Spencer Castro

Spencer Castro is a National Science Foundation Pre-Doctoral Graduate Research Fellow (GRFP) at the University of Utah, working with Dr. David Strayer. Spencer was awarded the NSF GRFP for research on the capacity of attention under cognitive workload, particularly in the context of technology and multitasking. He focuses on the validity of reaction time and accuracy as measures of different aspects of workload, as well as quantifying the risk of adverse outcomes due to these workload metrics in driving. He employs advanced cognitive modeling techniques to examine the mechanisms of attentional capacity, multitasking, and performance. In a recent publication in the Journal of Experimental Psychology: Human Performance and Perception, Spencer and collaborators propose new mathematical models for analyzing reaction time data that captures the classically difficult tradeoff between speed and accuracy.


As a member of the Paiute and Southern Sierra Miwuk Nations, Spencer was awarded a Postdoctoral Fellowship for Underrepresented and Disadvantaged Scholars from the University of Utah to support his on-going research on cognitive modeling. Spencer is a strong advocate for minoritized groups and is the president of the Diversity G.A.P. (Graduate Application Preparation) at the University of Utah, which prepares underrepresented students to apply to graduate school.

Education

Graduate Student, Cognition and Neural Science, University of Utah, Salt Lake City, 2015-2019

Master of Science, Cognitive Psychology, University of California, Santa Cruz, 2013-2015

Bachelor of Science, Science Technology and Society, Stanford University, 2007-2011

Publications

(Full Curriculum Vitae)

Peer-Reviewed Publications

Manuscripts Under Review

Manuscripts in Preparation

Conference Presentations

Poster Presentations

Research

  • Computational Modeling

  • Decision Making Applied to Cognitive Workload Measurement

    People around the world endanger the lives of themselves and others every day by dividing their attention across multiple tasks, such as driving and talking on a cell phone. These dangers result from splitting and overtaxing our limited voluntary attentional efforts. Current tools for measuring attentional effort, also known as cognitive workload, lack insight into cognitive factors that can cause fatal errors. With the advent of new distracting technology in cars, if we do not effectively measure cognitive workload fatal human errors may grow. To quantify cognitive workload under a simulated driving-like task, the current study details our application of mathematical modeling to an International Standard for measuring ongoing cognitive workload in the vehicle. This research provides a framework for accurately quantifying cognitive workload and the factors that contribute to it, which will allow future researchers and policy makers to determine the danger inherent in many tasks within the vehicle.


    Read more at: Castro, S.C., Strayer, D.L., Matzke, D., & Heathcote, A. (In Press). Cognitive workload measurement and modeling. Journal of Experimental Psychology: Human Performance and Perception.
  • Providing Automation with Situational Awareness of Operator Workload

    Human operators – particularly in demanding defence jobs – experience workload levels varying from light to complete overload. These workload fluctuations can be associated with sub-optimal performance, which can lead to poor outcomes or even mission failure. Automation, in the form of artificial intelligence that can take over routine tasks and/or recommend smart options, promises to alleviate some of these concerns but raises its own problem. Under-load can cause mind-wandering, sometimes called “automation neglect”, leaving the operator ill prepared for emergencies, and automated recommenders, lacking the situational awareness of human assistants, can cause failures by intruding at critical times or providing options that overload the operator’s capacity. This project aims to develop a hardware and software package for monitoring and predicting operator engagement and workload in real time. -Andrew Heathcote, PI

  • R Package Developement

    R package for advanced methods of analyzing reaction time data - under development here.

Classes Taught

Lecturer

Co-Lecturer

Teaching Assistant

Guest Lecturer

Contact

Get in touch

Diversity Graduate School Application Advisory

University of Utah

Spencer.Castro[at]psych.utah.edu

LinkedIn

ResearchGate

Academia.edu

Spencer Castro