scholarly journals Distinguishing Between Investigator Discriminability and Eyewitness Discriminability: A Method for Creating Full Receiver Operating Characteristic Curves of Lineup Identification Performance

2020 ◽  
Vol 15 (3) ◽  
pp. 589-607
Author(s):  
Andrew M. Smith ◽  
Yueran Yang ◽  
Gary L. Wells

The conceptual frameworks provided by both the lineups-as-experiments analogy and signal detection theory have proven important to understanding how eyewitness lineups work. The lineups-as-experiments analogy proposes that when investigators use a lineup procedure, they are acting as experimenters and should therefore follow the same tried-and-true procedures that experimenters follow when executing an experiment. Signal detection theory offers a framework for distinguishing between factors that improve the trade-off between culprit and innocent-suspect identifications and factors that affect the frequency of suspect identifications. We integrate these two conceptual frameworks. We argue that an eyewitness lineup procedure is characterized by two simultaneous signal detection tasks. On one hand, the witness is tasked with determining whether the culprit is present in the lineup and identifying that person. On the other hand, the investigator knows which lineup member is the suspect and which lineup members are known-innocent fillers and is therefore tasked only with determining whether the suspect is the culprit. The investigator uses the witness’s identification decision and associated level of confidence to decide whether the suspect is the culprit. We leverage this realization to demonstrate a method for creating full receiver operating characteristic curves for eyewitness lineup procedures.

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.


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