Grant: Discovery Grants Program - Individual

This research will enhance knowledge of face recognition as well as improve how the legal system weighs face recognition evidence from eyewitnesses by refining my predictive cognitive model of eyewitness accuracy with existing tools and new tools created and validated in my lab. Face recognition errors by eyewitnesses (i.e., identifications of innocent suspects from lineups, which are arrays of faces or people) are a leading contributor to wrongful convictions in Canada. And these errors are two-pronged: they result in innocent people suffering and guilty people escaping consequences. A promising path for reducing these errors is to develop better ways of judging whether a specific eyewitness' face recognition decision is accurate. Face recognition performance is shaped by cognitive processes. My research program will produce new knowledge about how cognitive processes relate to face recognition and create easy-to-use tools to help legal officials use this knowledge. My research program will refine the cognitive model I have been developing for predicting eyewitness accuracy. My long-term objective is to develop a sufficiently predictive cognitive model that eyewitness errors rarely go undetected-which will significantly improve the use of eyewitness evidence and inform uses of face recognition in areas such as intelligence and passport control. Model predictions will be based on existing measures (e.g., decision speed, confidence) and measures of cognitive processes, many of which I will create and validate. My short-term objectives are to: STO 1)build on my ongoing work by using best practices in psychometrics to develop measures of cognitive processes related to eyewitness accuracy using self-report scales, STO 2)validate existing measures and my new measures of cognitive processes by testing their ability to predict eyewitness accuracy and to measure the intended cognitive processes, and STO 3)refine my cognitive model for predicting eyewitness accuracy by integrating measures of cognitive processes (via self-reports) and testing its ability to predict eyewitness accuracy. My method involves: 1)studying the relationship between self-reported individual differences in cognitive processes and eyewitness accuracy to develop measures of cognitive processes that predict eyewitness accuracy, 2)validating these measures by replicating their ability to predict eyewitness accuracy (predictive validity) and testing their ability to predict performance on extant measures of cognitive processes (criterion validity), and 3)updating my cognitive model for predicting eyewitness accuracy by integrating validated measures of cognitive processes. In addition to creating new knowledge about face recognition, my research program will produce tools for predicting eyewitness accuracy, which will improve the legal system's ability to use eyewitness evidence, thereby reducing wrongful convictions of the innocent and missed convictions of the guilty.