Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling

Nitzan Shahar, Tobias U Hauser, Michael Moutoussis, Rani Moran, Mehdi Keramati, Consortium Nspn and Raymond J Dolan

PLOS Computational Biology, 2019, vol. 15, issue 2, 1-25

Abstract: A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.Author summary: In this paper, we report a reliability analysis for the estimation of “model basedness”—a psychological construct that informs a wealth of studies in animal, human and clinical research. We consider an exemplar paradigm, the two-step task, widely used in the recent literature. We report low reliability for model-agnostic model-based estimates, as well as computational model parameter estimates. We suggest how a model-based/free trade-off might affect reaction-time variability in this task, and go on to suggest use of model parameter estimates based on a combination of choice and RT. Finally, we demonstrate that combining choice and RT estimates improves both model-agnostic and algorithmic model-based estimates.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1006803

DOI: 10.1371/journal.pcbi.1006803

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