Algorithm for inferring chunks in discrete sequence production tasks using response times and errors
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Multi-faceted aspects of chunking enable robust algorithms: implementation

This package implements the method described in "Multi-faceted aspects of chunking enable robust algorithms" by Daniel E. Acuna, Nicholas F. Wymbs, Chelsea A. Reynolds, Nathalie Picard, Robert S. Turner, Peter L. Strick, Scott Grafton, and Konrad Kording, accepted in the Journal of Neurophysiology (http://jn.physiology.org/content/112/8/1849).

The algorithm was implemented by Daniel E. Acuna (daniel.acuna@northwestern.edu) If you have any questions, send him an email.

To run the algorithm, you need to first detrend your data so that you remove aspects of training that are largely unrelated to chunking. In our paper, we described a simple model that does this detrending.

We provide a full worked out example in 'demo.m', using data from one subject and one sequence. Our code needs the Statistics Toolbox from Matlab for detrending.