cp\u003eOur findings can be interpreted within the framework of contemporary neurobiological models of addiction. According to these models, addiction involves neuroadaptations in reward and motivational systems, leading to heightened incentive salience of addiction-related cues, habitual responding, and diminished executive control (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The specific bias toward unhealthy food images observed in our food addiction participants may reflect the enhanced incentive value assigned to these cues through repeated association with rewarding food consumption experiences.\u003c/p\u003e \u003cp\u003eThe practical and theoretical implications of our findings are substantial. First, the PIC task could serve as an operant behavioral measure complementing self-report assessments of food addiction. Given the stability of PIC performance over time, it might be particularly useful for tracking the efficacy of interventions targeting implicit food-related biases. Second, the identification of a specific bias toward unhealthy food images suggests that interventions focused on modifying automatic responses to food cues might be beneficial for individuals with food addiction. Such approaches could include attention bias modification (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), cognitive bias modification (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), or mindfulness-based techniques (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) that increase awareness of automatic responses to food cues. Beyond the practical and clinical implications, these findings have important theoretical significance, confirming that the same implicit biases observed in substance use disorders are also found in food addiction.\u003c/p\u003e \u003cp\u003eAn important consideration about this research is that our aim was to focus on behavioral bias towards drugs belonging to different pharmacological classes, and to investigate whether similar biases could be observed with food, with a longer-term aim of testing this approach in clinical populations. Accordingly, our focus was on behavioral processes, and not on the \"substance\" aspects of food addiction, i.e., which foods are more likely to be associated to food addiction as successfully done by other groups (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This is in line with the original idea of this paper to use the PIC task, which has been shown to reveal generalized motivational and attentional biases toward reward-related stimuli. Thus, we chose images that 1) could be easily recognized as highly palatable and highly caloric for people living in France; 2) had low levels of fibers; 3) were selected from the food-pics database (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Whereas our results show that our choice allowed identifying bias in the specific population in this study, it is possible that a different selection, for example oversampling ultra-processed foods (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), would have yielded stronger effects, and images in the PIC task may be adapted to specific aims and to specific populations to amplify the targeted effects. For example, in our panel of unhealthy food, we included 2/3 of images of salty/fat food and 1/3 of images of sweet food. Since women have been shown to be particularly sensitive to sweet food (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), a higher proportion of sweet food images may have further increased unhealthy food bias in this population.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, although we use the term food addicted individuals in our manuscript, this definition is not related to a medical diagnosis but to classification based on responses in the YFAS questionnaire. However, it should be noted that food addiction is not yet considered a mental disorder in the DSM-5 and, for the time being, the YFAS self-questionnaire represents the standard in the field to determine the presence and the severity of food addiction. A related limitation in terms of clinical usefulness of this study is that the participants in the study were not seeking treatment. Therefore, it is important to investigate whether similar results would be obtained in a clinical population and whether results in the PIC may actually help diagnosis or prognosis. Similarly, it may be interesting to investigate whether PIC task performance correlates with and predict real-world eating behaviors using ecological momentary assessment approaches (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In addition, we did not control hunger levels of participants which could represent a confounding factor for the results. However, given the large population sampled, it is unlikely that this has considerably affected the results. On the other hand, in laboratory study, it may be important to control the time from the last meal and even investigate how hunger levels influence behavior in the PIC task. Third, the population sampled lived in Ireland or the UK and therefore, cultural factors may have influenced our results.