Profiling Decision-Making Mechanisms in Binge Eating Disorder

Profiling Decision-Making Mechanisms in Binge Eating Disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Profiling Decision-Making Mechanisms in Binge Eating Disorder Emily Colton, Chanel Agosta, Holly Carey, Emily Giddens, Brittany Noy, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8254234/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2026 Read the published version in Journal of Eating Disorders → Version 1 posted 7 You are reading this latest preprint version Abstract Objective Binge eating disorder (BED) is a highly prevalent mental disorder associated with metabolic complications, reduced functioning, and poor quality of life, resulting in significant disease burden. Disordered decision-making is thought to drive behaviour in BED, but the specific mechanisms underlying this dysfunction remain unclear. Methods This study compared multiple aspects of decision-making between people with BED and higher weight (BED, n = 57), a control group matched by body mass index (BMI) without binge eating (HWC, n = 54), and lower weight controls (LWC, n = 54). We applied profile analyses to cognitive measures capturing three stages of decision-making: preference formation, choice implementation, and feedback processing. Additionally, we examined domains of psychological functioning shown to interact with cognitive mechanisms during decision-making – negative emotionality, maladaptive eating-related tendencies, and impulsive traits. Results We found generalised decision-making dysfunction in individuals with BED compared to the LWC but not the HWC group. However, BMI did not explain these differences. Poor overall psychological functioning clearly distinguished BED from both control groups, with elevated depressive symptoms and lack of perseverance emerging as key psychological characteristics. Discussion By mapping BED profiles across multiple components of decision-making, our findings indicate that domain-general cognitive dysfunction is an important mechanism in BED, alongside more well-recognised psychological features. These findings may further efforts to refine aetiological models of binge eating, providing more holistic and explanatory theories. They may also form a foundation for novel interventions and personalised approaches to treatment. Binge Eating Disorder Cognitive Functioning Decision-Making Impulsivity Psychological traits Figures Figure 1 Figure 2 Figure 3 1. Introduction Binge eating disorder (BED) is a serious and common mental health condition, with a lifetime prevalence estimate of 1.3% globally (Giel et al., 2022 ). BED is associated with high incidence of co-occurring psychiatric symptoms, somatic health complications, and poor quality of life (Hilbert, 2019 ), low rates of detection and treatment engagement (Bryant et al., 2022 ), and suboptimal treatment outcomes (Linardon, 2018 ; Monteleone et al., 2022 ). Thus, individual and societal disease burdens are considerable (Ahmed et al., 2024 ). BED is characterised by recurrent binge eating episodes; that is, when people experience loss-of-control over their eating behaviour and consume a large quantity of food in a short time, accompanied by negative self-focused thoughts and significant distress (APA, 2022 ). BED can thus be characterised by cycles of unwanted behaviours, cognitions, and emotions that are repeated despite negative consequences; however, the specific mechanisms underpinning these symptoms are unclear (Giel et al., 2022 ). Mechanisms of treatment action are also uncertain, hindering the optimisation of existing treatments and the development of novel, or more personalised interventions (Hilbert, 2023 ). Research that clarifies critical BED mechanisms is therefore a key priority for the field (Aouad et al., 2023 ). Several theories have attempted to explain critical mechanisms in BED onset and maintenance, which broadly fall into two categories: those focused on cognitive systems, and those emphasising emotional processes (Burr et al., 2025b ; Neyland et al., 2020 ). In terms of cognitive systems, one approach to conceptualising the mechanisms by which observed alterations in cognitive functioning influence BED is to consider core processes that unfold before, during, and after binge-eating episodes (Bodell & Racine, 2022 ; Morelli et al., 2022 ). One such model maps nine cognitive domains across three stages of decision-making (Ernst & Paulus, 2005 ; Verdejo-Garcia et al., 2018 ). The first stage involves Forming Preferences , which includes identifying and gathering information about choice options ( Reflection ), and attributing subjective value to their potential outcomes - both known ( Risk Evaluation ) and unknown ( Uncertainty Evaluation ). Thus, heightened value and salience of food rewards (Vrieze & Leenaerts, 2023 ), and difficulties identifying effective choice strategies in the context of uncertainty or ambiguity (Aloi et al., 2020 ) may promote binge eating. During Choice Implementation , resources are allocated to realise selected options ( Action Initiation ), while inhibiting competing actions ( Cognitive Inhibition ), or tempting but suboptimal choices ( Delay Discounting ). Here, enhanced motivation to pursue food rewards (Forester et al., 2024 ; Racine et al., 2019 ), along with difficulty inhibiting responses to food-related cues (Leehr et al., 2018 ; Schag et al., 2013 ), may work together to drive loss-of-control eating behaviour. In the final stage, Feedback Processing involves monitoring positive and negative feedback ( Reinforcement Learning ) and sampling recent and more distant decision outcomes ( Memory ), then using these sources of feedback to optimise ongoing decisions ( Consistency ) and adjust subsequent choices ( Cognitive Flexibility/Shifting ). Binge eating may thus be reinforced by enhanced tendencies to learn from food rewards combined with reduced learning from punishing outcomes (Schaefer & Steinglass, 2021 ; Voon et al., 2015 ; Waltmann et al., 2024 ), and difficulties integrating feedback, particularly changes from positive to negative feedback, to optimise decision-making (Banca et al., 2016 ; Kollei et al., 2018 ). This results in difficulties changing behaviour despite distress, guilt, and shame, and other negative consequences (Wollenhaupt et al., 2019 ). This multi-stage decision-making approach has proven valuable as a framework to meta-analyse findings from prior studies of cognition in BED (Colton et al., 2023 ). However, this multi-stage perspective has yet to be used to assess decision-making mechanisms in BED in a comprehensive study. While examining specific cognitive domains of decision-making in isolation has resulted in significant knowledge gain, it has also left important gaps in our understanding of these mechanisms (Dennison et al., 2022 ). Firstly, reviews have highlighted significant inconsistencies between studies (Carr et al., 2021 ; Waltmann et al., 2021 ). This has been particularly evident in the Cognitive Inhibition domain, which has received considerable attention, but produced few clear-cut outcomes (Smith et al., 2018 ). Similarly, though some studies have indicated BED is associated with significant tendencies to prioritise smaller-sooner rewards over larger-later benefits, others have reported null findings, or indicate Delay Discounting interacts with other motivational and individual characteristics (Blume et al., 2019 ; Colton et al., 2025 ; Steward et al., 2017 ). Reviews have also highlighted methodological issues, including variability in sampling approaches, differences in demographic profiles, and co-occurring conditions, that are not well accounted for when interpreting and integrating findings (Lavagnino et al., 2016 ; Leenaerts et al., 2022 ; Smith et al., 2018 ). In particular, individual characteristics (including age, sex, and education) and BED features (such as higher BMI, co-occurring mood symptoms, and disease severity and duration) have been identified as significant moderators of decision-making functioning and as confounds in prior studies. There have also been difficulties translating findings, such that interventions designed to target individual cognitive processes in isolation have had little success in reducing BED symptoms (Brockmeyer et al., 2019 ; Giel et al., 2017a ; Turton et al., 2018 ). Thus, approaches that consider cognitive functions from an integrated, system-based perspective may be key to moving the field forward (Bodell & Racine, 2022 ; Schaefer & Steinglass, 2021 ) In addition to interactions among domains of cognitive functioning, it is also important that decision-making does not occur in a vacuum; rather, these processes are modulated by emotional contexts (Goschke, 2014 ; Morelli et al., 2022 ). Emotion-focused theories of BED emphasise higher levels of negative emotionality and difficulties regulating emotions – such as depressive symptoms, states of stress or anxiety, or discomfort arising from interpersonal conflict, dietary restriction and cravings, and negative self-evaluations – as critical mechanisms in BED (Blackburn et al., 2006 ; Haedt-Matt & Keel, 2011 ; Heatherton & Baumeister, 1991 ; Stice et al., 1996 ; Wilfley et al., 1997 ). From these perspectives, Preference Formation is influenced by expectancies that food and eating will soothe, provide escape from, or regulate these aversive emotional states (Burr et al., 2025a ). Emotion-focused perspectives also emphasise trait-level tendencies to act impulsively in the context of low mood, such that emotional states interact with decision-making processes to influence binge eating (Kenny et al., 2019 ; Leenaerts et al., 2023 ; Pearson et al., 2015 ). Recent studies utilising ecological momentary assessment (EMA) methods further indicate that cognitive and emotional mechanisms dynamically interact throughout the Choice Implementation and Feedback Processing stages of decision-making. For example, trait delay discounting and momentary attentional bias before binge eating episodes have been shown to strengthen relationships between affective states and subsequent episodes (Smith et al., 2019 ; Smith et al., 2020 ). Similarly, negative affect has been shown to modulate attentional and reinforcement learning processes during and after binge eating episodes, to shape subsequent eating-related decisions (Schaefer et al., 2021 ; Smith et al., 2020 ). Such evidence has proven valuable in extending theoretical conceptions of binge eating, improving our understanding of how emotional and cognitive mechanisms interact in unique ways for different individuals (Burton & Abbott, 2019 ; Schaefer et al., 2023 ). For example, a recent study used a latent profile analysis approach to group individuals with BED according to patterns of psychological risk and maintenance factors, identifying four sub-profiles of BED with distinct combinations of psychological drivers of binge eating (O'Loghlen et al., 2025 ). A similar process was used to personalise psychological treatment, showing potential to improve outcomes (Levinson et al., 2021 ). These advances in emotional and psychological phenotyping contrast with the relative lack of progress in relation to identifying and addressing critical BED mechanisms in the cognitive domain (Allott et al., 2025 ; Brucar et al., 2025 ). In the present study, we aimed to concurrently assess cognitive mechanisms in BED from an integrated multi-stage decision-making perspective, alongside key psychological mechanisms that influence choice processes, using a profile analysis approach. To isolate mechanisms in BED as distinct from those associated with higher weight, we incorporated a higher-weight control group matched by BMI to the clinical group (HWC), and a lower-weight control group (LWC). To address additional common confounds and limitations in prior research (Leenaerts et al., 2022 ; Smith et al., 2018 ; Steward & Berner, 2020 ), all groups were matched according to sex, age, and education, and we included individuals with history of depressive disorders. We hypothesised the BED group would differ significantly from control groups according to their overall level of functioning and the shape or pattern of functioning across their multi-stage decision-making profile (incorporating behavioural indices of uncertainty evaluation , cognitive inhibition, delay discounting, reinforcement learning, consistency , and cognitive flexibility/set-shifting ), and across their psychological profile (comprising self-reported measures of depression, anxiety, and stress symptoms, tendencies toward restrained, emotional, and external eating behaviour, and five facets of impulsivity). 