The drift diffusion model as the choice rule in inter-temporal and risky choice: A case study in medial orbitofrontal cortex lesion patients and controls

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1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls.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.

Reliability assessment of temporal discounting measures in virtual reality environments

“…First, leveraging the information contained in the full RT distributions can improve the stability of parameter estimates [37,38] . Second, by conceiving decision making as a dynamic diffusion process, a more detailed picture of the underlying latent processes emerges [39][40][41][42][43] . Recent studies, for instance, applied these techniques to temporal discounting, where they revealed novel insights into effects of pharmacological manipulation of the dopamine system on choice dynamics [42] .…”

“…Likewise, we applied these techniques to examine the processes underlying reinforcement learning impairments in gambling disorder [44] and decision-making alterations following medial orbitofrontal cortex lesions [41] . Importantly, most standard lab-based testing settings use keyboards, button boxes and computer screens to record responses and display stimuli during behavioral tasks.…”

“…Next, we incorporated response times (RTs) into the model by replacing the softmax choice rule with the drift diffusion model (DDM) [39][40][41][42] . The DDM models choices between two options as a noisy evidence accumulation that terminates as soon as the accumulated evidence exceeds one of two boundaries.…”

“…One drawback of a linear representation of the relationship between the drift-rate v and trial-wise value differences is that v might increase infinitely with high value differences, which can lead the model to under-predict RTs for high value differences [41] . In line with previous work [41,42] we thus included a third version of the DDM, that assumes a non-linear sigmoidal mapping from trial-wise value differences to drift rates (eq. 5 and 6) [39] :…”

“…Recent studies, for instance, applied these techniques to temporal discounting, where they revealed novel insights into effects of pharmacological manipulation of the dopamine system on choice dynamics [46] . Likewise, we applied these techniques to examine the processes underlying reinforcement learning impairments in gambling disorder [48] and decision-making alterations following medial orbitofrontal cortex lesions [45] . Importantly, most standard lab-based testing settings use keyboards, button boxes and computer screens to record responses and display stimuli during behavioral tasks.…”