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

Mark D’Esposito,Jan Peters,Ulrik R. Beierholm

doi:10.1371/journal.pcbi.1007615

Mark D’Esposito, Jan Peters + Show 1 more

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https://doi.org/10.1371/journal.pcbi.1007615

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1) reproduced our previous results regarding temporal discounting in vmPFC/mOFC patients and 2) showed in a previously unpublished data set on risky choice that vmPFC/mOFC patients exhibit increased risk-taking relative to controls.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

Highlights

Understanding the neuro-cognitive mechanisms underlying decision-making and reinforcement learning[1,2,3] has potential implications for many neurological and psychiatric disorders associated with maladaptive choice behavior[4,5,6]
We carried out a number of simple sanity checks which confirmed that log (k) parameters estimated via standard softmax and via the drift diffusion model (DDM) showed good correspondence (S3 Fig)
We examined DDM mixture models to test whether vmPFC/mOFC damage affected the proportion of trials that were best described by a value DDM as compared to the DDM0

Summary

Introduction

Understanding the neuro-cognitive mechanisms underlying decision-making and reinforcement learning[1,2,3] has potential implications for many neurological and psychiatric disorders associated with maladaptive choice behavior[4,5,6]. In perceptual decision-making, sequential sampling models such as the drift diffusion model (DDM) that account for the observed choices and for the full response time (RT) distributions have a long tradition[9,10,11]. A drift rate of zero would indicate chance level performance, as the evidence accumulation process would have an equal likelihood of terminating at the upper or lower boundaries (for a neutral bias). The starting point or bias parameter z determines the starting point of the evidence accumulation process in units of the boundary separation, and the non-decision time τ reflects components of the RT related to stimulus encoding and/or response preparation that are unrelated to the evidence accumulation process. The DDM can account for a wide range of experimental effects on RT distributions during two-alternative forced choice tasks[9]

Results

Discussion

Conclusion