2. Methods 2.1 Participants We employed a cross-sectional, case-control design. We aimed to recruit 150 participants across three groups – 50 with BED and higher BMI (BED), 50 with higher BMI without BED (HWC), and 50 lower-weight controls (LWC), which was deemed sufficient to detect medium effect size differences between the study groups according to a priori power analyses. Participants were recruited using META advertisements, mailings to internal databases, and the TrialFacts research recruitment agency. Inclusion criteria for the BED group were currently meeting DSM-5-TR criteria for BED or OSFED-BED (supplementary materials, Table S1), assessed by SCID-5-TR semi-structured clinical interviews (First et al., 2015). Inclusion criteria for all groups were age 18-60, ability to attend in-person study sessions in Notting Hill, VIC, Australia, and BMI >30 (BED and HWC) or 18.5-25 (LWC). Exclusion criteria for all groups were history of psychosis, bipolar disorder, neurological disease, traumatic brain injury, seizure, or intellectual disability, and current major depressive episode or suicidality. Additional exclusion criteria for control groups were history of any eating disorder. In total, 165 eligible participants completed the study between April 2023 and August 2025 (125 [75.75%] female; mean age 43.90 [ SD 11.24], range 18.37-60.76). Analyses confirmed groups were well-matched according to sex, age, and education, and that BED and HWC groups were also matched according to BMI (Table 1). Groups were also confirmed to differ significantly on self-reported binge eating symptom severity, according to the Binge Eating Scale (BES; (Gormally et al., 1982). Groups were found to differ based on race, with a lower proportion of white participants and a higher proportion of Asian participants in the LWC compared to BED and HWC groups, p < .05. Table 1. Participant characteristics Mean (SD) Range BED ( n = 57) HWC ( n =54) LWC ( n = 54) Group comparisons Age 45.2 (11.8) 18.8-60.5 44.5 (10.8) 18.4-59.9 41.8 (11.0) 19.3-60.8 F (2,162) = 1.4, p = .25 Education 15.8 (2.51) 12-21 16.3 (3.24) 6-25 15.7 (2.78) 11-25 F (2,162) = 0.35, p = .71 BMI 36.1 (5.68) 27.9-51.5 35.2 (5.19) 29.1-60.1 22.6 (2.05) 18.7-26.0 F (2,162) = 147, p < .0001 b BES score 25.23 (6.83) 10-40 12.72 (6.73) 0-28 8.11 (6.28) 0-26 F (2,162) = 99.9, p < .0001 b Sex n(%) Female 43 (75.4) Male 14 (24.6) Female 42 (77.8) Male 12 (22.2) Female 40 (74.1) Male 14 (25.9) Race n(%) White Asian Multiple/Other 39 (68.4) 11 (19.3) 6 (10.6) 35 (64.8) 13 (24.1) 2 (3.7) 24 (44.4) 25 (46.9) 4 (7.4) ED Diagnoses n(%) BED OSFED-BED 35 (61.4) 22 (38.6) 0 (0) 0 (0) 0 (0) 0 (0) DD Diagnoses n(%) Any None Past only Current only Current & past 43 (75.4) 14 (24.6) 30 (52.6) 3 (5.3) 10 (17.5) 13 (24.1) 41 (75.9) 12 (2.2) 0 (0) 1 (1.8) 9 (16.7) 45 (81.8) 9 (16.7) 0 (0) 0 (0) Notes: Age – calculated from date-of-birth on the day participants completed the eligibility questionnaire. Education - self-reported years of completed education (participants were prompted that in Australia, 13 years equates to completing high school). BMI - estimated BMI calculated from self-reported height and weight was used during eligibility screening; BMI measured using a Tanita BC-545N segmental body composition monitor was used in all data analyses. BES total score - higher scores indicate greater binge eating symptomology. Sex - biological sex assigned at birth. We also acquired self-reported gender identity but used the binary sex variable in our analyses to aid comparison to prior research. Additionally, all participants reported their gender identity as congruent with their biological sex. Race – self-identified race was missing in six cases; Multiple/Other includes five individuals who self-identified as more than one race, one individual who identified as Pacific Islander, and six individuals who selected the option ‘other’. ED Diagnoses – eating disorder diagnoses according to SCID-V-TR clinical interview. DD Diagnoses – depressive disorder diagnoses (including MDE, MDD, PDD, and PMDD) according to SCID-V-TR clinical interview. a Tukey’s HSD post-hoc tests confirmed estimated BMI (self-reported in the eligibility survey) differed significantly between BED and LWC, and between HWC and LWC groups, but not between BED and HWC groups. b Tukey’s HSD post-hoc tests confirmed all pairwise comparisons were significant. 2.2 Materials 2.2.1 Screening Measures A demographics survey collected data on participant age (calculated from date-of-birth), biological sex, gender identity, years of education, and estimated BMI (initially calculated from self-reported height and weight and subsequently corroborated via body composition scale), along with data on inclusion and exclusion criteria. The Binge Eating Scale (BES) (Gormally et al., 1982) assessed frequency and severity of binge eating behaviours, cognitions, and emotions. Participants responded to 16 items, selecting the most appropriate from 3 (2 items) or 4 (14 items) response options (e.g., “I feel incapable of controlling urges to eat. I have a fear of not being able to stop eating voluntarily”). Continuous total scores range from 0-46 and are categorised as indicating none/minimal (0-17), mild (18-26), or moderate/severe (27-46) binge eating symptomology. In this study, Cronbach’s alpha internal consistency was 0.95 (excellent). 2.2.2 Decision-making Measures Decision-making variables were informed by our hypothesised stages of dysfunctional decision-making in BED (Figure 1). Uncertainty Evaluation was defined as optimal responding in a two-alternative information accumulation task, accounting for speed/accuracy trade-off. To parse out attentional inhibition and response inhibition components of Cognitive Inhibition (Hamilton et al., 2015; Nigg, 2017), we applied signal detection theory (Green & Swets, 1966) to calculate d’ and criterion ( c ) values from a cued Go/No-go task. Delay Discounting rate k quantified the extent to which monetary rewards were devalued as time to receiving them increases (Odum, 2011). A general measure of Consistency was operationalised as the total score in a Probabilistic Reversal Learning task (Verdejo-Garcia et al., 2018), while Reinforcement Learning was calculated from the same task as proportion of trials in which the same option was selected after a win (win-stay) less the proportion of trials in which the alternative option was selected after a loss (lose-shift) (Dennison et al., 2022). Finally, Cognitive Flexibility/Set-Shifting scores incorporated response time and accuracy to quantify the ability to switch between target dimensions (Phillipou & Miles, 2025). If necessary, domains were reverse-scored such that higher scores always indicated better decision-making functioning. Decision-making stages, domains, tasks, and behavioural outcome calculations are summarised in supplementary materials, section S2.2. Five tasks were used to assess multi-stage decision-making functioning. The Cognitive Impulsivity Suite (CIS) (Verdejo-Garcia et al., 2021) assesses multiple domains of cognitive impulsivity in an integrated, gamified environment with a cohesive ‘Wild West’ theme. The tasks have well-established psychometric properties and relationships to real-world impulsive behaviours. Three tasks are completed in a single online session with the task order randomised across participants: Caravan Spotter (two-alternative choice); Bounty Hunter (cued go/no-go) and Prospector’s Gamble (probabilistic reversal-learning). All tasks have a similar structure with instruction and practice phases followed by four testing blocks, with participant choices captured by keyboard responses. In Caravan Spotter , participants are instructed to safely guide a caravan its journey by correctly identifying animals and landscape features. On each trial, participants must determine whether an initially ambiguous (pixelated) image belongs to one of two categories, using the A (e.g., buffalo) or L (e.g., cougar) keys. The target image is presented at 50% pixelation and gradually disambiguated over a response window of 2000ms. The available reward for accurate responding also declines over this window, such that players are incentivised to respond both quickly and accurately. The pairs of categories change after each block of 40 trials. In Bounty Hunter , participants are instructed to shoot bandits (respond to ‘go’ stimuli) and avoid shooting sheriffs (withhold responses to ‘no-go’ stimuli) by pressing the space bar as quickly as possible. Each block has 60 trials with a response window of 700ms, and in each trial, the stimulus onset is preceded by a cue (a daytime or nighttime ‘camp’ setting). Stimulus onset asynchronies and the alignment of cues and stimuli are varied to establish prepotent responses and to challenge selective and sustained attention. Participants are rewarded 50 points for correct responses and correct withholds and are deducted 50 points for commission errors (no-go stimuli responses) and omission errors (go-stimuli withholds). In Prospector’s Gamble , participants are instructed to select the ‘luckiest’ of two gold miners by determining which is more likely to return rewards rather than losses. On each trial, participants must select the prospector located on the left or right of the screen by using the A or L keys within a response window of 1000ms, and the prospectors randomly switch screen locations between trials. Choosing the correct prospector results in positive feedback in 80% of trials in blocks 1 & 2, and in 70% of trials in blocks 3 & 4. Feedback may be basic (+50 points reward, 0 punishment), or enhanced (+200 points reward, -100 punishment). Contingencies are reversed after each block of 40 trials, which participants must also determine based on the feedback received. The Monetary Choice Questionnaire (MCQ) (Kirby et al., 1999) assessed Delay Discounting . In this task, participants responded to 27 hypothetical choices between smaller immediate and larger delayed monetary options, with varying dollar values and time delays between items (e.g., “Would you prefer $25 today, or $60 in 14 days?”). Discounting rate k is calculated for each participant ranging from -0.603 to -3.801 with values closer to zero indicating greater tendencies to select the smaller, sooner options (i.e., steeper discounting). The NIH Toolbox Dimensional Change Card Sort (DCCS) test assessed Cognitive Flexibility/Set-Shifting (Gershon et al., 2010). Target images were presented that vary on dimensions of shape and colour, and participants are instructed to match the target to two test pictures based on a specified dimension, then after varying numbers of trials, on the opposing dimension. To standardise motor movements, participants are instructed to return their index finger to a reference point on the desk (“home base”) after each response tap on the iPad screen. After the instruction and practice phases, the test phase contains 30 items. NIH Toolbox tasks have well-established reliability, validity, and age group norms. The computed score incorporates accuracy and response time, such that higher scores indicate faster and more accurate responding. 2.2.3 Psychological Trait Measures The Depression, Anxiety, and Stress Scales (DASS-21) (Henry & Crawford, 2005; Lovibond & Lovibond, 1995) assessed depression, anxiety, and stress symptoms. Participants rated the extent to which 21 items (7 for each subscale) applied to them in the past 7 days on a five-point Likert scale ranging from 0 did not apply to me at all to 3 applied to be very much or most of the time (e.g., “I felt down-hearted and blue”). Summed scores were calculated for each subscale, which are categorised as: Depression - Normal (0-4), Mild (5-6), Moderate, (7- 10), Severe (11-13), Extremely Severe (14+); Anxiety - Normal (0- 3), Mild (4), Moderate, (5- 7), Severe (8- 9), Extremely Severe (10+); Stress - Normal (0-7), Mild (8- 9), Moderate, (10-12), Severe (13-16), Extremely Severe (17+). Cronbach’s alpha internal consistency was calculated for each subscale, with Depression a = 0.92 (excellent), Anxiety a = 0.81 (good), and Stress a = 0.89 (good). The Dutch Eating Behaviour Questionnaire (van Strien et al., 1986) examined three eating-related traits. Participants rated 33 items on a five-point Likert scale ranging from 1 never to 5 very often (e.g., “If food tastes good to you, do you eat more than usual?”). Scores are averaged across the items such that each subscale is scored from 1 to 5, with higher scores indicating greater tendencies toward each trait. Cronbach’s alpha was excellent for all subscales: Emotional Eating a = 0.98, External Eating a = 0.91, and Restrained Eating a = 0.93. The Short UPPS-P Impulsive Behavior Scale (S-UPPS-P) (Cyders et al., 2014; Lynam, 2013) was used to measure five dimensions of trait impulsivity. Participants responded to 20 items on scale from 1 strongly agree to 4 strongly disagree (e.g., “When I am upset I often act without thinking”). Items from the (Lack of) Perseverance and (Lack of) Premeditation subscales were reverse coded before subscale scores were calculated (sum of four items, ranging from 4-16), such that higher scores always indicated more impulsive behaviour. Cronbach’s alpha was calculated for each subscale: Negative Urgency a = 0.80 (good); (Lack of) Perseverance a = 0.64 (questionable); (Lack of) Premeditation a = 0.74 (acceptable); Sensation Seeking a = 0.66 (questionable); Positive Urgency a = 0.79 (acceptable). 2.3 Procedure All procedures were approved by Monash University Human Research Ethics Committee (MUHREC), reference 34518. After reviewing the explanatory statement and providing informed consent, participants completed the eligibility questionnaire and BES online. Participants who met initial inclusion criteria then completed SCID-5-TR semi-structured clinical interviews over Zoom with a member of the research team to confirm eligibility and identify clinical diagnoses. To minimise participant burden, tasks validated for online administration (i.e., MCQ and CIS) were also completed online prior to attending laboratory sessions. Laboratory sessions started at 9:00am and lasted approximately 3.5 hours. To allow certain measurements to be taken for other studies, participants arrived fasted and completed biometric assessments (heart function, blood, and body composition measurements) before being provided a standardised breakfast. They then completed a battery of cognitive tasks and self-report scales in standardised order. This included the NIH Toolbox, and the DASS-21, DEB-Q, and S-UPPS-P questionnaires, all of which were administered on an iPad. Additional data were collected for further projects during laboratory sessions that are not reported here. At completion, participants were provided an $80 dollars electronic gift card plus $12-13 dollars based on performance in one of the cognitive tasks reported elsewhere. 2.4 Data Analyses Study data were collected and stored in REDCap on secure Monash servers (Harris et al., 2019; Harris et al., 2009), and unless otherwise stated, analyses were conducted using RStudio, version 2025.05.0+496 (Posit Software, 2025). For a full list of R packages and citations, see supplementary materials, Table S3. 