Modeling face similarity in police lineups

Police investigators worldwide use lineups to test an eyewitness’s memory of a perpetrator. A typical lineup consists of one suspect (who is innocent or guilty) plus five or more fillers who resemble the suspect and who all known to be innocent. Although eyewitness identification decisions were once biased by police pressure and poorly constructed lineups, decades of social-science research led to the development of reformed lineup procedures that provide a more objective test memory. Under these improved testing conditions, cognitive models of memory can be used to better understand and ideally enhance eyewitness identification performance. In this regard, one question that has bedeviled the field for decades is how similar the lineup fillers should be to the suspect to optimize performance. Here, we model the effects of manipulating filler similarity to better understand why such manipulations have the intriguing effects they do. Our findings suggest that witnesses rely on a decision variable consisting of the degree to which the memory signal for a particular face in the lineup stands out relative to the crowd of memory signals generated by the set of faces in the lineup. The use of that decision variable helps to explain why discriminability is maximized by choosing fillers that match the suspect on basic facial features typically described by the eyewitness (e.g., age, race, gender, etc.) but who otherwise are maximally dissimilar to the suspect.