Food perception in Orthorexia Nervosa: worry or craving?

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Marilena Aiello, Alberto Massimiliano Umiltà, Giovanni Ottoboni, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6614417/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Orthorexia nervosa (ON) is characterized by an excessive preoccupation with eating healthy foods. This disorder shares similarities with various pathological conditions, including anorexia nervosa and addictive behaviours. The current research aimed to explore whether ON is driven by a fear of unhealthy foods, similar to anorexia nervosa, or by a craving for healthy foods, akin to the patterns observed in addictive disorders. Methods In an online study (Study 1), participants (n = 166 adults, mean age = 24.8 years, SD = 7.6 years, 48.8% female) reported liking, wanting, and frequency of intake of 20 healthy and 20 unhealthy foods. Additionally, they completed the Dusseldorf Orthorexia Scale, while BMI, hunger level, and risk of eating disorders were collected. In Study 2, participants (n = 73 adults, mean age = 23.4 years, SD = 3.5 years, 37% female) completed questionnaires on ON and the risk of eating disorders, and a visual probe task with images of healthy and unhealthy foods. Eye movements were also recorded for a subset of participants in the laboratory. Results The results suggested that individuals with higher ON tendencies exhibit decreased responsiveness to rewards and demonstrate a pattern of attentional avoidance toward unhealthy foods. This indicates that ON behaviors may be driven by a fear of unhealthy foods. Conclusions These results underscore the importance of elucidating the role of attentional and motivational mechanisms in ON and their clinical implications. Attentional bias Reward responsiveness Eating disorders Addiction Healthiness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Orthorexia nervosa (ON) describes a condition characterised by obsessional concern with eating healthy foods (Dunn & Bratman, 2016 ; Horovitz & Argyrides, 2023 ). Individuals with ON manifest concern regarding the quality and composition of meals and engage in rigid eating behaviour in an attempt to achieve health through "food purity". They follow severe dietary rules that result in excessive avoidance of "unhealthy" foods. For instance, cases of patients eating only seeds or unprocessed, organic plant-based foods, or even only animal products have been described (Barthels et al., 2024 ). Moreover, they spend a significant amount of time thinking, choosing, controlling food sources, buying, preparing, and consuming food (approximately 4–5 hours a day; Barthels et al., 2024 ). Eating foods considered "unhealthy" causes feelings of guilt, disgust, fear of becoming ill, and self-punishment behaviours. On the other hand, the feeling of having control over food is positively perceived and reinforces this behaviour, which, in the long term, can affect health as well as social relationships, leading to nutrient deficiency, malnutrition, and social isolation (Koven & Abry, 2015 ). To date, the prevalence of ON behaviours has been reported to range from 7 to 57% in the general population, from 29 to 34.9% among Italian university students and higher in some “risk groups,” such as healthcare professionals and dietitians (general population: Ramacciotti et al., 2011 ; university students: Dell’osso et al., 2018 ; dieticians: Kinzl et al. 2006 ). ON is not an official disorder and is not mentioned in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and its conceptualization has been debated (e.g., Meule et al., 2021; Haman et al., 2015). Crucially, ON overlaps with various existing psychiatric disorders. For instance, ON is believed to share characteristics with anorexia nervosa, including perfectionism, restrictive eating patterns, food-related preoccupation (e.g., Gramaglia et al., 2017 ), and obsessive-compulsive disorders, because orthorexic subjects manifest intrusive thoughts (similar to obsessions) about food, fear of health contamination and impurities associated with distress (Zagaria et al., 2022 ). ON has also been proposed to belong to the ‘‘behavioural’’ addiction disorder category, together with pathological gambling, internet addiction, and overtraining syndrome (Marazziti et al., 2014 ). Similarly to these conditions, ON appears to be characterized by repetitive dysfunctional habits that induce a progressive impairment of affective, working, and social spheres, and by loss of control over behaviour despite its negative consequences (Strahler et al., 2023 ). Therefore, additional research on ON, particularly concerning its similarities and differences with these conditions, is essential for enhancing our understanding of the cognitive processes underlying ON and developing more effective diagnostic and therapeutic strategies. Multiple forms of psychopathology have been found to be associated with dysregulation of attentional processes (Mansell et al., 2008 ) and attentional bias (AB), particularly, consisting of the selective allocation of attention to salient stimuli. AB to negative disease-salient information perceived as threatening has been described in anxiety disorders (e.g., Todd et al., 2022 ), and it is believed to reflect high levels of worry. For instance, individuals who fear spiders or snakes show both an attentional bias toward words or pictures signalling these stimuli (for example, Lavy et al., 1993 ) and a time course characterized by an early attentional bias toward threat stimuli, followed by intentional avoidance of these same stimuli (Rinck et al., 2006). Similar patterns have been observed with caloric food or body pictures in anorexia nervosa (Stott et al., 2021 ). However, in addiction disorders, AB may reflect craving and hedonic motivation (Field & Cox, 2008 ; Werthmann et al., 2015 ). Craving is described as the irresistible desire to engage in specific behaviour and is the result of learning processes during which relevant cues become associated with the rewarding features of addictive behaviour (Drummond, 2001 ). An AB for drugs or disorder-related stimuli has been observed in individuals with drug addiction (Field & Cox, 2008 ), problem gambling (Farr et al., 2023 ), internet gaming disorders (Chia et al., 2020), and binge eating disorders (Stott et al., 2021 ). To date, only two studies have investigated AB for healthy/unhealthy food cues in orthorexic individuals (Albery et al., 2020 ; 2022 ), and both suggest that an attentional bias toward healthy food words characterizes ON. In the first study, ON tendencies were associated with increased attentional preference for healthy food-related words in a modified Stroop task (Albery et al., 2020 ). Similar findings were obtained in a second study, in which the allocation of attention to healthy/unhealthy food-related words was evaluated using a Dot Probe task (Albery et al. 2022 ). In the dot-probe task, a pair of stimuli is presented simultaneously on the screen. In target trials, one of these stimuli is considered relevant (healthy food words such as salad or unhealthy food words such as salami ), while the other is neutral (for instance, salary ). Immediately after the stimulus pair is removed, a probe (a dot) is presented at one of the two stimulus locations, and the participants are asked to indicate the location of the probe. Participants are assumed to react faster to a stimulus that appears in a location where their attention is already focused than to a stimulus appearing in an unattended location. Using this task in a sample of vegan/vegetarians, Albery et al. ( 2022 ) confirmed a significant correlation between attentional bias for healthy food words and ON tendencies. Despite these interesting results, no information on the time course or hedonic motivation toward healthy/unhealthy foods was provided in these studies. In this research, we aimed to investigate both reward responsiveness and attentional bias toward healthy and unhealthy food pictures in individuals with varying levels of ON through two distinct studies. Study 1 examined the ratings of liking and wanting for healthy and unhealthy foods. Liking and wanting are two essential components of the brain's reward system. "Liking" refers to the pleasurable experience associated with consuming food, driven by hedonic hotspots in the limbic brain structures. In contrast, "wanting" involves incentive salience—a motivational process that enhances the appeal of palatable foods and can trigger cravings. This process is regulated by dopamine-driven mesocorticolimbic networks (Berridge, 2009 ). Study 2 investigates AB toward healthy and unhealthy food cues through a dot-probe task. In a subsample of participants in Study 2, we employed an eye-tracking methodology to record gaze patterns during the task. Attentional engagement (early processing) can be assessed by measuring the proportion of trials in which the first saccade is made toward a stimulus. Similarly, we recorded the total gaze duration on a stimulus as a measure of maintained attention (late processing) (Kerr-Gaffney et al., 2019 ). Both indices can be considered as indicators of visual attentional bias. If Orthorexia is akin to addiction disorders, increased wanting for healthy foods and an AB for these stimuli should be observed. On the other hand, similarly to individuals with anorexia, people with ON may demonstrate negative evaluations of unhealthy food compared to healthy foods pictures on an explicit level, and aberrant attentional processing toward them, because of their preoccupation with and/or fear of food being considered unhealthy. Their preoccupation may be represented by initial engagement, followed by avoidance, as observed in anorexia nervosa. 2. Study 1. Reward responsiveness toward healthy and unhealthy food cues and ON 2.1 Participants and Procedure Participants were eligible for inclusion if they met the following criteria: 1) aged 18 or older, 2) native Italian speakers, 3) absence of neurological/psychiatric disease, and 4) absence of diabetes and food restrictions (e.g., vegetarianism, allergy, or others). A total of 166 eligible participants completed the survey on the Qualtrics platform (age mean 24.8 years, age SD =7.6 age range =18–60; 48.8% female). Participants were recruited via social media platforms and university mailing lists. The study ads informed participants about the study's aim and provided a link to complete the study on Qualtrics ( www.qualtrics.com ). The participants were not compensated for their participation and were informed that they were free to withdraw from participation at any time. The Ethics Committee of the University of Bologna approved the study (protocol no. 0062640 of 08/03/2023). After obtaining informed consent, the participants provided information about their demographic characteristics, health status, and eating habits. We asked them to report their hunger level (from 0 = not hungry to 7 = extremely hungry), hours from their last meals and their weight and height to compute their body mass index (BMI). Participants then completed an explicit evaluation task assessing reward responsiveness toward healthy and unhealthy foods, and questionnaires on ON and eating disorder risk. 2.2 Task and Questionnaires Explicit Evaluation of Foods . This task was aimed at investigating reward responsiveness to healthy and unhealthy foods. In this task, 20 pictures [10 healthy (HF) and 10 non-healthy (NHF) foods] were presented and participants were asked to respond to the following questions: 1) “How pleasant would it be to experience a mouthful of this food now?” (Liking); 2) "How much do you want this food now?" (Wanting). In addition, we presented two additional questions: 3) "How frequently do you eat this food?" and 4) "How healthy is it?" Participants indicated their responses on a 100-point visual analogue scale anchored at each end with “not at all” and “extremely” (for a similar procedure, see Aiello et al., 2017 ). Food pictures were taken from FRIDA and FoodPics databases (Foroni et al., 2013 ; Blechert et al., 2014 ). Healthy and unhealthy food images were selected through an online questionnaire filled out by an independent sample of 20 participants (six females, age mean 20.3 age SD 1.9). Importantly, healthy and unhealthy foods were matched for caloric content [t (18) = 0.82 p = 0.42], frequency of consumption [t (18) = -1.98 p = 0.06], and palatability [t (18) = 1.54 p = 0.14], whereas they significantly differed in perceived healthiness [t (18) = -8.05 p = 0.00). See Fig. 1 . Questionnaires. The Düsseldorf Orthorexia Scale (DOS) (Barthels et al. 2015 ; Cerolini et al., 2022 ) evaluates orthorexic tendencies and is composed of 10 items on a 4-point Likert scale, from 1 (it does not correspond to my behaviour at all) to 4 (it corresponds well to my behaviour). The maximum score is 40, with higher scores indicating more pronounced orthorexic behaviour. A score ≥ 30 is considered indicative of the presence of ON, while a score between 25 and 29 indicates the risk of ON. The Eating Attitudes Test (EAT-26) (Garner & Garfinkel, 1979 ; Dotti et al., 1998) assesses the risk of eating disorders. Responses to questions 1–25 are rated on a four-point scale: "always" is assigned 3 points, "usually" is assigned 2 points, "often" is assigned 1 point, and "sometimes," "rarely," or "never" receive 0 points. Reverse scoring is applied to item 26. A score ≥ 20 is considered indicative of disordered eating tendencies. 