Do not make decisions on an empty stomach: the impact of hunger state on attention and dietary choice processes

Hunger is a biological drive, with the function of motivating a mechanism to eat to reach homeostasis. Hungry participants are particularly likely to choose hedonic food options. Here we apply a version of a sequential sampling model to elucidate the mechanisms underlying the hunger-driven impairment in healthy dietary choice. We implemented a binary food choice task, in which two food images (representing tastiness of the option) and their respective Nutri-Score (representing healthiness of the option) appeared on the screen. Participants completed the task in a hungry and a satiated state (within-subject design) while their eye-movements were being recorded. In line with our hypothesis, behavioral evidence demonstrated that participants were more likely to choose tasty over healthy food items, and this difference was amplified by hunger state. To identify hunger-driven effects on decision processes, we used an extension of the Drift Diffusion Model, the multi attribute-time dependent Drift Diffusion Model (mtDDM) (Sullivan & Huettel, 2021, Nat Hum Behav), which allows the options’ underlying attributes (here taste and health) to influence the decision process with different latencies and different weights. Applying the mtDDM to our data, we found that in both conditions’ health latencies were significantly later than taste latencies and health weights were significantly lower than taste weights. When comparing conditions, there was no significant influence of hunger state on the attributes’ latencies. However, we found that health weights were significantly reduced in the hungry compared to the sated condition, while taste weights were unaffected. This suggests that poor dietary choices under hunger are driven primarily by an impairment in health consideration in the decision process. Notably, our modeling results also revealed that the mtDDM predicts that more than 20% of the estimated responses are made faster than the estimated latencies, that is, before any attribute information comes into play. While the purpose of the mtDDM is to predict multi-attribute choice, we would argue that it may not be suited for tasks in which the underlying attributes are represented distinctly. Further analyses of the eye-tracking data, combined with different implementations of dynamic process model of decision making will extend our understanding of the effects of hunger on attentional dynamics and preference formation in dietary decision making.