The effects of liking bias on eyewitness identifications

Lead Research Organisation: University of Portsmouth
Department Name: Psychology

Abstract

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Publications

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Description Eyewitness identification is less reliable than many people assume. We investigated one potential cause of such unreliability, witnesses' liking of lineup members. The idea is simple: Witnesses should be more likely to identify a lineup member they dislike as a perpetrator, and less likely to identify lineup members they like. We call this liking bias. Our aims were to explore underlying variables that affect the strength of liking bias, the nature of liking, the generality of liking bias, and potential ways of reducing it.

Our seven studies produced unexpected findings. Most problematic (from a researcher's point of view) was that the relatively low level of liking bias (compared to pilot research), impeded efforts to investigate underlying variables as initially planned. However, we discovered a variable that was strongly associated with liking bias: increased variability in the likeability of lineup members produced stronger liking bias. This should be followed up in future research, as it might predict which lineups are more vulnerable to liking bias. Liking bias effects also differed according to the measure of identification used. Although liking bias effects on identification decisions were inconsistent (and often absent), liking bias consistently influenced participants' confidence in lineup members' likely guilt.

Further, liking judgements were strongly related to both the perceived attractiveness and trustworthiness of lineup members (whereas these two variables were not so strongly related to each other). Also, liking bias generalised to the acoustic domain: For instance, hearing statements in a liked voice facilitated classifying them as positive.
Exploitation Route We discovered a variable that was strongly associated with liking bias: increased variability in the likeability of lineup members produced stronger liking bias. This should be followed up in future research, as it might predict which lineups are more vulnerable to liking bias.
Sectors Government, Democracy and Justice