2.3 Statistical analyses Participants were divided into two groups based on the median DOS value (median value = 20), with 83 participants in the HighON and 83 in the LowON groups. Normality was assessed using the Shapiro-Wilk test. Parametric data were analysed using 2 × 2 repeated-measures ANOVAs, where the type of food (two levels: HF and NHF) was included as a within-subject factor and the group of participants (two levels: HighON and LowON) as the between-subject factor. Non-parametric variables were analysed using the Mann-Whitney U and the Wilcoxon signed-rank tests. Pearson correlation coefficient was used to examine the relationships between DOS and food ratings. All analyses were performed using Statistica software (Statsoft, Tulsa, USA). Violin plots were made using online platform SRplot (Tange et al., 2023). 2.4 Results 2.4.1 Sample characteristics Overall, the average DOS score was 19.5 (SD = 5.4), with a range of 10–38. The average EAT-26 score was 8.1 (SD = 8.7), with a range of 0–44. The participants’ BMI ranged between 17 and 31.3 (Mean = 22.7, SD = 2.8). Finally, participants reported a mean hunger score of 2.8 (SD = 1.9), with a range of 0–7. 2.4.2 Explicit ratings Participants in the HighON and LowON groups did not differ in BMI (U = 3309, Z = 0.44, p = 0.66), hunger level [U = 3145, Z = 0.96, p = 0.33], or hours since their last meal (U = 3218, Z = 0.73, p = 0.46). However, a significant difference was observed in EAT-26 scores (U = 1651, Z = -5.79, p < 0.001). The ANOVA on liking ratings revealed a significant interaction between group and food category [F (1, 164) = 10.34, p = 0.001], while other main effects were not significant: group [F (1, 164) = 2.46, p = 0.11], category [F (1, 164) = 2.29, p = 0.13]. Post-hoc analyses showed that individuals in the HighON group liked unhealthy foods less than healthy foods ( p = 0.001), while no such difference was found in the LowON group ( p = 0.23). Additionally, individuals in the HighON group liked unhealthy foods less than those in the LowON group ( p = 0.02), whereas no significant difference emerged for healthy foods ( p = 0.90). The analysis of wanting ratings revealed that overall wanting was higher for healthy foods than unhealthy foods [F (1, 164) = 5.3, p = 0.02] while the main group effect was not significant [F (1, 164) = 0.18, p = 0.6]. Moreover, a significant interaction between group and food category also emerged [F (1, 164) = 7.03, p = 0.008]. Post-hoc analyses indicated that individuals in the HighON group wanted unhealthy food less than healthy food ( p 0.24). Moreover, participants reported consuming healthy foods more frequently than unhealthy foods ( p < 0.001). Individuals in the HighON group reported consuming healthy foods more frequently than those in the LowON group (U = 2407.5, Z = -3.34, p < = 0.01), whereas no difference in the consumption of unhealthy foods was found between the two groups (U = 2944.5, Z = 1.61, p = 0.10). Finally, as expected, healthy foods were rated as healthier than unhealthy foods ( p < 0.001), and no significant difference emerged in the perceived healthiness of the two food categories between the groups [healthy foods: U = 3402, Z = 0.13, p = 0.89; unhealthy foods: U = 3420, Z = 0.07, p = 0.93]. Pearson correlation results are shown in Table 1 . Table 1 HF- Liking UHF- Liking HF- Wanting UHF- Wanting HF- Consumption UHF- Consumption DOS 0.05 -0.18 0.12 -0.12 0.37 -0.14 p = 0.48 p = 0.02 p = 0.13 p = 0.13 p = 0.00 p = 0.07 3. Study 2: AB toward healthy and unhealthy foods in ON 3.1 Participants and Procedure This study was conducted using two independent samples. The first sample performed the task online, the second sample was tested in the laboratory, and eye movements were recorded. The inclusion criteria for both samples were the same as in Study 1 except for age. Given the attentional nature of the task, only participants aged between 18–45 years were included. Sample 1 was composed of 42 participants (age mean : 23.1, age SD =4.3, 45% female), whereas sample 2 was composed of 31 participants (age mean : = 23.9, age SD = 1.8, 29% female). None of the participants were compensated for their participation, and they were informed that they were free to withdraw from participation at any time. They provided informed consent to participate in the study, which was in line with the Declaration of Helsinki and approved by the Ethics Committee of Bologna University (Protocol no. 0062640 of 08/03/2023). For what concerns the online procedure, after obtaining informed consent, participants provided information about their demographic characteristics, health status, and eating patterns. Participants then completed a dot-probe task delivered over the Internet using the free software Jatos (Lange et al., 2015 ). Finally, they completed questionnaires on ON and the risk of eating disorders. Regarding laboratory procedures, we began the session by administering the dot-probe task and measuring eye movements. We then asked the participants to complete the two questionnaires. 3.2 Task and Questionnaires Dot-Probe Task. The task followed a procedure similar to the one employed by van Ens et al. ( 2019 ). Initially, a fixation cross was displayed in the centre of the screen for 100 milliseconds, followed by the presentation of a picture pair on either side of the centre for 500 milliseconds. Afterwards, a dot (1 × 1 degrees of visual angle) appeared either on the left or right side, and participants were required to indicate the dot's location by pressing the "S" key if it was on the left and the "L" key if it was on the right. A total of 30 pairs of images were used, with 20 being critical pairs (10 healthy foods versus non-food and 10 unhealthy foods versus non-food) and 10 being filler pairs (presenting two non-food pictures). Each pair was presented four times, resulting in a total of 120 trials. The trials were divided into two blocks of 60 trials, separated by a short break, with one block containing images of healthy foods and the other containing images of unhealthy foods. Blocks were counterbalanced across subjects. The position of the probe was counterbalanced. The food pictures used in this study were the same as those used in study one, while the non-food pictures were obtained from the FRIDA and FoodPics databases (Foroni et al., 2013 ; Blechert et al., 2014 ). Food and Non-food stimuli in the critical pairs were matched as closely as possible for colour and complexity (see Fig. 3 for an example). In the lab version, participants were seated 64 cm from the screen. Reaction time (RT) data was collected using Matlab software while a Tobii eye tracker was used to record eye movements. For what concerns RT data, incorrect responses, RTs exceeding the mean individual RT of the participant plus or minus two standard deviations were excluded from subsequent analysis. Three indices were calculated. RT bias scores were calculated by subtracting RTs of congruent trials (probe replaced the food image) from RTs of the incongruent trials (probe replaced the control image). Positive values indicate attention bias towards food images; negative values indicate attention bias away from food images and towards control images (van Ens et al., 2019 ). Regarding eye movement data, a fixation was defined as a period lasting at least 100 ms in which no saccades or blinks occurred (Werthmann et al., 2015 ). Direction bias was calculated as the percentage of the total initial fixations on food. A score above 50% indicates a direction bias towards food, whereas a score below 50% indicates a direction bias away from food. Direction bias is considered a measure of initial attentional orientation . Gaze duration bias, which is considered a measure of maintained attention , was calculated using the average gaze duration to a food image across all trials as a proportion of the average gaze duration to all images (food and control). A duration bias score of > 0.5, 0.5, or < 0.5 represents maintained attention to food pictures, no bias and maintained attention to control images respectively (see Doolan et al., 2014; van Ens et al., 2019 ). Questionnaires Participants completed the DOS and the EAT-26 as in Study 1. 3.3 Statistical analyses As no significant differences were found in reaction times (RTs) between individuals who performed the task online and those who did so in the laboratory for healthy congruent trials, healthy incongruent trials, unhealthy congruent trials, and unhealthy incongruent trials ( ps > 0.09), the data from both groups were combined for the RT analysis. The data were then divided into two groups based on the median DOS value (median value = 19), with 37 participants in the HighON group and 36 in the LowON group. A similar approach was used for analysing gaze data, where the data were divided into two groups based on the median DOS value (median value = 19), with 16 participants in the HighON group and 15 in the LowON group. Since the variables were not normally distributed, as assessed by the Shapiro-Wilk test, the Mann-Whitney U test and the Wilcoxon signed-rank test were used for data analysis. Spearman correlation analyses were conducted to examine the relationship between ON and RT bias, gaze direction bias, and gaze duration bias. All analyses were performed using Statistica software (StatSoft, Tulsa, USA). The bar charts were constructed using online platform SRplot (Tange et al., 2023). 3.4 Results 3.4.1 Reaction time data Sample characteristics The new sample was composed of 73 participants. In the pooled sample, the average score on the DOS was 19.7 (SD 5.7), with a range of 10–38. Furthermore, the average score on the EAT-26 was 6.5 (SD 7.3), with a range of 0–31. In the sample of participants who performed the dot probe and in which eye movements were tracked, the average score on the DOS was 18.8 (SD 4.5) with a range of 12–28. Furthermore, the average score on the EAT-26 was 4.3 (SD 4.6), with a range of 0–19. Table 2 Table 2 presents the reaction times and bias indices for all participants and separately for the two groups (HighON and LowON) for both healthy and unhealthy trials. Healthy Food (HF) Mean SD Unhealthy Food (UHF) Mean SD n = 73 RT Congruent trials (ms) 427.2 83.2 415.8 77.9 RT Incongruent trials (ms) 429.3 94.9 427.1 96.7 RT bias score 2.09 42.1 11.2 43.1 HighON RT Congruent trials (ms) 411.3 71.9 407.7 80.1 RT Incongruent trials (ms) 405.7 72.2 407.6 78.8 Bias index score -5.6 37.2 -0.2 25.6 LowON RT Congruent trials (ms) 443.7 91.6 424.2 76 RT Incongruent trials (ms) 453.7 109.5 447.1 109.8 Bias index score 10 45.7 23 53.6 In trials presenting healthy foods, there was no significant difference between congruent and incongruent trials for either the HighON or LowON groups ( ps > 0.30). In both groups, the bias scores did not differ significantly from zero ( ps > 0.30). Individuals in the HighON group were faster than those in the LowON group in both congruent and incongruent trials (Congruent: U = 480.5, Z = -2.04, p = 0.04; Incongruent: U = 428.5, Z = -2.62, p = 0.008). No significant difference emerged between the two groups in the bias index score (U = 563.5, Z = -1.13, p = 0.25). In trials presenting unhealthy foods, individuals in the LowON group were slower in incongruent trials compared to the congruent one ( p = 0.007) and exhibited a bias score significantly different from zero ( p = 0.007), indicating an attentional bias (AB) toward unhealthy food items. In contrast, individuals in the HighON group did not show a significant difference between congruent and incongruent trials ( p = 0.9), and the bias score did not differ from zero ( p = 0.92). Individuals in the HighON group were faster than those in the LowON group in the incongruent trials (U = 443.5, Z = -2.45, p = 0.01), but no difference emerged in the congruent trials (U = 529.5, Z = -1.5, p = 0.13). Importantly, the bias scores significantly differed between individuals in the LowON and those in the HighON groups (U = 480.5, Z = -2.04, p = 0.04). See Fig. 4 . No correlations were found between DOS scores and RT bias scores for unhealthy foods (rho=-0.20 p = 0.08) and RT bias scores for healthy foods (rho=-0.07 p = 0.53). 