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It is for this reason that the distribution of match values for the innocent suspect (the lure distribution) lies to the left of the one for the perpetrator. But, importantly, the average match across eyewitnesses between memory and an innocent suspect will be lower than between memory and the actual perpetrator. For idiosyncratic reasons, some eyewitnesses may experience a higher match between this innocent suspect and memory, and others a lower match. What happens in this case? The suspect may resemble the actual perpetrator to a greater or lesser degree.
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Of course, sometimes the police apprehend a suspect who is innocent of a crime. The hit and false-alarm rates for the criteria located at β 3, β 2, and β 1 correspond to the data in Table 1 for Conditions A (the careful students), B, and C. The three levels of instructional bias are denoted by β 1, β 2, and β 3, and X denotes the memory strength elicited by a suspect. A depiction of an equal-variance signal-detection model for a showup test. In the 1950s, the idea of an adjustable decision criterion was adopted (e.g., Egan, 1958), a shift that enhanced the psychological usefulness of signal detection theory and facilitated its adoption into basic research involving recognition memory ( Banks, 1970 Lockhart & Murdock, 1970).įig. He assumed that the decision threshold, or criterion, for discriminating the heaviness of two weights, for example, was located midway between the two Gaussian distributions that summarized the statistical sensory experience of each weight. Fechner's theory of discrimination posited that sensations were likewise perturbed by measurement error. Link (1994 Wixted & Mickes, 2018) traced this history to Fechner's (1860/1966) idea that perception involves “an unknown amount of error that interfered with the measurement of the true value” of a phenomenon of interest. At the heart of SDT is the idea that the psychological experience of an event is subject to noise (variability), and that that noise can be statistically modeled as arising from a normal distribution. What SDT brings to the study of eyewitness memory is a time-tested focus on measurement ( Wixted & Mickes, 2012). Swets, Dawes, and Monahan (2000) reviewed numerous applications of signal detection theory (SDT), including medical decision-making, predicting violence, detecting cracks in airplane wings, weather forecasting, and law school admissions. Signal detection is a theory of decision-making with wide applicability to tasks involving detection, discrimination, identification, and choice ( Green & Swets, 1966). Benjamin, in Psychology of Learning and Motivation, 2018 2.2 Signal detection theory (1992), more recent examples can be found in McFall and Treat (1999), DeCarlo (2005), and DeCarlo and Luthar (2000). The classic work on SDT is Green and Swets (1995), a basic introduction is McNicol (1972), two recent comprehensive references are Macmillan and Creelman (2005) and Wickens (2002).Ī few examples of SDT to education can be found in McDermott et al.
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SDT is used in very different domains from psychology (psychophysics, perception, memory), medical diagnostics (do the symptoms match a known diagnostic or can they be dismissed are irrelevant), to statistical decision (do the data indicate that the experiment has an effect or not). The second parameter called C (a variant of it is called β) reflects the strategy of response of the participant of being more willing to say, for example, yes rather than no. The first parameter, called d′, indicates the strength of the signal (relative to the noise). The goal of signal detection theory is to estimate two main parameters from the experimental data. Here the signal corresponds to a familiarity feeling generated by a memorized input, whereas the noise corresponds to a familiarity feeling generated by a new stimulus. For example, in a memory recognition experiment, participants have to decide if the input they currently see was presented before. But the notion of signal and noise can be somewhat metaphorical is some experimental contexts. This type of applications was the original framework of SDT (see the founding work of Green and Swets, 1966). For example, a radar operator must decide if what she sees on the radar screen indicates the presence of a plane (the signal) or the presence of parasites (the noise). Signal detection theory (often abridged as SDT) is used to analyze data coming from experiments where the task is to categorize ambiguous inputs which can be generated either by a known process (called the signal) or be obtained by chance (called the noise in the SDT framework). Abdi, in International Encyclopedia of Education (Third Edition), 2010 Overview