Modelling working memory precision in R

Functions for modelling continuous response working memory tasks

Update: I’ve since wrapped this code up in a R package.

I have translated Paul Bays’ Matlab functions for modeling continuous response data into R . Bays’ guide to the Matlab functions and their usage can be found here. I have a detailed guide to the R functions and their usage over on Github.

A typical precision task has participants recall a feature of stimulus on a continuous scale rather than using classic correct/incorrect scoring. The model described in Bayes et al. (2009) can be used to model responses on such a tasks as a mixture of different response distributions corresponding to recalling the target item, a non-target item, and guessing.

For more information on the theoretical perspective that has developed from the use of precision tasks see Ma et al. (2014) and Fallon et al. (2016).

If you use this code please refer to Bays et al. (2009) and Paul Bays’ website.


Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10):7, 1-11.
Fallon, S. J., Zokaei, N., & Husain, M. (2016). Causes and consequences of limitations in visual working memory. Annals of the New York Academy of Sciences.
Ma, W. J., Husain, M., & Bays, P. M. (2014). Changing concepts of working memory. Nature Neuroscience, 17(3), 347–56.

See also