Robert Jenkins

Successful refugee and migrant integration has been shown to generate novel opportunities for development, and to enrich countries economically, socially, and culturally. Nonetheless, integration is one of the most complex issues of our time. Here we review this problem from a behavioural science perspective. Behavioural science brings together insights from psychology, behavioural economics, neuroscience, and sociology to devise and improve population-level interventions and to develop more effective policies. One approach in behavioural science is commonly referred to as ‘nudging’. Recently there has been a growing interest in nudge strategies among both practitioners and academics, in part because the strategies are cheap to implement. Here we provide an overview of such strategies and their applicability to refugee integration. By addressing two sectors of society where behavioural science is currently applied (education and employment), we examine how behavioural evidence may be used to bypass barriers and facilitate drivers of integration. Our review (i) reveals that few interventions aimed at refugee integration use a behavioural science approach, (ii) highlights areas in which this approach could be especially effective, and (iii) identifies some behavioural science techniques that may be counterproductive.

Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework.

This article describes some differences between familiar and unfamiliar face processing. It presents the evidence that unfamiliar face recognition is poor. Since this poor performance has implications both practically and theoretically, it is important to establish the facts. The article analyses reasons that people appear to have little insight into their own poor performance with unfamiliar faces, and some sectors of society seem so keen to use faces as a means of proving identity. It reviews some historical research comparing familiar and unfamiliar face processing. The study shows evidence for the assertion that despite eliminating the memory-load of normal eyewitness situations, it turns out that people are surprisingly bad at matching two images of the same unfamiliar person. Finally, it suggests that the modern tendency to conflate familiar and unfamiliar face processing, and to theorize about “face recognition” in general, lies at the heart of practical failures in this field.