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This code is based and forked from the code related to the publication: Tajima, S., Drugowitsch, J., and Pouget, A. Optimal policy for value-based decision-making. Nature Communications, 7:12400, (2016).

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Optimal-policy-for-value-based-decision-making-with-value-sensitive-noise

This code is based and forked from the code of the paper by Tajima, Drugowitsch & Pouget (2016) [1].

We extended the code to run numerical simulations of the Drift Diffusion Model (DDM) with multiplicative noise on the input stimuli. For equal case alternatives, the DDM with multiplicative noise shows value-sensitive reaction time in agreement with experimental results on humans, primates, and invertebrates.

CITATION:

[1] Satohiro Tajima*, Jan Drugowitsch*, and Alexandre Pouget. Optimal policy for value-based decision-making. Nature Communications, 7:12400, (2016). *Equally contributed.

USAGE: 'valueDecisionBoundaryRR.m' generates figures for various values of multiplicative noise.

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This code is based and forked from the code related to the publication: Tajima, S., Drugowitsch, J., and Pouget, A. Optimal policy for value-based decision-making. Nature Communications, 7:12400, (2016).

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