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Pain-free Bayesian inference for psychometric functions


Macke,  J
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Neural Computation and Behaviour, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schütt, H., Harmeling, S., Macke, J., Wichmann, F., & Wichmann, F. A. (2014). Pain-free Bayesian inference for psychometric functions. Poster presented at 2014 European Mathematical Psychology Group Meeting (EMPG), Tübingen, Germany.

Cite as: https://hdl.handle.net/21.11116/0000-0001-327B-D
To estimate psychophysical performance, psychometric functions are usually modeled as sigmoidal functions, whose parameters are estimated by likelihood maximization. While this approach gives a point estimate, it ignores its reliability (its variance). This is in contrast to Bayesian methods, which in principle can determine the posterior of the parameters and thus the reliability of the estimates. However, using Bayesian methods in practice usually requires extensive expert knowledge, user interaction and computation time. Also many methods|including Bayesian ones|are vulnerable to non-stationary observers (whose performance is not constant). Our work provides an efficient Bayesian analysis, which runs within seconds on a common office computer, requires little user-interaction and improves robustness against non-stationarity. A Matlab implementation of our method, called PSIGNFIT 4, is freely available online. We additionally provide methods to combine posteriors to test the difference between psychometric functions (such as between conditions), obtain posterior distributions for the average of a group, and other comparisons of practical interest. Our method uses numerical integration, allowing robust estimation of a beta-binomial model that is stable against non-stationarities. Comprehensive simulations to test the numerical and statistical correctness and robustness of our method are in progress, and initial results look very promising.