Signal Detection Theory is a theoretical framework that aims to analyze and understand the process of detecting the presence of a signal within a background of noise. It is a branch of psychophysics that deals with the systematic study of decision-making in the presence of uncertainty and ambiguity.
According to Signal Detection Theory, the detection of a signal is not solely determined by the intensity or strength of the signal itself, but also by several factors such as the observer's sensitivity, response bias, and the variability of the background noise. It considers that the human perception of signals operates within a flexible threshold, allowing for variability and subjectivity in differentiating between signal and noise.
The theory introduces several important concepts, including hit rate, false alarm rate, sensitivity (d'), response bias (c), and receiver operating characteristic (ROC) curves. Hit rate refers to the rate at which a signal is successfully detected, while false alarm rate indicates the rate at which a signal is incorrectly detected in the absence of a true signal. Sensitivity (d') quantifies an observer's ability to discriminate between signal and noise, whereas response bias (c) captures an observer's willingness to respond positively or negatively. ROC curves are graphical representations illustrating the relationship between hit rates and false alarm rates.
Overall, Signal Detection Theory provides a comprehensive framework for understanding and quantifying decision-making processes in the presence of uncertain and ambiguous signals, contributing to fields such as psychology, neuroscience, and communication studies.
