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Healthful choices depend on the latency and rate of information accumulation


The drift diffusion model provides a parsimonious explanation of decisions across neurobiological, psychological and behavioural levels of analysis. Although most drift diffusion model implementations assume that only a single value guides decisions, choices often involve multiple attributes that could make separable contributions to choice. Here we fit incentive-compatible dietary choices to a multi-attribute, time-dependent drift diffusion model, in which taste and health could differentially influence the evidence accumulation process. We find that these attributes shaped both the relative value signal and the latency of evidence accumulation in a manner consistent with participants’ idiosyncratic preferences. Moreover, by using a dietary prime, we showed how a healthy choice intervention alters multi-attribute, time-dependent drift diffusion model parameters that in turn predict prime-dependent choices. Our results reveal that different decision attributes make separable contributions to the strength and timing of evidence accumulation, providing new insights into the construction of interventions to alter the processes of choice.

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Fig. 1: Examples of the decision process modelling within the mtDDM.
Fig. 2: Predicted results of the mtDDM.
Fig. 3: Behavioural results.
Fig. 4: Attribute drift latency parameters related to choice.

Data availability

Data generated during this study are posted at the Open Science Framework at

Code availability

Custom codes used for stimulus presentation, analysis and modelling are posted at the Open Science Framework at


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This work was conducted using institutional funding provided by Duke University. The authors thank I. Krajbich for suggesting the use of the term ‘latency’ to describe attribute-wise non-decision times.

Author information




N.J.S. and S.A.H. designed the study and wrote the paper. N.J.S. developed the models, collected data and analysed data.

Corresponding author

Correspondence to Nicolette J. Sullivan.

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Competing interests

The authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the interpretation of the article.

Additional information

Peer review information Nature Human Behaviour thanks Eran Eldar, Sanjay Manohar and Uku Vainik for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Flow of a choice trial.

Participants made 300 binary choices with free response time. Foods were displayed on the left and right sides of the screen. After keyboard response, the chosen food was highlighted in green for 200 ms to reflect participant response. Between trials, a fixation cross was displayed in the center of the screen for between 200 and 500 ms (mean 350 ms; i.i.d. distributed). Although icons are displayed here, stimuli used were high-resolution real common snack foods photographed on a black background. Icons made by Nikita Golubev & Vectors Market from

Extended Data Fig. 2 Difference between recovered and true simulated parameters.

a, Each ‘true’ mtDDM parameter is plotted against its recovered estimate. The distribution of differences between true and recovered parameters are shown below each scatterplot. b, The difference in Drift Slopes and Latencies for Taste and Health are plotted, with the true parameters of the simulations plotted against their recovered parameters. The distribution of differences in true and recovered parameter differences (Taste-Health) are shown below each scatterplot. In each scatter plot, the black line represents a perfect correlation line. In each histogram, the black line represents the mean difference between true and recovered parameter.

Extended Data Fig. 3 Model comparisons.

Please add a caption for Extended Data Fig. 3 here.

Extended Data Fig. 4 Association between health’s drift latency, response times, and healthy choices.

Models 3 and 4 use latency fit to one half of trials to predict healthy choice on the other half of trials.

Extended Data Fig. 5 Influence of prime on mtDDM parameters.

a, Drift slopes for food tastiness and healthfulness by prime condition. b, Difference in taste and health drift slopes by prime condition. For both plots, the center line is the median and box edges represent the 25th and 75th percentiles. The error bars represent the extent of the data MATLAB’s boxplot considered to be not outliers, and black crosses represent outliers. Outliers are determined using MATLAB’s default algorithm, in which outliers are data points larger (smaller) than the 75th (25th) percentile plus (minus) 1.5 times the difference between the 75th and 25th percentiles.

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Supplementary Methods, Supplementary Results, Supplementary Figs. 1–6 and Supplementary Table 1.

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Sullivan, N.J., Huettel, S.A. Healthful choices depend on the latency and rate of information accumulation. Nat Hum Behav (2021).

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