2.4.1 Data cleaning and pre-processing CIS task data were imported to MATLAB for pre-processing. At the first level, summary outcomes were calculated for each participant from their raw trial-by-trial data. At the second level, quality checks were performed and data was excluded on a task-by-task basis if it failed to reach criteria. This resulted in seven cases (4.2%) being excluded from the Uncertainty Evaluation domain, four cases (2.4%) being excluded from the Cognitive Inhibition domain, and six cases (3.6%) from the Reinforcement Learning and Consistency domains (one case was common across all domains). Additionally, one participant (0.6%) did not complete any CIS tasks. Delay discounting rate k was calculated from MCQ responses using an automated tool in MS Excel (Kaplan et al., 2016). Due to skewness and kurtosis in the k distribution, scores were log transformed and multiplied by -1 for greater ease of analysis and interpretation. Thus, - ln(k) values range from 1.39 (all smaller-sooner) to 8.75 (all larger-later), with lower scores indicating steeper discounting. Quality assessments indicated two participants always selected the smaller-sooner option, and five always selected larger-later, such that seven cases (4.2%) were excluded from the Delay Discounting domain in subsequent analyses (Gray et al., 2016; Kaplan et al., 2016). NIH DCCS test responses were scored automatically within the NIH Toolbox app before being exported from the testing iPad for analysis. Computed score outcomes were missing for three participants (1.8%) due to technical issues with the NIH Toolbox app. Questionnaire data for the BES (Gormally et al., 1982), DASS-21 (Henry & Crawford, 2005; Lovibond & Lovibond, 1995), DEB-Q (van Strien et al., 1986), and S-UPPS-P (Lynam, 2013; Lynam et al., 2006) were scored in REDCap according to published guidelines before being exported for further analysis. Internal reliability coefficient alpha (Cronbach, 1951) was calculated for each scale and/or subscale using the semTools package in RStudio (Jorgensen et al., 2022). Across these measures, only one case (0.6%) was missing S-UPPS-P data. Since meaningful missing data analyses therefore could not be conducted, the mean value was imputed for subsequent analyses. Descriptive statistics were calculated for each variable by group using the dplyr package in R (Wickham et al., 2023). 2.4.2 Hypothesis testing Prior to testing our hypotheses, assumption checks showed absence of multicollinearity was supported for both decision-making and psychological profiles (see supplementary materials, section S4.1). However, the assumption of homogeneity of variance-covariance matrices was only supported for the decision-making profile, thus for the psychological profile, we interpreted Pillai’s Trace robust alternative to the Wilks’ Lambda test of parallelism (Ateş et al., 2019). The assumption of multivariate normality was violated for both profiles. As profile analysis requires all variables to have the same units of measurement (Bulut & Desjardins, 2020), we therefore used median and median absolute deviation (MAD) values as a robust alternative to z -scores to standardise variables prior to hypothesis testing (Kappal, 2019). To test our hypotheses, we conducted profile analyses using the profileR package in RStudio (Bulut & Desjardins, 2018). Profile analyses allowed us to determine if the overall profiles of decision-making and psychological functioning in BED differed from control groups across multiple concurrent domains (Davison & Davenport Jr, 2002). The analysis examines three characteristics: equal levels (whether the overall mean levels of functioning across the profile differ between groups); parallelism (whether profiles differ in shape between groups); and flatness (whether there is variation between the domains within groups) (Mathai et al., 2022). For each of these three tests, post-hoc analyses were planned following significant main effects. Here, we used ANOVA models with Tukey’s HSD to examine pairwise group comparisons for the profile levels in Base R. MANCOVA multivariate models explored group differences at the domain level, as well as the influence of covariates on the profiles, using the jmv package (Selker et al., 2025). Covariates were mood disorder diagnoses and binge eating symptom severity, which had been identified as potential confounds and limitations in previous research (Colton et al., 2023; Leenaerts et al., 2022; Smith et al., 2018), and race, which had been found to differ between groups in our sample. In terms of flatness, repeated-measures ANOVA and Tukey’s HSD were planned to examine variability between domains within groups. 3. Results Descriptive statistics for profile variables are summarised in Table 2 . Table 2 Descriptive Statistics for the cognitive tasks and self-report measures BED ( n = 57) HWC ( n = 54) LWC ( n = 54) Decision-Making Profile Mean (SD) Uncertainty Evaluation 0.721 (0.09) 0.713 (0.07) 0.722 (0.07) Delay Discounting 4.57 (1.42) 4.68 (1.40) 5.00 (1.47) Attentional Inhibition d’ 3.38 (0.72) 3.36 (0.66) 3.64 (0.70) Response Inhibition c -0.039 (0.22) 0.013 (0.20) 0.076 (0.19) Consistency 8822 (2341) 9344 (3084) 9338 (2713) Reinforcement Learning -0.298 (0.16) -0.295 (0.21) -0.301 (0.18) Cognitive Flexibility/Set-Shifting 8.39 (1.20) 8.61 (0.85) 8.81 (0.85) Psychological Profile Mean (SD) DASS Depression 6.11 (4.24) 2.96 (3.19) 3.59 (3.47) DASS Anxiety 4.05 (3.45) 1.93 (1.94) 2.24 (2.31) DASS Stress 8.47 (4.58) 4.54 (3.11) 4.76 (3.61) DEB-Q Restrained Eating 2.77 (0.73) 2.49 (0.70) 2.34 (0.79) DEB-Q Emotional Eating 3.81 (0.75) 2.56 (0.96) 2.00 (0.71) DEB-Q External Eating 3.74 (0.58) 3.15 (0.62) 3.05 (0.53) S-UPPS-P Negative Urgency 10.39 (2.71) 8.43 (2.45) 8.37 (2.86) S-UPPS-P Positive Urgency 7.95 (2.92) 6.78 (1.90) 7.28 (2.20) S-UPPS-P Lack of Perseverance 8.12 (2.09) 6.94 (1.55) 6.91 (1.73) S-UPPS-P Lack of Premeditation 7.58 (1.84) 6.67 (1.98) 6.67 (1.91) S-UPPS-P Sensation Seeking 9.25 (2.96) 9.52 (2.62) 10.02 (2.62) Note: Descriptive statistics for covariates are reported in Table 1 . 3.1 Profile Analysis Results In the decision-making profile (Fig. 2 ), the test of equal levels was significant, F (2,140) = 5.67, p < .01, η 2 = .06, indicating there was a significant difference between groups in grand mean scores across the profile, with a medium effect size. Tukey’s HSD post-hoc pairwise comparisons showed that standardised grand mean decision-making scores were significantly higher in the LWC group compared to the BED group, mean difference = 0.294, p < .01. Pairwise comparisons between the BED and HWC group, and between the two control groups, were non-significant (supplementary table S4.2.1). To further understand this result, we compared linear models predicting grand mean decision-making scores from group and from BMI separately and together, then repeated these using waist-hip ratio as an alternative measure of body composition. These analyses indicated the best fit model was that including only Group (supplementary table S4.2.2). The tests of parallelism and of flatness were non-significant, indicating the overall shape of the decision-making profile did not differ between groups, and that standardised scores did not differ significantly between domains within groups. Note Higher scores indicate higher levels of decision-making functioning In the psychological profile analysis (Fig. 3 ), the test of equal levels was significant, F (2, 162) = 36, p < .0001, η 2 = .31, indicating there was a significant difference between groups in grand mean scores across the profile, with a large effect size. Tukey’s HSD pairwise comparisons showed standardised grand mean scores were significantly higher in the BED group compared to both the HWC and LWC groups ( p < .0001), while the control groups did not differ from each other (supplementary table S4.2.3). The test of parallelism was also significant, F (20, 308) = 3.53, p < .0001, Pillai’s Trace = 0.37, indicating the overall shape of the profiles differed significantly between groups with a moderate effect size. The post-hoc MANCOVA model showed that together with group, depressive disorder history ( p < .001), BE symptom severity ( p < .001), and race ( p < .01) significantly predicted the shape of the psychological profile at the multivariate level (supplementary table S4.2.4). At the univariate level, the BED group differed significantly from both control groups on all domains of psychological functioning excluding restrained eating, positive urgency, and sensation seeking (supplementary table S4.2.5). Finally, the test of flatness was significant, F (10, 153) = 2.95, p < .01, η 2 = .016 showing standardised scores varied between psychological domains within groups with a small effect size. Post-hoc pairwise comparisons indicated this was driven by elevated depression symptoms (supplementary table S4.2.6). Note Higher scores indicate higher levels of symptoms, or poorer psychological functioning. 4. Discussion We aimed to concurrently characterise cognitive and psychological mechanisms in binge eating disorder (BED) from a multi-stage decision-making perspective. Our hypotheses concerning the decision-making profile were partially supported. Individuals with BED showed an overall reduced level of decision-making functioning than lower-weight controls. Decision-making functioning in BED did not differ significantly from higher-weight controls. Contrary to hypotheses, group profiles were parallel, such that between groups, performance tended to fluctuate together relative to their grand means. Within groups, performance did not differ significantly between decision-making domains (i.e., profiles were flat). Our hypotheses concerning the psychological profile were supported. The BED group differed significantly in the overall level of their psychological functioning from both higher- and lower-weight control groups. The shape or nature of psychological profile also differed significantly between groups, and post-hoc tests showed BE symptom severity and depressive disorder diagnoses significantly predicted the shape of the psychological profile alongside BED diagnosis. Finally, the psychological profile was not flat, with the BED group showing particularly high levels of depressive symptoms, compared to other domains. Our primary finding that overall decision-making functioning is compromised may help to explain core features of BED that occur before, during, and after binge-eating episodes, including difficulties inhibiting eating behaviour as an established response to internal or external cues (Giel et al., 2017b; Stott et al., 2021), and difficulties adapting behaviour despite negative consequences and desire to change (Grant & Chamberlain, 2023). That unequal levels were found along with parallelism and flatness is an interesting feature of the profiles, showing decision-making functioning incorporated relatively similar strengths and weaknesses between groups, and was relatively consistent across domains within groups. This combination of outcomes suggests the profile is tapping into generalised, cross-domain dysfunctions as opposed to performance deficits that are unique to specific tasks or contexts (Goschke, 2014; Morelli et al., 2022). This emphasises the value of conceptualising cognitive functioning in BED from a holistic, multi-domain perspective (Dennison et al., 2022). A further interesting feature of the decision-making profile was that the BED group differed significantly from the lower-weight control group, but not from the higher-weight control group. This would suggest a shared role for decision-making dysfunction associated with excess weight among individuals with BED and without. However, our subsequent analyses indicated neither BMI nor waist-hip ratio explained this pattern of group comparisons in decision-making functioning. Our findings that overall psychological functioning is compromised in BED aligns with prior research and theory indicating elevated negative emotionality, maladaptive eating-related tendencies, and certain impulsive traits are critical characteristics of BED (Fairburn et al., 2003; Leenaerts et al., 2023; Pearson et al., 2015; Williamson et al., 2004). However, our profile analysis approach provides some unique insights. Firstly, it is striking that the psychological profile clearly differentiated individuals with BED from both control groups, and that the two control groups had such similar scores across all domains. This provides a clear answer to the long-standing question in the field that these symptoms and tendencies are related to BED specifically, rather than higher weight more generally (Agüera et al., 2021; Steptoe & Frank, 2023). Variability between and within profiles highlighted high levels of depressive symptoms as a key feature of the psychological profile. This has important methodological implications, given that co-occurring mood symptoms and disorders are frequently excluded from research or poorly accounted for in analyses (Smith et al., 2018). Interestingly, though considerable prior research has focused on negative urgency (and to a lesser extent, positive urgency) as the impulsive traits associated with binge eating (Lavender & Mitchell, 2015), our analysis indicated lack of perseverance (a tendency to struggle to resist distractions and give up early in the face of boredom, fatigue, or difficulty), as a key feature of the BED profile (Whiteside & Lynam, 2001). While this trait has been reported as distinguishing individuals with BED from controls in prior research (Kenny et al., 2019), it has received little attention in the eating disorder literature to date. This study combined several important methodological assets. Firstly, we recruited a relatively large sample in comparison to similar studies in the field, strengthening the statistical power and generalizability of findings. This sample was diverse in age and race, closely representing the Australian population (ABS, 2024). However, it is important to note our sample included relatively few men, few individuals who identified as having First Nations heritage, and no individuals who identified as gender diverse. By matching groups on age, sex, and education, defining BED status according to structured clinical interviews as well as self-report symptom measures, characterising our sample according to co-occurring depression diagnoses, and including two control groups defined according to weight status, we addressed key methodological limitations in prior research (Colton et al., 2023; Smith et al., 2018). It is important to note that all cognitive assessment tasks used in this study employed neutral rather than food- or eating-related stimuli. This has been shown to be an important factor in neurocognitive research, however, as disorder-specific stimuli often produce larger group differences, this supports confidence in our findings (Berner et al., 2017). As our findings are cross-sectional, this study is primarily descriptive. It would be advantageous for future research to employ longitudinal methods to assess prospective relationships between decision-making functioning and binge eating disorder, and to relate changes in decision-making functioning to treatment outcomes. In terms of clinical implications, evidence suggests poorer decision-making functioning predicts poorer treatment outcomes in BED, yet they are not routinely targeted by current treatment modalities (Goldschmidt et al., 2025; Lucas et al., 2021). Novel interventions that augment traditional psychotherapies by targeting broadly-defined decision-making mechanisms have shown promise in improving BED