Methodological Guide: Signal Detection Analysis for Perceptual Judgements of Deceptive and Genuine Actions

Abstract

Accurate judgements of others’ actions are essential during time-constrained interactions, but deceptive signals often make this difficult. Traditional analyses of deception rely on separate measures of response accuracy for genuine and deceptive trials, with researchers often making inferences from accuracy on deceptive trials alone. This approach is limited in that it does not directly measure the ability to differentiate between deceptive and genuine actions nor the possibility that expertise effects are confounded by differences in response bias. Signal detection analysis provides two key indices: discriminability (d′), reflecting the ability to differentiate genuine from deceptive actions, and bias (c), indicating tendencies toward judging actions as genuine or deceptive. This article outlines the conceptual advantages of signal detection analysis over conventional methods and provides practical guidance for calculating discriminability and bias, including corrections for extreme values, with a worked example for a repeated-measures ANOVA. By adopting signal detection analysis, researchers can better capture the perceptual and cognitive processes underlying perception of deception, quantifying susceptibility to, and detection of deception, as well as exploring contextual influences such as prior expectations. This approach offers a more comprehensive understanding of expertise effects and opens new avenues for training and perceptual research.