Forschungskolloquium

Many everyday decisions, such as buying a phone or choosing a hotel, require integrating information from multiple attributes to identify the best option. Our recently proposed Multi-Attribute Search & Choice (MASC; Gluth et al., 2024) theory suggests that this process follows a hierarchical Bayesian belief-updating scheme, where posterior beliefs about attribute values, and consequently about options, are iteratively updated based on information sampled during fixations. Once one option is deemed sufficiently superior, a choice is made. In this EEG and eye-tracking study, 57 participants performed a multi-attribute choice task, choosing between two smartphones based on star ratings for three attributes (battery capacity, screen size, storage space). We fitted MASC to individual eye-tracking data and, by simulating the model with the best-fitting parameters, derived predictions about the strength of belief updating at each fixation. Using linear deconvolution modeling, we used these model-derived estimates to predict EEG activity evoked by fixations, while also controlling for overlapping activity evoked by successive fixations and confounds such as saccade amplitude. Our findings demonstrate that MASC not only captures key behavioural patterns but also provides meaningful predictions at the neural level. Regression analyses, corrected for multiple comparisons using threshold-free cluster enhancement, revealed that predicted belief updating at the attribute level was associated with a transient, P3-like modulation over central electrodes, while belief updating at the option level corresponded to a later, more sustained centroparietal positivity. These findings offer converging behavioural and neural evidence that multi-attribute decision-making involves dynamic, fixation-driven belief updating consistent with Bayesian inference as formalized by MASC.

Compulsive-use disorders like binge eating affect millions and impose substantial costs through psychosocial impairment and health risks. Despite their prevalence, the cognitive mechanisms maintaining compulsive behavior remain poorly understood. Individuals with binge eating preferentially pursue highly palatable foods while forgoing alternative rewards, suggesting aberrant reward processing. However, prevailing models fail to explain how this altered reward processing maintains compulsive eating or how emotional distress modulates these processes.This talk presents a neurocomputational framework proposing that impaired reward discriminability—the ability to detect differences between potential rewards—drives maladaptive over-selection of highly palatable foods in binge-type eating disorders. I will first present behavioral and neural evidence showing that healthy adults exhibit enhanced discriminability for high-value versus low-value rewards, motivating a computational model of value-sensitive decision-making. Next, I will demonstrate how negative emotions bias food choices toward taste over health considerations, with this effect amplified in bulimia nervosa. Finally, I will discuss ongoing work testing whether individuals with binge eating show reward-type-specific discriminability deficits: enhanced for food rewards but impaired for non-food rewards. Together, these findings illuminate the neurocomputational mechanisms underlying maladaptive reward seeking and suggest novel intervention targets for eating disorders.