What is signal detection theory?

Signal detection theory is a framework for understanding how individuals make decisions in the presence of uncertainty. It was originally developed in the field of psychophysics to study how humans and animals detect signals in noisy and uncertain environments.

Key concepts in signal detection theory include:

• Signal: A stimulus or event of interest that the individual is trying to detect, such as the presence of a target in a visual display or the sound of a faint noise in a noisy environment.
• Noise: Random background information or distractions that can interfere with the detection of the signal.
• Decision criteria: The individual's internal threshold for determining whether a stimulus is present or absent. This can vary depending on factors such as the importance of correctly detecting the signal or the consequences of a false alarm.
• Sensitivity: The ability of the individual to discriminate between the signal and the noise. This is typically measured by d' (d prime), which represents the distance between the means of the signal and noise distributions divided by their standard deviations.
• Response bias: The tendency of the individual to lean towards responding "yes" or "no" in situations of uncertainty. This can lead to errors such as false alarms (detecting a signal when none is present) or misses (failing to detect a signal that is present).

Signal detection theory has been applied in various fields beyond psychophysics, including cognitive psychology, economics, marketing, and clinical psychology. It can help researchers and practitioners understand how individuals make decisions in complex and uncertain situations, and how factors like motivation, experience, and cognitive resources influence their performance.