These codes modify the analysis presented in the paper by Tajima, Drugowitsch & Pouget (2016) [1], to examine the implication of maximising geometric-discounted future rewards on the optimal policy, rather than maximising arithmetic mean reward rate.
N.B. reward rate across trials is not maximised, but set to 0; this has the effect of considering only single trial dynamics, since the cost of waiting for the next trial becomes zero.
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 used in Figs. 3 or 6 in the paper. You can switch the utility function and reward calculation assumed in the model by removing commented-out lines in the code, and toggling the value of the geometric variable, respectively