Sub-optimality in motor planning is not improved by explicit observation of motor uncertainty

This repository accompanies the manusript by Ota et al., (2019), accepted in Scientific Reports. It includes subject's data and the scripts for generating figures reported in the paper.

Overview of the data structure

The data structure is as follows:

Decision task

bdt_optAim: optimal aim point in each block

bdt_optEG: maximized expected gain

sigma: standard deviation of subject's endpoint

aimpoint: subject's mean endpoint

miss: number of mistrials

EG: expected gain, subject's total score / 50 trials

to: number of timeout trials

Full information task

dot_noise: standard deviation used in a sample from a Gaussian distribution

dot_optAim: theoretical optimal setting

dot_endpoint: subjective optimal setting in each trial

Variables of dotsL, gauss, gaussL, uni, and uniL have the same structure above. L means a wide width condition.
gauss and uni indicate a Gaussian probability density function and uniform PDF, respectively.

If you have any questions, please let me know.

Reference

Ota, K., Shinya, M., Maloney, L. T., & Kudo, K. (2019). Sub-optimality in motor planning is not improved by explicit observation of motor uncertainty. Scientific Reports, 9:14850.