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Articles 61 - 70 of 70

Full-Text Articles in Cognitive Science

Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke May 2022

Understanding The Influence Of Perceptual Noise On Visual Flanker Effects Through Bayesian Model Fitting, Jordan Deakin, Dietmar Heinke

MODVIS Workshop

No abstract provided.

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Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality', Logan Scott Parker May 2022

Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality', Logan Scott Parker

Honors Theses

With the recent explosion of popularity of virtual and mixed reality, an important question has arisen: “Is there a way to create a better blend of real and virtual worlds in a mixed reality experience?” This research attempts to determine whether a visual filter can be created and applied to virtual objects to better convince the brain into interpreting a composite of virtual and real views as one seamless view. The method devised in this thesis is being called 'Diminished Virtual Reality'. The results found in this study show that when presented with a scene composed of a combination of …

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Eye-Tracking Using Deep Learning, Sam Trenter Apr 2022

Eye-Tracking Using Deep Learning, Sam Trenter

Theses

Eye-tracking can be valuable for researchers in many domains. Most eye-tracking technologies require an extra piece of costly hardware. Several other available eye-tracking solutions are usually not very accurate and require a costly subscription. Our project was oriented at creating a free and open-source alternative that does not require additional equipment. We developed a deep learning-based solution as a prototype for this project. Specifically, we developed a deep learning model to predict a user’s gaze position on the screen. We created our training data set using a commercially available eye-tracker to train the model. Each training sample consists of a …

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Usability Of Health-Related Websites By Filipino-American Adults And Nursing Informatics Experts, Kathleen Begonia Feb 2022

Usability Of Health-Related Websites By Filipino-American Adults And Nursing Informatics Experts, Kathleen Begonia

Dissertations, Theses, and Capstone Projects

Filipino-Americans are an understudied minority group with high prevalence and mortality from chronic conditions, such as cardiovascular disease and diabetes. Facing barriers to care and lack of culturally appropriate health resources, they frequently use the internet to obtain health information. It is unknown whether they perceive health-related websites to be useful or easy to use because there are no published usability studies involving this population. Using the Technology Acceptance Model as a theoretical framework, this study investigated the difference between website design ratings by experts and the perceptions of Filipino-American users to determine if usability guidelines influenced the perceived ease …

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Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel Jan 2022

Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel

Computer Science Faculty Scholarship

The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing …

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Dark Mode Vogue: Do Light-On-Dark Displays Have Measurable Benefits To Users?, Tara Sethi Jan 2022

Dark Mode Vogue: Do Light-On-Dark Displays Have Measurable Benefits To Users?, Tara Sethi

Theses and Dissertations - All Years

In recent years, dark mode displays have become a popular user interface design trend. Major software providers have promised several benefits to using dark mode (negative polarity) displays. However, most of the prior research showed that light more (positive polarity) is more beneficial to human performance. In this work, we investigated the effect of display polarity (negative and positive) on cognitive load, subjective mental effort, subjective task difficulty, and emotion to assess whether the popularity of these displays is related to aesthetic qualities or true physiological benefits. As the dark mode trend has been observed mostly in younger populations, two …

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Using Ai-Enabled Gaze Tracking System To Understand Comprehension Patterns Of Computer Science Students, Bradley Boswell Jan 2022

Using Ai-Enabled Gaze Tracking System To Understand Comprehension Patterns Of Computer Science Students, Bradley Boswell

College of Graduate Studies: Theses & Dissertations

Academic institutions and instructors lack the ability to accurately assess the moment-to-moment attentiveness of students in classrooms where students’ faces are obscured by computer monitors. This can cause the lectures of Computer Science, Information Technology, or other lab-based courses to be incorrectly-paced, which can lead to students having an overall poorer grasp of the subject material. We propose a system for real-time accurate detection of classroom attentiveness using monitor-mounted webcams and eye trackers, along with a Convolutional Neural Network machine learning model (NiCATS). Through the use of a neural network, we produce an initial attentiveness score based on student webcam …

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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 Sep 2021

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 …

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Action Real-Time Strategy Gaming Experience Related To Enhanced Capacity Of Visual Working Memory, Yutong Yao, Ruifang Cui, Yi Li, Lu Zeng, Jinliang Jiang, Nan Qiu, Li Dong, Diakun Gong, Guojian Yan, Weiyi Ma, Tiejun Liu Aug 2020

Action Real-Time Strategy Gaming Experience Related To Enhanced Capacity Of Visual Working Memory, Yutong Yao, Ruifang Cui, Yi Li, Lu Zeng, Jinliang Jiang, Nan Qiu, Li Dong, Diakun Gong, Guojian Yan, Weiyi Ma, Tiejun Liu

General Human Environmental Sciences Faculty Publications and Presentations

Action real-time strategy gaming (ARSG)—a major genre of action video gaming (AVG)—has both action and strategy elements. ARSG requires attention, visual working memory (VWM), sensorimotor skills, team cooperation, and strategy-making abilities, thus offering promising insights into the learning-induced plasticity. However, it is yet unknown whether the ARSG experience is related to the development of VWM capacity. Using both behavioral and event-related potential (ERP) measurements, this study tested whether ARSG experts had larger VWM capacity than non-experts in a change detection task. The behavioral results showed that ARSG experts had higher accuracy and larger VWM capacity than non-experts. In addition, the …

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Distributed Artificial Neural Network (Dann): Model Of Human Memory Behavior, Karen Meriweather Dec 2004

Distributed Artificial Neural Network (Dann): Model Of Human Memory Behavior, Karen Meriweather

All-Inclusive List of Electronic Theses and Dissertations

It is believed that a distributed network system will provide a general means of understanding cognitive functions that occur in the brain. This study provides a framework for modeling human memory behavior using a Distributed Artificial Neural Network (DANN) architecture. This approach presents a different method for modeling cognitive functions using peer-to-peer computing. The computers on the network represents different memory stores, and by using Hopfield Neural Network Techniques, along with the software application Joone, the final product is a DANN system that exhibits human memory behavior and the ability to associate input vectors from the Fisher's Iris Classification output …

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