Experimental Design

a) Food rating task. Participants rated all food images and their corresponding Nutri-Scores in terms of taste, health, wanting and perceived caloric content on a continuous scale b) Trial sequence of food choice task. In each trial, participants made a binary choice between two food options represented by food image and corresponding Nutri-Scores; Feedback and fixation-based fixation dots were implemented c) Experimental procedures; blue refers to sated, yellow to hungry condition (order counterbalanced). VAS refers to visual analogue scale used to assess subjective feelings of hunger. PANAS refers to a questionnaire assessing mood (see SOM1). FEV II refers to a questionnaire assessing eating behavior (see SOM2); *indicates that these steps were only required in the first session.

Behavioral Results

a) Manipulation check: The green boxplot displays the difference (hungry-sated) in hunger state at arrival at the lab, yellow and blue boxplots display the difference (last timepointfirst timepoint) in hunger state in the hungry and sated condition, respectively. b) RT quantile plot displaying the cumulative probability of tasty (dashed lines) and healthy choices (solid lines) separately for the two conditions (quantiles are .1, .3, .5, .7, .9 of choices). c) and d) Probability to choose the left option as a function of taste and health value difference (leftright), respectively. Importantly, the dependency of choice on health information was eliminated under hunger. e) and f) Corresponding mean RTs as a function of taste and health value difference, respectively. For illustration purposes, value differences were segmented into 25 bins, and a locally weighted scatterplot smoothing technique was applied with a span of 0.75. Plots c-f) are based on all trials. Transparent shades indicate the standard errors of the smoothed choice probability and RT for the respective value bins (see also Figure S5).

Eye-Tracking Results

a) Dwell time difference between the tasty and healthy option was positively associated with the probability of choosing the tasty option in both conditions. b) The average probability to look at food image (taste attribute) compared to Nutri-Score (health attribute) was even higher in the hungry than sated condition. c) Path diagram with posterior means of the parameters, associated 95%-credible interval in squared brackets.

Quantitative Model Comparison

Posterior Predictive Checks maaDDM2ϕ

Quantile plots of simulated data with fitted parameters of the maaDDM2ϕ in blue (sated) and yellow (hungry) with highest density intervals (HDI) of each quantile (vertical lines) and behavior. Posterior predictive checks were performed by drawing 1000 parameter values from the individual posterior parameter distribution to simulate the new data.

Parameter estimates of maaDDM2ϕ

Group parameter estimates (blue = sated, yellow = hungry; left panels) and the effect of hunger state (gray; right panels). Dashed black lines indicate the 95% HDI. a) Estimated taste weights. In both conditions the weight is larger than .5, indicating a higher weight on taste compared to health. This preference was even stronger under hunger. b-f) Parameter estimates of d, nDT, α, θ and ϕT, and the corresponding effects of hunger state. g) Parameter estimates of ϕH and the corresponding effects of hunger state, showing that the attention-driven discounting of health information was amplified under hunger.

Illustration of the maaDDM2ϕ

The decision-making process underlying choice and RT data as conceived by the maaDDM2ϕ. The decision is assumed to emerge from a noisy evidence-accumulation process commencing from the starting point (β) and terminating at one of the two boundaries (here: 0 = healthy boundary and α = tasty boundary) representing the tasty and healthy choice, respectively. The non-decision time (nDT) reflects processes unrelated to the decision itself, here illustrated as stimulus encoding time. The drift rate represents the rate of evidence accumulation. It is determined by the scaled value difference (VD) of the displayed options, which in turn is given by the taste (T) and health (H) ratings of the options, the relative weight of tastiness ω vs. healthiness (1- ω) as well as the currently attended item on the screen (as illustrated by the differently colored segments and the corresponding images). The coloring scheme of the VD equation shows which part of the equation define the drift rate at any given attended item. Attending to the tasty option (here: chocolate bar with Nutri-Score E), and in particular to its taste information (i.e., the image), increases the drift towards the tasty boundary, while attending to the healthy option (here: cucumber with Nutri-Score B), and in particular to its health information (i.e., the Nutri-Score) increases the drift towards the healthy boundary.