Keynote Speaker

Nilli Lavie, FBA


Nilli Lavie is a Professor of Psychology and Brain Sciences at UCL Institute of Cognitive Neuroscience, where she heads the Attention and Cognitive Control laboratory. She is an elected fellow of the British Academy, Royal Society of Biology, British Psychological Society, the Association for Psychological Science, US; and an honorary life member of the Experimental Psychological Society, UK. Among the awards she received are the British Psychological Society award for ‘outstanding contribution to research on human cognition’ and the Experimental Psychological Society ‘Mid-Career award’ for her distinguished research record.  She is renowned for her ‘Load Theory’ of attention, perception and cognitive control, which has provided a resolution to the four-decades long debate in Cognitive Sciences on the locus of capacity limits in human information processing.

Her research concerns attention, perception, multi-sensory integration, emotion, and cognitive control over behavior. She uses a combination of research methods spanning neuroimaging (fMRI, EEG, MEG, Spectroscopy), behavioral experiments, psychophysics, and machine learning. Prof Lavie’s research is supported by Toyota Motor Europe and Jaguar Land Rover, to address the fundamentals of human brain and cognition during driving.

She was previously a scientist at the MRC Applied Psychology Unit in Cambridge, UK; A postdoctoral Miller research fellow at UC Berkeley; and has received her PhD and BA degrees from Tel Aviv University.

Keynote Abstract

Understanding Auto-UI in Highly Automated Driving: A Cognitive Neuroscience Approach

Advances in cognitive neuroscience allow us to achieve a comprehensive understanding of the ways in which human mind and brain interacts with tasks and their interfaces. In this talk, I describe our application of a cognitive neuroscience approach to establish several ground principles of the human mind and brain engagement during driving, with a focus on Highly Automated Driving (HAD). HAD involves new forms of user interaction with the car interfaces, including the opportunity for the user to engage in a wide range of non-driving tasks and activities during the autonomous driving mode. I describe our work using a combination of brain science (specifically neuroimaging with Electroencephalography (EEG) and functional Near Infrared Spectroscopy), computer vision (deep learning predictive models), physiological (e.g. pupillometry) and behavioural performance measures, as well as psychological tests to establish new understanding of driver task engagement (including in non-driving tasks) during HAD. This work offers both general principles and specific methods to measure and monitor user engagement levels, and their impact on attention, perception and task switching ability (for example upon the presentation of a takeover request). We have also begun to establish individual differences in these cognitive capacities, paving the way for the design of personalised Auto UI.