In terms of the model shown in Fig. A signal-detection-based diagnostic-feature-detection model of eyewitness identification. Other signal detection models applied to lineups assume that the decision is based on a transformed memory signal, such as the sum of the memory signals associated with the faces in the lineup (Duncan, 2006), but we consider only the simpler Independent Observations signal detection model here. The probability of observing filler memory strength x1 from a target-present lineup is given by Eq. Then again, this analysis would not conclusively establish that the simultaneous procedure is necessarily superior outside of the tested FAR range (i.e., outside of 0 to FAR 2.

Condition B to test theory-based predictions about d' More specifically, DR = HR/FAR. Perception & Psychophysics, 62, 425–451. pROC: An open-source package for R and S+ to analyze and compare ROC curves. Here, we advanced the argument that when it comes to informing real-world policy decisions about eyewitness identification procedures, an empirical measure of discriminability (pAUC, or its parametric counterpart, when necessary) takes precedence over a theoretical measure of the degree to which memory signals overlap (d'

PubMed What does the pAUC measure actually tell you? BMC Bioinformatics, 12, 77. Because the FAR for a lineup is limited to a range that is less than 0 to 1, the relevant measure of empirical discriminability for a lineup is the partial area under the curve (pAUC). For this latent variable, d’2AFC > d’old/new. For example, the area under the ROC curve is a single-number index of discriminability (National Research Council, 2014, p. 86). Thus, our suggestion that policy decisions are informed by area under the ROC, not by a theoretical estimate of underlying discriminability, is new to the field of eyewitness identification but is not a new suggestion generally speaking. The diagnostic feature-detection theory attributes the pAUC effect to a higher d' m If a different model is assumed – even a slight variant that merely assumes unequal variances – then a different measure of discriminability would apply, such as d The same receiver operating characteristic (ROC) data as in Fig. {x}_1\right|{x}_1\right)=\prod \limits_{j=2}^k\Phi \left(\frac{x_1-{\mu}_j}{\sigma_j}\right). Criterion variability might be higher for the sequential procedure because instead of making only one decision per lineup, as a witness presented with a simultaneous lineup does, a witness presented with a sequential lineup makes as many as six decisions, with each decision providing an opportunity for the placements of the confidence criteria to change.

Evaluating imaging and computer-aided detection and diagnosis devices at the FDA. A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays. , the model would have to be cognizant of this decision rule. PubMed Google Scholar.

Practical knowledge is knowledge that is acquired by day-to-day hands-on experiences. International Association of Chiefs of Police (2006). In other words, our main focus is on the measures that once led the field to conclude that sequential lineups are diagnostically superior to simultaneous lineups. 2 but with three important differences described next. In other words, there would be a dissociation between pAUC and d' Which FAR – the one associated with the less conservative procedure or the one associated with the more conservative procedure – should determine the FAR

theoretical the information that is set.

Computer software is needed to precisely measure the size of the shaded area, and the tutorial videos associated with Gronlund, Wixted, and Mickes (2014) explain how to use one such R program, called pROC (Robin et al., 2011), to do that. By considering the empirical literature in this way, we seek to determine whether and how relevant theoretical perspectives on human morality and the types of research questions they raise are reflected in empirical studies carried out. National Research Council (2003). However, it is possible for them to disagree, and that fact can lead to confusion about which condition actually yields higher discriminability. Cognitive Research: Principles and Implications.

PubMed However, when a large degree of criterion variability is introduced (lower panel of Fig. Like the DR, a measure of true empirical discriminability also makes use of the HR and FAR computed from IDs made to suspects in the lineup. and σ https://doi.org/10.1016/j.jarmac.2014.03.004. Instead of switching from Procedure A to Procedure B to achieve a FAR as low as .02, it would make more sense to stick with Procedure A and to induce more conservative responding. The posterior odds of guilt can, of course, also be computed for the other two outcomes, namely, filler IDs and lineup rejections (Wells, Yang, & Smalarz, 2015). One focus of their work was to adjudicate the debate over whether the diagnosticity ratio or ROC analysis offers the best approach for comparing competing eyewitness identification procedures. https://doi.org/10.1002/acp.3344. The pROC software uses a bootstrap procedure to determine if the apparent difference in the two pAUC values is statistically significant.

