Institute for Human & Machine Cognition, Pensacola, FL
This summer, I had the great opportunity to be an intern at the Institute for Human & Machine Cognition (IHMC) in Pensacola, Fla. Pensacola is a beautiful city by the sea, and it’s close to not only pristine beaches, but also the Navy Base. The Institute for Human & Machine Cognition is truly a wonderful place to learn and do research. The machines, equipment, and technology are impressively cutting-edge; one can set up experiments to resemble aircraft flights in the style of amusement park rides configured into a virtual reality framework, among many other options.
I have always been intrigued with flight simulation, biometric feedback (i.e. EEG, Electrocardiogram), and machine learning/deep learning. The work has an overarching goal of predicting and optimizing cognitive functions, especially relating to safety and efficiency in air crafts. I was involved in optimizing experimental design and analyzing empirical data under various experimental and real-life physical configuration conditions. Getting the opportunity to work with pilot subjects, in designing and operating the experimental setup, and in utilizing a wide variety of empirical and statistical methods made this a highly valuable and enriching experience.
The most significant aspect that I have learned from my experience is the real-world processes of scientific and industrial advancement.
At Williams, I have taken a wide variety of classes that have helped me a great deal in my internship. From invaluable and useful technical, STEM-related classes in mathematics, statistical experimental design, and computer science, to fascinating and informative social science subjects of cognitive psychology, cognitive science and neuroscience-themed, to highly enjoyable humanities classes that inquire the philosophical nature of the mind and the self and the art of fostering a harmonious, synchronized, and enhanced human experience.
At the Institute for Human & Machine Cognition, I found that success in research truly takes a combination of technical knowledge and creativity in problem solving as well as patience, prudent planning, and teamwork. Multi-stage planning was essential for carrying out the experiments, and mindful statistical design of experiments was essential to carrying out experimental protocol within the constraints of experimental validity, human subject availability, and budget and tangible experimental resources.
Specifically, I enjoyed the process of implementing psychologic tests (like the famous N-back test, which is a continuous performance task that is commonly used as an assessment in cognitive neuroscience to measure a part of working memory and working memory capacity) on aviation student pilots while they are flying in various modes of flight simulation for experimental variation. I also enjoyed utilizing the experimental machinery, synchronizing both hardware and software.
A combination of several different software programming skills that I have learned from classes and projects I completed at Williams (like R, JMP, MATLAB, etc.) have been helpful in various steps of performing data analysis. I have found that using Python for coding and performing data analysis and machine learning has been the most useful method of data analysis—many new and interesting Python libraries allowed me to easily analyze data pulled from eclectic sources and integrate them into overall data analysis. Identifying and setting up parameters like reaction time, percent correct of target letter identifications, and indicators of fatigue all made data analysis and drawing conclusions from the experiments possible.
The pairing of subjects with specific experimental conditions was especially noteworthy in experimental design and data analysis: by using a Latin Square experimental design, much fewer trials were needed than the ostensibly necessary full gamut of pairings of each experimental condition with each other. Learning about this highly apt and agile method of carrying out statistical experiments at Williams, I had not anticipated the ubiquity of this method of carrying out experiments. I now appreciate that this manner of experimental design is used increasingly often in a wide array of contexts since the time and money of using this sound experimental design can be reduced in the realms of whole magnitudes lower. Similarly, I have enjoyed using the art of correcting and offsetting to standardize experimental units to focus in on the specific time frames and data frames involved in the experiments.
I have gained a great deal of experience at the Institute for Human & Machine Cognition—learning the ins-and-outs of scientific research and making the outcomes of research to improve public well-being. Furthermore, this summer internship has impacted me positively both academically and professionally since I have learned many new technical and hands-on skills in data science and experimental design. Personally, I learned that I really enjoy research and implementing the technology of human and machine cognition: I would like to continue to be involved with IHMC in future endeavors and support new research and initiatives there.
I am so grateful for having this opportunity at IHMC. I would like to thank the Class of 1972, the ’68 Center for Career Exploration, Dr. Anil R., Maggie F., and the many talented and generous scientists, researchers, and staff at the Institute for Human & Machine Cognition that have made my summer internship possible and be an enriching experience. Their support has made summer 2019 a highly enlightening experience for me that has solidified my passion for making advancements in human and machine cognition.