\u003c/p\u003e \u003cp\u003eIn conclusion, this study show that PIC task provides an operant measure of the ability of visual cues related to unhealthy food to attract the attention and drive the behavior of people with symptoms of severe food addiction and obesity. Importantly, women with food addiction were particularly sensitive to the incentive value of unhealthy food and this, independently from obesity. Therefore, the PIC task may be useful tool to investigate the behavioral and neurobiological mechanisms underlying food addiction. Future studies in clinical settings should be performed to determine whether the PIC task may also be useful in the classification, diagnosis, and prognosis of food addiction and related pathological conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Centre National pour la Recherche Scientifique, the Institut National de la Sant\u0026eacute; et de la Recherche M\u0026eacute;dicale, the University of Poitiers, the IRESP and the Aviesan Alliance (IRESP-19-ADDICTIONS-20, to MS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of competing interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eData and images are freely accessible via the Open Science Framework link: https://osf.io/u73k4/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMS: Conceptualization \u0026amp; Design, Data Collection \u0026amp; Curation, Analysis, Writing original draft, Funding acquisition\u003c/p\u003e\n\u003cp\u003eSI: Data Collection \u0026amp; Curation, Analysis, Writing original draft\u003c/p\u003e\n\u003cp\u003eCC: Conceptualization, Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003eED: Data Collection \u0026amp; Curation, Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003eBM: Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003eCLC: Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003eNJ: Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003eAC: Conceptualization \u0026amp; Design, Data Collection \u0026amp; Curation, Writing reviewing and editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the authors used Claude Sonnet 4.5 in order to improve readability, and grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelaal M, le Roux CW, Docherty NG. Morbidity and mortality associated with obesity. Ann Transl Med. 2017 Apr;5(7):161. \u003c/li\u003e\n\u003cli\u003eOkunogbe A, Nugent R, Spencer G, Ralston J, Wilding J. Economic impacts of overweight and obesity: current and future estimates for eight countries. BMJ Global Health. 2021 Oct 1;6(10):e006351. \u003c/li\u003e\n\u003cli\u003eGalmiche M, D\u0026eacute;chelotte P, Lambert G, Tavolacci MP. Prevalence of eating disorders over the 2000\u0026ndash;2018 period: a systematic literature review. The American Journal of Clinical Nutrition. 2019 May;109(5):1402\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003ede Zwaan M. Binge eating disorder and obesity. Int J Obes. 2001 May;25(1):S51\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eMeule A. Back by Popular Demand: A Narrative Review on the History of Food Addiction Research. YALE JOURNAL OF BIOLOGY AND MEDICINE. 2015;88. \u003c/li\u003e\n\u003cli\u003eMeule A, Gearhardt A. Food Addiction in the Light of DSM-5. Nutrients. 2014 Sept 16;6(9):3653\u0026ndash;71. \u003c/li\u003e\n\u003cli\u003ePursey KM, Stanwell P, Gearhardt AN, Collins CE, Burrows TL. The Prevalence of Food Addiction as Assessed by the Yale Food Addiction Scale: A Systematic Review. Nutrients. 2014 Oct;6(10):4552\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eLeary M, Pursey KM, Verdejo-Garcia A, Burrows TL. Current Intervention Treatments for Food Addiction: A Systematic Review. Behavioral Sciences. 2021 May 23;11(6):80. \u003c/li\u003e\n\u003cli\u003eGearhardt AN, Corbin WR, Brownell KD. Development of the Yale Food Addiction Scale Version 2.0. Psychology of Addictive Behaviors. 2016 Feb;30(1):113\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003eGearhardt AN, Corbin WR, Brownell KD. Preliminary validation of the Yale Food Addiction Scale. Appetite. 2009 Apr;52(2):430\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eSkinner JA, Garg ML, Dayas CV, Burrows TL. Using participant ratings to construct food image paradigms for use in the Australian population \u0026ndash; A pilot study. Food Quality and Preference. 