 The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controlsJan Peters and Mark D’Esposito PLOS Computational Biology, 2020, vol. 16, issue 4, 1-25 Abstract: Sequential sampling models such as the drift diffusion model (DDM) have a long tradition in research on perceptual decision-making, but mounting evidence suggests that these models can account for response time (RT) distributions that arise during reinforcement learning and value-based decision-making. Building on this previous work, we implemented the DDM as the choice rule in inter-temporal choice (temporal discounting) and risky choice (probability discounting) using hierarchical Bayesian parameter estimation. We validated our approach in data from nine patients with focal lesions to the ventromedial prefrontal cortex / medial orbitofrontal cortex (vmPFC/mOFC) and nineteen age- and education-matched controls. Model comparison revealed that, for both tasks, the data were best accounted for by a variant of the drift diffusion model including a non-linear mapping from value-differences to trial-wise drift rates. Posterior predictive checks confirmed that this model provided a superior account of the relationship between value and RT. We then applied this modeling framework and 1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls. Analyses of DDM parameters revealed that patients showed substantially increased non-decision times and reduced response caution during risky choice. In contrast, vmPFC/mOFC damage abolished neither scaling nor asymptote of the drift rate. Relatively intact value processing was also confirmed using DDM mixture models, which revealed that in both groups >98% of trials were better accounted for by a DDM with value modulation than by a null model without value modulation. Our results highlight that novel insights can be gained from applying sequential sampling models in studies of inter-temporal and risky decision-making in cognitive neuroscience.Author summary: Maladaptive changes in decision-making are associated with many psychiatric and neurological disorders, e.g. when people are making impulsive or risky decisions. For understanding the processes of how such decisions arise, it can be informative to examine not only the choices that people make, but also the response times associated with these decisions. Here we show that response times during impulsive and risky decision-making are well accounted for by a model that has been developed to describe perceptual decision-making, the drift diffusion model. Furthermore, we use this model to examine impulsive and risky choice following damage to a core regions of the brains decision-making circuitry, the ventromedial / orbitofrontal cortex. Although this region has repeatedly been shown to contribute to value processing, modeling revealed that lesions to this area do not render reponse times less dependent on value. Our results highlight that novel insights can be gained from applying such models in studies of impulsive and risky choice in cognitive neuroscience. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1007615 DOI: 10.1371/journal.pcbi.1007615 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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