我要吃瓜

Suppose you are being questioned by police about a serious crime. They show you a picture of the victim, or of your accomplice, either of whom you should know only if you were involved. You therefore deny knowing them. The aim of this project is to find ways to help police detect such lies, by advancing our understanding of the markers of recognition. The proposed experiments are specifically about recognising faces, not detecting lies more generally. Our approach is known as the concealed information test (CIT). This is widely used in Japan to uncover guilty knowledge about a crime - something such as a murder weapon, which only the person who did it should know. So, for example, a suspect might be presented with a series of pictures of possible murder weapons. When the true weapon appears, a guilty suspect produces a rapid recognition response that is hard to control. Used carefully, it has been shown to be a useful source of information, unlike standard lie-detector tests which are very error-prone. There is, however, little work on using the CIT with faces. We plan to use eye-tracking, together with other measures such as skin conductance, facial expressions and vocal cues to provide objective evidence of recognition. There are systematic differences between the patterns of eye movement on familiar and unfamiliar faces. For example, recognition causes the first fixation to a familiar face to be longer, while pupils also tend to dilate. Part of our work will be to look for ways to combine different measures into one overall indicator of concealed recognition. We plan to study two other, novel approaches to detecting recognition of faces, which rely on the differences in how we process familiar and unfamiliar faces. Pilot work indicates that all three of our proposed methods show promise. We aim to identify the most reliable measures. We need a method that is robust in two different ways. First, it should produce a signal that can be detected in a single person - and across as many different people as possible. Second, it should be resistant to attempts to conceal deception of the sort that make traditional lie-detectors so unreliable. Again, our pilot data are promising. Our results should help understand better how our brains recognise faces. The ultimate aim is to develop a method that will help with the detection of crime and breaking criminal and terrorist networks.