![]() It describes the basics of SDT and demonstrates its applicability, with examples drawn largely from auditory and speech perception. This seminal book, more than any other, introduced SDT to researchers in psychology. Signal detection theory and psychophysics. ![]() This information will most interest readers who seek to understand how neural noise and other basic factors limit human perception and decision making. ![]() Opening chapters examine the ROCs resulting from the traditional assumption of Gaussian noise distributions the text then considers Poisson and other distributions. This book focuses on receiver operating characteristics (ROCs), which are integral to SDT and describe how changes in bias affect hit and false alarm rates. Signal detection theory and ROC analysis. Readers who find these books challenging may wish to first examine McNicol 1972 and Wickens 2002, or some of the articles listed under Sample Applications of Signal Detection Theory and Methodological Considerations.Įgan, J. P. They have a broad scope, while Swets 1996, Swets and Pickett 1982, and Egan 1975 have a narrower focus that may suit the needs of readers with specific interests. However, the references provide a wealth of useful information to contemporary readers-online reviews frequently describe them as “invaluable classics.” Green and Swets 1966 and Macmillan and Creelman 2005 are essential readings for any serious scholar of SDT. None were published recently even Macmillan and Creelman 2005 is based upon a 1991 edition. The references in this section are all books that provide solid overviews of SDT, though they vary in the degree to which they emphasize the mathematical underpinnings of SDT and extensions of the theory. A final group of publications examines models that extend SDT by relaxing the assumptions upon which it is based, considering novel and complex applications, or exploring links to other widely used models. Other publications examine how closely human decision-making approaches the theoretical optimum described by SDT. A frequent goal is to demonstrate how the understanding of a particular phenomenon changes when sensitivity is distinguished from bias. Indeed, the literature is filled with publications that apply SDT to a wide range of problems. These measures are now routinely assessed in such diverse areas as memory, medicine and clinical diagnosis, library science, weather forecasting, and hazard detection by motor vehicle operators. These early SDT publications derived “pure” measures of sensitivity, including d’ and A’, and “pure” measures of bias, such as β and c. For example, the percentage of correct responses is often conceptualized as reflecting sensitivity, but it changes when bias changes. Early SDT publications demonstrated that common performance measures confound sensitivity and bias. A listener will more likely report hearing a faint tone when each hit earns $10 and each false alarm costs $1 (bias is set to favor hits), than when the rewards and penalties are reversed (bias is set to avoid false alarms). Bias is the tendency to state that a signal is present, and it also affects hit and false alarm rates. For example, sensitivity to an auditory tone increases when the tone becomes louder or when the noise in which it is presented becomes quieter. Sensitivity is the ability to distinguish the presence of a signal from its absence. A key notion here is that perception involves decision: Was that faint tone simply imagined, or was it actually presented? SDT addresses this problem by recognizing that hit and false alarm rates reflect two factors, sensitivity and bias. A challenge similar to the detection of signals by radars arises when humans listen for weak auditory stimuli. However, radars may also mistake noise for signals these events are false alarms, and the corresponding probability is the false alarm rate. As radars become more sensitive (capable of detecting weaker and weaker signals), they are increasingly able to correctly detect when signals are present these events are called hits, and their probability of occurrence is the hit rate. Signal detection theory (SDT) was originally developed to describe the performance of radars, which must detect signals against a background of noise.
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