Cognitive Science Commons^™

Examining Differences In Concept Representation Across Similarity Spaces Between Humans And Large Language Models, Krishnachandra Nair

Computer Science Senior Theses

The replication of human concept representation is a critical task for the pursuit of artificial general intelligence. With the recent influx of large language models that demonstrate text-generation capabilities nearly on par with humans, the question stands on whether these large language models can capture concepts within language. We examine this question by exploring differences in concept representation across similarity spaces between humans and LLMs. We find that, while concept representation within LLMs does partially mimic human concept representation, LLMs are greatly limited by their dependence on semantic information and cannot therefore develop an understanding of human social code or …

Alterations In Brain Morphometric Networks And Their Relationship With Memory Dysfunction In Patients With Type 2 Diabetes Mellitus, Rye Young Kim, Yoonji Joo, Eunji Ha, Haejin Hong, Chaewon Suh, Youngeun Shim, Hyeonji Lee, Yejin Kim, Jae-Hyoung Cho, Sujung Yoon, In Kyoon Lyoo

Faculty, Staff and Student Publications

Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years’ duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups’ …

Unveiling Gene Interactions In Alzheimer's Disease By Integrating Genetic And Epigenetic Data With A Network-Based Approach, Keith L Sanders, Astrid M Manuel, Andi Liu, Boyan Leng, Xiangning Chen, Zhongming Zhao

Faculty, Staff and Student Publications

Alzheimer’s Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD’s pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted …

Improving Educational Delivery And Content In Juvenile Detention Centers, Yomna Elmousalami

Undergraduate Research Symposium

Students in juvenile detention centers have the greatest need to receive improvements in educational delivery and content; however, they are one of the “truly disadvantaged” populations in terms of receiving those improvements. This work presents a qualitative data analysis based on a focus group meeting with stakeholders at a local Juvenile Detention Center. The current educational system in juvenile detention centers is based on paper worksheets, single-room style teaching methods, outdated technology, and a shortage of textbooks and teachers. In addition, detained students typically have behavioral challenges that are deemed "undesired" in society. As a result, many students miss classes …

Bayesian Inference In Reinforcement Learning Neural Networks During A Markov Decision Processes?, Katherine Graham

Scripps Senior Theses

The predictive mind theory proposes that brains work in a way that makes predictions about future stimuli to process information efficiently and accurately. Bayesian brain theory suggests that the brain utilizes Bayesian probability models to make predictions, while the free-energy minimization hypothesis proposes that these predictions are made to minimize energy or uncertainty, ensuring accurate perceptions. Vertechi et al. (2020) explored animal participants’ utilization of stimulus-bound strategy versus inference-based strategy to solve a Markov decision process with a 2-state environment, one of which is always active. These sites have a certain probability of switching to a different site and the …

Enhancing Students’ User Experience With A Code Critiquer, Laura E. Albrant

Dissertations, Master's Theses and Master's Reports

This thesis explores the role of human factors in the realm of code critiquers and students’ experiences with them. Across three studies, the work utilized Design Thinking to improve the user experience of WebTA for introductory engineering students learning MATLAB. The first two studies gathered observational and interview data to empathize, define, and ideate a new user interface (UI). Said UI was prototyped and then tested with the third study. Overall, the surveys’ data suggests that most students found the new design to be more appealing, useful, and purposeful; however, there is still plenty of room for improvement. Additionally, analysis …

What’S In A Word? A Study Of Emotional Valence, Trisha Patel, Cameron Smith, Olivia Williams, Raul Rodriguez-Calva

Science University Research Symposium (SURS)

Cognitive stress affects humans mentally and physically, and specifically may impact emotional recognition. Previous studies have focused on the perceived emotional valence of nouns vs. adjectives and the emotional recognition of photographs. While this has led to the creation of an emotional valence database, there has yet to be a study to explore how stress can change perceived emotional valence. Using data from the Affective Norms for English Words (ANEW), the Portland Arithmetic Stress Test (PAST), and the Montreal Imaging Stress Task (MIST), a study was created to explore how participants emotional valence of words would change after being placed …

Depaul Digest

DePaul Magazine

College of Education Professor Jason Goulah fosters hope, happiness and global citizenship through DePaul’s Institute for Daisaku Ikeda Studies in Education. Associate Journalism Professor Jill Hopke shares how to talk about climate change. News briefs from DePaul’s 10 colleges and schools: Occupational Therapy Standardized Patient Program, Financial Planning Certificate program, Business Education in Technology and Analytics Hub, Racial Justice Initiative, Teacher Quality Partnership grant, Intimate Partner Violence and Brain Injury collaboration, School of Music Career Closet, Sports Photojournalism course, DePaul Migration Collaborative’s Solutions Lab, Inclusive Screenwriting courses. New appointments: School of Music Dean John Milbauer, College of Education Dean Jennifer …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

Weakly Supervised Spatial Relation Extraction From Radiology Reports, Surabhi Datta, Kirk Roberts

Faculty, Staff and Student Publications

OBJECTIVE: Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.