treatment outcomes in limited scale trials (Eichen et al., 2023; Juarascio et al., 2023; Schag et al., 2019). Similarly, neurostimulation interventions that target the biological underpinnings of decision-making mechanisms are also showing promising early results (Chmiel et al., 2024). However, larger scale trials are needed to confirm these initial findings. Lack of perseverance has also been shown to predict treatment outcomes in disorders that share some characteristics with BED, such as ADHD and behavioural addiction (Mallorquí-Bagué et al., 2019; Way et al., 2024). Given that treatments for eating disorders can be challenging and slow-going, bolstering perseverance through motivational, compassion-focused, and similar interventions could improve BED treatment engagement and success. This study shows a generalised profile of decision-making dysfunction is an important feature of binge eating disorder, alongside more well-recognised characteristics of elevated depression, anxiety, and stress symptoms, emotional- and external-eating tendencies, negative urgency, lack of perseverance, and lack of premeditation. By mapping out profiles across both decision-making and psychological domains of functioning concurrently, our findings align with a recent theoretical model of binge eating, which attempted to integrate cognitive and emotional drivers (Schaefer et al., 2023). This novel approach suggests there is value in breaking down barriers between typically siloed areas of research. To date, a single study has used a multi-modal assessment incorporating cognitive and psychological assessments to characterise subgroups with BED (Brucar et al., 2025). In this study, decision-making dysfunctions clustered with emotional characteristics to define three distinct subtypes of BED, in which negative emotionality, impulsive behaviour, and harm avoidance were dominant features respectively. Such insights hold promise for much needed improvements in BED treatment, indicating outcomes may be improved by tailoring approaches according to mechanistic phenotypes (Bryant et al., 2025; Levinson et al., 2025; Levinson et al., 2024). Thus far, individual mechanistic profiles in the psychological domain have been used effectively to guide personalised treatment (Levinson et al., 2021), however gaps remain in the decision-making domain (Allott et al., 2025). Declarations Funding Declaration EC is supported by an Australian Research Training Program (RTP) scholarship, and by an Australian Eating Disorders Research and Translation Centre Translat ED scholarship funded by the Australian Government, Commonwealth Department of Health. CA, HC, EG, & BN are also supported by Australian Research Training Program (RTP) scholarships. TC is supported by the Australian Research Council (DP250102224, FT220100294). AVG is supported by an Australian National Health and Medical Research Council Investigators grant (2009464), and by the Australian Eating Disorders Research and Translation Centre funded by the Australian Government, Commonwealth Department of Health. The authors are not aware of any conflicts of interest relevant to this study. Human Ethics & Consent to Participate Declaration All procedures were approved by Monash University Human Research Ethics Committee (MUHREC), reference 34518. All participants were provided a written explanatory statement of these procedures and gave informed consent. Data Availability statement Deidentified data is available by reasonable request to the corresponding author. CRediT author contributions EC: Conceptualization; methodology; investigation; data curation; formal analysis; writing – original draft; writing – review & editing; visualisation. CA, HC, KF, EG, BN, LT, KW: Investigation;data curation;writing – review and editing. 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12:05:32","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":262160,"visible":true,"origin":"","legend":"","description":"","filename":"43b04b58305646aca7b5973def816c171structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/55e7e46d232f8d2b47ff284d.xml"},{"id":98624359,"identity":"a0459bcd-0bd8-40a0-b8a4-4dfb5608155f","added_by":"auto","created_at":"2025-12-19 17:08:21","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":281409,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/01797d2b804789fc17afabb1.html"},{"id":98625019,"identity":"0e70dc6c-edc4-44d2-9586-56039d7d46d5","added_by":"auto","created_at":"2025-12-19 17:08:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53772,"visible":true,"origin":"","legend":"\u003cp\u003eStages of Dysfunctional Decision-Making in BED. Adapted from Colton et al. (2023).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/32ff9a76d03dc6a84c5b57f0.png"},{"id":98624240,"identity":"9ea25d51-1adc-4fa6-a3b8-18f4d52d2115","added_by":"auto","created_at":"2025-12-19 17:08:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71104,"visible":true,"origin":"","legend":"\u003cp\u003eDecision-Making Profiles\u003c/p\u003e\n\u003cp\u003eNote: Higher scores indicate higher levels of decision-making functioning\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/21daafa59f71d9fd6eec31a3.png"},{"id":98513050,"identity":"300028e2-4728-4a39-aba0-b3e9ef04a2e1","added_by":"auto","created_at":"2025-12-18 12:05:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68545,"visible":true,"origin":"","legend":"\u003cp\u003ePsychological trait profiles\u003c/p\u003e\n\u003cp\u003eNote: Higher scores indicate higher levels of symptoms, or poorer psychological functioning.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/d27e3f392a7c3db742484743.png"},{"id":104739721,"identity":"e2c93fd8-c052-4640-8229-cd1bfd15004d","added_by":"auto","created_at":"2026-03-16 16:12:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1128158,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/c199ccea-a34a-4a3b-ade1-97e79566272b.pdf"},{"id":98513058,"identity":"f549568f-a215-406d-bef1-d28d0adfddd4","added_by":"auto","created_at":"2025-12-18 12:05:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6457962,"visible":true,"origin":"","legend":"","description":"","filename":"ProfilingdecisionmakingmechanismsinBEDSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8254234/v1/d91c19fcf9068bab927e36c5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Profiling Decision-Making Mechanisms in Binge Eating Disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBinge eating disorder (BED) is a serious and common mental health condition, with a lifetime prevalence estimate of 1.3% globally (Giel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). BED is associated with high incidence of co-occurring psychiatric symptoms, somatic health complications, and poor quality of life (Hilbert, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), low rates of detection and treatment engagement (Bryant et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and suboptimal treatment outcomes (Linardon, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Monteleone et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thus, individual and societal disease burdens are considerable (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). BED is characterised by recurrent binge eating episodes; that is, when people experience loss-of-control over their eating behaviour and consume a large quantity of food in a short time, accompanied by negative self-focused thoughts and significant distress (APA, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). BED can thus be characterised by cycles of unwanted behaviours, cognitions, and emotions that are repeated despite negative consequences; however, the specific mechanisms underpinning these symptoms are unclear (Giel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mechanisms of treatment action are also uncertain, hindering the optimisation of existing treatments and the development of novel, or more personalised interventions (Hilbert, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research that clarifies critical BED mechanisms is therefore a key priority for the field (Aouad et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral theories have attempted to explain critical mechanisms in BED onset and maintenance, which broadly fall into two categories: those focused on cognitive systems, and those emphasising emotional processes (Burr et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e; Neyland et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In terms of cognitive systems, one approach to conceptualising the mechanisms by which observed alterations in cognitive functioning influence BED is to consider core processes that unfold before, during, and after binge-eating episodes (Bodell \u0026amp; Racine, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One such model maps nine cognitive domains across three stages of decision-making (Ernst \u0026amp; Paulus, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Verdejo-Garcia et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The first stage involves \u003cem\u003eForming Preferences\u003c/em\u003e, which includes identifying and gathering information about choice options (\u003cem\u003eReflection\u003c/em\u003e), and attributing subjective value to their potential outcomes - both known (\u003cem\u003eRisk Evaluation\u003c/em\u003e) and unknown (\u003cem\u003eUncertainty Evaluation\u003c/em\u003e). Thus, heightened value and salience of food rewards (Vrieze \u0026amp; Leenaerts, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and difficulties identifying effective choice strategies in the context of uncertainty or ambiguity (Aloi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) may promote binge eating. During \u003cem\u003eChoice Implementation\u003c/em\u003e, resources are allocated to realise selected options (\u003cem\u003eAction Initiation\u003c/em\u003e), while inhibiting competing actions (\u003cem\u003eCognitive Inhibition\u003c/em\u003e), or tempting but suboptimal choices (\u003cem\u003eDelay Discounting\u003c/em\u003e). Here, enhanced motivation to pursue food rewards (Forester et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Racine et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), along with difficulty inhibiting responses to food-related cues (Leehr et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schag et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), may work together to drive loss-of-control eating behaviour. In the final stage, \u003cem\u003eFeedback Processing\u003c/em\u003e involves monitoring positive and negative feedback (\u003cem\u003eReinforcement Learning\u003c/em\u003e) and sampling recent and more distant decision outcomes (\u003cem\u003eMemory\u003c/em\u003e), then using these sources of feedback to optimise ongoing decisions (\u003cem\u003eConsistency\u003c/em\u003e) and adjust subsequent choices (\u003cem\u003eCognitive Flexibility/Shifting\u003c/em\u003e). Binge eating may thus be reinforced by enhanced tendencies to learn from food rewards combined with reduced learning from punishing outcomes (Schaefer \u0026amp; Steinglass, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Voon et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Waltmann et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and difficulties integrating feedback, particularly changes from positive to negative feedback, to optimise decision-making (Banca et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kollei et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This results in difficulties changing behaviour despite distress, guilt, and shame, and other negative consequences (Wollenhaupt et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This multi-stage decision-making approach has proven valuable as a framework to meta-analyse findings from prior studies of cognition in BED (Colton et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this multi-stage perspective has yet to be used to assess decision-making mechanisms in BED in a comprehensive study.\u003c/p\u003e \u003cp\u003eWhile examining specific cognitive domains of decision-making in isolation has resulted in significant knowledge gain, it has also left important gaps in our understanding of these mechanisms (Dennison et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Firstly, reviews have highlighted significant inconsistencies between studies (Carr et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Waltmann et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This has been particularly evident in the \u003cem\u003eCognitive Inhibition\u003c/em\u003e domain, which has received considerable attention, but produced few clear-cut outcomes (Smith et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, though some studies have indicated BED is associated with significant tendencies to prioritise smaller-sooner rewards over larger-later benefits, others have reported null findings, or indicate \u003cem\u003eDelay Discounting\u003c/em\u003e interacts with other motivational and individual characteristics (Blume et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Colton et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Steward et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Reviews have also highlighted methodological issues, including variability in sampling approaches, differences in demographic profiles, and co-occurring conditions, that are not well accounted for when interpreting and integrating findings (Lavagnino et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Leenaerts et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In particular, individual characteristics (including age, sex, and education) and BED features (such as higher BMI, co-occurring mood symptoms, and disease severity and duration) have been identified as significant moderators of decision-making functioning and as confounds in prior studies. There have also been difficulties translating findings, such that interventions designed to target individual cognitive processes in isolation have had little success in reducing BED symptoms (Brockmeyer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Giel et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Turton et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, approaches that consider cognitive functions from an integrated, system-based perspective may be key to moving the field forward (Bodell \u0026amp; Racine, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schaefer \u0026amp; Steinglass, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn addition to interactions among domains of cognitive functioning, it is also important that decision-making does not occur in a vacuum; rather, these processes are modulated by emotional contexts (Goschke, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Morelli et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Emotion-focused theories of BED emphasise higher levels of negative emotionality and difficulties regulating emotions \u0026ndash; such as depressive symptoms, states of stress or anxiety, or discomfort arising from interpersonal conflict, dietary restriction and cravings, and negative self-evaluations \u0026ndash; as critical mechanisms in BED (Blackburn et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Haedt-Matt \u0026amp; Keel, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Heatherton \u0026amp; Baumeister, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Stice et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Wilfley et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). From these perspectives, \u003cem\u003ePreference Formation\u003c/em\u003e is influenced by expectancies that food and eating will soothe, provide escape from, or regulate these aversive emotional states (Burr et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e). Emotion-focused perspectives also emphasise trait-level tendencies to act impulsively in the context of low mood, such that emotional states interact with decision-making processes to influence binge eating (Kenny et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leenaerts et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pearson et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Recent studies utilising ecological momentary assessment (EMA) methods further indicate that cognitive and emotional mechanisms dynamically interact throughout the \u003cem\u003eChoice Implementation\u003c/em\u003e and \u003cem\u003eFeedback Processing\u003c/em\u003e stages of decision-making. For example, trait delay discounting and momentary attentional bias before binge eating episodes have been shown to strengthen relationships between affective states and subsequent episodes (Smith et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, negative affect has been shown to modulate attentional and reinforcement learning processes during and after binge eating episodes, to shape subsequent eating-related decisions (Schaefer et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such evidence has proven valuable in extending theoretical conceptions of binge eating, improving our understanding of how emotional and cognitive mechanisms interact in unique ways for different individuals (Burton \u0026amp; Abbott, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schaefer et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, a recent study used a latent profile analysis approach to group individuals with BED according to patterns of psychological risk and maintenance factors, identifying four sub-profiles of BED with distinct combinations of psychological drivers of binge eating (O'Loghlen et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A similar process was used to personalise psychological treatment, showing potential to improve outcomes (Levinson et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These advances in emotional and psychological phenotyping contrast with the relative lack of progress in relation to identifying and addressing critical BED mechanisms in the cognitive domain (Allott et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Brucar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to concurrently assess cognitive mechanisms in BED from an integrated multi-stage decision-making perspective, alongside key psychological mechanisms that influence choice processes, using a profile analysis approach. To isolate mechanisms in BED as distinct from those associated with higher weight, we incorporated a higher-weight control group matched by BMI to the clinical group (HWC), and a lower-weight control group (LWC). To address additional common confounds and limitations in prior research (Leenaerts et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Steward \u0026amp; Berner, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), all groups were matched according to sex, age, and education, and we included individuals with history of depressive disorders. We hypothesised the BED group would differ significantly from control groups according to their overall level of functioning and the shape or pattern of functioning across their multi-stage decision-making profile (incorporating behavioural indices of \u003cem\u003euncertainty evaluation\u003c/em\u003e, \u003cem\u003ecognitive inhibition, delay discounting, reinforcement learning, consistency\u003c/em\u003e, and \u003cem\u003ecognitive flexibility/set-shifting\u003c/em\u003e), and across their psychological profile (comprising self-reported measures of depression, anxiety, and stress symptoms, tendencies toward restrained, emotional, and external eating behaviour, and five facets of impulsivity).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1 Participants\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We employed a cross-sectional, case-control design. We aimed to recruit 150 participants across three groups \u0026ndash; 50 with BED and higher BMI (BED), 50 with higher BMI without BED (HWC), and 50 lower-weight controls (LWC), which was deemed sufficient to detect medium effect size differences between the study groups according to \u003cem\u003ea priori\u003c/em\u003e power analyses. Participants were recruited using META advertisements, mailings to internal databases, and the TrialFacts research recruitment agency. Inclusion criteria for the BED group were currently meeting DSM-5-TR criteria for BED or OSFED-BED (supplementary materials, Table S1), assessed by SCID-5-TR semi-structured clinical interviews (First et al., 2015). Inclusion criteria for all groups were age 18-60, ability to attend in-person study sessions in Notting Hill, VIC, Australia, and BMI \u0026gt;30 (BED and HWC) or 18.5-25 (LWC). Exclusion criteria for all groups were history of psychosis, bipolar disorder, neurological disease, traumatic brain injury, seizure, or intellectual disability, and current major depressive episode or suicidality. Additional exclusion criteria for control groups were history of any eating disorder. In total, 165 eligible participants completed the study between April 2023 and August 2025 (125 [75.75%] female; mean age 43.90 [\u003cem\u003eSD\u003c/em\u003e 11.24], range 18.37-60.76). Analyses confirmed groups were well-matched according to sex, age, and education, and that BED and HWC groups were also matched according to BMI (Table 1). Groups were also confirmed to differ significantly on self-reported binge eating symptom severity, according to the Binge Eating Scale (BES; (Gormally et al., 1982). Groups were found to differ based on race, with a lower proportion of white participants and a higher proportion of Asian participants in the LWC compared to BED and HWC groups, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. \u003cem\u003eParticipant characteristics\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003cp\u003eRange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBED\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHWC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e =54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLWC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003cem\u003en\u003c/em\u003e = 54)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e45.2 (11.8)\u003c/p\u003e\n \u003cp\u003e18.8-60.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e44.5 (10.8)\u003c/p\u003e\n \u003cp\u003e18.4-59.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e41.8 (11.0)\u003c/p\u003e\n \u003cp\u003e19.3-60.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e(2,162) = 1.4, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e15.8 (2.51)\u003c/p\u003e\n \u003cp\u003e12-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e16.3 (3.24)\u003c/p\u003e\n \u003cp\u003e6-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e15.7 (2.78)\u003c/p\u003e\n \u003cp\u003e11-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e(2,162) = 0.35, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e36.1 (5.68)\u003c/p\u003e\n \u003cp\u003e27.9-51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e35.2 (5.19)\u003c/p\u003e\n \u003cp\u003e29.1-60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e22.6 (2.05)\u003c/p\u003e\n \u003cp\u003e18.7-26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(2,162) = 147,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026lt; .0001\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBES score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e25.23 (6.83)\u003c/p\u003e\n \u003cp\u003e10-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e12.72 (6.73)\u003c/p\u003e\n \u003cp\u003e0-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e8.11 (6.28)\u003c/p\u003e\n \u003cp\u003e0-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e(2,162) = 99.9,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026lt; .0001\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003en(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFemale 43 (75.4)\u003c/p\u003e\n \u003cp\u003eMale 14 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFemale 42 (77.8)\u003c/p\u003e\n \u003cp\u003eMale 12 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eFemale 40 (74.1)\u003c/p\u003e\n \u003cp\u003eMale 14 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u0026nbsp;\u003c/strong\u003en(%)\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003cp\u003eMultiple/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39 (68.4)\u003c/p\u003e\n \u003cp\u003e11 (19.3)\u003c/p\u003e\n \u003cp\u003e6 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35 (64.8)\u003c/p\u003e\n \u003cp\u003e13 (24.1)\u003c/p\u003e\n \u003cp\u003e2 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24 (44.4)\u003c/p\u003e\n \u003cp\u003e25 (46.9)\u003c/p\u003e\n \u003cp\u003e4 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eED Diagnoses\u0026nbsp;\u003c/strong\u003en(%)\u003c/p\u003e\n \u003cp\u003eBED\u003c/p\u003e\n \u003cp\u003eOSFED-BED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35 (61.4)\u003c/p\u003e\n \u003cp\u003e22 (38.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDD Diagnoses\u003c/strong\u003e n(%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Any\u003c/p\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003cp\u003ePast only\u003c/p\u003e\n \u003cp\u003eCurrent only\u003c/p\u003e\n \u003cp\u003eCurrent \u0026amp; past\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43 (75.4)\u003c/p\u003e\n \u003cp\u003e14 (24.6)\u003c/p\u003e\n \u003cp\u003e30 (52.6)\u003c/p\u003e\n \u003cp\u003e3 (5.3)\u003c/p\u003e\n \u003cp\u003e10 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13 (24.1)\u003c/p\u003e\n \u003cp\u003e41 (75.9)\u003c/p\u003e\n \u003cp\u003e12 (2.2)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (16.7)\u003c/p\u003e\n \u003cp\u003e45 (81.8)\u003c/p\u003e\n \u003cp\u003e9 (16.7)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003eNotes:\u003c/p\u003e\n \u003cp\u003eAge \u0026ndash; calculated from date-of-birth on the day participants completed the eligibility questionnaire.\u003c/p\u003e\n \u003cp\u003eEducation - self-reported years of completed education (participants were prompted that in Australia, 13 years equates to completing high school).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eBMI - estimated BMI calculated from self-reported height and weight was used during eligibility screening; BMI measured\u0026nbsp;using a\u0026nbsp;Tanita BC-545N segmental body composition monitor\u0026nbsp;was used in all data analyses.\u003c/p\u003e\n \u003cp\u003eBES total score - higher scores indicate greater binge eating symptomology.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSex - biological sex assigned at birth. We also acquired self-reported gender identity but used the binary sex variable in our analyses to aid comparison to prior research. Additionally, all participants reported their gender identity as congruent with their biological sex.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRace \u0026ndash; self-identified race was missing in six cases; Multiple/Other includes five individuals who self-identified as more than one race, one individual who identified as Pacific Islander, and six individuals who selected the option \u0026lsquo;other\u0026rsquo;.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eED Diagnoses \u0026ndash; eating disorder diagnoses according to SCID-V-TR clinical interview.\u003c/p\u003e\n \u003cp\u003eDD Diagnoses \u0026ndash; depressive disorder diagnoses (including MDE, MDD, PDD, and PMDD) according to SCID-V-TR clinical interview.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Tukey\u0026rsquo;s HSD post-hoc tests confirmed estimated BMI (self-reported in the eligibility survey) differed significantly between BED and LWC, and between HWC and LWC groups, but not between BED and HWC groups.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Tukey\u0026rsquo;s HSD post-hoc tests confirmed all pairwise comparisons were significant.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e2.2 Materials\u003c/p\u003e\n\u003cp\u003e2.2.1 Screening Measures\u003c/p\u003e\n\u003cp\u003eA demographics survey collected data on participant age (calculated from date-of-birth), biological sex, gender identity, years of education, and estimated BMI (initially calculated from self-reported height and weight and subsequently corroborated via body composition scale), along with data on inclusion and exclusion criteria.\u003c/p\u003e\n\u003cp\u003eThe Binge Eating Scale (BES) (Gormally et al., 1982) assessed frequency and severity of binge eating behaviours, cognitions, and emotions. Participants responded to 16 items, selecting the most appropriate from 3 (2 items) or 4 (14 items) response options (e.g., \u0026ldquo;I feel incapable of controlling urges to eat. I have a fear of not being able to stop eating voluntarily\u0026rdquo;). Continuous total scores range from 0-46 and are categorised as indicating none/minimal (0-17), mild (18-26), or moderate/severe (27-46) binge eating symptomology. In this study, Cronbach\u0026rsquo;s alpha internal consistency was 0.95 (excellent).