Cognitive Science | Open Access Articles | Digital Commons Network™

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

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

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

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

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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Ue-Based Estimation Of Available Uplink Data Rates In Cellular Networks, Christian Beder, Julia Blanke, Martin Klepal

NIMBUS Articles

Behaviour Demand Response (BDR) is the process of communicating with the building occupants and integrating their behavioural flexibility into the energy value chain. In this paper we will present an integrated behavioural model based on well-established behavioural theories and show how it can be used to provide predictable flexibility to the production schedule optimisation. The proposed approach is two-fold: the model can be used to predict the expected behavioural flexibility of occupants as well as to generate optimal communication to trigger reliable BDR events. A system architecture will be presented showing how BDR can be integrated into simulation passed building/district …

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Off The Lip Conference - Transdisciplinary Approaches To Cognitive Innovation. Conference Proceedings, Sue Denham, Michael Punt, Edith Doove, Martha Blassnigg, Raluca Briazu, Kathryn Francis, Agi Haynes, Guy Edmonds, Adam Benjamin, Matthew Emmett, Iris Garrelfs, Christopher B. Germann, Joanna Griffin, Diane Humphrey, Bryanna Lucyk, Christie Purchase, Rachel Sansone, Emily Baxter, Amy Ione, Frank Loesche, Abigail Jackson, Alexis Kirke, Eduardo Miranda, Luke Rendell, Simon Ingram, Yutaka Nakamura, Gi Taek Ryoo, Eugenia Stamboliev, Michael Straeubig, Chun-Wei Hsu, Pinar Oztop, Mihaela Taranu, Sundar Sarukkai, James Sweeting, Minami Hirayama

Off the Lip Conference - Transdisciplinary Approaches to Cognitive Innovation

The promise of cognitive innovation as a collaborative project in the sciences, arts and humanities is that we can approach creativity as a bootstrapping cognitive process in which the energies that shape the poem are necessarily indistinguishable from those that shape the poet. For the purposes of this conference the exploration of the idea of cognitive innovation concerns an understanding of creativity that is not exclusively concerned with conscious human thought and action but also as intrinsic to our cognitive development. As a consequence, we see the possibility for cognitive innovation to provide a theoretical and practical platform from which …

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Using Data Analytics To Further Understand The Role That Boredom, Loneliness, Social Anxiety, Social Gratification, And Social Relationships (Brag) Play In A Driver’S Decision To Text, Nathan White, Yair Levy, Steven R. Terrell, Steve Bronsburg

All Faculty Scholarship for the College of Education and Professional Studies

Texting while driving is a growing problem that current efforts have failed to curtail. This behavior has serious, and sometimes fatal, consequences, and the factors that cause a driver to text are not well understood. This study investigates the influence that boredom, social relationships, social anxiety, and social gratification (BRAG) have upon the texting driver. A survey instrument was used to collect data from 297 respondents at a mid-sized regional university in the Pacific Northwest of the United States. The data was evaluated with PLS-SEM, which indicated that social gratification plays a very significant role in a driver’s decision to …

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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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Cognitive Science Commons^™

Full-Text Articles in Cognitive Science

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

Computer Science Faculty Scholarship

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

Theses and Dissertations - All Years

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

College of Graduate Studies: Theses & Dissertations

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

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

Ue-Based Estimation Of Available Uplink Data Rates In Cellular Networks, Christian Beder, Julia Blanke, Martin Klepal

NIMBUS Articles

Off the Lip Conference - Transdisciplinary Approaches to Cognitive Innovation

Using Data Analytics To Further Understand The Role That Boredom, Loneliness, Social Anxiety, Social Gratification, And Social Relationships (Brag) Play In A Driver’S Decision To Text, Nathan White, Yair Levy, Steven R. Terrell, Steve Bronsburg

All Faculty Scholarship for the College of Education and Professional Studies

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

All-Inclusive List of Electronic Theses and Dissertations