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Abstract

Whether looking for your car keys, wallet or simply where your coffee mug is to have a sip, eye movements provide a window into how information is initially processed, prioritized and integrated. Early models linked eye movements to the visual properties and stimulus features that would pull attention according to saliency. However, current models of eye movements include the influences of current task goals, previous experience, and general world knowledge.

In this talk, I will use visual processing of complex real-world images to examine how these influences are linked to attention and eye movement patterns. These studies not only have implications for how we perceive the real-world in our day-to-day lives, but also how these influences affect our actions, judgements, and prioritization of information. Taken together, investigating attention through eye movement patterns can improve our understanding of complex information, but must be considered in a larger context.

Biography

Monica Castelhano is a Professor in the Department of Psychology at Queen’s University and is an internationally-renowned scholar examining attentional and memory processes in real-world scenes using eye tracking technology. She was recently appointed a Visiting Professor through the Leverhulme Trust at the University of Central Lancashire with Simon P. Liversedge and Valerie Benson. She received her PhD in 2005 from Michigan State University and joined the lab of the late Keith Rayner as a postdoctoral fellow from 2005-2007 at the University of Massachusetts Amherst. She joined Queen's University in 2007, where she is the current Chair of the Cognitive Neuroscience research program. For her work, Dr. Castelhano has been awarded an Early Researcher Award from the Ontario Government, and most recently received a prestigious NSERC Accelerator Discovery grant. Her research not only explores attention and memory processing in complex environments, but also investigates the function of these underlying processes in clinical populations (e.g. Autism Spectrum Conditions) and how current theory can inform emerging technologies.