3.4.3 Gaze bias Gaze direction and duration bias scores are presented in Table 3 for all participants and separately for the HighON and LowON groups. Table 3 Healthy Food (HF) Mean SD Unhealthy Food (UHF) Mean SD n = 31 Gaze direction bias 49.9 6.0 49.3 6.4 Gaze duration bias 0.51 0.1 0.52 0.1 HighON Gaze direction bias 50.8 6.1 51.3 6.5 Gaze duration bias 0.52 0.1 0.53 0.1 LowON Gaze direction bias 49.0 5.8 47.3 5.7 Gaze duration bias 0.51 0.05 0.52 0.04 For gaze direction bias, individuals in the LowON group did not show any significant difference between healthy and unhealthy foods ( p = 0.42). Wilcoxon matched-pairs test analysis revealed that the direction bias scores for healthy and unhealthy food images were not significantly different from a test score of 50% (HF: p = 0.70, UHF: p = 0.10). Similarly, the HighON group did not show any significant difference between healthy and unhealthy foods in gaze direction bias ( p = 0.67), and the direction bias scores were not significantly different from a test score of 50% (HF: p = 0.93, UHF: p = 0.50). No difference emerged between groups for either healthy food (U = 109.5 Z = 0.41 p = 0.68) or unhealthy food (U = 75 Z = 1.78 p = 0.07). See Fig. 5 . For gaze duration bias, no significant difference emerged between healthy and unhealthy food images in individuals in the LowON group ( p = 0.39). Furthermore, the duration bias score for healthy food images was not significantly different from a test score of 0.5 ( p = 0.53), while the duration bias score for unhealthy food images was significantly different from 0.5 ( p = 0.05), indicating that participants in this group spent more time looking at unhealthy images compared to non-food images. In individuals in the HighON group, no significant difference was found between healthy and unhealthy gaze duration bias ( p = 0.16), and the duration bias scores did not differ significantly from a test score of 0.5 (HF: p = 0.46, UHF: p = 0.25). When comparing the two groups, no significant differences were found for both healthy (U = 114 Z = 0.23 p = 0.81) and unhealthy food (U = 108 Z=-0.47 p = 0.63). See Fig. 5 A Spearman correlation revealed a significant correlation between DOS and gaze direction bias for unhealthy foods. See Table 5 . Table 5 Spearman Correlations DOS - Gaze Direction bias HF 0.06 0.73 DOS - Gaze Direction bias UF 0.41 0.02 DOS - Gaze Duration bias HF -0.02 0.99 DOS - Gaze Duration bias UF -0.01 0.94 Discussion Orthorexia nervosa (ON) is characterized by an excessive preoccupation with healthy eating, manifesting in stringent dietary restrictions and the avoidance of perceived unhealthy foods. Through two distinct studies, we investigated what is the predominant component in characterizing ON: the craving and hedonic motivation for healthy foods or the concern regarding foods deemed unhealthy. Our findings indicated that elevated ON tendencies were associated with lower reward responsiveness and a pattern of attentional avoidance of unhealthy foods. These findings are discussed below. In Study 1, we measured liking and wanting for healthy and unhealthy foods and ON tendencies. According to Berridge's model (Berrridge, 2009), two separate but interconnected processes govern motivation: liking, which pertains to hedonic preference or palatability, and wanting, which represents the motivational drive towards a particular food. Liking and wanting can manifest themselves at both explicit conscious and implicit unconscious levels, and interact with homeostatic circuitry to regulate eating behaviour (Berridge et al., 2009). Using explicit ratings, we found that ON tendencies were associated with both lower liking and wanting for unhealthy foods. Additionally, individuals with higher ON scores reported consuming healthy foods more frequently. These findings suggest that the reduced intake of unhealthy foods and the increased consumption of healthy foods typical of ON may be driven by both a diminished motivational drive and a lack of hedonic enjoyment of unhealthy food, which are not perceived as rewarding. This pattern aligns with observations in anorexia nervosa and high-calorie foods (see Lloy & Steinglass, 2018 for a review). Notably, anhedonia has been shown to facilitate food avoidance (Murray et al., 2022). In Study 2, we employed an implicit task, i.e. a dot-probe task and recorded eye movements to investigate attentional processes associated with the processing of healthy and unhealthy food in ON. The findings seem to suggest that ON is characterized by a pattern of attentional avoidance of unhealthy foods. First, while individuals with lower ON scores exhibited an attentional bias toward unhealthy foods, consistent with their higher palatability, this pattern was not observed in individuals with higher ON scores. Second, higher ON scores were associated with a greater proportion of initial fixations on unhealthy food items. However, individuals with higher ON scores did not fixate on unhealthy food more than non-food stimuli, unlike those with lower ON scores. These suggest that individuals with higher ON scores show an initial heightened vigilance of unhealthy food cues followed by an attentional shift away from these stimuli. These findings are inconsistent with those of Albery et al. ( 2020 ), who reported an attentional bias toward healthy food in ON. A potential explanation for this discrepancy could be that, unlike these two studies, we utilized pictorial stimuli. Pictures may indeed be more salient (e.g., Stormark et al., 2004), convey more affective information (De Houwer & Hermans, 1994 ), and demonstrate higher ecological validity when compared to words. Importantly, it has been shown that attentional bias outcomes toward food vary depending on whether words or pictures are used, and whether they are high or low-calorie (Freijy et al., 2014 ). Interestingly, the attentional pattern that we observed aligns with observations in patients with anorexia nervosa (Giel et al., 2011 ; Werthmann et al., 2019 ; Meregalli et al., 2023 ) and corresponds well with the vigilance-avoidance model, according to which threatening stimuli automatically capture attention at initial stage but, in later stages, they are avoided as a strategy to reduce arousal and anxiety (Mogg & Bradley, 1999 ). Crucially, this pattern may even contribute to reinforcing food restriction in anorexia nervosa (Werthmann et al., 2019 ). As a matter of fact, the optimal conceptualization of ON has been debated, with rationales proposed for considering the condition as an eating disorder (e.g. Gramaglia et al., 2017 ), a form of obsessive disorder (e.g. Zagaria et al., 2022 ), or even a form of behavioural addiction (e.g. Strahler et al., 2023 ). Our study suggests that this group exhibits reward system dysregulation specifically toward unhealthy foods, manifested as altered attentional salience. ON is characterized by a pattern of low reward responsiveness to unhealthy foods rather than heightened responsiveness to healthy foods. On a more implicit level, no AB to healthy foods is observed. Instead, the study seem to reveal a pattern of avoidance of unhealthy foods similar to the one observed in anxiety and anorexia (Stott et al., 2021 ), which suggests that shared cognitive mechanisms may underlie these disorders. Further studies are required to investigate motivational and attentional alterations in ON. Similarly, more comparisons of the neurocognitive profiles across these pathological conditions would be helpful in further understanding the commonalities between disorders. Finally, it must be pointed out the present study had several limitations that warrant consideration. Firstly, reward responsiveness and attentional biases toward food were assessed in two independent samples, with a different proportion of females and males. More studies employing implicit and explicit tasks in the same sample of participants should be performed to clarify whether unhealthy/healthy foods evoke conflicting implicit and explicit evaluations in individuals with ON that differ from those without ON. In addition, our experiments involved individuals with ON tendencies. Although several subjects in the study demonstrated ON on a validated scale, no formal diagnostic criteria for ON currently exist, which limits the generalizability of the findings. In sum, our observations may suggest the hypothesis that altered motivation and visual orientation may contribute to impaired function in ON. However, future research should investigate whether and how motivational and attentional bias could serve as a potential marker of ON, and how targeting this bias could be applied in treatment interventions. Declarations Funding source This work was supported by Progetti PRIMA - Call 2022 Section 1 Agri-food IA- Grant Agreement No: [2231] [CIPROMED] granted to A.T. Author Contribution M.A.: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. A.M.U.: Investigation; Writing - Review & Editing; G.O.: Resources; Writing - Review & Editing; A.T.: Conceptualization, Resources, Funding acquisition, Supervision, Writing - Review & Editing. Acknowledgement The authors are grateful to Michele Marzocchi for his technical assistance. Data Availability Data will be made available on request References Aiello, M., Eleopra, R., Foroni, F., Rinaldo, S., & Rumiati, R. I. (2017). Weight gain after STN-DBS: The role of reward sensitivity and impulsivity. Cortex, 92, 150-161. http://dx.doi.org/10.1016/j.cortex.2017.04.005 Albery, I. P., Michalska, M., Moss, A. C., & Spada, M. (2020). Selective attentional bias to food-related stimuli in healthy individuals with characteristics towards orthorexia nervosa. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 25, 1225-1233. http://dx.doi.org/10.1007/s40519-019-00755-z Albery, I. P., Shove, E., Bartlett, G., Frings, D., & Spada, M. M. (2022). Individual differences in selective attentional bias for healthy and unhealthy food-related stimuli and social identity as a vegan/vegetarian dissociate “healthy” and “unhealthy” orthorexia nervosa. Appetite, 178, 106261. http://dx.doi.org/10.1016/j.appet.2022.106261 Barthels, F., Fischer, M., Keskini, R., Schöl, A. M., & Pietrowsky, R. (2024). The various facets of orthorexic eating behavior: five case reports of individuals with supposed orthorexia nervosa. Journal of Eating Disorders, 12(1), 31. http://dx.doi.org/10.1186/s40337-024-00988-z Barthels, F., Meyer, F., & Pietrowsky, R. (2015). Orthorexic eating behavior. A new type of disordered eating. Ernahrungs Umschau, 62(10), 156-161. Berridge, K. C. (2009). ‘Liking’and ‘wanting’food rewards: brain substrates and roles in eating disorders. Physiology & behavior, 97(5), 537-550. http://dx.doi.org/10.1016/j.physbeh.2009.02.044 Blechert, J., Meule, A., Busch, N. A., & Ohla, K. (2014). Food-pics: an image database for experimental research on eating and appetite. Frontiers in psychology, 5, 617. http://dx.doi.org/10.3389/fpsyg.2014.00617 Cerolini, S., Vacca, M., Zagaria, A., Donini, L. M., Barbaranelli, C., & Lombardo, C. (2022). Italian adaptation of the Düsseldorf Orthorexia Scale (I-DOS): psychometric properties and prevalence of orthorexia nervosa among an Italian sample. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 1-9. http://dx.doi.org/10.1007/s40519-021-01278-2 Chia, D. X., & Zhang, M. W. (2020). A scoping review of cognitive bias in internet addiction and internet gaming disorders. International Journal of Environmental Research and Public Health, 17(1), 373. http://dx.doi.org/10.3390/ijerph17010373 Dell’Osso, L., Carpita, B., Muti, D., Cremone, I. M., Massimetti, G., Diadema, E., ... & Carmassi, C. (2018). Prevalence and characteristics of orthorexia nervosa in a sample of university students in Italy. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 23, 55-65. http://dx.doi.org/10.1007/s40519-017-0460-3 Doolan, K. J., Breslin, G., Hanna, D., & Gallagher, A. M. (2015). Attentional bias to food-related visual cues: is there a role in obesity?. Proceedings of the Nutrition Society, 74(1), 37-45. http://dx.doi.org/10.1017/S002966511400144X Dotti, A., & Lazzari, R. (1998). Validation and reliability of the Italian EAT-26. Eating and weight disorders-studies on anorexia, bulimia and obesity, 3, 188-194. http://dx.doi.org/10.1007/BF03340009 Drummond, D. C. (2001). Theories of drug craving, ancient and modern. Addiction, 96(1), 33-46. Dunn, T. M., & Bratman, S. (2016). On orthorexia nervosa: A review of the literature and proposed diagnostic criteria. Eating behaviors, 21, 11-17. http://dx.doi.org/10.1046/j.1360-0443.2001.961333.x Farr, Z., Broomfield, N. M., & Coventry, K. R. (2023). A Systematic Review of Attentional Bias in Problem Gambling. Journal of Gambling Studies, 1-27. http://dx.doi.org/10.1007/s10899-023-10260-9 Field, M., & Cox, W. M. (2008). Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug and alcohol dependence, 97(1-2), 1-20. http://dx.doi.org/10.1016/j.drugalcdep.2008.03.030 Foroni, F., Pergola, G., Argiris, G., & Rumiati, R. I. (2013). The FoodCast research image database (FRIDa). Frontiers in human neuroscience, 7, 51. http://dx.doi.org/10.3389/fnhum.2013.00051 Freijy, T., Mullan, B., & Sharpe, L. (2014). Food-related attentional bias. Word versus pictorial stimuli and the importance of stimuli calorific value in the dot probe task. Appetite, 83, 202-208. http://dx.doi.org/10.1016/j.appet.2014.08.037 Garner DM, Garfinkel P . The eating attitude test: an index of the symptoms of anorexia nervosa. Psychol Med 1979; 9: 273–279. Giel, K. E., Friederich, H. C., Teufel, M., Hautzinger, M., Enck, P., & Zipfel, S. (2011). Attentional processing of food pictures in individuals with anorexia nervosa—An eye-tracking study. Biological psychiatry, 69(7), 661-667. http://dx.doi.org/10.1016/j.biopsych.2010.09.047 Gramaglia, C., Brytek-Matera, A., Rogoza, R., & Zeppegno, P. (2017). Orthorexia and anorexia nervosa: two distinct phenomena? A cross-cultural comparison of orthorexic behaviours in clinical and non-clinical samples. BMC psychiatry, 17, 1-5. http://dx.doi.org/10.1186/s12888-017-1241-2 Håman, L., Barker-Ruchti, N., Patriksson, G., & Lindgren, E. C. (2015). Orthorexia nervosa: An integrative literature review of a lifestyle syndrome. International journal of qualitative studies on health and well-being, 10(1), 26799. http://dx.doi.org/10.3402/qhw.v10.26799 Horovitz, O., & Argyrides, M. (2023). Orthorexia and orthorexia nervosa: A comprehensive examination of prevalence, risk factors, diagnosis, and treatment. Nutrients, 15(17), 3851. http://dx.doi.org/10.3390/nu15173851 Houwer, J. D., & Hermans, D. (1994). Differences in the affective processing of words and pictures. Cognition & Emotion, 8(1), 1-20. http://dx.doi.org/10.1080/02699939408408925 Kerr‐Gaffney, J., Harrison, A., & Tchanturia, K. (2019). Eye‐tracking research in eating disorders: A systematic review. International Journal of Eating Disorders, 52(1), 3-27. http://dx.doi.org/10.1002/eat.22998 Kim, H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry & endodontics, 38(1), 52-54. http://dx.doi.org/10.5395/rde.2013.38.1.52 Kinzl, J. F., Hauer, K., Traweger, C., & Kiefer, I. (2006). Orthorexia nervosa in dieticians. Psychotherapy and psychosomatics, 75(6), 395. http://dx.doi.org/10.1159/000095447 Koven, N. S., & Abry, A. W. (2015). The clinical basis of orthorexia nervosa: emerging perspectives. Neuropsychiatric disease and treatment, 385-394. http://dx.doi.org/10.2147/NDT.S61665 Lange, K., Kühn, S., & Filevich, E. (2015). "Just another tool for online studies” (JATOS): An easy solution for setup and management of web servers supporting online studies. PloS one, 10(6), e0130834. http://dx.doi.org/10.1371/journal.pone.0130834 Lavy, E., Van den Hout, M., & Arntz, A. (1993). Attentional bias and spider phobia: Conceptual and clinical issues. Behaviour research and therapy, 31(1), 17-24. doi: 10.1016/0005-7967(93)90038-v Mansell, W., Harvey, A., Watkins, E. R., & Shafran, R. (2008). Cognitive behavioral processes across psychological disorders: A review of the utility and validity of the transdiagnostic approach. International Journal of Cognitive Therapy, 1(3), 181-191. https://doi.org/10.1521/ijct.2008.1.3.179 Marazziti, D., Presta, S., Baroni, S., Silvestri, S., & Dell'Osso, L. (2014). Behavioral addictions: a novel challenge for psychopharmacology. CNS spectrums, 19(6), 486-495. http://dx.doi.org/10.1017/S1092852913001041 Meregalli, V., Tenconi, E., Madan, C. R., Somà, E., Meneguzzo, P., Ceccato, E.,... & Collantoni, E. (2023). Beyond body image: what body schema and motor imagery can tell us about the way patients with anorexia nervosa experience their body. Psychiatry and Clinical Neurosciences, 77(2), 94-101. http://dx.doi.org/10.1111/pcn.13501 Meule, A., & Voderholzer, U. (2021). Orthorexia nervosa—It is time to think about abandoning the concept of a distinct diagnosis. Frontiers in Psychiatry, 12, 640401. http://dx.doi.org/10.3389/fpsyt.2021.640401 Mogg, K., & Bradley, B. P. (1999). Selective attention and anxiety: A cognitive-motivational perspective. Handbook of cognition and emotion, 145-170. Murray, S. M., Brown, C. S., Kaye, W. H., & Wierenga, C. E. (2021). Anhedonia in eating disorders. In Anhedonia: Preclinical, Translational, and Clinical Integration (pp. 219-236). Cham: Springer International Publishing. http://dx.doi.org/10.1002/0470013494.ch8 Pool, E., Sennwald, V., Delplanque, S., Brosch, T., & Sander, D. (2016). Measuring wanting and liking from animals to humans: A systematic review. Neuroscience & Biobehavioral Reviews, 63, 124-142. doi: 10.1016/j.neubiorev.2016.01.006. Ramacciotti, C. E., Perrone, P., Coli, E., Burgalassi, A., Conversano, C., Massimetti, G., & Dell’Osso, L. (2011). Orthorexia nervosa in the general population: a preliminary screening using a self-administered questionnaire (ORTO-15). Eating and weight disorders-Studies on Anorexia, Bulimia and Obesity, 16, e127-e130. http://dx.doi.org/10.1007/BF03325318 Rinck, M., & Becker, E. S. (2006). Spider fearful individuals attend to threat, then quickly avoid it: evidence from eye movements. Journal of abnormal psychology, 115(2), 231. http://dx.doi.org/10.1037/0021-843X.115.2.231 Robinson, T. E., & Berridge, K. C. (2008). The incentive sensitization theory of addiction: some current issues. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1507), 3137-3146. http://dx.doi.org/10.1098/rstb.2008.0093 Stormark, K. M., & Torkildsen, Ø. (2004). Selective processing of linguistic and pictorial food stimuli in females with anorexia and bulimia nervosa. Eating behaviors, 5(1), 27-33. http://dx.doi.org/10.1016/j.eatbeh.2003.07.002 Stott, N., Fox, J. R., & Williams, M. O. (2021). Attentional bias in eating disorders: A meta‐review. International Journal of Eating Disorders, 54(8), 1377-1399. http://dx.doi.org/10.1002/eat.23560 Strahler, J., Moser, L., & Wachten, H. (2023). Orthorexic Eating and Addictions: Links with Substance Use, Behavioral Addictions, and Research Gaps. In Eating Disorders (pp. 1327-1352). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-031-16691-4_79 Tang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. Todd, J., Wilson, E., Coutts-Bain, D., & Clarke, P. J. (2022). Attentional bias variability and its association with psychological and health symptoms: A meta-analysis. Neuroscience & Biobehavioral Reviews, 140, 104812. http://dx.doi.org/10.1016/j.neubiorev.2022.104812 van Ens, W., Schmidt, U., Campbell, I. C., Roefs, A., & Werthmann, J. (2019). Test-retest reliability of attention bias for food: Robust eye-tracking and reaction time indices. Appetite, 136, 86-92. http://dx.doi.org/10.1016/j.appet.2019.01.020 Werthmann, J., Jansen, A., & Roefs, A. (2015). Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples. Proceedings of the Nutrition Society, 74(2), 99-114. http://dx.doi.org/10.1017/S0029665114001451 Werthmann, J., Simic, M., Konstantellou, A., Mansfield, P., Mercado, D., van Ens, W., & Schmidt, U. (2019). Same, same but different: Attention bias for food cues in adults and adolescents with anorexia nervosa. International Journal of Eating Disorders, 52(6), 681-690. http://dx.doi.org/10.1002/eat.23064 Zagaria, A., Vacca, M., Cerolini, S., Ballesio, A., & Lombardo, C. (2022). Associations between orthorexia, disordered eating, and obsessive–compulsive symptoms: A systematic review and meta‐analysis. International Journal of Eating Disorders, 55(3), 295-312. http://dx.doi.org/10.1002/eat.23654 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Example of explicit liking and wanting ratings. B. Pictures of food used in the study.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/3aef5cb45153953ba152d083.jpeg"},{"id":93537564,"identity":"42fa0d3c-357c-4be7-8cfb-8548b5fe36da","added_by":"auto","created_at":"2025-10-15 02:06:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104921,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots showing the ratings distributions for HighON and LowON groups for the healthy and unhealthy food pictures. Dotted lines represent the interquartile range of the distribution; the horizontal line in the middle represents the median. The width of each plot shows the density of the data. The bottom and top of each whisker represents the lowest and highest data point, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/85afe122b7c7a95f035babc9.png"},{"id":93537565,"identity":"4a20a3ff-6498-4ced-8744-034181b2c652","added_by":"auto","created_at":"2025-10-15 02:06:50","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69213,"visible":true,"origin":"","legend":"\u003cp\u003eA. On the left, a pair of unhealthy food versus non-food is shown. On the right, a pair of healthy food versus non-food. B. An example of congruent and incongruent trials.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/e2d889afce8817b4ebadc177.jpeg"},{"id":93537567,"identity":"aa9cee18-2c22-4ccb-b2e6-2c5d9567e0af","added_by":"auto","created_at":"2025-10-15 02:06:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23190,"visible":true,"origin":"","legend":"\u003cp\u003eBar charts with standard error bars displaying RT bias for healthy (HF) and unhealthy (UHF) food in individuals with HighON and LowON.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/65a286056bf41576c526b2ed.png"},{"id":93539258,"identity":"9562ccdc-e4dc-4a80-8166-e9fd80156f47","added_by":"auto","created_at":"2025-10-15 02:14:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60148,"visible":true,"origin":"","legend":"\u003cp\u003eBar charts with standard error bars displaying Direction and Duration bias for healthy (HF) and unhealthy (UHF) food in individuals with HighON and LowON.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/8d46c19694d5acac9cef1768.png"},{"id":93541473,"identity":"bf7a3b56-d295-4478-be2e-4d6dfdcecc35","added_by":"auto","created_at":"2025-10-15 02:30:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6614417/v1/9fd8c8e8-7c0a-4426-8de7-dee7d0e88bef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Food perception in Orthorexia Nervosa: worry or craving?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOrthorexia nervosa (ON) describes a condition characterised by obsessional concern with eating healthy foods (Dunn \u0026amp; Bratman, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Horovitz \u0026amp; Argyrides, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Individuals with ON manifest concern regarding the quality and composition of meals and engage in rigid eating behaviour in an attempt to achieve health through \"food purity\". They follow severe dietary rules that result in excessive avoidance of \"unhealthy\" foods. For instance, cases of patients eating only seeds or unprocessed, organic plant-based foods, or even only animal products have been described (Barthels et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, they spend a significant amount of time thinking, choosing, controlling food sources, buying, preparing, and consuming food (approximately 4\u0026ndash;5 hours a day; Barthels et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Eating foods considered \"unhealthy\" causes feelings of guilt, disgust, fear of becoming ill, and self-punishment behaviours. On the other hand, the feeling of having control over food is positively perceived and reinforces this behaviour, which, in the long term, can affect health as well as social relationships, leading to nutrient deficiency, malnutrition, and social isolation (Koven \u0026amp; Abry, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To date, the prevalence of ON behaviours has been reported to range from 7 to 57% in the general population, from 29 to 34.9% among Italian university students and higher in some \u0026ldquo;risk groups,\u0026rdquo; such as healthcare professionals and dietitians (general population: Ramacciotti et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; university students: Dell\u0026rsquo;osso et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; dieticians: Kinzl et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eON is not an official disorder and is not mentioned in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and its conceptualization has been debated (e.g., Meule et al., 2021; Haman et al., 2015). Crucially, ON overlaps with various existing psychiatric disorders. For instance, ON is believed to share characteristics with anorexia nervosa, including perfectionism, restrictive eating patterns, food-related preoccupation (e.g., Gramaglia et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and obsessive-compulsive disorders, because orthorexic subjects manifest intrusive thoughts (similar to obsessions) about food, fear of health contamination and impurities associated with distress (Zagaria et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ON has also been proposed to belong to the \u0026lsquo;\u0026lsquo;behavioural\u0026rsquo;\u0026rsquo; addiction disorder category, together with pathological gambling, internet addiction, and overtraining syndrome (Marazziti et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly to these conditions, ON appears to be characterized by repetitive dysfunctional habits that induce a progressive impairment of affective, working, and social spheres, and by loss of control over behaviour despite its negative consequences (Strahler et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, additional research on ON, particularly concerning its similarities and differences with these conditions, is essential for enhancing our understanding of the cognitive processes underlying ON and developing more effective diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eMultiple forms of psychopathology have been found to be associated with dysregulation of attentional processes (Mansell et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and attentional bias (AB), particularly, consisting of the selective allocation of attention to salient stimuli. AB to negative disease-salient information perceived as threatening has been described in anxiety disorders (e.g., Todd et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and it is believed to reflect high levels of worry. For instance, individuals who fear spiders or snakes show both an attentional bias toward words or pictures signalling these stimuli (for example, Lavy et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and a time course characterized by an early attentional bias toward threat stimuli, followed by intentional