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.2 Decision-making Measures\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDecision-making variables were informed by our hypothesised stages of dysfunctional decision-making in BED (Figure 1). \u003cem\u003eUncertainty Evaluation\u003c/em\u003e was defined as optimal responding in a two-alternative information accumulation task, accounting for speed/accuracy trade-off. To parse out attentional inhibition and response inhibition components of \u003cem\u003eCognitive Inhibition\u0026nbsp;\u003c/em\u003e(Hamilton et al., 2015; Nigg, 2017), we applied signal detection theory (Green \u0026amp; Swets, 1966) to calculate \u003cem\u003ed\u0026rsquo;\u003c/em\u003e and criterion (\u003cem\u003ec\u003c/em\u003e) values from a cued Go/No-go task. \u003cem\u003eDelay Discounting\u003c/em\u003e rate \u003cem\u003ek\u003c/em\u003e quantified the extent to which monetary rewards were devalued as time to receiving them increases (Odum, 2011). A general measure of \u003cem\u003eConsistency\u003c/em\u003e was operationalised as the total score in a Probabilistic Reversal Learning task (Verdejo-Garcia et al., 2018), while \u003cem\u003eReinforcement Learning\u003c/em\u003e was calculated from the same task as proportion of trials in which the same option was selected after a win (win-stay) less the proportion of trials in which the alternative option was selected after a loss (lose-shift) (Dennison et al., 2022). Finally, \u003cem\u003eCognitive Flexibility/Set-Shifting\u003c/em\u003e scores incorporated response time and accuracy to quantify the ability to switch between target dimensions (Phillipou \u0026amp; Miles, 2025). If necessary, domains were reverse-scored such that higher scores always indicated better decision-making functioning. Decision-making stages, domains, tasks, and behavioural outcome calculations are summarised in supplementary materials, section S2.2.\u003c/p\u003e\n\u003cp\u003eFive tasks were used to assess multi-stage decision-making functioning. The Cognitive Impulsivity Suite (CIS) (Verdejo-Garcia et al., 2021) assesses multiple domains of cognitive impulsivity in an integrated, gamified environment with a cohesive \u0026lsquo;Wild West\u0026rsquo; theme. The tasks have well-established psychometric properties and relationships to real-world impulsive behaviours. Three tasks are completed in a single online session with the task order randomised across participants: Caravan Spotter (two-alternative choice); Bounty Hunter (cued go/no-go) and Prospector\u0026rsquo;s Gamble (probabilistic reversal-learning). All tasks have a similar structure with instruction and practice phases followed by four testing blocks, with participant choices captured by keyboard responses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eCaravan Spotter\u003c/em\u003e, participants are instructed to safely guide a caravan its journey by correctly identifying animals and landscape features. On each trial, participants must determine whether an initially ambiguous (pixelated) image belongs to one of two categories, using the A (e.g., buffalo) or L (e.g., cougar) keys. The target image is presented at 50% pixelation and gradually disambiguated over a response window of 2000ms. The available reward for accurate responding also declines over this window, such that players are incentivised to respond both quickly and accurately. The pairs of categories change after each block of 40 trials.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eBounty Hunter\u003c/em\u003e, participants are instructed to shoot bandits (respond to \u0026lsquo;go\u0026rsquo; stimuli) and avoid shooting sheriffs (withhold responses to \u0026lsquo;no-go\u0026rsquo; stimuli) by pressing the space bar as quickly as possible. Each block has 60 trials with a response window of 700ms, and in each trial, the stimulus onset is preceded by a cue (a daytime or nighttime \u0026lsquo;camp\u0026rsquo; setting). Stimulus onset asynchronies and the alignment of cues and stimuli are varied to establish prepotent responses and to challenge selective and sustained attention. Participants are rewarded 50 points for correct responses and correct withholds and are deducted 50 points for commission errors (no-go stimuli responses) and omission errors (go-stimuli withholds).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eProspector\u0026rsquo;s Gamble\u003c/em\u003e, participants are instructed to select the \u0026lsquo;luckiest\u0026rsquo; of two gold miners by determining which is more likely to return rewards rather than losses. On each trial, participants must select the prospector located on the left or right of the screen by using the A or L keys within a response window of 1000ms, and the prospectors randomly switch screen locations between trials. Choosing the correct prospector results in positive feedback in 80% of trials in blocks 1 \u0026amp; 2, and in 70% of trials in blocks 3 \u0026amp; 4. Feedback may be basic (+50 points reward, 0 punishment), or enhanced (+200 points reward, -100 punishment). Contingencies are reversed after each block of 40 trials, which participants must also determine based on the feedback received.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Monetary Choice Questionnaire (MCQ) (Kirby et al., 1999) assessed \u003cem\u003eDelay Discounting\u003c/em\u003e. In this task, participants responded to 27 hypothetical choices between smaller immediate and larger delayed monetary options, with varying dollar values and time delays between items (e.g., \u0026ldquo;Would you prefer $25 today, or $60 in 14 days?\u0026rdquo;). Discounting rate \u003cem\u003ek\u003c/em\u003e is calculated for each participant ranging from -0.603 to -3.801 with values closer to zero indicating greater tendencies to select the smaller, sooner options (i.e., steeper discounting).\u003c/p\u003e\n\u003cp\u003eThe NIH Toolbox Dimensional Change Card Sort (DCCS) test assessed \u003cem\u003eCognitive Flexibility/Set-Shifting\u003c/em\u003e (Gershon et al., 2010). Target images were presented that vary on dimensions of shape and colour, and participants are instructed to match the target to two test pictures based on a specified dimension, then after varying numbers of trials, on the opposing dimension. To standardise motor movements, participants are instructed to return their index finger to a reference point on the desk (\u0026ldquo;home base\u0026rdquo;) after each response tap on the iPad screen. After the instruction and practice phases, the test phase contains 30 items. NIH Toolbox tasks have well-established reliability, validity, and age group norms. The computed score incorporates accuracy and response time, such that higher scores indicate faster and more accurate responding.\u003c/p\u003e\n\u003cp\u003e2.2.3 Psychological Trait Measures\u003c/p\u003e\n\u003cp\u003eThe Depression, Anxiety, and Stress Scales (DASS-21) (Henry \u0026amp; Crawford, 2005; Lovibond \u0026amp; Lovibond, 1995) assessed depression, anxiety, and stress symptoms. Participants rated the extent to which 21 items (7 for each subscale) applied to them in the past 7 days on a five-point Likert scale ranging from 0 \u003cem\u003edid not apply to me at all\u0026nbsp;\u003c/em\u003eto 3 \u003cem\u003eapplied to be very much or most of the time\u003c/em\u003e (e.g., \u0026ldquo;I felt down-hearted and blue\u0026rdquo;). Summed scores were calculated for each subscale, which are categorised as: Depression - Normal (0-4), Mild (5-6), Moderate, (7- 10), Severe (11-13), Extremely Severe (14+); Anxiety - Normal (0- 3), Mild (4), Moderate, (5- 7), Severe (8- 9), Extremely Severe (10+); Stress - Normal (0-7), Mild (8- 9), Moderate, (10-12), Severe (13-16), Extremely Severe (17+). Cronbach\u0026rsquo;s alpha internal consistency was calculated for each subscale, with Depression a = 0.92 (excellent), Anxiety a = 0.81 (good), and Stress a = 0.89 (good).\u003c/p\u003e\n\u003cp\u003eThe Dutch Eating Behaviour Questionnaire (van Strien et al., 1986) examined three eating-related traits. Participants rated 33 items on a five-point Likert scale ranging from 1 \u003cem\u003enever\u0026nbsp;\u003c/em\u003eto 5 \u003cem\u003every often\u003c/em\u003e (e.g., \u0026ldquo;If food tastes good to you, do you eat more than usual?\u0026rdquo;). Scores are averaged across the items such that each subscale is scored from 1 to 5, with higher scores indicating greater tendencies toward each trait. Cronbach\u0026rsquo;s alpha was excellent for all subscales: Emotional Eating a = 0.98, External Eating a = 0.91, and Restrained Eating a = 0.93.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Short UPPS-P Impulsive Behavior Scale (S-UPPS-P) (Cyders et al., 2014; Lynam, 2013) was used to measure five dimensions of trait impulsivity. Participants responded to 20 items on scale from 1 \u003cem\u003estrongly agree\u003c/em\u003e to 4 \u003cem\u003estrongly disagree\u0026nbsp;\u003c/em\u003e(e.g., \u0026ldquo;When I am upset I often act without thinking\u0026rdquo;). \u0026nbsp; Items from the (Lack of) Perseverance and (Lack of) Premeditation subscales were reverse coded before subscale scores were calculated (sum of four items, ranging from 4-16), such that higher scores always indicated more impulsive behaviour. Cronbach\u0026rsquo;s alpha was calculated for each subscale: Negative Urgency a = 0.80 (good); (Lack of) Perseverance a = 0.64 (questionable); (Lack of) Premeditation a = 0.74 (acceptable); Sensation Seeking a = 0.66 (questionable); Positive Urgency a = 0.79 (acceptable).\u003c/p\u003e\n\u003cp\u003e2.3 Procedure\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All procedures were approved by Monash University Human Research Ethics Committee (MUHREC), reference 34518. After reviewing the explanatory statement and providing informed consent, participants completed the eligibility questionnaire and BES online. Participants who met initial inclusion criteria then completed SCID-5-TR semi-structured clinical interviews over Zoom with a member of the research team to confirm eligibility and identify clinical diagnoses. To minimise participant burden, tasks validated for online administration (i.e., MCQ and CIS) were also completed online prior to attending laboratory sessions. Laboratory sessions started at 9:00am and lasted approximately 3.5 hours. To allow certain measurements to be taken for other studies, participants arrived fasted and completed biometric assessments (heart function, blood, and body composition measurements) before being provided a standardised breakfast. They then completed a battery of cognitive tasks and self-report scales in standardised order. This included the NIH Toolbox, and the DASS-21, DEB-Q, and S-UPPS-P questionnaires, all of which were administered on an iPad. Additional data were collected for further projects during laboratory sessions that are not reported here. At completion, participants were provided an $80 dollars electronic gift card plus $12-13 dollars based on performance in one of the cognitive tasks reported elsewhere.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4 Data Analyses\u003c/p\u003e\n\u003cp\u003eStudy data were collected and stored in REDCap on secure Monash servers (Harris et al., 2019; Harris et al., 2009), and unless otherwise stated, analyses were conducted using\u0026nbsp;RStudio, version 2025.05.0+496\u0026nbsp;(Posit Software, 2025).\u0026nbsp;For a full list of R packages and citations, see supplementary materials, Table S3.\u003c/p\u003e\n\u003cp\u003e2.4.1 Data cleaning and pre-processing\u003c/p\u003e\n\u003cp\u003eCIS task data were imported to MATLAB for pre-processing. At the first level, summary outcomes were calculated for each participant from their raw trial-by-trial data. At the second level, quality checks were performed and data was excluded on a task-by-task basis if it failed to reach criteria. This resulted in seven cases (4.2%) being excluded from the \u003cem\u003eUncertainty Evaluation\u003c/em\u003e domain, four cases (2.4%) being excluded from the \u003cem\u003eCognitive Inhibition\u003c/em\u003e domain, and six cases (3.6%) from the \u003cem\u003eReinforcement Learning\u003c/em\u003e and \u003cem\u003eConsistency\u0026nbsp;\u003c/em\u003edomains (one case was common across all domains). Additionally, one participant (0.6%) did not complete any CIS tasks.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDelay discounting rate \u003cem\u003ek\u003c/em\u003e was calculated from MCQ responses using an automated tool in MS Excel (Kaplan et al., 2016). Due to skewness and kurtosis in the \u003cem\u003ek\u003c/em\u003e distribution,\u003cem\u003e\u0026nbsp;\u003c/em\u003escores were log transformed and multiplied by -1 for greater ease of analysis and interpretation. Thus, -\u003cem\u003eln(k)\u003c/em\u003e values range from 1.39 (all smaller-sooner) to 8.75 (all larger-later), with lower scores indicating steeper discounting. Quality assessments indicated two participants always selected the smaller-sooner option, and five always selected larger-later, such that seven cases (4.2%) were excluded from the Delay Discounting domain in subsequent analyses (Gray et al., 2016; Kaplan et al., 2016).\u003c/p\u003e\n\u003cp\u003eNIH DCCS test responses were scored automatically within the NIH Toolbox app before being exported from the testing iPad for analysis. Computed score outcomes were missing for three participants (1.8%) due to technical issues with the NIH Toolbox app.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuestionnaire data for the BES (Gormally et al., 1982), DASS-21 (Henry \u0026amp; Crawford, 2005; Lovibond \u0026amp; Lovibond, 1995), DEB-Q (van Strien et al., 1986), and S-UPPS-P (Lynam, 2013; Lynam et al., 2006) were scored in REDCap according to published guidelines before being exported for further analysis. Internal reliability coefficient alpha (Cronbach, 1951) was calculated for each scale and/or subscale using the \u003cem\u003esemTools\u003c/em\u003e package in RStudio (Jorgensen et al., 2022). Across these measures, only one case (0.6%) was missing S-UPPS-P data. Since meaningful missing data analyses therefore could not be conducted, the mean value was imputed for subsequent analyses. Descriptive statistics were calculated for each variable by group using the \u003cem\u003edplyr\u003c/em\u003e package in R (Wickham et al., 2023).