MATERIALS AND METHODS: Our weak supervision approach is based on data programming that uses rules (or labeling functions) relying on domain-specific dictionaries and radiology language characteristics to generate weak labels. The labels correspond to different spatial relations that are critical to understanding radiology reports. These weak labels are then used to fine-tune a pretrained Bidirectional Encoder Representations from …

Say That Again: The Role Of Multimodal Redundancy In Communication And Context, Brandon Javier Dormes

Cognitive Science Senior Theses

With several modes of expression, such as facial expressions, body language, and speech working together to convey meaning, social communication is rich in redundancy. While typically relegated to signal preservation, this study investigates the role of cross-modal redundancies in establishing performance context, focusing on unaided, solo performances. Drawing on information theory, I operationalize redundancy as predictability and use an array of machine learning models to featurize speakers' facial expressions, body poses, movement speeds, acoustic features, and spoken language from 24 TEDTalks and 16 episodes of Comedy Central Stand-Up Presents. This analysis demonstrates that it is possible to distinguish between these …

Inferring Personalized Treatment Effect Of Antihypertensives On Alzheimer's Disease Using Deep Learning, Pulakesh Upadhyaya, Yaobin Ling, Luyao Chen, Yejin Kim, Xiaoqian Jiang

Faculty, Staff and Student Publications

Alzheimer's disease (AD) is one of the leading causes of death in the United States, especially among the elderly. Recent studies have shown how hypertension is related to cognitive decline in elderly patients, which in turn leads to increased mortality as well as morbidity. There have been various studies that have looked at the effect of antihypertensive drugs in reducing cognitive decline, and their results have proved inconclusive. However, most of these studies assume the treatment effect is similar for all patients, thus considering only the average treatment effects of antihypertensive drugs. In this paper, we assume that the effect …

Predicting The Risk Of Alzheimer's Disease And Related Dementia In Patients With Mild Cognitive Impairment Using A Semi-Competing Risk Approach, Zhaoyi Chen, Yuchen Yang, Dazheng Zhang, Jingchuan Guo, Yi Guo, Xia Hu, Yong Chen, Jiang Bian

Faculty, Staff and Student Publications

Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) are a group of progressive neurodegenerative diseases. The progression of AD can be conceptualized as a continuum in which patients progress from normal cognition to preclinical AD (i.e., no symptoms but biological changes in the brain) to mild cognitive impairment (MCI) due to AD (i.e., mild symptoms but not interfere with daily activities), followed by increasing severity of dementia due to AD. Early detection and prediction models for the transition of MCI to AD/ADRD are needed, and efforts have been made to build predictions of MCI conversion to AD/ADRD. However, most existing studies …

The Model 2.0 And Friends: An Interim Report, Garrison W. Cottrell, Martha Gahl, Shubham Kulkarni, Shashank Venkatramani, Yash Shah, Keyu Long, Xuzhe Zhi, Shivaank Agarwal, Cody Li, Jingyuan He, Thomas Fischer

MODVIS Workshop

Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!

We look forward to the group’s suggestions for these projects.

How Object Segmentation And Perceptual Grouping Emerge In Noisy Variational Autoencoders, Ben Lonnqvist, Zhengqing Wu, Michael H. Herzog

MODVIS Workshop

Many animals and humans can recognize and segment objects from their backgrounds. Whether object segmentation is necessary for object recognition has long been a topic of debate. Deep neural networks (DNNs) excel at object recognition, but not at segmentation tasks - this has led to the belief that object recognition and segmentation are separate mechanisms in visual processing. Here, however, we show evidence that in variational autoencoders (VAEs), segmentation and faithful representation of data can be interlinked. VAEs are encoder-decoder models that learn to represent independent generative factors of the data as a distribution in a very small bottleneck layer; …

Beyond Machine Learning: An Fmri Domain Adaptation Model For Multi-Study Integration, Lauryn Michelle Burleigh

LSU Doctoral Dissertations

Traditional machine learning analyses are challenging with functional magnetic
resonance imaging (fMRI) data, not only because of the amount of data that needs to be
collected, adding a particular challenge for human fMRI research, but also due to the change in
hypothesis being addressed with various analytical techniques. Domain adaptation is a type of
transfer learning, a step beyond machine learning which allows for multiple related, but not
identical, data to contribute to a model, can be beneficial to overcome the limitation of data
needed but may address different hypothesis questions than anticipated given the analysis
computation. This dissertation assesses …

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 …

Types Of Questions Teachers Ask To Engage Students In Making Sense Of A Student Contribution, Nishat B. Alam

Dissertations, Master's Theses and Master's Reports

In the student-centered classroom, a teacher’s interpretation and response to student mathematical contributions plays an important role to shape and direct students’ opportunities for sense-making. This research used a scenario-based survey questionnaire to examine what types of questions middle and high school mathematics teachers indicate they would ask to engage students in making sense of a high-leverage student mathematical contribution and their reasoning about why particular questions are or are not productive. From the results, it could be concluded that teachers asked more productive questions after seeing a set of possible questions. Their beliefs about the productivity of the questions …