2020 June 1;82:103885. \u003c/li\u003e\n\u003cli\u003eLaFata EM, Allison KC, Audrain-McGovern J, Forman EM. Ultra-Processed Food Addiction: A Research Update. Current Obesity Reports. 2024 May 18;13(2):214. \u003c/li\u003e\n\u003cli\u003eMcCausland HC, Gearhardt AN, Peralta JM, LaFata EM. A Critical Evaluation of the Terms Used To Describe Foods Implicated in Addictive-Like Eating. Curr Addict Rep. 2025 Oct 7;12(1):71. \u003c/li\u003e\n\u003cli\u003eKenny PJ. Common cellular and molecular mechanisms in obesity and drug addiction. Nat Rev Neurosci. 2011 Nov;12(11):638\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eSmith DG, Robbins TW. The neurobiological underpinnings of obesity and binge eating: a rationale for adopting the food addiction model. Biol Psychiatry. 2013 May 1;73(9):804\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eVolkow ND, Wise RA, Baler R. The dopamine motive system: implications for drug and food addiction. Nat Rev Neurosci. 2017 16;18(12):741\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eBerridge KC. \u0026lsquo;Liking\u0026rsquo; and \u0026lsquo;wanting\u0026rsquo; food rewards: Brain substrates and roles in eating disorders. Physiology \u0026amp; Behavior. 2009 July;97(5):537\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eBerridge KC, Robinson TE. Liking, wanting, and the incentive-sensitization theory of addiction. Am Psychol. 2016 Nov;71(8):670\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eHyman SE. Addiction: A Disease of Learning and Memory. AJP. 2005 Aug 1;162(8):1414\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eDi Chiara G. Drug addiction as dopamine-dependent associative learning disorder. Eur J Pharmacol. 1999 June 30;375(1\u0026ndash;3):13\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eSchulte EM, Yokum S, Jahn A, Gearhardt AN. Food cue reactivity in food addiction: A functional magnetic resonance imaging study. Physiology \u0026amp; Behavior. 2019 Sept;208:112574. \u003c/li\u003e\n\u003cli\u003eBoswell RG, Kober H. Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obesity Reviews. 2016;17(2):159\u0026ndash;77. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Maloney T, Parvaz MA, Dunning JP, Alia-Klein N, Woicik PA, et al. Enhanced Choice for Viewing Cocaine Pictures in Cocaine Addiction. Biol Psychiatry. 2009 July;66(2):169\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Okita K, Robertson CL, Ballard ME, Konova AB, Goldstein RZ, et al. Low Striatal Dopamine D2-type Receptor Availability is Linked to Simulated Drug Choice in Methamphetamine Users. Neuropsychopharmacology. 2018 Mar;43(4):751\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Hanley AW, Garland EL. Behavioral preference for viewing drug \u003cem\u003ev.\u003c/em\u003e pleasant images predicts current and future opioid misuse among chronic pain patients. Psychol Med. 2020 Mar;50(4):644\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eSolinas M, Chauvet C, Lafay-Chebassier C, Vanderkam P, Barillot L, Moeller SJ, et al. Tobacco Images Choice and its Association With Craving and Dependence in People Who Smoke Cigarettes. Nicotine and Tobacco Research. 2024 Nov 22;26(12):1636\u0026ndash;45. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Maloney T, Parvaz MA, Alia-Klein N, Woicik PA, Telang F, et al. Impaired insight in cocaine addiction: laboratory evidence and effects on cocaine-seeking behaviour. Brain. 2010 May;133(5):1484\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Zilverstand A, Konova AB, Kundu P, Parvaz MA, Preston-Campbell R, et al. Neural Correlates of Drug-Biased Choice in Currently Using and Abstinent Individuals With Cocaine Use Disorder. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018 May;3(5):485\u0026ndash;94. \u003c/li\u003e\n\u003cli\u003ePedram P, Wadden D, Amini P, Gulliver W, Randell E, Cahill F, et al. Food Addiction: Its Prevalence and Significant Association with Obesity in the General Population. PLOS ONE. 2013 Sept 4;8(9):e74832. \u003c/li\u003e\n\u003cli\u003eKanter R, Caballero B. Global Gender Disparities in Obesity: A Review. Advances in Nutrition. 2012 July;3(4):491\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCooper AJ, Gupta SR, Moustafa AF, Chao AM. Sex/Gender Differences in Obesity Prevalence, Comorbidities, and Treatment. Curr Obes Rep. 2021 Dec 1;10(4):458\u0026ndash;66. \u003c/li\u003e\n\u003cli\u003eHallam J, Boswell RG, DeVito EE, Kober H. Gender-related Differences in Food Craving and Obesity. YALE JOURNAL OF BIOLOGY AND MEDICINE. 2016;89:161\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eBlechert J, Lender A, Polk S, Busch NA, Ohla K. Food-Pics_Extended\u0026mdash;An Image Database for Experimental Research on Eating and Appetite: Additional Images, Normative Ratings and an Updated Review. Front Psychol [Internet]. 