Taylor R. Hayes, Ph.D. Email and Phone Number

- Trained and deployed a deep learning model to automate the estimation of the distribution of semantic information density across real-world scenes.- Wrote an analysis pipeline that applied linear mixed effects models to eye movement data to understand scene attention.- Combined a vector space semantic model with eye movement data to reveal the role of semantic object similarity in scenes.

- Conducted and coordinated all aspects of 15+ research studies on how humans guide attention in real-world scenes.- Authored 20+ articles and presented work at 10 conferences.- Developed a novel survey protocol and computational approach that estimated the distribution of semantic information in scenes.- Tested 800+ eye tracking participants and collected over 1 million remote survey responses.- Wrote an end-to-end eye tracking data preprocessing and analysis pipeline that was adopted lab-wide due to speed.- Supervised graduate students and trained/managed 10+ research assistants.

- Led all aspects of 8 mixed-methods research studies on the role of attention on relational reasoning and fluid intelligence.- Managed 5 research assistants and collected data from 500+ eye tracking participants.- Collected and annotated think aloud protocols from 100+ participants and analyzed using verbal protocol analysis.- Developed an approach to reduce pupil size distortions in image-based eye tracking by 97%.- Authored 4 papers and presented research at 5 conferences.

- Pioneered the use of reinforcement learning algorithms to extract predictive patterns from sequential eye movements.- Used sequential eye movement patterns to show a serious flaw in the claims of the billion dollar cognitive brain training industry.- Developed a model of the asymmetric transfer of perceptual learning of luminance- and contrast-modulated motion.