Signal detection analyses refer to a statistical method used in various fields, including psychology, neuroscience, and engineering, to measure and interpret the ability to differentiate between meaningful information (signal) and meaningless noise (background). It focuses on identifying and quantifying the sensitivity of a system or individual in detecting a specific stimulus or event from among various distractors.
Signal detection analyses take into account two key components: sensitivity and response bias. Sensitivity refers to the ability to accurately detect the presence (signal) or absence (noise) of a target stimulus. It measures how well the system or individual discriminates between signal and noise, typically assessed by metrics such as d' (d-prime).
Response bias, on the other hand, relates to an individual's predisposition to favor one response over another, even in the absence of clear evidence. It quantifies factors such as a tendency to be more conservative (responding "no" more frequently to avoid false alarms) or more liberal (responding "yes" more often to avoid misses) in decision-making.
Signal detection analyses employ various statistical models, such as receiver operating characteristic (ROC) curves, hit rate, false alarm rate, and related parameters, to characterize the discriminability (sensitivity) and decision-making criteria (response bias) of a system or individual. By providing a quantitative approach to evaluate and compare performance across different conditions, signal detection analyses contribute to a deeper understanding of information processing, the detection of weak or subtle signals, and decision-making processes.
