Signal Detection Analysis

Purpose

This skill encodes expert methodological knowledge for applying Signal Detection Theory (SDT) to behavioral and cognitive science data. SDT separates an observer's perceptual sensitivity from their decision criterion -- a distinction that raw accuracy conflates. A competent programmer without cognitive science training would typically compute percent correct, missing the critical insight that two observers with identical accuracy can differ drastically in their ability to detect signals vs. their willingness to say "yes."

When to Use SDT (Not Simple Accuracy)

Use SDT whenever:

• Stimuli belong to two classes (signal vs. noise, old vs. new, present vs. absent) and the observer makes a binary classification
• You need to distinguish how well someone can discriminate (sensitivity) from how willing they are to respond in a particular way (bias/criterion)
• Response bias may differ across conditions, groups, or time points, making raw accuracy misleading
• You want a measure that is independent of base rates and payoff structures

Do not use standard SDT when:

• There are more than two stimulus classes (use multi-class extensions or confusion matrices)
• Responses are continuous rather than categorical (use regression-based approaches)