Interdisciplinary collaboration, and investigation on a role of motivational salience on face recognition memory across a period of sleep

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Humans are innately good at face recognition. A study estimated that young adults in the UK recognize approximately 1,000-10,000 faces (Jenkins et al., 2018). On the other side, approximately 1.3 million (2%) people in the UK are suffering from face blindness. Those people are incapable to recognize faces of acquaintances, which causes mental distress, conflict in relationship or even job loss (Yardley et al., 2008). To support the well-being of people with face blindness, we need to understand the mechanisms underlying human face recognition, especially mechanisms of flexibility in face recognition such as, "How do we recognize faces of people that are varying over time, for example through aging or makeup?", and "How do we maintain memories of each face in such large numbers?, and "How do we not confuse different faces, especially when these look very similar to each other (e.g., faces of twins)?".The recent two decades of research on the relationship between sleep and memory suggests that memory is subject to change from its original form over a period of sleep. For instance, recent work demonstrated that sleep actively promotes generalization of memories (Gomez, et al., 2006), extraction of generalized ideas or gist of memories (Lewis, 2011), and even produces more errors if they are matching the extracted gist, compared to an equal amount of wakefulness (Payne et al., 2009).In a continuum of this recent research field on sleep and memory, my PhD work focused on an active role of sleep in face recognition memory. My PhD work revealed that sleep is also important in distinguishing memories of faces when they are visually similar to each other (e.g., faces of twins). Furthermore, my doctoral work using the functional MRI technique called Representational Similarity Analysis (RSA), which measures distinctiveness of brain activity patterns between conditions, revealed that this memory distinction across a period of sleep was linked with dissociating patterns of brain responses to the visually similar faces in the hippocampus (a crucial area in the brain's memory network). Altogether, the combination of recent findings on sleep and memory and my doctoral work provides the foundation to support a tentative mechanism underlying flexibility in human face recognition, in which memories of visually similar faces are gradually dissociated from each other.Motivational salience (e.g. rewards) is also important when maintaining memories over time. For example, salience is closely linked to hippocampus-linked memory formation (Gruber, et al., 2014) and modulates effects of sleep on memory transformation (Fischer et al., 2009).During the fellowship, therefore, I will promote my doctoral work and recent trends of work on sleep and memory to the community of researchers in face perception and develop interdisciplinary collaboration, as well as running pilot work extending my doctoral work. The pilot work will test how the presence of motivational salience contributes to an alteration of face recognition memory across a period of sleep, in a similar manner to behavioral tasks developed during my PhD. This pilot work will be key to apply for future funding and/or fellowships in which I will use the developed behavioural paradigm to investigate the neural mechanisms underlying salience, face recognition memory and sleep. To maximise the impact of my work, I will publish my research in highly-respected journals. I will also attend national and international conferences such as an annual meeting organized by Cognitive Neuroscience Society (CNS) so that my work can be seen by people working in academic research relating to human face recognition and sleep and memory. In addition, I plan to visit scholars, such as Prof Michael Banissy in London and Dr Rob Jenkins in York, who are key researchers in face recognition memory and face perception more generally, present my work to them and exchange ideas to develop a network of collaborators.