Cognitive Science Commons^™

Using Machine Learning Methods To Evaluate Undergraduates’ Executive Function In The Context Of Game-Based Measurement, Ming Chen

Dissertations, Theses, and Capstone Projects

Traditional performance-based measurements have faced criticism due to their potential to induce test fatigue and their limited ecological validity. In contrast, game-based assessments have demonstrated the capacity to engage participants effectively in tests and enhance ecological validity by replicating real-world scenarios. Nevertheless, conventional statistical approaches may prove insufficient in capturing the intricate, non-linear associations between participants' gaming performance and their cognitive capabilities. Leveraging the potential of machine learning (ML) techniques, which can model complex problems, offers a promising avenue for estimating students' performance in video games. This dissertation aims to explore various ML methods for the validation of three game-based …

Constructing A Parameterized Stimuli Space Suitable To Induce Controlled Changes In Human And Machine Perceptual Systems, Andrew Frankel

Dissertations, Theses, and Capstone Projects

Traditional computer vision applications focus on objective tasks, such as object detection, classification, or segmentation. Much of human experience is inherently subjective, such as our personal response to artwork. Individualized experiences are challenging to replicate in a controlled laboratory environment, as those experiences depend on the unique history and internal models of the observer. This work presents a technique to construct a 2-dimensional parameterized stimuli space suitable to induce controlled changes in the perceptual systems of both human and machine observers. Our contributions are threefold:

1. Using a balanced dataset of 8,800 fine art images spanning 11 art movements and 88 …

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Machine Learning-Driven Music Genre Recognition, Redeate Kidanue

All Undergraduate Theses and Capstone Projects

Music is a tool that has been integrated into society for thousands of years; it has influenced social aspects of life and has also aided in communication. Today we have various uses for music that go past our traditional uses for entertainment and self-expression. For example, music therapy has been seen to show improvements in patients with Alzheimer’s disease, depression, and PTSD. Additionally, music has played a role in political movements, demonstrating its emotional power. Social media relies heavily on the music industry as many social media posts include music either in the background, or as the forefront of posts. …

Blood-Based Transcriptomic Biomarkers Are Predictive Of Neurodegeneration Rather Than Alzheimer's Disease, Artur Shvetcov, Shannon Thomson, Jessica Spathos, Ann-Na Cho, Heather M Wilkins, Shea J Andrews, Fabien Delerue, Timothy A Couttas, Jasmeen Kaur Issar, Finula Isik, Simranpreet Kaur, Eleanor Drummond, Carol Dobson-Stone, Shantel L Duffy, Natasha M Rogers, Daniel Catchpoole, Wendy A Gold, Russell H Swerdlow, David A Brown, Caitlin A Finney

Faculty, Staff and Student Publications

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as …

A Machine Learning Approach To Deepfake Detection, Delaney Conrad

All Undergraduate Theses and Capstone Projects

The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …

Screen Media Use Among Children And Adolescents – Applications Of Supervised And Unsupervised Machine Learning And Sentiment Analysis, Yifan Zhang

Graduate Theses, Dissertations, and Problem Reports (ETD)

Screen media has become increasingly pervasive in our everyday lives and has profoundly changed the way people communicate and interact with each other. However, we are still unclear about the long-term influence of screen media use on our physical health, mental health, and social wellbeing. Children and adolescents are in an important stage of brain development and are susceptible to the environmental influence that screen media possess. This dissertation pursued three aims to address research gaps related to screen media use among children and adolescents: 1) identify topics and knowledge gaps in screen media use research among children and adolescents …

Cognition-Enhanced Machine Learning For Better Predictions With Limited Data, Florian Sense, Ryan Wood, Michael G. Collins, Joshua Fiechter, Aihua W. Wood, Michael Krusmark, Tiffany Jastrzembski, Christopher W. Myers

Faculty Publications

The fields of machine learning (ML) and cognitive science have developed complementary approaches to computationally modeling human behavior. ML's primary concern is maximizing prediction accuracy; cognitive science's primary concern is explaining the underlying mechanisms. Cross-talk between these disciplines is limited, likely because the tasks and goals usually differ. The domain of e-learning and knowledge acquisition constitutes a fruitful intersection for the two fields’ methodologies to be integrated because accurately tracking learning and forgetting over time and predicting future performance based on learning histories are central to developing effective, personalized learning tools. Here, we show how a state-of-the-art ML model can …