avoidance of these same stimuli (Rinck et al., 2006). Similar patterns have been observed with caloric food or body pictures in anorexia nervosa (Stott et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, in addiction disorders, AB may reflect craving and hedonic motivation (Field \u0026amp; Cox, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Werthmann et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Craving is described as the irresistible desire to engage in specific behaviour and is the result of learning processes during which relevant cues become associated with the rewarding features of addictive behaviour (Drummond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). An AB for drugs or disorder-related stimuli has been observed in individuals with drug addiction (Field \u0026amp; Cox, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), problem gambling (Farr et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), internet gaming disorders (Chia et al., 2020), and binge eating disorders (Stott et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, only two studies have investigated AB for healthy/unhealthy food cues in orthorexic individuals (Albery et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and both suggest that an attentional bias toward healthy food words characterizes ON. In the first study, ON tendencies were associated with increased attentional preference for healthy food-related words in a modified Stroop task (Albery et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar findings were obtained in a second study, in which the allocation of attention to healthy/unhealthy food-related words was evaluated using a Dot Probe task (Albery et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the dot-probe task, a pair of stimuli is presented simultaneously on the screen. In target trials, one of these stimuli is considered relevant (healthy food words such as \u003cem\u003esalad\u003c/em\u003e or unhealthy food words such as \u003cem\u003esalami\u003c/em\u003e), while the other is neutral (for instance, \u003cem\u003esalary\u003c/em\u003e). Immediately after the stimulus pair is removed, a probe (a dot) is presented at one of the two stimulus locations, and the participants are asked to indicate the location of the probe. Participants are assumed to react faster to a stimulus that appears in a location where their attention is already focused than to a stimulus appearing in an unattended location. Using this task in a sample of vegan/vegetarians, Albery et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) confirmed a significant correlation between attentional bias for healthy food words and ON tendencies. Despite these interesting results, no information on the time course or hedonic motivation toward healthy/unhealthy foods was provided in these studies.\u003c/p\u003e \u003cp\u003eIn this research, we aimed to investigate both reward responsiveness and attentional bias toward healthy and unhealthy food pictures in individuals with varying levels of ON through two distinct studies. Study 1 examined the ratings of liking and wanting for healthy and unhealthy foods. Liking and wanting are two essential components of the brain's reward system. \"Liking\" refers to the pleasurable experience associated with consuming food, driven by hedonic hotspots in the limbic brain structures. In contrast, \"wanting\" involves incentive salience\u0026mdash;a motivational process that enhances the appeal of palatable foods and can trigger cravings. This process is regulated by dopamine-driven mesocorticolimbic networks (Berridge, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Study 2 investigates AB toward healthy and unhealthy food cues through a dot-probe task. In a subsample of participants in Study 2, we employed an eye-tracking methodology to record gaze patterns during the task. Attentional engagement (early processing) can be assessed by measuring the proportion of trials in which the first saccade is made toward a stimulus. Similarly, we recorded the total gaze duration on a stimulus as a measure of maintained attention (late processing) (Kerr-Gaffney et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Both indices can be considered as indicators of visual attentional bias. If Orthorexia is akin to addiction disorders, increased wanting for healthy foods and an AB for these stimuli should be observed. On the other hand, similarly to individuals with anorexia, people with ON may demonstrate negative evaluations of unhealthy food compared to healthy foods pictures on an explicit level, and aberrant attentional processing toward them, because of their preoccupation with and/or fear of food being considered unhealthy. Their preoccupation may be represented by initial engagement, followed by avoidance, as observed in anorexia nervosa.\u003c/p\u003e"},{"header":"2. Study 1. Reward responsiveness toward healthy and unhealthy food cues and ON","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and Procedure\u003c/h2\u003e \u003cp\u003eParticipants were eligible for inclusion if they met the following criteria: 1) aged 18 or older, 2) native Italian speakers, 3) absence of neurological/psychiatric disease, and 4) absence of diabetes and food restrictions (e.g., vegetarianism, allergy, or others). A total of 166 eligible participants completed the survey on the Qualtrics platform (age\u003csub\u003emean\u003c/sub\u003e 24.8 years, age\u003csub\u003eSD\u003c/sub\u003e=7.6 age\u003csub\u003erange\u003c/sub\u003e=18–60; 48.8% female). Participants were recruited via social media platforms and university mailing lists. The study ads informed participants about the study's aim and provided a link to complete the study on Qualtrics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.qualtrics.com\u003c/span\u003e\u003cspan address=\"http://www.qualtrics.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The participants were not compensated for their participation and were informed that they were free to withdraw from participation at any time. The Ethics Committee of the University of Bologna approved the study (protocol no. 0062640 of 08/03/2023). After obtaining informed consent, the participants provided information about their demographic characteristics, health status, and eating habits. We asked them to report their hunger level (from 0 = not hungry to 7 = extremely hungry), hours from their last meals and their weight and height to compute their body mass index (BMI). Participants then completed an explicit evaluation task assessing reward responsiveness toward healthy and unhealthy foods, and questionnaires on ON and eating disorder risk.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Task and Questionnaires\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eExplicit Evaluation of Foods\u003c/em\u003e. This task was aimed at investigating reward responsiveness to healthy and unhealthy foods. In this task, 20 pictures [10 healthy (HF) and 10 non-healthy (NHF) foods] were presented and participants were asked to respond to the following questions: 1) “How pleasant would it be to experience a mouthful of this food now?” (Liking); 2) \"How much do you want this food now?\" (Wanting). In addition, we presented two additional questions: 3) \"How frequently do you eat this food?\" and 4) \"How healthy is it?\" Participants indicated their responses on a 100-point visual analogue scale anchored at each end with “not at all” and “extremely” (for a similar procedure, see Aiello et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Food pictures were taken from FRIDA and FoodPics databases (Foroni et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Blechert et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Healthy and unhealthy food images were selected through an online questionnaire filled out by an independent sample of 20 participants (six females, age\u003csub\u003emean\u003c/sub\u003e 20.3 age\u003csub\u003eSD\u003c/sub\u003e 1.9). Importantly, healthy and unhealthy foods were matched for caloric content [t (18) = 0.82 \u003cem\u003ep\u003c/em\u003e = 0.42], frequency of consumption [t (18) = -1.98 \u003cem\u003ep\u003c/em\u003e = 0.06], and palatability [t (18) = 1.54 \u003cem\u003ep\u003c/em\u003e = 0.14], whereas they significantly differed in perceived healthiness [t (18) = -8.05 \u003cem\u003ep\u003c/em\u003e = 0.00). See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eQuestionnaires.\u003c/em\u003e The \u003cem\u003eDüsseldorf Orthorexia Scale\u003c/em\u003e (DOS) (Barthels et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cerolini et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) evaluates orthorexic tendencies and is composed of 10 items on a 4-point Likert scale, from 1 (it does not correspond to my behaviour at all) to 4 (it corresponds well to my behaviour). The maximum score is 40, with higher scores indicating more pronounced orthorexic behaviour. A score ≥ 30 is considered indicative of the presence of ON, while a score between 25 and 29 indicates the risk of ON. The Eating Attitudes Test (EAT-26) (Garner \u0026amp; Garfinkel, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; Dotti et al., 1998) assesses the risk of eating disorders. Responses to questions 1–25 are rated on a four-point scale: \"always\" is assigned 3 points, \"usually\" is assigned 2 points, \"often\" is assigned 1 point, and \"sometimes,\" \"rarely,\" or \"never\" receive 0 points. Reverse scoring is applied to item 26. A score ≥ 20 is considered indicative of disordered eating tendencies.\u003c/p\u003e\n\u003ch3\u003e2.3 Statistical analyses\u003c/h3\u003e\n\u003cp\u003eParticipants were divided into two groups based on the median DOS value (median value = 20), with 83 participants in the HighON and 83 in the LowON groups. Normality was assessed using the Shapiro-Wilk test. Parametric data were analysed using 2 × 2 repeated-measures ANOVAs, where the type of food (two levels: HF and NHF) was included as a within-subject factor and the group of participants (two levels: HighON and LowON) as the between-subject factor. Non-parametric variables were analysed using the Mann-Whitney U and the Wilcoxon signed-rank tests. Pearson correlation coefficient was used to examine the relationships between DOS and food ratings. All analyses were performed using Statistica software (Statsoft, Tulsa, USA). Violin plots were made using online platform SRplot (Tange et al., 2023).\u003c/p\u003e \u003cp\u003e2.4 Results\u003c/p\u003e\n\u003ch3\u003e2.4.1 Sample characteristics\u003c/h3\u003e\n\u003cp\u003eOverall, the average DOS score was 19.5 (SD = 5.4), with a range of 10–38. The average EAT-26 score was 8.1 (SD = 8.7), with a range of 0–44. The participants’ BMI ranged between 17 and 31.3 (Mean = 22.7, SD = 2.8). Finally, participants reported a mean hunger score of 2.8 (SD = 1.9), with a range of 0–7.\u003c/p\u003e\n\u003ch3\u003e2.4.2 Explicit ratings\u003c/h3\u003e\n\u003cp\u003eParticipants in the HighON and LowON groups did not differ in BMI (U = 3309, Z = 0.44, \u003cem\u003ep\u003c/em\u003e = 0.66), hunger level [U = 3145, Z = 0.96, \u003cem\u003ep\u003c/em\u003e = 0.33], or hours since their last meal (U = 3218, Z = 0.73, \u003cem\u003ep\u003c/em\u003e = 0.46). However, a significant difference was observed in EAT-26 scores (U = 1651, Z = -5.79, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e \u003cp\u003eThe ANOVA on liking ratings revealed a significant interaction between group and food category [F (1, 164) = 10.34, \u003cem\u003ep\u003c/em\u003e = 0.001], while other main effects were not significant: group [F (1, 164) = 2.46, \u003cem\u003ep\u003c/em\u003e = 0.11], category [F (1, 164) = 2.29, \u003cem\u003ep\u003c/em\u003e = 0.13]. Post-hoc analyses showed that individuals in the HighON group liked unhealthy foods less than healthy foods (\u003cem\u003ep\u003c/em\u003e = 0.001), while no such difference was found in the LowON group (\u003cem\u003ep\u003c/em\u003e = 0.23). Additionally, individuals in the HighON group liked unhealthy foods less than those in the LowON group (\u003cem\u003ep\u003c/em\u003e = 0.02), whereas no significant difference emerged for healthy foods (\u003cem\u003ep\u003c/em\u003e = 0.90). The analysis of wanting ratings revealed that overall wanting was higher for healthy foods than unhealthy foods [F (1, 164) = 5.3, \u003cem\u003ep\u003c/em\u003e = 0.02] while the main group effect was not significant [F (1, 164) = 0.18, \u003cem\u003ep\u003c/em\u003e = 0.6]. Moreover, a significant interaction between group and food category also emerged [F (1, 164) = 7.03, \u003cem\u003ep\u003c/em\u003e = 0.008]. Post-hoc analyses indicated that individuals in the HighON group wanted unhealthy food less than healthy food (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while no such difference was observed in the LowON group (\u003cem\u003ep\u003c/em\u003e = 0.81). No differences were found between groups in wanting ratings (\u003cem\u003eps\u003c/em\u003e \u0026gt; 0.24).\u003c/p\u003e \u003cp\u003eMoreover, participants reported consuming healthy foods more frequently than unhealthy foods (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Individuals in the HighON group reported consuming healthy foods more frequently than those in the LowON group (U = 2407.5, Z = -3.