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This Wiki hosts a manual and comments on our psignifit 4 software. If you require additional information, or to report errors or questions please contact us: heiko.schuett@nyu.edu or felix.wichmann@uni-tuebingen.de
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A paper describing our method in detail and showing tests for the congruency of our method is published at Vision Research: Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data by Heiko H. Schütt, Stefan Harmeling, Jakob H. Macke, and Felix A. Wichmann.
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A clone of our toolbox in python is available at https://github.com/wichmann-lab/python-psignifit
First, install the toolbox.
Then, have a look at the Basic Usage or at the "demo_001.m" file, which cover similar content.
A more detailed tour can be found in our Demos, which cover most of the toolbox's content.
We would like to thank previous members of the Wichmann-lab who were involved in developing MCMC based methods of Bayesian inference for the psychometric function, most notably Ingo Fründ, Valentin Hänel, Frank Jäkel and Malte Kuss.
Furthermore, our thanks go to the reviewers of our manuscript and the students and colleagues who read the paper or tested the software and provided feedback: Nicole Eichert, Frank Jäkel, David Janssen, Britta Lewke, Lars Rothkegel, Joshua Solomon, Hans Strasburger, Tom Wallis, Uli Wannek, Christian Wolf and Bei Xiao
Finally we thank the user "wrongu" for an additional plotting function.