2019 Mar 7 [cited 2025 May 26];10. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2019.00307/full\u003c/li\u003e\n\u003cli\u003eMedawar E, Thieleking R, Witte AV. Dietary Fiber and WHO Food Categories Extension for the Food-Pics_Extended Database. Front Psychol [Internet]. 2022 June 10 [cited 2025 Dec 16];13. Available from: https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2022.818471/full\u003c/li\u003e\n\u003cli\u003eBradley MM, Lang PJ. International Affective Picture System. In: Zeigler-Hill V, Shackelford TK, editors. Encyclopedia of Personality and Individual Differences [Internet]. Cham: Springer International Publishing; 2017 [cited 2025 May 26]. p. 1\u0026ndash;4. Available from: https://doi.org/10.1007/978-3-319-28099-8_42-1\u003c/li\u003e\n\u003cli\u003eSchulte EM, Gearhardt AN. Development of the Modified Yale Food Addiction Scale Version 2.0. European Eating Disorders Review. 2017;25(4):302\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMoeller SJ, Beebe-Wang N, Woicik PA, Konova AB, Maloney T, Goldstein RZ. Choice to view cocaine images predicts concurrent and prospective drug use in cocaine addiction. Drug Alcohol Depend. 2013 June;130(1\u0026ndash;3):178\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eParikh A, Moeller SJ, Garland EL. Simulated opioid choice linked to opioid use disorder severity among veterans with chronic pain: initial validation of a novel paradigm. Am J Drug Alcohol Abuse. 2022 July 4;48(4):403\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eMcClain N, Ceceli AO, Kronberg G, Alia-Klein N, Goldstein RZ. Moving beyond self-report in characterizing drug addiction: Using drug-biased behavior to prospectively inform treatment adherence in opioid use disorder [Internet]. 2025 [cited 2025 Apr 27]. Available from: http://medrxiv.org/lookup/doi/10.1101/2025.01.01.25319860\u003c/li\u003e\n\u003cli\u003eDelgado-Rodr\u0026iacute;guez R, Moreno-Padilla M, Moreno-Dom\u0026iacute;nguez S, Cepeda-Benito A. Food addiction correlates with emotional and craving reactivity to industrially prepared (ultra-processed) and home-cooked (processed) foods but not unprocessed or minimally processed foods. Food Quality and Preference. 2023 Aug 1;110:104961. \u003c/li\u003e\n\u003cli\u003ePursey KM, Collins CE, Stanwell P, Burrows TL. The stability of \u0026lsquo;food addiction\u0026rsquo; as assessed by the Yale Food Addiction Scale in a non-clinical population over 18-months. Appetite. 2016 Jan;96:533\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eHeitmann J, Bennik EC, Van Hemel-Ruiter ME, De Jong PJ. The effectiveness of attentional bias modification for substance use disorder symptoms in adults: a systematic review. Syst Rev. 2018 Dec;7(1):160. \u003c/li\u003e\n\u003cli\u003eBoffo M, Zerhouni O, Gronau QF, van Beek RJJ, Nikolaou K, Marsman M, et al. Cognitive Bias Modification for Behavior Change in Alcohol and Smoking Addiction: Bayesian Meta-Analysis of Individual Participant Data. Neuropsychology Review [Internet]. 2019 Jan 14 [cited 2019 May 3]; Available from: http://link.springer.com/10.1007/s11065-018-9386-4\u003c/li\u003e\n\u003cli\u003eLarsen JK, Hollands GJ, Garland EL, Evers AWM, Wiers RW. Be more mindful: Targeting addictive responses by integrating mindfulness with cognitive bias modification or cue exposure interventions. Neuroscience \u0026amp; Biobehavioral Reviews. 2023 Oct 1;153:105408. \u003c/li\u003e\n\u003cli\u003eVarnado A, Smith A, Mason TB, Smith KE. The Ecological Validity of the Yale Food Addiction Scale 2.0 and Momentary Food Addiction Symptoms. Psychol Addict Behav. 2024 Aug;38(5):628\u0026ndash;36. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTABLE 1\u003c/strong\u003e Demographic characteristics and weight-related variables.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3303%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1307%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5082%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3303%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.1307%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eNot disclosed/Other\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eNot disclosed/Other\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eNot disclosed/Other\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7931%;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e401\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.2377%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e42.3 (13.6)\u003c/p\u003e\n \u003cp\u003e41.2 (14.0)\u003c/p\u003e\n \u003cp\u003e45.3 (13.9)\u003c/p\u003e\n \u003cp\u003e41.8 (13.8)\u003c/p\u003e\n \u003cp\u003e27.80 (7.98)\u003c/p\u003e\n \u003cp\u003e26.38 (5.26)\u003c/p\u003e\n \u003cp\u003e31.92 (16.3)\u003c/p\u003e\n \u003cp\u003e27.15 (6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5082%;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e18-74\u003c/p\u003e\n \u003cp\u003e20-77\u003c/p\u003e\n \u003cp\u003e27-64\u003c/p\u003e\n \u003cp\u003e18-77\u003c/p\u003e\n \u003cp\u003e15.82-72.40\u003c/p\u003e\n \u003cp\u003e16.62-52.57\u003c/p\u003e\n \u003cp\u003e17.93-58.59\u003c/p\u003e\n \u003cp\u003e15.82-72.