\u003c/p\u003e\n\u003cp\u003e2.4.2 Hypothesis testing\u003c/p\u003e\n\u003cp\u003ePrior to testing our hypotheses, assumption checks showed absence of multicollinearity was supported for both decision-making and psychological profiles (see supplementary materials, section S4.1). However, the assumption of homogeneity of variance-covariance matrices was only supported for the decision-making profile, thus for the psychological profile, we interpreted Pillai\u0026rsquo;s Trace robust alternative to the Wilks\u0026rsquo; Lambda test of parallelism (Ateş et al., 2019). The assumption of multivariate normality was violated for both profiles. As profile analysis requires all variables to have the same units of measurement (Bulut \u0026amp; Desjardins, 2020), we therefore used median and median absolute deviation (MAD) values as a robust alternative to \u003cem\u003ez\u003c/em\u003e-scores to standardise variables prior to hypothesis testing (Kappal, 2019).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test our hypotheses, we conducted profile analyses using the \u003cem\u003eprofileR\u003c/em\u003e package in RStudio (Bulut \u0026amp; Desjardins, 2018). Profile analyses allowed us to determine if the overall profiles of decision-making and psychological functioning in BED differed from control groups across multiple concurrent domains (Davison \u0026amp; Davenport Jr, 2002). The analysis examines three characteristics: equal levels (whether the overall mean levels of functioning across the profile differ between groups); parallelism (whether profiles differ in shape between groups); and flatness (whether there is variation between the domains within groups) (Mathai et al., 2022). For each of these three tests, post-hoc analyses were planned following significant main effects. Here, we used ANOVA models with Tukey\u0026rsquo;s HSD to examine pairwise group comparisons for the profile levels in Base R. MANCOVA multivariate models explored group differences at the domain level, as well as the influence of covariates on the profiles, using the \u003cem\u003ejmv\u003c/em\u003e package (Selker et al., 2025). Covariates were mood disorder diagnoses and binge eating symptom severity, which had been identified as potential confounds and limitations in previous research (Colton et al., 2023; Leenaerts et al., 2022; Smith et al., 2018), and race, which had been found to differ between groups in our sample. In terms of flatness, repeated-measures ANOVA and Tukey\u0026rsquo;s HSD were planned to examine variability between domains within groups.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eDescriptive statistics for profile variables are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDescriptive Statistics for the cognitive tasks and self-report measures\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBED (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHWC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLWC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDecision-Making Profile\u003c/b\u003e Mean (SD)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertainty Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.721 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.713 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.722 (0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay Discounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.57 (1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.68 (1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.00 (1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttentional Inhibition \u003cem\u003ed\u0026rsquo;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.64 (0.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse Inhibition \u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.039 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076 (0.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8822 (2341)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9344 (3084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9338 (2713)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReinforcement Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.298 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.295 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.301 (0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Flexibility/Set-Shifting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.39 (1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.61 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.81 (0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological Profile\u003c/b\u003e Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.11 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.96 (3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.59 (3.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05 (3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.93 (1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24 (2.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASS Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.47 (4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54 (3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.76 (3.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEB-Q Restrained Eating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.77 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49 (0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34 (0.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEB-Q Emotional Eating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81 (0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.56 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (0.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEB-Q External Eating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.74 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.15 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05 (0.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-UPPS-P Negative Urgency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.39 (2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.43 (2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.37 (2.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-UPPS-P Positive Urgency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.95 (2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.78 (1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.28 (2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-UPPS-P Lack of Perseverance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.12 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.94 (1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.91 (1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-UPPS-P Lack of Premeditation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.58 (1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.67 (1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.67 (1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-UPPS-P Sensation Seeking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.25 (2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.52 (2.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.02 (2.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Descriptive statistics for covariates are reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Profile Analysis Results\u003c/h2\u003e \u003cp\u003eIn the decision-making profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the test of equal levels was significant, \u003cem\u003eF\u003c/em\u003e (2,140)\u0026thinsp;=\u0026thinsp;5.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.06, indicating there was a significant difference between groups in grand mean scores across the profile, with a medium effect size. Tukey\u0026rsquo;s HSD post-hoc pairwise comparisons showed that standardised grand mean decision-making scores were significantly higher in the LWC group compared to the BED group, mean difference\u0026thinsp;=\u0026thinsp;0.294, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. Pairwise comparisons between the BED and HWC group, and between the two control groups, were non-significant (supplementary table S4.2.1). To further understand this result, we compared linear models predicting grand mean decision-making scores from group and from BMI separately and together, then repeated these using waist-hip ratio as an alternative measure of body composition. These analyses indicated the best fit model was that including only Group (supplementary table S4.2.2). The tests of parallelism and of flatness were non-significant, indicating the overall shape of the decision-making profile did not differ between groups, and that standardised scores did not differ significantly between domains within groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eHigher scores indicate higher levels of decision-making functioning\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn the psychological profile analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the test of equal levels was significant, \u003cem\u003eF\u003c/em\u003e (2, 162)\u0026thinsp;=\u0026thinsp;36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.31, indicating there was a significant difference between groups in grand mean scores across the profile, with a large effect size. Tukey\u0026rsquo;s HSD pairwise comparisons showed standardised grand mean scores were significantly higher in the BED group compared to both the HWC and LWC groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001), while the control groups did not differ from each other (supplementary table S4.2.3). The test of parallelism was also significant, \u003cem\u003eF\u003c/em\u003e (20, 308)\u0026thinsp;=\u0026thinsp;3.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.0001, Pillai\u0026rsquo;s Trace\u0026thinsp;=\u0026thinsp;0.37, indicating the overall shape of the profiles differed significantly between groups with a moderate effect size. The post-hoc MANCOVA model showed that together with group, depressive disorder history (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), BE symptom severity (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;.001), and race (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01) significantly predicted the shape of the psychological profile at the multivariate level (supplementary table S4.2.4). At the univariate level, the BED group differed significantly from both control groups on all domains of psychological functioning excluding restrained eating, positive urgency, and sensation seeking (supplementary table S4.2.5). Finally, the test of flatness was significant, \u003cem\u003eF\u003c/em\u003e (10, 153)\u0026thinsp;=\u0026thinsp;2.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01, \u003cem\u003eη\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.016 showing standardised scores varied between psychological domains within groups with a small effect size. Post-hoc pairwise comparisons indicated this was driven by elevated depression symptoms (supplementary table S4.2.6).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eHigher scores indicate higher levels of symptoms, or poorer psychological functioning.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe aimed to concurrently characterise cognitive and psychological mechanisms in binge eating disorder (BED) from a multi-stage decision-making perspective. Our hypotheses concerning the decision-making profile were partially supported. Individuals with BED showed an overall reduced level of decision-making functioning than lower-weight controls. Decision-making functioning in BED did not differ significantly from higher-weight controls. Contrary to hypotheses, group profiles were parallel, such that between groups, performance tended to fluctuate together relative to their grand means. Within groups, performance did not differ significantly between decision-making domains (i.e., profiles were flat). Our hypotheses concerning the psychological profile were supported. The BED group differed significantly in the overall level of their psychological functioning from both higher- and lower-weight control groups. The shape or nature of psychological profile also differed significantly between groups, and post-hoc tests showed BE symptom severity and depressive disorder diagnoses significantly predicted the shape of the psychological profile alongside BED diagnosis. Finally, the psychological profile was not flat, with the BED group showing particularly high levels of depressive symptoms, compared to other domains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur primary finding that overall decision-making functioning is compromised may help to explain core features of BED\u0026nbsp;that occur before, during, and after binge-eating episodes, including difficulties\u0026nbsp;inhibiting eating behaviour as an established response to internal or external cues (Giel et al., 2017b; Stott et al., 2021), and\u0026nbsp;difficulties adapting behaviour despite\u0026nbsp;negative consequences and desire to change\u0026nbsp;(Grant \u0026amp; Chamberlain, 2023). That unequal levels were found along with parallelism and flatness is an interesting feature of the profiles, showing decision-making functioning incorporated relatively similar strengths and weaknesses between groups, and was relatively consistent across domains within groups. This combination of outcomes suggests the profile is tapping into generalised, cross-domain dysfunctions as opposed to performance deficits that are unique to specific tasks or contexts\u0026nbsp;(Goschke, 2014; Morelli et al., 2022). This emphasises the value of conceptualising cognitive functioning in BED from a holistic, multi-domain perspective\u0026nbsp;(Dennison et al., 2022). A further interesting feature of the decision-making profile was that the BED group differed significantly from the lower-weight control group, but not from the higher-weight control group. This would suggest a shared role for decision-making dysfunction associated with excess weight among individuals with BED and without. However, our subsequent analyses indicated neither BMI nor waist-hip ratio explained this pattern of group comparisons in decision-making functioning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings that overall psychological functioning is compromised in BED aligns with prior research and theory indicating elevated negative emotionality, maladaptive eating-related tendencies, and certain impulsive traits are critical characteristics of BED (Fairburn et al., 2003; Leenaerts et al., 2023; Pearson et al., 2015; Williamson et al., 2004). However, our profile analysis approach provides some unique insights. Firstly, it is striking that the psychological profile clearly differentiated individuals with BED from both control groups, and that the two control groups had such similar scores across all domains. This provides a clear answer to the long-standing question in the field that these symptoms and tendencies are related to BED specifically, rather than higher weight more generally (Ag\u0026uuml;era et al., 2021; Steptoe \u0026amp; Frank, 2023). Variability between and within profiles highlighted high levels of depressive symptoms as a key feature of the psychological profile. This has important methodological implications, given that co-occurring mood symptoms and disorders are frequently excluded from research or poorly accounted for in analyses (Smith et al., 2018). Interestingly, though considerable prior research has focused on negative urgency (and to a lesser extent, positive urgency) as the impulsive traits associated with binge eating (Lavender \u0026amp; Mitchell, 2015), our analysis indicated lack of perseverance (a tendency to struggle to resist distractions and give up early in the face of boredom, fatigue, or difficulty), as a key feature of the BED profile (Whiteside \u0026amp; Lynam, 2001). While this trait has been reported as distinguishing individuals with BED from controls in prior research (Kenny et al., 2019), it has received little attention in the eating disorder literature to date.