Now accepting applications for graduate positions with an emphasis on the topics of research below (applications on other topics of interest to the lab are also welcome. For more information email firstname.lastname@example.org). We are an interdisciplinary laboratory and welcome students with backgrounds in mathematics, computer science, engineering, physics, biology, neuroscience and experimental/cognitive psychology.
Some news stories on recent work by the VIU lab:
Humans vs. Deep Neural Networks:
Collective wisdom and search:
Scene context and the brain:
Individual differences in eye movements to faces
Computational Cognitive Neuroscience:
This position is to elucidate the neural basis of attention, learning and decision making using electroencephalogram (EEG), functional magnetic resonance imaging (fMRI) and machine learning algorithms to relate neural activity to human behavior and computational models.
Visual Attention and Eye movements during search
How do humans and animals use the statistical structure of environments to plan eye movements and deploy attention to search for objects? This position is to study this question using computational modeling, fixed and mobile eye tracker technology to measure human eye movements, and analysis of natural images with the goal of explaining human search strategies.
Eye movments and Face Recognition tasks
Identfying people from faces, their gender and emotions are all evolutionary important perceptual tasks that humans routinely engage in on a daily basis. How do humans plan their eye movements to scrutinize faces? Do eye movement strategies vary across individuals and why? This position is to study these questions using computational modeling, fixed and mobile eye tracker technology to measure human eye movements, analysis of faces, and neuroimaging with the goal of explaining human eye movement strategies during face recognition taks.
There is a tradition of of a line of research in the VIU lab studying how physicians scrutinize images to make diagnostic decisions and developing engineering tools and models to optimize medical image quality. This position is to study either theoretical issues in the perception of medical image quality and/or developing engineering tools to optimize medical image quality.
Computational Collective Wisdom
Decisions by groups of individuals or animals can often lead to more accurate decisions than that attained by any individual in the group. This is known as collective wisdom. How do groups of humans combine information to reach decisions? And what are the neural basis of collective wisdom? This position addresses these questions using computtional tools, behavioral studies and electrophysiology.
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