Cogn. 1. New York: Springer. Theoretical discriminability is measured by a statistic like d', which is the standardized distance between the means of two underlying strength distributions that are assumed to be Gaussian in form and to have equal variance. Rotello & Chen, 2016). If, for some reason, policymakers preferred a FAR of approximately .06 because of the higher HR that could be achieved, the fact that pAUCSIM > pAUCSEQ over the tested FAR range (0 to FAR m That person would be identified even if one or more of the other faces in the lineup also generate a memory signal that exceeds the decision criterion. For theoreticians, theoretical discriminability (e.g., d') is the measure of interest, but for policymakers, empirical discriminability (e.g., area under the ROC) is the measure of interest. Wixted, J.T., Mickes, L. Theoretical vs. empirical discriminability: the application of ROC methods to eyewitness identification. The confidence ratings themselves provide the multiple decision criteria needed to construct an ROC, as illustrated earlier in Fig. However, in the presence of criterion variability (equated across the two procedures), simultaneous lineups yielded higher empirical discriminability (measured by pAUC) than showups. 2. More formally, Bayes’ theorem compares the odds in favor of one hypothesis over another. argued against the following idea, which they attributed to us: “…the procedure that produces superior underlying discriminability produces superior applied utility” (Smith et al., 2017, p. 127, emphasis added). m Thus, in this example, both measures – d'

7, σ

More specifically, given the standard assumptions of signal detection theory, it should be the case that d'2AFC = (√2) d'old/new (Macmillan & Creelman, 2005). 4 atheoretically.

This is true even though underlying discriminability has not changed and is still set to d' Research 3, 9 (2018). We might refer to that memory-based d' as d' Gallas, B. D., Chan, H. P., D'Orsi, C. J., Dodd, L. E., Giger, M. L., Gur, D., … Zuley, M. L. (2012). Foil What are the implications of the fact that theoretical and empirical measures of discriminability are capable of yielding conclusions that point in opposite directions? Filler IDs do not (because, as noted earlier, fillers are known to be innocent) and neither do no IDs, but suspect IDs do, whether the identified suspect is innocent or guilty. Google Scholar. However, in a fair lineup ROC, it usually ranges from 0 to a value less than 1, such as .80. max The likelihood functions for fitting a signal detection model to data from a fair lineup can be worked out by specifying the joint probabilities of the events that result in a given outcome (suspect ID, filler ID, or no ID). Although d' Law and Human Behavior, 39, 99–122. However, unlike Fig. from lineup data, one needs to fit a model that is cognizant of the task demands (e.g., whether the task is a showup, a 2AFC task, a simultaneous lineup, or a sequential lineup) and that can also separate the effects of variability in criterion placement from the effects of variability in memory signals. 3. Medical Physics, 23, 1709–1725. For this simulation, d' 1

This approach would avoid extrapolating the ROC curve to the origin (0,0). C Alexandria: IACP Law Enforcement Policy Center. Applied Psychology in Criminal Justice, 13, 96–109. The two hypotheses of interest here are: where D is the data (a suspect ID in this case), P(H1|D)/P(H2|D) represents the posterior odds of H1 compared to H2 (i.e., the odds of guilt after a suspect ID has been made), P(D|H1)/P(D|H2) represents the likelihood ratio (i.e., the diagnosticity ratio) and P(H1)/P(H2) represents the prior odds of H1 compared to H2 (i.e., the odds of guilt before a suspect ID has been made). m

j These PPV values are not very impressive for either procedure, but the task used by Lindsay and Wells (1985) involved an innocent suspect who closely resembled the perpetrator (i.e., it was designed to be a hard task). m

.

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