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study combined several important methodological assets. Firstly, we recruited a relatively large sample in comparison to similar studies in the field, strengthening the statistical power and generalizability of findings. This sample was diverse in age and race, closely representing the Australian population (ABS, 2024). However, it is important to note our sample included relatively few men, few individuals who identified as having First Nations heritage, and no individuals who identified as gender diverse. By matching groups on age, sex, and education, defining BED status according to structured clinical interviews as well as self-report symptom measures, characterising our sample according to co-occurring depression diagnoses, and including two control groups defined according to weight status, we addressed key methodological limitations in prior research (Colton et al., 2023; Smith et al., 2018). It is important to note that all cognitive assessment tasks used in this study employed neutral rather than food- or eating-related stimuli. This has been shown to be an important factor in neurocognitive research, however, as disorder-specific stimuli often produce larger group differences, this supports confidence in our findings (Berner et al., 2017). As our findings are cross-sectional, this study is primarily descriptive. It would be advantageous for future research to employ longitudinal methods to assess prospective relationships between decision-making functioning and binge eating disorder, and to relate changes in decision-making functioning to treatment outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn terms of clinical implications, evidence suggests poorer decision-making functioning predicts poorer treatment outcomes in BED, yet they are not routinely targeted by current treatment modalities (Goldschmidt et al., 2025; Lucas et al., 2021). Novel interventions that augment traditional psychotherapies by targeting broadly-defined decision-making mechanisms have shown promise in improving BED treatment outcomes in limited scale trials (Eichen et al., 2023; Juarascio et al., 2023; Schag et al., 2019). Similarly, neurostimulation interventions that target the biological underpinnings of decision-making mechanisms are also showing promising early results (Chmiel et al., 2024). However, larger scale trials are needed to confirm these initial findings. Lack of perseverance has also been shown to predict treatment outcomes in disorders that share some characteristics with BED, such as ADHD and behavioural addiction (Mallorqu\u0026iacute;-Bagu\u0026eacute; et al., 2019; Way et al., 2024). Given that treatments for eating disorders can be challenging and slow-going, bolstering perseverance through motivational, compassion-focused, and similar interventions could improve BED treatment engagement and success.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study shows a generalised profile of decision-making dysfunction is an important feature of binge eating disorder, alongside more well-recognised characteristics of elevated depression, anxiety, and stress symptoms, emotional- and external-eating tendencies, negative urgency, lack of perseverance, and lack of premeditation. By mapping out profiles across both decision-making and psychological domains of functioning concurrently, our findings align with a recent theoretical model of binge eating, which attempted to integrate cognitive and emotional drivers (Schaefer et al., 2023). This novel approach suggests there is value in breaking down barriers between typically siloed areas of research. To date, a single study has used a multi-modal assessment incorporating cognitive and psychological assessments to characterise subgroups with BED (Brucar et al., 2025). In this study, decision-making dysfunctions clustered with emotional characteristics to define three distinct subtypes of BED, in which negative emotionality, impulsive behaviour, and harm avoidance were dominant features respectively. Such insights hold promise for much needed improvements in BED treatment, indicating outcomes may be improved by tailoring approaches according to mechanistic phenotypes (Bryant et al., 2025; Levinson et al., 2025; Levinson et al., 2024). Thus far, individual mechanistic profiles in the psychological domain have been used effectively to guide personalised treatment (Levinson et al., 2021), however gaps remain in the decision-making domain (Allott et al., 2025).\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e is supported by an Australian Research Training Program (RTP) scholarship, and by an Australian Eating Disorders Research and Translation Centre Translat\u003cem\u003eED\u003c/em\u003e scholarship funded by the Australian Government, Commonwealth Department of Health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA, HC, EG, \u0026amp; BN\u003c/strong\u003eare also supported by Australian Research Training Program (RTP) scholarships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e is supported by the Australian Research Council (DP250102224, FT220100294).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVG\u0026nbsp;\u003c/strong\u003eis supported by an Australian National Health and Medical Research Council Investigators grant (2009464), and by the Australian Eating Disorders Research and Translation Centre funded by the Australian Government, Commonwealth Department of Health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors are not aware of any conflicts of interest relevant to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics \u0026amp; Consent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by Monash University Human Research Ethics Committee (MUHREC), reference 34518. All participants were provided a written explanatory statement of these procedures and gave informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeidentified data is available by reasonable request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT author contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEC:\u003c/strong\u003e Conceptualization; methodology; investigation; data curation; formal analysis; writing – original draft; writing – review \u0026amp; editing; visualisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCA, HC, KF, EG, BN, LT, KW:\u0026nbsp;\u003c/strong\u003eInvestigation;data curation;writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLBP; JM:\u003c/strong\u003e Software; data curation; project administration;writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTT-JC\u003c/strong\u003e: Methodology; supervision; writing – review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVG:\u003c/strong\u003e Conceptualization; funding acquisition; methodology; project administration; resources; supervision; writing – review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenerative AI was not used in the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the additional members of the Monash FoodCODE study research team, particularly Aryan Gupta and Dr Melissa Pelly, as well as all its participants.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eABS. 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The Five Factor Model and impulsivity: using a structural model of personality to understand impulsivity. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e,\u003cem\u003e\u0026nbsp;30\u003c/em\u003e(4), 669-689. https://doi.org/10.1016/S0191-8869(00)00064-7\u003c/li\u003e\n \u003cli\u003eWickham, H., Fran\u0026ccedil;ois, R., Henry, L., M\u0026uuml;ller, K., \u0026amp; Vaughn, D. (2023). \u003cem\u003edplyr: A grammar of data manipulation\u003c/em\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003eIn https://dplyr.tidyverse.org\u003c/li\u003e\n \u003cli\u003eWilfley, D. E., Pike, K. M., \u0026amp; Striegel-Moore, R. H. (1997). Towards an integrated model of risk for binge-eating disorder. \u003cem\u003eJournal of Gender, Culture, and Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2\u003c/em\u003e, 1-3.\u003c/li\u003e\n \u003cli\u003eWilliamson, D. A., White, M. A., York-Crowe, E., \u0026amp; Stewart, T. M. (2004). Cognitive-behavioral theories of eating disorders. \u003cem\u003eBehav Modif\u003c/em\u003e,\u003cem\u003e\u0026nbsp;28\u003c/em\u003e(6), 711-738. https://doi.org/10.1177/0145445503259853\u003c/li\u003e\n \u003cli\u003eWollenhaupt, C., Wilke, L., Erim, Y., Rauh, M., Steins-Loeber, S., \u0026amp; Paslakis, G. (2019). The association of leptin secretion with cognitive performance in patients with eating disorders. \u003cem\u003ePsychiatry Research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;276\u003c/em\u003e, 269-277. https://doi.org/10.1016/j.psychres.2019.05.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Binge Eating Disorder, Cognitive Functioning, Decision-Making, Impulsivity, Psychological traits","lastPublishedDoi":"10.21203/rs.3.rs-8254234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8254234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eBinge eating disorder (BED) is a highly prevalent mental disorder associated with metabolic complications, reduced functioning, and poor quality of life, resulting in significant disease burden. Disordered decision-making is thought to drive behaviour in BED, but the specific mechanisms underlying this dysfunction remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study compared multiple aspects of decision-making between people with BED and higher weight (BED, n\u0026thinsp;=\u0026thinsp;57), a control group matched by body mass index (BMI) without binge eating (HWC, n\u0026thinsp;=\u0026thinsp;54), and lower weight controls (LWC, n\u0026thinsp;=\u0026thinsp;54). We applied profile analyses to cognitive measures capturing three stages of decision-making: preference formation, choice implementation, and feedback processing. Additionally, we examined domains of psychological functioning shown to interact with cognitive mechanisms during decision-making \u0026ndash; negative emotionality, maladaptive eating-related tendencies, and impulsive traits.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found generalised decision-making dysfunction in individuals with BED compared to the LWC but not the HWC group. However, BMI did not explain these differences. Poor overall psychological functioning clearly distinguished BED from both control groups, with elevated depressive symptoms and lack of perseverance emerging as key psychological characteristics.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eBy mapping BED profiles across multiple components of decision-making, our findings indicate that domain-general cognitive dysfunction is an important mechanism in BED, alongside more well-recognised psychological features. These findings may further efforts to refine aetiological models of binge eating, providing more holistic and explanatory theories. They may also form a foundation for novel interventions and personalised approaches to treatment.\u003c/p\u003e","manuscriptTitle":"Profiling Decision-Making Mechanisms in Binge Eating Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 12:05:27","doi":"10.21203/rs.3.rs-8254234/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T21:34:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-24T19:59:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240893558351617044896997524736409585191","date":"2026-01-12T17:07:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T13:27:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T09:19:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T09:18:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Eating Disorders","date":"2025-12-01T22:33:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-eating-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"joed","sideBox":"Learn more about [Journal of Eating Disorders](http://jeatdisord.biomedcentral.com)","snPcode":"40337","submissionUrl":"https://submission.nature.com/new-submission/40337/3","title":"Journal of Eating Disorders","twitterHandle":"@JEatDisord","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5a1c654-f148-4bad-86f1-429af61a540c","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:07:43+00:00","versionOfRecord":{"articleIdentity":"rs-8254234","link":"https://doi.org/10.1186/s40337-026-01563-4","journal":{"identity":"journal-of-eating-disorders","isVorOnly":false,"title":"Journal of Eating Disorders"},"publishedOn":"2026-03-14 15:58:56","publishedOnDateReadable":"March 14th, 2026"},"versionCreatedAt":"2025-12-18 12:05:27","video":"","vorDoi":"10.1186/s40337-026-01563-4","vorDoiUrl":"https://doi.org/10.1186/s40337-026-01563-4","workflowStages":[]},"version":"v1","identity":"rs-8254234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8254234","identity":"rs-8254234","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}