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LICENSE.txt
README.md
analyze_outputs.m
common_to_all_curves.m
family_of_curves.m
family_of_distributions.m
importance_sampler.m
jbfill.m
likratiotest.m
logsumexp.m
preprocessing_setup.m
round_to.m
run_importance_sampler.m
savesamesize.m
scale_data.m
simulate_data.m
truncated_normal.m

README.md

The Probabilistic Curve Induction and Testing (P-CIT) toolbox was developed to estimate the shape of a curve relating a predictor variable (e.g., fMRI classifier estimates of memory ctivation) to a dependent variable (e.g., subsequent memory recall). The toolbox consists of a single directory of Matlab files with thorough documentation for ease of use and deployment. The toolbox was developed in Ken Norman's Computational Memory Lab at Princeton University ( http://compmem.princeton.edu/ )

Users of P-CIT should read the following documents, in the following order:

  1. Detre, G. J., Natarajan, A., Gershman, S. J., and Norman, K. A. (submitted). Moderate levels of activation lead to forgetting in the think/no-think paradigm. Neuropsychologia.

  2. The supplementary materials for the above paper.

  3. The P-CIT Toolbox Manual

Note that the Detre et al. paper and the accompanying supplementary materials are not yet in press, and they may change in minor ways before the paper is published.

DISCLAIMER: At this point in time, the P-CIT toolbox is beta software, which we are making available to anyone who might find it useful. While we have tested the toolbox extensively on our own data, it is still possible that bugs or other issues might arise (especially if your data differ substantially from the data that we have used for testing). If you find a bug or have any further suggestions (either for the software or for the documentation), you should let us know by emailing Ken Norman at knorman at princeton dot edu.