Tuesday, October 24, 2023

Using Computational Neuroimaging to Characterize Neural Priority Maps Supporting Visual Cognition

4:00 PM to 5:30 PM

Lurie Biomedical Engineering Building (LBME), Room 1170

Functional MRI Speaker Series 

Much of the visual system is organized according to visual retinotopic space, and activation patterns within each retinotopically-defined region (e.g., V1) can be considered as neural ‘priority maps’ – maps of the relative importance of different elements in the visual environment. In my lab’s research, we seek to understand how visual regions index priority aspects based on image-computable stimulus salience and an observer’s behavioral goals. To accomplish this goal, we develop and apply computational neuroimaging methods to reconstruct and quantify population-level neural representations and assay predictive neural encoding models. In this talk, I will describe the methods we’ve developed and show results from several key empirical tests of priority map theory establishing how different retinotopic visual regions in the human cortex differentially compute priority maps based on stimulus properties (luminance contrast, salience-defining feature) and task demands (behaviorally-relevant location or feature). Additionally, based on data acquired in the absence of visual stimulation, I will show how Bayesian generative models can be used to show how activation patterns in these priority maps support performance on tasks requiring visual working memory. Overall, I hope to convince you that these results support a theoretical framework whereby visual-spatial cognition can operate via multiple interacting neural priority maps, with different regions preferentially indexing stimulus and task-related priority aspects.

*Light refreshments will be served.

Thomas Sprague, Ph.D.

Assistant Professor
University of California, San Diego

Thomas (Tommy) Sprague received his BA in Cognitive Sciences from Rice University in Houston, TX in 2010 and his PhD in Neurosciences with a Specialization in Computational Neurosciences from the University of California, San Diego in 2016. His graduate work with John Serences sought to develop and apply novel multivariate analysis methods to human neuroimaging techniques to understand how neural systems represent information in support of dynamic behavioral goals. Prior to joining the faculty at UCSB, Dr. Sprague worked as a postdoctoral fellowship with Clayton Curtis and Wei Ji Ma studying how neural systems represent both the contents of visual working memory, but also their ‘uncertainty’, by building new multivariate analysis methods.