Figures and data
Affect induction and task design.
(A) Study timeline. BN and HC participants completed the study tasks on two separate days. On session 1, participants were randomly assigned to the neutral mood or negative mood induction before completing the Food Choice Task. On session 2, participants experienced the alternative mood induction before completing another run of the Food Choice Task. The mood inductions involved combinations of music and autobiographical writing. (B) Food Choice Task. During the Food Choice Task, both BN and HC participants rated 43 food items across three phases. In the Tastiness Ratings and Healthiness phases, participants rated each item on a 5-point Likert scale from Bad to Good and Unhealthy to Healthy, respectively. In the Choice phase, participants indicated their strength of preference for a presented food item, compared to a personally tailored neutral reference item.
(A) Food choice task behavior estimated from regression models. Re-analysis of the raw data excluding outlier response times (2.5% of trials n = 85) replicated original findings (Gianini et al., 2019). While both groups were less likely to choose high-fat foods (over the neutral reference item) than low-fat foods (over the neutral reference item), the BN group was even less likely than the HC group to choose high-fat food items. However, we did not identify any significant effects of the Affect Condition on choices. Error bars indicate 95% confidence intervals of the estimated effects. (B) Influence of health and taste ratings on food choice. Health ratings influenced food choice more in the BN group than in the HC group. Within the HC group, food choice was influenced more strongly by taste ratings than health ratings. Error bars indicate standard errors of the estimated coefficients. Note. Corresponding statistics are presented in Table S8. HC = healthy controls; BN = bulimia nervosa.
Example evidence accumulation trajectories predicted by the starting time diffusion decision model (stDDM).
Each trajectory is a simulated agent that considers one attribute alone before beginning to consider both tastiness and healthiness attributes together. (A) Tastiness onset delay. The dashed, green trajectory represents a case where positive healthiness information about a low-fat food item quickly biases the agent towards the accept threshold before taste information has time to influence the evidence accumulation process. The solid purple trajectory illustrates a case where evidence related to a high-fat food is initially biased towards the reject threshold while the aversive healthiness information dominates the evidence accumulation process. Once information about the item’s appetitive tastiness comes online, the evidence accumulation changes its trajectory towards the accept threshold. (B) Healthiness onset delay. The dashed, orange trajectory illustrates a case where evidence related to the highly appetitive tastiness attribute dominates the evidence accumulation process, influencing the trajectory to terminate at the accept threshold of a high-fat food before the healthiness attribute is considered. For the solid brown trajectory, a less appetitive high-fat food item is ultimately rejected once aversive healthiness information enters the evidence accumulation process. (C-D) Hypothesized model of affect-induced binge-eating. Each trajectory is a simulated agent with BN making a decision involving a high-fat food. During neutral affect (gray solid line), the high-fat food is trending towards the accept threshold until the onset of aversive healthiness information biases the trajectory towards the reject threshold. (C) Attribute-weight hypothesis. During negative affect, either the attribute weight for taste information increases (red dashed line) or the attribute weight for healthiness information decreases (blue dashed line). In both cases, the trajectory is ultimately biased towards the accept threshold. (D) Attribute-onset hypothesis. During negative affect, the initial delay shifts and taste information is accumulated longer before healthiness information comes online. With a longer delay in the onset of healthiness information, the evidence accumulation for the high-fat food has enough time to reach the accept threshold.
Parameter estimates.
Colors indicate diagnosis: purple = bulimia nervosa (BN); green = healthy controls (HC). Error bars represent the standard error of the mean. (A) Attribute onset. In the neutral condition, we observed a Group by Food Type cross-over effect: while the BN group showed a greater initial bias towards accumulating tastiness information of high-fat foods than low-fat foods, the HC group showed a greater tastiness information bias for low-fat foods than high-fat foods. After the negative affect induction, any Food Type-based distinctions disappeared, and both groups’ biases towards tastiness information increased, but this effect was more pronounced in the BN group. (B) Weight on tastiness information. The HC group put more weight on tastiness information than the BN group. The negative affect induction reduced tastiness weights for the HC group, but not the BN group. (C) Weight on healthiness information. The BN group put more weight on healthiness information than the HC group, especially for high-fat foods. The negative affect induction did not have significant effects on healthiness weights for either group.
Affect-induced changes in information onset predict subjective binge episodes.
