Trial-by-trial estimates of mixture component probabilities

This code is an extension to a function we released previously for fitting a mixture model [1] to errors on working memory tasks. The new method is described in [2]. The MATLAB code can be downloaded here [zip].

This code is released under a GNU General Public License: feel free to use and adapt these functions as you like, but credit should be given if they contribute to your work, by citing:

Schneegans S & Bays PM. No fixed item limit in visuospatial working memory. Cortex 83: 181-193 (2016)

If you refer to this code directly, please also include the URL of this webpage, or my homepage bayslab.com.

Notes on use

The new CO16_fit function operates identically to the previous JV10_fit function (described here) except it returns an additional output W:

[B LL W] = CO16_fit(X, T, NT)

W is an (n×3) matrix of trial-by-trial posterior probabilities that responses are drawn from each of the three mixture components. Each row corresponds to a separate trial and is of the form [wT wN wU], corresponding to the probability that the response on that trial came from the target, non-target or uniform response distributions, respectively.

References

[1] Bays PM, Catalao RFG & Husain M. The precision of visual working memory is set by allocation of a shared resource. Journal of Vision 9(10): 7, 1-11 (2009) [pdf]

[2] Schneegans S & Bays PM. No fixed item limit in visuospatial working memory. Cortex 83: 181-193 (2016) [pdf]