Detecting categorical perception in continuous discrimination data

We present a method for assessing categorical perception from continuous discrimination data. Until recently, categorical perception of speech has exclusively been measured by
discrimination and identification experiments with a small number of repeatedly presented stimuli. Experiments by Rogers and Davis [1] have shown that using non-repeating
stimuli along a densely-sampled phonetic continuum yields a more reliable measure of categorization. However, no analysis method has been proposed that would preserve the continuous nature of the obtained discrimination data. In the present study, we describe a method of analysis that can be applied to continuous discrimination data without having to discretize the raw data at any time during the analysis.