Deficient Goal-Directed Control in a Population Characterized by Extreme Goal Pursuit

Abstract

Research in computational psychiatry has sought to understand the basis of compulsive behavior by relating it to basic psychological and neural mechanisms: specifically, goal-directed versus habitual control. These psychological categories have been further identified with formal computational algorithms, model-based and model-free learning, which helps to provide quantitative tools to distinguish them. Computational psychiatry may be particularly useful for examining phenomena in individuals with anorexia nervosa (AN), whose self-starvation appears both excessively goal directed and habitual. However, these laboratory-based studies have not aimed to examine complex behavior, as seen outside the laboratory, in contexts that extend beyond monetary rewards. We therefore assessed (1) whether behavior in AN was characterized by enhanced or diminished model-based behavior, (2) the domain specificity of any abnormalities by comparing learning in a food-specific (i.e., illness-relevant) context as well as in a monetary context, and (3) whether impairments were secondary to starvation by comparing learning before and after initial treatment. Across all conditions, individuals with AN, relative to healthy controls, showed an impairment in model-based, but not model-free, learning, suggesting a general and persistent contribution of habitual over goal-directed control, across domains and time points. Thus, eating behavior in individuals with AN that appears very goal-directed may be under more habitual than goal-directed control, and this is not remediated by achieving weight restoration.

Food task menu used to determine outcomes for the food version of the 2-step task. Participants numbered items in order of preference. Performance on the task determined access to food items with better performance granting access to increasingly preferred items.

Figure 1.

Two-step decision tasks used to assess model-free and model-based learning. (A) Decision task with monetary outcomes. Alien treasure pieces were converted into a monetary bonus paid out at the end of the task to all participants. (B) Task with food outcomes. Food tokens were converted into access to a range of preferred food items, and the selected food was consumed as a snack after the task. (A, B) In both tasks, the Stage 1 choice determined the transition to the next stage according to a fixed probability. One choice was associated with transition to one particular Stage 2 state 70% of the time (Common transition) and the other state 30% of the time (Rare transition). At Stage 2, participants made choices followed by reward or no reward (both monetary and food outcomes were actualized after the task). Each Stage 2 option was associated with a probabilistic reward, which ranged from 0.25 to 0.75 and varied gradually (according to a Gaussian random walk) and independently across trials (see examples in bottom rows of A and B). (C) Example trial steps from the monetary task.

Figure 2.

Overall model-free and model-based contributions to learning for HCs and individuals with AN across task type (monetary and food) and session (Time 1 and Time 2). Error bars represent SEM.

Figure 3.

(A) Model-based contributions to learning for the monetary and food tasks collapsed across session (Time 1 and Time 2). (B) Model-based contributions to learning at Time 1 and Time 2 collapsed across task type (monetary and food task). Error bars represent SEM.

Figure 4.

Behavioral data—stay probability at Stage 1 as a function of transition type (common/rare) and the outcome (rewarded/unrewarded) on the previous trial—across groups, task types, and sessions. (A) Food outcome task at Time 1. (B) Food outcome task at Time 2. (C) Money outcome task at Time 1. (D) Money outcome task at Time 2. Error bars represent SEM.

Figure 5.

Raw data (A, C) and simulations (B, D) for overall stay probability at Stage 1 (A, B) and model-based effect (contrast measuring size of Reward × Rare interaction) for task outcome types and sessions (C, D). Error bars represent SEM.

Figure 6.

Association between model-based learning for food versus money outcomes and the YBC-EDS Total score (left) and Preoccupations subscale (right) in individuals with AN.