In the negative affect condition (right facet), longer delays in accumulating healthiness information (i.e., reduced τS) for high-fat foods compared to low-fat foods was associated with more frequent subjective binge episodes. Line type and shape refer to Food Type: Solid lines and circles = Low fat foods; dashed lines and triangles = High fat foods.
Revised model of affect-induced binge-eating.
Each trajectory is a simulated individual with BN who considers the taste attribute alone (pink shaded sides of the panels) before considering both tastiness and healthiness attributes together (blue shaded sides of the panels). In both panels the solid purple trajectory illustrates a decision involving a low-fat food, where tastiness information has a weak, positive weight and healthiness information has a strong, positive weight. The dashed blue trajectory illustrates a decision involving a high-fat food, where tastiness information has a strong, positive weight and healthiness information has a strong, negative weight. (A) Low Negative affect. During neutral affect, the high-fat food is trending towards the accept threshold until the onset of aversive healthiness information biases the trajectory towards the reject threshold. (B) High Negative affect. During negative affect, the initial delay is shifted and tastiness information is accumulated longer before healthiness information comes online. With a longer delay in the onset of healthiness information, the evidence accumulation for the high-fat food has enough time to reach the accept threshold.
Linear mixed-effects regression analyzing overall negative affect across Group, Affect Condition and Timing
Linear mixed-effects regression analyzing attribute onset (τs) across Group, Affect Condition, and Food Type.
Simple effects analyses from linear mixed effects model predicting attribute onset (τs).
Linear mixed-effects regression analyzing tastiness attribute weights (ωtaste) across Group, Affect Condition, and Food Type
Simple effects analyses from linear mixed effects model predicting taste weights (ωtaste).
Linear mixed-effects regression analyzing healthiness attribute weights (ωhealth) across Group, Affect Condition, and Food Type.
Simple effects analyses from linear mixed effects model predicting health weights (ωhealth).
Negative binomial models of symptom severity.
Logistic mixed-effects regression analyzing food choice across Affect Condition and Food Type split by Group.
Logistic mixed-effects regression analyzing choice across Group and Affect Condition using tastiness and healthiness attribute ratings.
Logistic mixed-effects regression analyzing self-control across Group and Affect Condtion.
Linear mixed-effects regression analyzing log-transformed response times across Group and Affect Condition using tastiness and healthiness attribute ratings.
Model fit metrics for alternative model specifications.
All models were estimated with the time-varying drift rate but differed in which parameters varied by Food Type.
Confusion matrix indicating frequency with which each candidate model was selected for each simulated model
Analysis of variance using mixed effects models to assess deviance of M0 predicted choice frequencies compared to empirical data
Analysis of variance using mixed effects models to assess deviance of M1 predicted choice frequencies compared to empirical data
Analysis of variance using mixed effects models to assess deviance of M2 predicted choice frequencies compared to empirical data
Analysis of variance using mixed effects models to assess deviance of M3 predicted choice frequencies compared to empirical data
Logistic mixed-effects regression assessing choice predictions from Model M0.
Logistic mixed-effects regression assessing choice predictions from Model M1.
Logistic mixed-effects regression assessing choice predictions from Model M2.
Logistic mixed-effects regression assessing choice predictions from Model M3.
Linear mixed-effects regression assessing non-decision time (rND) across Group and Affect Condition,
Linear mixed-effects regression assessing boundary separation (a) across Group and Affect Condition,
Simple effects analyses from linear mixed effects model predicting boundary separation (a).
Linear mixed-effects regression assessing starting point (z) across Group and Affect Condition,
Linear mixed-effects regression assessing the Anger POMS subscale as a function of Group, Affect Condition, and Timing.
Linear mixed-effects regression assessing the Confusion POMS subscale as a function of Group, Affect Condition, and Timing.
Linear mixed-effects regression assessing the Depression POMS subscale as a function of Group, Affect Condition, and Timing.
Linear mixed-effects regression assessing the Fatigue POMS subscale as a function of Group, Affect Condition, and Timing.
Linear mixed-effects regression assessing the Tension POMS subscale as a function of Group, Affect Condition, and Timing.
Linear mixed-effects regression assessing the Vigor POMS subscale as a function of Group, Affect Condition, and Timing.
Linear regression models of negative urgency.
Linear regression models of the restraint subscale of EDE-Q.
Results from the parameter recovery exercise.
Note, α = boundary separation; ωhealth = health coefficient, ωtaste = taste coefficient, τND = non-decision time, τs = relative starting time, z = starting point bias.
