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Abstract

In this tutorial, we will discuss some statistical techniques that one can use in order to obtain a more accurate statistical model of the relationship between experimental variables and psychophysical performance. We will use models which include the effect of additional, non-stimulus determinants of behaviour, and which therefore give us additional flexibility in analysing psychophysical data. For example, these models will allow us to estimate the effect of experimental history on the responses on an observer, and to automatically correct for errors which can be attributed to such history-effects. By reanalysing a large data-set of low-level psychophysical data, we will show that the resulting models have vastly superior statistical goodness of fit, give more accurate estimates of psychophysical functions and allow us to detect and capture interesting temporal structure in psychophysical data. In summary, the approach presented in this tutorial does not only yield more accurate models of the data, but also has the potential to reveal unexpected structure in the kind of data that every visual scientist has plentiful-- classical psychophysical data with binary responses.