Code for comment on Tajima et al. (2019). Optimal policy for multi-alternative decisions. Nature Neuroscience.
These codes modify the analysis presented in the paper by Tajima et al. (2019), 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.
All scripts are written in MATLAB and have been tested with MATLAB R2018b. They should also run on earlier versions of MATLAB.
To run the scripts, download them, extract them into a folder of your choice, and navigate to this folder within MATLAB.
To use any of the script in the fig folders, you need to first point MATLAB to the shared utility functions by calling
addpath('shared/')
addpath('shared/bads-1.0.4/')at the MATLAB command line. See the individual fig folders for further usage instructions.
To switch between maxisation of geometrically-discounted rewards and arithmetic mean reward rate, toggle the value of the geometric variable.
To scale option values change the value of the valscale variable.
To change between linear and various nonlinear utility functions uncomment the appropriate utilityFunc definition.