Putting the individual into face recognition: Bridging theory and application

This project will examine the neural basis of a fundamental social skill - the ability to recognise the people we know from their faces. We recognise our relatives, friends, and colleagues dozens of times every day, but surprisingly little is known about how we achieve this. We propose that one reason for this is that previous research has not acknowledged the idiosyncrasy of familiar face recognition. We all have our own unique set of familiar faces, and these tend to overlap only partly with others. Here, we address the problem of familiar face recognition using methods that allow incorporation of individual, idiosyncratic familiarity. Taking idiosyncratic familiarity into account will result in substantially more reliable measures of face recognition and will allow us to develop an innovative theoretical and methodological focus. Previous work has largely established where in the brain and when in time a familiar face is recognised. Here, we will, for the first time, systematically examine how the visual appearance of known faces is stored in the brain. In addition, we will examine a question of practical importance - the reliable detection of familiarity with a face, even when participants are motivated to conceal such knowledge.
In a first strand, we will tackle the problem that the same face can look very different in different conditions (e.g. due to changes in lighting, viewing angle, or make-up). How then is it possible for the brain to recognise it as the same familiar face? We have developed two potentially complementary theoretical views to explain this phenomenon. The first suggests that we store the "gist" of a face in our memory, i.e. the information that is commonly observed in all circumstances. The second suggests that, in addition to this abstract representation, we may have many specific memories of a face - similar to "snapshots" taken on different occasions - and thus information about what a face looked like in a particular encounter. Our project will provide decisive evidence for the 'gist' and 'snapshot' views.
In a second strand, we will examine how neural representations of different faces are organised to allow for efficient recognition. We know literally thousands of faces. How then is it possible not to constantly mix them up? Maybe because the different faces we know are organised in a way that allows only the "best match" to become activated while other, potentially similar looking faces are inhibited. This idea is based on computer models of face recognition, but has rarely been tested with human viewers and its neural basis is completely unknown. We have now developed a series of novel experimental studies to fill this gap.
Finally, while previous cognitive neuroscience research has mostly been constrained to examine face processing in groups, i.e. by collapsing data across a number of participants, it is crucial for any potential real-life application that familiarity can be detected in individual participants. In applied situations, it is not useful to know that a group of 10-20 participants on average shows a certain brain response, as evidence is typically required about an individual witness or suspect. This project will therefore develop a novel neural measure to detect familiarity in individuals. Moreover, to be of practical relevance, such a measure needs to work reliably even when participants are trying to conceal familiarity - for example to avoid implicating conspirators in criminal investigations. We will therefore test for robustness against attempted deceit.
Overall, by taking idiosyncratic familiarity into account as well as by shifting the research focus from the "where/when" to "how", and from the group to the individual level, this project will generate innovative findings on how the human brain recognises familiar faces - a question of high theoretical importance. In addition, our results will contribute to solving a problem of substantial practical relevance.