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviation: BMI, body mass index; SD, standard deviation.\u003c/em\u003e\u003cbr\u003e\u003cem\u003eNote that sample sizes vary due to missing or invalid data.\u0026nbsp;\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 2\u003c/strong\u003e Prevalence of Obesity and Food addiction in our population.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8147%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.0077%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7413%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0772%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3591%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.8147%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYFAS score\u003c/p\u003e\n \u003cp\u003eFA diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.0077%;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003cp\u003eNormal Weight\u003c/p\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003cp\u003eModerate\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7413%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003cp\u003e389\u003c/p\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0772%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/p\u003e\n \u003cp\u003e3.39 (3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3591%;\"\u003e\n \u003cp\u003e28.28\u003c/p\u003e\n \u003cp\u003e25.96\u003c/p\u003e\n \u003cp\u003e43.96\u003c/p\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.29\u003c/p\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e14.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviation: BMI, body mass index; YFAS, Yale Food Addiction Scale; FA, food addiction; SD, standard deviation. \u003cem\u003eNote that sample sizes vary due to missing or invalid data.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 3.\u003c/strong\u003e Prevalence of obesity in people as a function of food addiction and prevalence of food addiction in people as a function of obesity.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.00474%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3254%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7441%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6919%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9558%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4281%;\"\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSevere FA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNonObese\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.00474%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.3254%;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003eSevere FA\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003eSevere FA\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7441%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e321\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6919%;\"\u003e\n \u003cp\u003e17.31 (0.81) 22.33 (1.70) 27.2 (1.4)\u003c/p\u003e\n \u003cp\u003e36.02 (8.34)\u003c/p\u003e\n \u003cp\u003e26.37 (6.77)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e21.79 (1.99)\u003c/p\u003e\n \u003cp\u003e27.01 (1.43)\u003c/p\u003e\n \u003cp\u003e36.24 (5.33)\u003c/p\u003e\n \u003cp\u003e31.60 (7.15)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e15.82-17.96\u003c/p\u003e\n \u003cp\u003e18.52-25.00\u003c/p\u003e\n \u003cp\u003e25.00-29.90\u003c/p\u003e\n \u003cp\u003e30.06-72.40\u003c/p\u003e\n \u003cp\u003e15.82-72.40\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.11-24.80\u003c/p\u003e\n \u003cp\u003e25.05-29.94\u003c/p\u003e\n \u003cp\u003e30.04-48.19\u003c/p\u003e\n \u003cp\u003e19.11-48.19\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9558%;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003cp\u003e49.22\u003c/p\u003e\n \u003cp\u003e27.72\u003c/p\u003e\n \u003cp\u003e20.87\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17.54\u003c/p\u003e\n \u003cp\u003e24.56\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;57.89\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.37\u003c/p\u003e\n \u003cp\u003e8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: BMI, body mass index; FA, food addiction; SD, standard deviation. \u003cem\u003eNote that sample sizes vary due to missing or invalid data.