A More Parsimonious Approach to Estimating Signal Detection Theory Measures

Abstract

Signal Detection Theory (SDT) has proven to be an effective tool for analyzing perceptual and decision-making performance in many different areas. The main contribution of SDT is its capability to separate the discrimination process from the decision process by distinguishing between measures of sensitivity and measures of response bias. However, the usefulness of the traditional SDT measures of sensitivity, d', and response bias, c, is questionable in applied settings where observers do not make decisions based on an underlying psychophysical continuum. The purpose of our research was to derive alternative measures of sensitivity and response bias, overcoming the limitations of the traditional SDT model. Results from a Monte Carlo simulation showed support for our more parsimonious model.

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