Explicit Rule Learning: A Cognitive Tutorial Method To Train Users Of Artificial Intelligence/Machine Learning Systems, Anne Linja

Dissertations, Master's Theses and Master's Reports

Today’s intelligent software systems, such as Artificial Intelligence/Machine Learning systems, are sophisticated, complicated, sometimes complex systems. In order to effectively interact with these systems, novice users need to have a certain level of understanding. An awareness of a system’s underlying principles, rationale, logic, and goals can enhance the synergistic human-machine interaction. It also benefits the user to know when they can trust the systems’ output, and to discern boundary conditions that might change the output. The purpose of this research is to empirically test the viability of a Cognitive Tutorial approach, called Explicit Rule Learning. Several approaches have been used …

Investigating Collaborative Explainable Ai (Cxai)/Social Forum As An Explainable Ai (Xai) Method In Autonomous Driving (Ad), Tauseef Ibne Mamun

Dissertations, Master's Theses and Master's Reports

Explainable AI (XAI) systems primarily focus on algorithms, integrating additional information into AI decisions and classifications to enhance user or developer comprehension of the system's behavior. These systems often incorporate untested concepts of explainability, lacking grounding in the cognitive and educational psychology literature (S. T. Mueller et al., 2021). Consequently, their effectiveness may be limited, as they may address problems that real users don't encounter or provide information that users do not seek.

In contrast, an alternative approach called Collaborative XAI (CXAI), as proposed by S. Mueller et al (2021), emphasizes generating explanations without relying solely on algorithms. CXAI centers …

A Picture Worth A Thousand Words: Factors Influencing Disability Accommodations, Alicia E. Martin

Cal Poly Humboldt theses and projects

Because not all disabilities look the same it is difficult to label a person with disabilities just by looking at them. Given that our knowledge, attitudes, and perceptions impact how we interpret our world and our willingness to act, people, including professors, may be biased toward providing accommodations for those with easily recognizable disabilities and biased against those with non-recognizable disabilities, and this may impact the disabled person’s ability to learn. This thesis aims to address whether professors’ disability-related attitudes, perceptions of accommodation reasonableness, and willingness to provide accommodations differ when the disability is recognizable (student is pictured in a …

Transfer Of Personality Through Text Style, Michael O'Mahony, Robert Ross

Other resources

The style of generated text is how something is said rather than what is said. We hypothesize that changing the style of generated text can change the perceived personality of the text generation agent. Dialogue systems that aim to imitate a human agent can appear to have a consistent personality through a consistent, controllable style of conversation. Some recent work on the style of generated text [1] performs impressively in the small number of domains selected for their experiments using transformer and LSTM-based models. Lin et al. [1] used weak supervised learning as their data set lacks parallel data. The …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

Publications and Research

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Legislative Language For Success, Sanjana Gundala

Master's Theses

Legislative committee meetings are an integral part of the lawmaking process for local and state bills. The testimony presented during these meetings is a large factor in the outcome of the proposed bill. This research uses Natural Language Processing and Machine Learning techniques to analyze testimonies from California Legislative committee meetings from 2015-2016 in order to identify what aspects of a testimony makes it successful. A testimony is considered successful if the alignment of the testimony matches the bill outcome (alignment is "For" and the bill passes or alignment is "Against" and the bill fails). The process of finding what …

The Significance Of Sonic Branding To Strategically Stimulate Consumer Behavior: Content Analysis Of Four Interviews From Jeanna Isham’S “Sound In Marketing” Podcast, Ina Beilina

Student Theses and Dissertations

Purpose:
Sonic branding is not just about composing jingles like McDonald’s “I’m Lovin’ It.” Sonic branding is an industry that strategically designs a cohesive auditory component of a brand’s corporate identity. This paper examines the psychological impact of music and sound on consumer behavior reviewing studies from the past 40 years and investigates the significance of stimulating auditory perception by infusing sound in consumer experience in the modern 2020s.

Design/methodology/approach:
Qualitative content analysis of audio media was used to test two hypotheses. Four archival oral interview recordings from Jeanna Isham’s podcast “Sound in Marketing” featuring the sonic branding experts …

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

MODVIS Workshop

No abstract provided.

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 …

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 …

Counterfactual Analysis Of Differential Comorbidity Risk Factors In Alzheimer’S Disease And Related Dementias, Yejin Kim, Kai Zhang, Sean I Savitz, Luyao Chen, Paul E Schulz, Xiaoqian Jiang

Faculty, Staff and Student Publications

Alzheimer’s disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with various comorbidities. There is also significant heterogeneity in the prevalence of ADRD across diverse demographics groups. Association studies on such heterogeneous comorbidity risk factors are limited in their ability to determine causation. We aim to compare counterfactual treatment effects of various comorbidity in ADRD in different racial groups (African Americans and Caucasians). We used 138,026 ADRD and 1:1 matched older adults without ADRD from nationwide electronic health records, which extensively cover a large population’s long medical history in breadth. We matched African Americans …

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 …