Dynamic Face Processing in Infants, Children, and Adults

点击翻译按钮获取中文摘要

Human faces are one of the most important stimuli in our visual environment. Our decision to fight, to flee, to mate, to cooperate, and to seek protection often depends on the outcome of successful processing of human faces. Inability to process faces can lead to debilitating adaptive problems (e.g., autism). Given the importance of faces, it is no wonder that extensive cognitive and neuroscience research has been devoted to investigating face processing abilities in adults, and how such abilities emerge and develop from early infancy to adulthood. However, although faces we encounter on a daily basis are mostly dynamically moving faces that turn, nod, smile, and talk, most of what we currently know about human face processing has come from studies using static faces. Our recent NSERC-funded research has revealed that adults and infants process static and dynamic faces in a dramatically differently manner. This new discovery leads us to ask a fundamental question: Can our current knowledge based on static faces be used to account for how humans actually process faces in real life that are dynamically moving? Building on our recent discoveries, the present proposed program of research continues and extends my current NSERC-funded research significantly. We aim to investigate the specific role that facial motion information plays in our processing of human faces. Specifically, we will use psychophysical and eye-tracking methodologies to examine similarities and differences between static and dynamic face processing in infants, children, and adults. Our project will bridge a major gap in the literature and provide much needed evidence regarding the role of motion in face processing and its development. We hope the findings from our research program will lead to a re-evaluation of current theories and assumptions and the development of new and more ecologically valid accounts of face processing in infants, children, and adults. Further, our normative data will help us assess and perhaps train individuals with face processing deficits (e.g., autism) using more naturalistic stimuli.