\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTABLE 4\u003c/strong\u003e Gender differences in obesity and FA status.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eNonObese\u003c/p\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eNonObese\u003c/p\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eFA\u003c/p\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003eFA\u003c/p\u003e\n \u003cp\u003eNoFA\u003c/p\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e70.21\u003c/p\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.95\u003c/p\u003e\n \u003cp\u003e22.05\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.39\u003c/p\u003e\n \u003cp\u003e80.61\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003cp\u003e89.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: FA, food addiction\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote that sample sizes vary due to missing or invalid data.\u003c/em\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Food addiction, obesity, probabilistic image choice task, gender differences, unhealthy food bias","lastPublishedDoi":"10.21203/rs.3.rs-7827708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7827708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aims:\u003c/strong\u003e Food addiction (FA) shares behavioral and neurobiological similarities with substance use disorders, but objective behavioral measures of FA are lacking. This study aimed to adapt the Probabilistic Image Choice (PIC) task to assess behavioral bias toward unhealthy food, and to determine its association with FA and obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign:\u003c/strong\u003eCross-sectional study with a one-year follow-up in a subset of participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting:\u003c/strong\u003eOnline study with participants from the UK and Ireland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants:\u003c/strong\u003e 415 adults (50% women) recruited via the Prolific platform, with 72 completing a follow-up assessment after 12 months.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements:\u003c/strong\u003eParticipants completed the Yale Food Addiction Scale (YFAS 2.0), reported height and weight for BMI calculation, and performed the adapted PIC task featuring four image categories: neutral, pleasant, healthy food, and unhealthy food. Primary outcome measures included unhealthy food bias (difference between unhealthy and healthy food image selections) and its relationship with food addiction and obesity status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003eIndividuals with severe FA or obesity showed greater selection of unhealthy food images compared to those without food addiction or obesity. Gender analyses revealed that unhealthy food bias was larger in women than in men with food addiction\u003c/p\u003e\n\u003cp\u003eTest-retest analysis showed high stability of the unhealthy food bias over a one-year period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The FA PIC task provides an operant behavioral measure of unhealthy food bias that is associated with food addiction, particularly in women. The stability of this bias over time suggests it may represent a trait-like characteristic with potential diagnostic and prognostic value for FA and related disorders.\u003c/p\u003e","manuscriptTitle":"Unhealthy Food Bias in Food Addiction: Behavioral Evidence from a Probabilistic Image Choice Task","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 00:08:25","doi":"10.21203/rs.3.rs-7827708/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T23:18:26+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T22:58:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T22:56:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T14:12:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Eating Disorders","date":"2025-12-18T10:58:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba39a1d7-5924-4f56-a2c3-d0ded80722ce","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:02:46+00:00","versionOfRecord":{"articleIdentity":"rs-7827708","link":"https://doi.org/10.1186/s40337-026-01590-1","journal":{"identity":"journal-of-eating-disorders","isVorOnly":false,"title":"Journal of Eating Disorders"},"publishedOn":"2026-04-03 15:57:55","publishedOnDateReadable":"April 3rd, 2026"},"versionCreatedAt":"2025-12-30 00:08:25","video":"","vorDoi":"10.1186/s40337-026-01590-1","vorDoiUrl":"https://doi.org/10.1186/s40337-026-01590-1","workflowStages":[]},"version":"v1","identity":"rs-7827708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7827708","identity":"rs-7827708","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}