Signal Detection Theory is a framework used to understand how people make decisions under uncertainty. It helps explain how we distinguish between a true signal (like a sound or light) and noise (irrelevant information) in various situations, such as in medical diagnoses or security screenings. The theory considers both the sensitivity to the signal and the decision-making criteria of the observer. In this theory, two key concepts are hit rate and false alarm rate. A hit occurs when a signal is correctly identified, while a false alarm happens when noise is mistakenly identified as a signal. By analyzing these rates, researchers can assess an individual's ability to detect signals amidst distractions.