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; = 0.01), whereas no difference in the consumption of unhealthy foods was found between the two groups (U = 2944.5, Z = 1.61, \u003cem\u003ep\u003c/em\u003e = 0.10). Finally, as expected, healthy foods were rated as healthier than unhealthy foods (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and no significant difference emerged in the perceived healthiness of the two food categories between the groups [healthy foods: U = 3402, Z = 0.13, \u003cem\u003ep\u003c/em\u003e = 0.89; unhealthy foods: U = 3420, Z = 0.07, \u003cem\u003ep\u003c/em\u003e = 0.93].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePearson correlation results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHF-\u003cem\u003eLiking\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUHF-\u003cem\u003eLiking\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHF-\u003cem\u003eWanting\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUHF-\u003cem\u003eWanting\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHF-\u003cem\u003eConsumption\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUHF-\u003cem\u003eConsumption\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e = 0.07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e "},{"header":"3. Study 2: AB toward healthy and unhealthy foods in ON","content":"\u003ch2\u003e3.1 Participants and Procedure\u003c/h2\u003e\u003cp\u003eThis study was conducted using two independent samples. The first sample performed the task online, the second sample was tested in the laboratory, and eye movements were recorded. The inclusion criteria for both samples were the same as in Study 1 except for age. Given the attentional nature of the task, only participants aged between 18–45 years were included. Sample 1 was composed of 42 participants (age\u003csub\u003emean\u003c/sub\u003e: 23.1, age\u003csub\u003eSD\u003c/sub\u003e=4.3, 45% female), whereas sample 2 was composed of 31 participants (age\u003csub\u003emean\u003c/sub\u003e: = 23.9, age\u003csub\u003eSD\u003c/sub\u003e = 1.8, 29% female). None of the participants were compensated for their participation, and they were informed that they were free to withdraw from participation at any time. They provided informed consent to participate in the study, which was in line with the Declaration of Helsinki and approved by the Ethics Committee of Bologna University (Protocol no. 0062640 of 08/03/2023). For what concerns the online procedure, after obtaining informed consent, participants provided information about their demographic characteristics, health status, and eating patterns. Participants then completed a dot-probe task delivered over the Internet using the free software Jatos (Lange et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Finally, they completed questionnaires on ON and the risk of eating disorders. Regarding laboratory procedures, we began the session by administering the dot-probe task and measuring eye movements. We then asked the participants to complete the two questionnaires.\u003c/p\u003e\u003ch3\u003e3.2 Task and Questionnaires\u003c/h3\u003e\u003cp\u003e\u003cem\u003eDot-Probe Task.\u003c/em\u003e The task followed a procedure similar to the one employed by van Ens et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Initially, a fixation cross was displayed in the centre of the screen for 100 milliseconds, followed by the presentation of a picture pair on either side of the centre for 500 milliseconds. Afterwards, a dot (1 × 1 degrees of visual angle) appeared either on the left or right side, and participants were required to indicate the dot's location by pressing the \"S\" key if it was on the left and the \"L\" key if it was on the right. A total of 30 pairs of images were used, with 20 being critical pairs (10 healthy foods versus non-food and 10 unhealthy foods versus non-food) and 10 being filler pairs (presenting two non-food pictures). Each pair was presented four times, resulting in a total of 120 trials. The trials were divided into two blocks of 60 trials, separated by a short break, with one block containing images of healthy foods and the other containing images of unhealthy foods. Blocks were counterbalanced across subjects. The position of the probe was counterbalanced. The food pictures used in this study were the same as those used in study one, while the non-food pictures were obtained from the FRIDA and FoodPics databases (Foroni et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Blechert et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Food and Non-food stimuli in the critical pairs were matched as closely as possible for colour and complexity (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for an example).\u003c/p\u003e\u003cp\u003eIn the lab version, participants were seated 64 cm from the screen. Reaction time (RT) data was collected using Matlab software while a Tobii eye tracker was used to record eye movements.\u003c/p\u003e\u003cp\u003eFor what concerns RT data, incorrect responses, RTs exceeding the mean individual RT of the participant plus or minus two standard deviations were excluded from subsequent analysis. Three indices were calculated. \u003cem\u003eRT bias scores\u003c/em\u003e were calculated by subtracting RTs of congruent trials (probe replaced the food image) from RTs of the incongruent trials (probe replaced the control image). Positive values indicate attention bias towards food images; negative values indicate attention bias away from food images and towards control images (van Ens et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Regarding eye movement data, a fixation was defined as a period lasting at least 100 ms in which no saccades or blinks occurred (Werthmann et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cem\u003eDirection bias\u003c/em\u003e was calculated as the percentage of the total initial fixations on food. A score above 50% indicates a direction bias towards food, whereas a score below 50% indicates a direction bias away from food. Direction bias is considered a measure of \u003cem\u003einitial attentional orientation\u003c/em\u003e. Gaze duration bias, which is considered a measure of \u003cem\u003emaintained attention\u003c/em\u003e, was calculated using the average gaze duration to a food image across all trials as a proportion of the average gaze duration to all images (food and control). A duration bias score of \u0026gt; 0.5, 0.5, or \u0026lt; 0.5 represents maintained attention to food pictures, no bias and maintained attention to control images respectively (see Doolan et al., 2014; van Ens et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eQuestionnaires\u003c/h2\u003e\u003cp\u003eParticipants completed the DOS and the EAT-26 as in Study 1.\u003c/p\u003e\u003ch2\u003e3.3 Statistical analyses\u003c/h2\u003e\u003cp\u003eAs no significant differences were found in reaction times (RTs) between individuals who performed the task online and those who did so in the laboratory for healthy congruent trials, healthy incongruent trials, unhealthy congruent trials, and unhealthy incongruent trials (\u003cem\u003eps\u003c/em\u003e \u0026gt; 0.09), the data from both groups were combined for the RT analysis. The data were then divided into two groups based on the median DOS value (median value = 19), with 37 participants in the HighON group and 36 in the LowON group. A similar approach was used for analysing gaze data, where the data were divided into two groups based on the median DOS value (median value = 19), with 16 participants in the HighON group and 15 in the LowON group. Since the variables were not normally distributed, as assessed by the Shapiro-Wilk test, the Mann-Whitney U test and the Wilcoxon signed-rank test were used for data analysis. Spearman correlation analyses were conducted to examine the relationship between ON and RT bias, gaze direction bias, and gaze duration bias. All analyses were performed using Statistica software (StatSoft, Tulsa, USA). The bar charts were constructed using online platform SRplot (Tange et al., 2023).\u003c/p\u003e\u003ch2\u003e3.4 Results\u003c/h2\u003e\u003cp\u003e3.4.1 Reaction time data\u003c/p\u003e\u003ch2\u003eSample characteristics\u003c/h2\u003e\u003cp\u003e The new sample was composed of 73 participants. In the pooled sample, the average score on the DOS was 19.7 (SD 5.7), with a range of 10–38. Furthermore, the average score on the EAT-26 was 6.5 (SD 7.3), with a range of 0–31. In the sample of participants who performed the dot probe and in which eye movements were tracked, the average score on the DOS was 18.8 (SD 4.5) with a range of 12–28. Furthermore, the average score on the EAT-26 was 4.3 (SD 4.6), with a range of 0–19.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents the reaction times and bias indices for all participants and separately for the two groups (HighON and LowON) for both healthy and unhealthy trials.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHealthy Food (HF)\u003c/p\u003e \u003cp\u003eMean SD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnhealthy Food (UHF)\u003c/p\u003e \u003cp\u003eMean SD\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en = 73\u003c/em\u003e\u003c/p\u003e \u003cp\u003eRT Congruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e427.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e415.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT Incongruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT bias score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHighON\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT Congruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e411.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT Incongruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e407.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBias index score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLowON\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT Congruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e443.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e424.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT Incongruent trials (ms)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e453.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e447.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBias index score\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eIn trials presenting healthy foods, there was no significant difference between congruent and incongruent trials for either the HighON or LowON groups (\u003cem\u003eps\u003c/em\u003e \u0026gt; 0.30). In both groups, the bias scores did not differ significantly from zero (\u003cem\u003eps\u003c/em\u003e \u0026gt; 0.30). Individuals in the HighON group were faster than those in the LowON group in both congruent and incongruent trials (Congruent: U = 480.5, Z = -2.04, \u003cem\u003ep\u003c/em\u003e = 0.04; Incongruent: U = 428.5, Z = -2.62, \u003cem\u003ep\u003c/em\u003e = 0.008). No significant difference emerged between the two groups in the bias index score (U = 563.5, Z = -1.13, \u003cem\u003ep\u003c/em\u003e = 0.25).\u003c/p\u003e\u003cp\u003eIn trials presenting unhealthy foods, individuals in the LowON group were slower in incongruent trials compared to the congruent one (\u003cem\u003ep\u003c/em\u003e = 0.007) and exhibited a bias score significantly different from zero (\u003cem\u003ep\u003c/em\u003e = 0.007), indicating an attentional bias (AB) toward unhealthy food items. In contrast, individuals in the HighON group did not show a significant difference between congruent and incongruent trials (\u003cem\u003ep\u003c/em\u003e = 0.9), and the bias score did not differ from zero (\u003cem\u003ep\u003c/em\u003e = 0.92). Individuals in the HighON group were faster than those in the LowON group in the incongruent trials (U = 443.5, Z = -2.45, \u003cem\u003ep\u003c/em\u003e = 0.01), but no difference emerged in the congruent trials (U = 529.5, Z = -1.5, \u003cem\u003ep\u003c/em\u003e = 0.13). Importantly, the bias scores significantly differed between individuals in the LowON and those in the HighON groups (U = 480.5, Z = -2.04, \u003cem\u003ep\u003c/em\u003e = 0.04). See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eNo correlations were found between DOS scores and RT bias scores for unhealthy foods (rho=-0.20 \u003cem\u003ep\u003c/em\u003e = 0.08) and RT bias scores for healthy foods (rho=-0.07 \u003cem\u003ep\u003c/em\u003e = 0.53).\u003c/p\u003e\u003cp\u003e3.4.3 Gaze bias\u003c/p\u003e\u003cp\u003eGaze direction and duration bias scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for all participants and separately for the HighON and LowON groups.