Assessing Eyewitness Identification

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This project will build a database summarizing the results of over 1,000 facial identification studies published in the last five decades. Using this database, the project will address four research questions: 1. The effect of 80 factors on facial identification performance will be summarized. 2. The generalizability (application) of findings from eyewitness laboratory research to actual eyewitness performance will be examined by testing whether variations in research methods affect patterns of research results. 3. The implications of new witness performance measures (based on signal detection theory) will be assessed to see if they provide an adequate account of research findings as compared to prior performance measures. 4. The pattern of relationships among more than 60 indices of eyewitness performance proposed by various researchers will be investigated. It is anticipated that these measures are highly redundant and eyewitness performance can be fully reflected in a handful of readily understood measures. This project will build a comprehensive database of facial identification research that includes over 1,000 studies published in the last five decades; it will expand the database constructed by Penrod and his colleagues in 1986 and 2002. Using this database, four research questions, which are crucial for better understanding eyewitness performance, will be addressed. These questions are as follows: First, the effect of certain independent variables in facial identification research that influence identification performance will be investigated. The accumulated database includes approximately 80 independent variables that facilitate a review of the relative contribution of each variable to identification performance. This will not only help summarize findings from previous literature on facial identification, but also suggest a direction for future research by revealing valuable variables deserving particular attention. Second, the generalizability of findings from eyewitness laboratory research to actual eyewitness performance in the real world will be tested. Some researchers warn of the risk to the generalizability of eyewitness research results because of methodological homogeneity across studies and low external validity in research methodology. To test the generalizability of finings, this project will investigate whether research methodology (e.g., participant type, stimulus type, experimental procedures, etc.) moderates the effects of independent variables on eyewitness performance and if so, whether laboratory research under-estimates or over-estimates real-world performance. Third, the implications of a new signal detection model for eyewitness performance during lineups and test its validity through analysis of the cumulative database will be tested. Past eyewitness research based on signal detection theory has generally focused on identification of a guilty suspect (i.e., hit rate) and an innocent suspect (i.e., false alarm rate) while ignoring filler identifications (which carry costs such as "burning" witnesses, missing perpetrators, and failing to clear innocent suspects). In contrast to past research, the proposed signal detection model will clarify the theoretical and practical importance of filler identifications in eyewitness performance. Finally, the psychometric structure of indices measuring eyewitness performance with the cumulative database will be investigated. Although researchers have frequently used very few selected indices (e.g., DR, PPV and others) to measure eyewitness performance, there are more than 60 indices measuring or relevant to eyewitness performance. It is anticipated that eyewitness performance is reflected in a handful of components and that each index measures one or multiple components to a different degree. The project aims to reveal how the various indices relate to one another, that is, reveal in accessible terms--what components of eyewitness performance these indices measure, and arrive at suggestions about which (preferably accessible) metrics best characterize which aspects of performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.