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHealthy Food (HF)\u003c/p\u003e \u003cp\u003eMean SD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUnhealthy Food (UHF)\u003c/p\u003e \u003cp\u003eMean SD\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en = 31\u003c/em\u003e\u003c/p\u003e \u003cp\u003eGaze direction bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaze duration bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHighON\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaze direction bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaze duration bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLowON\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaze direction bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaze duration bias\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eFor gaze direction bias, individuals in the LowON group did not show any significant difference between healthy and unhealthy foods (\u003cem\u003ep\u003c/em\u003e = 0.42). Wilcoxon matched-pairs test analysis revealed that the direction bias scores for healthy and unhealthy food images were not significantly different from a test score of 50% (HF: p = 0.70, UHF: p = 0.10). Similarly, the HighON group did not show any significant difference between healthy and unhealthy foods in gaze direction bias (\u003cem\u003ep\u003c/em\u003e = 0.67), and the direction bias scores were not significantly different from a test score of 50% (HF: \u003cem\u003ep\u003c/em\u003e = 0.93, UHF: \u003cem\u003ep\u003c/em\u003e = 0.50). No difference emerged between groups for either healthy food (U = 109.5 Z = 0.41 p = 0.68) or unhealthy food (U = 75 Z = 1.78 p = 0.07). See Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFor gaze duration bias, no significant difference emerged between healthy and unhealthy food images in individuals in the LowON group (\u003cem\u003ep\u003c/em\u003e = 0.39). Furthermore, the duration bias score for healthy food images was not significantly different from a test score of 0.5 (\u003cem\u003ep\u003c/em\u003e = 0.53), while the duration bias score for unhealthy food images was significantly different from 0.5 (\u003cem\u003ep\u003c/em\u003e = 0.05), indicating that participants in this group spent more time looking at unhealthy images compared to non-food images. In individuals in the HighON group, no significant difference was found between healthy and unhealthy gaze duration bias (\u003cem\u003ep\u003c/em\u003e = 0.16), and the duration bias scores did not differ significantly from a test score of 0.5 (HF: p = 0.46, UHF: p = 0.25). When comparing the two groups, no significant differences were found for both healthy (U = 114 Z = 0.23 \u003cem\u003ep\u003c/em\u003e = 0.81) and unhealthy food (U = 108 Z=-0.47 \u003cem\u003ep\u003c/em\u003e = 0.63). See Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA Spearman correlation revealed a significant correlation between DOS and gaze direction bias for unhealthy foods. See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSpearman Correlations\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaze Direction bias HF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaze Direction bias UF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaze Duration bias HF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGaze Duration bias UF\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOrthorexia nervosa (ON) is characterized by an excessive preoccupation with healthy eating, manifesting in stringent dietary restrictions and the avoidance of perceived unhealthy foods. Through two distinct studies, we investigated what is the predominant component in characterizing ON: the craving and hedonic motivation for healthy foods or the concern regarding foods deemed unhealthy. Our findings indicated that elevated ON tendencies were associated with lower reward responsiveness and a pattern of attentional avoidance of unhealthy foods. These findings are discussed below.\u003c/p\u003e \u003cp\u003eIn Study 1, we measured liking and wanting for healthy and unhealthy foods and ON tendencies. According to Berridge's model (Berrridge, 2009), two separate but interconnected processes govern motivation: liking, which pertains to hedonic preference or palatability, and wanting, which represents the motivational drive towards a particular food. Liking and wanting can manifest themselves at both explicit conscious and implicit unconscious levels, and interact with homeostatic circuitry to regulate eating behaviour (Berridge et al., 2009). Using explicit ratings, we found that ON tendencies were associated with both lower liking and wanting for unhealthy foods. Additionally, individuals with higher ON scores reported consuming healthy foods more frequently. These findings suggest that the reduced intake of unhealthy foods and the increased consumption of healthy foods typical of ON may be driven by both a diminished motivational drive and a lack of hedonic enjoyment of unhealthy food, which are not perceived as rewarding. This pattern aligns with observations in anorexia nervosa and high-calorie foods (see Lloy \u0026amp; Steinglass, 2018 for a review). Notably, anhedonia has been shown to facilitate food avoidance (Murray et al., 2022).\u003c/p\u003e \u003cp\u003eIn Study 2, we employed an implicit task, i.e. a dot-probe task and recorded eye movements to investigate attentional processes associated with the processing of healthy and unhealthy food in ON. The findings seem to suggest that ON is characterized by a pattern of attentional avoidance of unhealthy foods. First, while individuals with lower ON scores exhibited an attentional bias toward unhealthy foods, consistent with their higher palatability, this pattern was not observed in individuals with higher ON scores. Second, higher ON scores were associated with a greater proportion of initial fixations on unhealthy food items. However, individuals with higher ON scores did not fixate on unhealthy food more than non-food stimuli, unlike those with lower ON scores. These suggest that individuals with higher ON scores show an initial heightened vigilance of unhealthy food cues followed by an attentional shift away from these stimuli. These findings are inconsistent with those of Albery et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who reported an attentional bias toward healthy food in ON. A potential explanation for this discrepancy could be that, unlike these two studies, we utilized pictorial stimuli. Pictures may indeed be more salient (e.g., Stormark et al., 2004), convey more affective information (De Houwer \u0026amp; Hermans, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), and demonstrate higher ecological validity when compared to words. Importantly, it has been shown that attentional bias outcomes toward food vary depending on whether words or pictures are used, and whether they are high or low-calorie (Freijy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Interestingly, the attentional pattern that we observed aligns with observations in patients with anorexia nervosa (Giel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Werthmann et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meregalli et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and corresponds well with the vigilance-avoidance model, according to which threatening stimuli automatically capture attention at initial stage but, in later stages, they are avoided as a strategy to reduce arousal and anxiety (Mogg \u0026amp; Bradley, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Crucially, this pattern may even contribute to reinforcing food restriction in anorexia nervosa (Werthmann et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a matter of fact, the optimal conceptualization of ON has been debated, with rationales proposed for considering the condition as an eating disorder (e.g. Gramaglia et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a form of obsessive disorder (e.g. Zagaria et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), or even a form of behavioural addiction (e.g. Strahler et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study suggests that this group exhibits reward system dysregulation specifically toward unhealthy foods, manifested as altered attentional salience. ON is characterized by a pattern of low reward responsiveness to unhealthy foods rather than heightened responsiveness to healthy foods. On a more implicit level, no AB to healthy foods is observed. Instead, the study seem to reveal a pattern of avoidance of unhealthy foods similar to the one observed in anxiety and anorexia (Stott et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which suggests that shared cognitive mechanisms may underlie these disorders. Further studies are required to investigate motivational and attentional alterations in ON. Similarly, more comparisons of the neurocognitive profiles across these pathological conditions would be helpful in further understanding the commonalities between disorders.\u003c/p\u003e \u003cp\u003eFinally, it must be pointed out the present study had several limitations that warrant consideration. Firstly, reward responsiveness and attentional biases toward food were assessed in two independent samples, with a different proportion of females and males. More studies employing implicit and explicit tasks in the same sample of participants should be performed to clarify whether unhealthy/healthy foods evoke conflicting implicit and explicit evaluations in individuals with ON that differ from those without ON. In addition, our experiments involved individuals with ON tendencies. Although several subjects in the study demonstrated ON on a validated scale, no formal diagnostic criteria for ON currently exist, which limits the generalizability of the findings.\u003c/p\u003e \u003cp\u003eIn sum, our observations may suggest the hypothesis that altered motivation and visual orientation may contribute to impaired function in ON. However, future research should investigate whether and how motivational and attentional bias could serve as a potential marker of ON, and how targeting this bias could be applied in treatment interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding source\u003c/h2\u003e \u003cp\u003eThis work was supported by Progetti PRIMA - Call 2022 Section 1 Agri-food IA- Grant Agreement No: [2231] [CIPROMED] granted to A.T.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.A.: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization. A.M.U.: Investigation; Writing - Review \u0026amp; Editing; G.O.: Resources; Writing - Review \u0026amp; Editing; A.T.: Conceptualization, Resources, Funding acquisition, Supervision, Writing - Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Michele Marzocchi for his technical assistance.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAiello, M., Eleopra, R., Foroni, F., Rinaldo, S., \u0026amp; Rumiati, R. I. (2017). Weight gain after STN-DBS: The role of reward sensitivity and impulsivity. Cortex, 92, 150-161. http://dx.doi.org/10.1016/j.cortex.2017.04.005\u003c/li\u003e\n\u003cli\u003eAlbery, I. P., Michalska, M., Moss, A. C., \u0026amp; Spada, M. (2020). Selective attentional bias to food-related stimuli in healthy individuals with characteristics towards orthorexia nervosa. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 25, 1225-1233. http://dx.doi.org/10.1007/s40519-019-00755-z\u003c/li\u003e\n\u003cli\u003eAlbery, I. P., Shove, E., Bartlett, G., Frings, D., \u0026amp; Spada, M. M. (2022). Individual differences in selective attentional bias for healthy and unhealthy food-related stimuli and social identity as a vegan/vegetarian dissociate \u0026ldquo;healthy\u0026rdquo; and \u0026ldquo;unhealthy\u0026rdquo; orthorexia nervosa. Appetite, 178, 106261. http://dx.doi.org/10.1016/j.appet.2022.106261\u003c/li\u003e\n\u003cli\u003eBarthels, F., Fischer, M., Keskini, R., Sch\u0026ouml;l, A. M., \u0026amp; Pietrowsky, R. (2024). The various facets of orthorexic eating behavior: five case reports of individuals with supposed orthorexia nervosa. Journal of Eating Disorders, 12(1), 31. http://dx.doi.org/10.1186/s40337-024-00988-z\u003c/li\u003e\n\u003cli\u003eBarthels, F., Meyer, F., \u0026amp; Pietrowsky, R. (2015). Orthorexic eating behavior. A new type of disordered eating. Ernahrungs Umschau, 62(10), 156-161.\u003c/li\u003e\n\u003cli\u003eBerridge, K. C. (2009). \u0026lsquo;Liking\u0026rsquo;and \u0026lsquo;wanting\u0026rsquo;food rewards: brain substrates and roles in eating disorders. Physiology \u0026amp; behavior, 97(5), 537-550. http://dx.doi.org/10.1016/j.physbeh.2009.02.044\u003c/li\u003e\n\u003cli\u003eBlechert, J., Meule, A., Busch, N. A., \u0026amp; Ohla, K. (2014). Food-pics: an image database for experimental research on eating and appetite. Frontiers in psychology, 5, 617. http://dx.doi.org/10.3389/fpsyg.2014.00617\u003c/li\u003e\n\u003cli\u003eCerolini, S., Vacca, M., Zagaria, A., Donini, L. M., Barbaranelli, C., \u0026amp; Lombardo, C. (2022). Italian adaptation of the D\u0026uuml;sseldorf Orthorexia Scale (I-DOS): psychometric properties and prevalence of orthorexia nervosa among an Italian sample. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 1-9. http://dx.doi.org/10.1007/s40519-021-01278-2\u003c/li\u003e\n\u003cli\u003eChia, D. X., \u0026amp; Zhang, M. W. (2020). A scoping review of cognitive bias in internet addiction and internet gaming disorders. International Journal of Environmental Research and Public Health, 17(1), 373. http://dx.doi.org/10.3390/ijerph17010373\u003c/li\u003e\n\u003cli\u003eDell\u0026rsquo;Osso, L., Carpita, B., Muti, D., Cremone, I. M., Massimetti, G., Diadema, E., ... \u0026amp; Carmassi, C. (2018). Prevalence and characteristics of orthorexia nervosa in a sample of university students in Italy. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 23, 55-65. http://dx.doi.org/10.1007/s40519-017-0460-3\u003c/li\u003e\n\u003cli\u003eDoolan, K. J., Breslin, G., Hanna, D., \u0026amp; Gallagher, A. M. (2015). Attentional bias to food-related visual cues: is there a role in obesity?. Proceedings of the Nutrition Society, 74(1), 37-45. http://dx.doi.org/10.1017/S002966511400144X\u003c/li\u003e\n\u003cli\u003eDotti, A., \u0026amp; Lazzari, R. (1998). Validation and reliability of the Italian EAT-26. Eating and weight disorders-studies on anorexia, bulimia and obesity, 3, 188-194. http://dx.doi.org/10.1007/BF03340009\u003c/li\u003e\n\u003cli\u003eDrummond, D. C. (2001). Theories of drug craving, ancient and modern. Addiction, 96(1), 33-46.\u003c/li\u003e\n\u003cli\u003eDunn, T. M., \u0026amp; Bratman, S. (2016). On orthorexia nervosa: A review of the literature and proposed diagnostic criteria. Eating behaviors, 21, 11-17. http://dx.doi.org/10.1046/j.1360-0443.2001.961333.x\u003c/li\u003e\n\u003cli\u003eFarr, Z., Broomfield, N. M., \u0026amp; Coventry, K. R. (2023). A Systematic Review of Attentional Bias in Problem Gambling. Journal of Gambling Studies, 1-27. http://dx.doi.org/10.1007/s10899-023-10260-9\u003c/li\u003e\n\u003cli\u003eField, M., \u0026amp; Cox, W. M. (2008). Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug and alcohol dependence, 97(1-2), 1-20. http://dx.doi.org/10.1016/j.drugalcdep.2008.03.030\u003c/li\u003e\n\u003cli\u003eForoni, F., Pergola, G., Argiris, G., \u0026amp; Rumiati, R. I. (2013). The FoodCast research image database (FRIDa). Frontiers in human neuroscience, 7, 51. http://dx.doi.org/10.3389/fnhum.2013.00051\u003c/li\u003e\n\u003cli\u003eFreijy, T., Mullan, B., \u0026amp; Sharpe, L. (2014). Food-related attentional bias. Word versus pictorial stimuli and the importance of stimuli calorific value in the dot probe task. Appetite, 83, 202-208. http://dx.doi.org/10.1016/j.appet.2014.08.037\u003c/li\u003e\n\u003cli\u003eGarner DM, Garfinkel P . The eating attitude test: an index of the symptoms of anorexia nervosa. Psychol Med 1979; 9: 273\u0026ndash;279.\u003c/li\u003e\n\u003cli\u003eGiel, K. E., Friederich, H. C., Teufel, M., Hautzinger, M., Enck, P., \u0026amp; Zipfel, S. (2011). Attentional processing of food pictures in individuals with anorexia nervosa\u0026mdash;An eye-tracking study. Biological psychiatry, 69(7), 661-667. http://dx.doi.org/10.1016/j.biopsych.2010.09.047\u003c/li\u003e\n\u003cli\u003eGramaglia, C., Brytek-Matera, A., Rogoza, R., \u0026amp; Zeppegno, P. (2017). Orthorexia and anorexia nervosa: two distinct phenomena? A cross-cultural comparison of orthorexic behaviours in clinical and non-clinical samples. BMC psychiatry, 17, 1-5. http://dx.doi.org/10.1186/s12888-017-1241-2\u003c/li\u003e\n\u003cli\u003eH\u0026aring;man, L., Barker-Ruchti, N., Patriksson, G., \u0026amp; Lindgren, E. C. (2015). Orthorexia nervosa: An integrative literature review of a lifestyle syndrome. International journal of qualitative studies on health and well-being, 10(1), 26799. http://dx.doi.org/10.3402/qhw.v10.26799\u003c/li\u003e\n\u003cli\u003eHorovitz, O., \u0026amp; Argyrides, M. (2023). Orthorexia and orthorexia nervosa: A comprehensive examination of prevalence, risk factors, diagnosis, and treatment. Nutrients, 15(17), 3851. http://dx.doi.org/10.3390/nu15173851\u003c/li\u003e\n\u003cli\u003eHouwer, J. D., \u0026amp; Hermans, D. (1994). Differences in the affective processing of words and pictures. Cognition \u0026amp; Emotion, 8(1), 1-20. http://dx.doi.org/10.1080/02699939408408925\u003c/li\u003e\n\u003cli\u003eKerr‐Gaffney, J., Harrison, A., \u0026amp; Tchanturia, K. (2019). Eye‐tracking research in eating disorders: A systematic review. International Journal of Eating Disorders, 52(1), 3-27. http://dx.doi.org/10.1002/eat.22998\u003c/li\u003e\n\u003cli\u003eKim, H. Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative dentistry \u0026amp; endodontics, 38(1), 52-54. http://dx.doi.org/10.5395/rde.2013.38.1.52\u003c/li\u003e\n\u003cli\u003eKinzl, J. F., Hauer, K., Traweger, C., \u0026amp; Kiefer, I. (2006). Orthorexia nervosa in dieticians. Psychotherapy and psychosomatics, 75(6), 395. http://dx.doi.org/10.1159/000095447\u003c/li\u003e\n\u003cli\u003eKoven, N. S., \u0026amp; Abry, A. W. (2015). The clinical basis of orthorexia nervosa: emerging perspectives. Neuropsychiatric disease and treatment, 385-394. http://dx.doi.org/10.2147/NDT.S61665\u003c/li\u003e\n\u003cli\u003eLange, K., K\u0026uuml;hn, S., \u0026amp; Filevich, E. (2015). \u0026quot;Just another tool for online studies\u0026rdquo; (JATOS): An easy solution for setup and management of web servers supporting online studies. PloS one, 10(6), e0130834. http://dx.doi.org/10.1371/journal.pone.0130834\u003c/li\u003e\n\u003cli\u003eLavy, E., Van den Hout, M., \u0026amp; Arntz, A. (1993). Attentional bias and spider phobia: Conceptual and clinical issues. Behaviour research and therapy, 31(1), 17-24. doi: 10.1016/0005-7967(93)90038-v\u003c/li\u003e\n\u003cli\u003eMansell, W., Harvey, A., Watkins, E. R., \u0026amp; Shafran, R. (2008). Cognitive behavioral processes across psychological disorders: A review of the utility and validity of the transdiagnostic approach. International Journal of Cognitive Therapy, 1(3), 181-191. https://doi.org/10.1521/ijct.2008.1.3.179\u003c/li\u003e\n\u003cli\u003eMarazziti, D., Presta, S., Baroni, S., Silvestri, S., \u0026amp; Dell\u0026apos;Osso, L. (2014). Behavioral addictions: a novel challenge for psychopharmacology. CNS spectrums, 19(6), 486-495. http://dx.doi.org/10.1017/S1092852913001041\u003c/li\u003e\n\u003cli\u003eMeregalli, V., Tenconi, E., Madan, C. R., Som\u0026agrave;, E., Meneguzzo, P., Ceccato, E.,... \u0026amp; Collantoni, E. (2023). Beyond body image: what body schema and motor imagery can tell us about the way patients with anorexia nervosa experience their body. Psychiatry and Clinical Neurosciences, 77(2), 94-101. http://dx.doi.org/10.1111/pcn.13501\u003c/li\u003e\n\u003cli\u003eMeule, A., \u0026amp; Voderholzer, U. (2021). Orthorexia nervosa\u0026mdash;It is time to think about abandoning the concept of a distinct diagnosis. Frontiers in Psychiatry, 12, 640401. http://dx.doi.org/10.3389/fpsyt.2021.640401\u003c/li\u003e\n\u003cli\u003eMogg, K., \u0026amp; Bradley, B. P. (1999). Selective attention and anxiety: A cognitive-motivational perspective. Handbook of cognition and emotion, 145-170.\u003c/li\u003e\n\u003cli\u003eMurray, S. M., Brown, C. S., Kaye, W. H., \u0026amp; Wierenga, C. E. (2021). Anhedonia in eating disorders. In Anhedonia: Preclinical, Translational, and Clinical Integration (pp. 219-236). Cham: Springer International Publishing. http://dx.doi.org/10.1002/0470013494.ch8\u003c/li\u003e\n\u003cli\u003ePool, E., Sennwald, V., Delplanque, S., Brosch, T., \u0026amp; Sander, D. (2016). Measuring wanting and liking from animals to humans: A systematic review. Neuroscience \u0026amp; Biobehavioral Reviews, 63, 124-142. doi: 10.1016/j.neubiorev.2016.01.006.\u003c/li\u003e\n\u003cli\u003eRamacciotti, C. E., Perrone, P., Coli, E., Burgalassi, A., Conversano, C., Massimetti, G., \u0026amp; Dell\u0026rsquo;Osso, L. (2011). Orthorexia nervosa in the general population: a preliminary screening using a self-administered questionnaire (ORTO-15). Eating and weight disorders-Studies on Anorexia, Bulimia and Obesity, 16, e127-e130. http://dx.doi.org/10.1007/BF03325318\u003c/li\u003e\n\u003cli\u003eRinck, M., \u0026amp; Becker, E. S. (2006). Spider fearful individuals attend to threat, then quickly avoid it: evidence from eye movements. Journal of abnormal psychology, 115(2), 231. http://dx.doi.org/10.1037/0021-843X.115.2.231\u003c/li\u003e\n\u003cli\u003eRobinson, T. E., \u0026amp; Berridge, K. C. (2008). The incentive sensitization theory of addiction: some current issues. Philosophical Transactions of the Royal Society B: Biological Sciences, 363(1507), 3137-3146. http://dx.doi.org/10.1098/rstb.2008.0093\u003c/li\u003e\n\u003cli\u003eStormark, K. M., \u0026amp; Torkildsen, \u0026Oslash;. (2004). Selective processing of linguistic and pictorial food stimuli in females with anorexia and bulimia nervosa. Eating behaviors, 5(1), 27-33. http://dx.doi.org/10.1016/j.eatbeh.2003.07.002\u003c/li\u003e\n\u003cli\u003eStott, N., Fox, J. R., \u0026amp; Williams, M. O. (2021). Attentional bias in eating disorders: A meta‐review. International Journal of Eating Disorders, 54(8), 1377-1399. http://dx.doi.org/10.1002/eat.23560\u003c/li\u003e\n\u003cli\u003eStrahler, J., Moser, L., \u0026amp; Wachten, H. (2023). Orthorexic Eating and Addictions: Links with Substance Use, Behavioral Addictions, and Research Gaps. In Eating Disorders (pp. 1327-1352). Cham: Springer International Publishing. http://dx.doi.org/10.1007/978-3-031-16691-4_79\u003c/li\u003e\n\u003cli\u003eTang D, Chen M, Huang X, Zhang G, Zeng L, Zhang G, Wu S, Wang Y. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023 Nov 9;18(11):e0294236. doi: 10.1371/journal.pone.0294236. \u003c/li\u003e\n\u003cli\u003eTodd, J., Wilson, E., Coutts-Bain, D., \u0026amp; Clarke, P. J. (2022). Attentional bias variability and its association with psychological and health symptoms: A meta-analysis. Neuroscience \u0026amp; Biobehavioral Reviews, 140, 104812. http://dx.doi.org/10.1016/j.neubiorev.2022.104812\u003c/li\u003e\n\u003cli\u003evan Ens, W., Schmidt, U., Campbell, I. C., Roefs, A., \u0026amp; Werthmann, J. (2019). Test-retest reliability of attention bias for food: Robust eye-tracking and reaction time indices. Appetite, 136, 86-92. http://dx.doi.org/10.1016/j.appet.2019.01.020\u003c/li\u003e\n\u003cli\u003eWerthmann, J., Jansen, A., \u0026amp; Roefs, A. (2015). Worry or craving? A selective review of evidence for food-related attention biases in obese individuals, eating-disorder patients, restrained eaters and healthy samples. Proceedings of the Nutrition Society, 74(2), 99-114. http://dx.doi.org/10.1017/S0029665114001451\u003c/li\u003e\n\u003cli\u003eWerthmann, J., Simic, M., Konstantellou, A., Mansfield, P., Mercado, D., van Ens, W., \u0026amp; Schmidt, U. (2019). Same, same but different: Attention bias for food cues in adults and adolescents with anorexia nervosa. International Journal of Eating Disorders, 52(6), 681-690. http://dx.doi.org/10.1002/eat.23064\u003c/li\u003e\n\u003cli\u003eZagaria, A., Vacca, M., Cerolini, S., Ballesio, A., \u0026amp; Lombardo, C. (2022). Associations between orthorexia, disordered eating, and obsessive\u0026ndash;compulsive symptoms: A systematic review and meta‐analysis. International Journal of Eating Disorders, 55(3), 295-312. http://dx.doi.org/10.1002/eat.23654\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Attentional bias, Reward responsiveness, Eating disorders, Addiction, Healthiness","lastPublishedDoi":"10.21203/rs.3.rs-6614417/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6614417/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOrthorexia nervosa (ON) is characterized by an excessive preoccupation with eating healthy foods. This disorder shares similarities with various pathological conditions, including anorexia nervosa and addictive behaviours. The current research aimed to explore whether ON is driven by a fear of unhealthy foods, similar to anorexia nervosa, or by a craving for healthy foods, akin to the patterns observed in addictive disorders.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn an online study (Study 1), participants (n\u0026thinsp;=\u0026thinsp;166 adults, mean age\u0026thinsp;=\u0026thinsp;24.8 years, SD\u0026thinsp;=\u0026thinsp;7.6 years, 48.8% female) reported liking, wanting, and frequency of intake of 20 healthy and 20 unhealthy foods. Additionally, they completed the Dusseldorf Orthorexia Scale, while BMI, hunger level, and risk of eating disorders were collected. In Study 2, participants (n\u0026thinsp;=\u0026thinsp;73 adults, mean age\u0026thinsp;=\u0026thinsp;23.4 years, SD\u0026thinsp;=\u0026thinsp;3.5 years, 37% female) completed questionnaires on ON and the risk of eating disorders, and a visual probe task with images of healthy and unhealthy foods. Eye movements were also recorded for a subset of participants in the laboratory.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results suggested that individuals with higher ON tendencies exhibit decreased responsiveness to rewards and demonstrate a pattern of attentional avoidance toward unhealthy foods. This indicates that ON behaviors may be driven by a fear of unhealthy foods.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results underscore the importance of elucidating the role of attentional and motivational mechanisms in ON and their clinical implications.\u003c/p\u003e","manuscriptTitle":"Food perception in Orthorexia Nervosa: worry or craving?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:06:45","doi":"10.21203/rs.3.rs-6614417/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a96f47da-0f47-4e47-89b1-5b890f730b06","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T14:29:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:06:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6614417","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6614417","identity":"rs-6614417","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}