Matching Items (4)
Description
Bully victimization has been associated with blunted cardiovascular responses to stress as well as elevated responses to stress. The difference between these altered physiological responses to stress is largely unknown. This study explored several possible moderators to the relationship between chronic stress and future cardiac output (an indicator of increased stress) in response to future stressors. These moderators include the difference between social and physical stressors and individual levels of loneliness. Participants were administered measures of loneliness and victimization history, and led to anticipate either a "social" (recorded speech) or "non-social" (pain tolerance test ) stressor, neither of which occurred. EKG and impedance cardiography were measured throughout the session. When anticipating both stressors, loneliness and victimization were associated with increased CO. A regression revealed a three-way interaction, with change in cardiac output depending on victimization history, loneliness, and condition in the physical stressor condition. Loneliness magnified the CO output levels of non-bullied individuals when facing a physical stressor. These results suggest that non- bullied participants high in loneliness are more stressed out when facing stressors, particularly stressors that are physically threatening in nature.
ContributorsHaneline, Magen (Author) / Newman, Matt (Thesis advisor) / Salerno, Jessica (Committee member) / Miller, Paul (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and model emotions which later can be used to improve these learning systems.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
The first contribution proposes an event-crossover (ECO) methodology to analyze performance in learning environments. The methodology is relevant to studies where it is desired to evaluate the relationships between sentinel events in a learning environment and a physiological measurement which is provided in real time.
The second contribution introduces analytical methods to study relationships between multi-dimensional physiological signals and sentinel events in a learning environment. The methodology proposed learns physiological patterns in the form of node activations near time of events using different statistical techniques.
The third contribution addresses the challenge of performance prediction from physiological signals. Features from the sensors which could be computed early in the learning activity were developed for input to a machine learning model. The objective is to predict success or failure of the student in the learning environment early in the activity. EEG was used as the physiological signal to train a pattern recognition algorithm in order to derive meta affective states.
The last contribution introduced a methodology to predict a learner's performance using Bayes Belief Networks (BBNs). Posterior probabilities of latent nodes were used as inputs to a predictive model in real-time as evidence was accumulated in the BBN.
The methodology was applied to data streams from a video game and from a Damage Control Simulator which were used to predict and quantify performance. The proposed methods provide cognitive scientists with new tools to analyze subjects in learning environments.
ContributorsLujan Moreno, Gustavo A. (Author) / Runger, George C. (Thesis advisor) / Atkinson, Robert K (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Villalobos, Rene (Committee member) / Arizona State University (Publisher)
Created2017
Description
For my graduate thesis, I present an annotated bibliography that evaluates and summarizes a list of resources available for use in future research. The resources focus on
how pectus excavatum, a congenital birth anomaly affecting the thoracic wall, may
impact identity formation and subsequent behavior during a period characterized by
significant physical and psychological development, from the ages 12 to 18, known as
adolescence. I examine resources that specifically look at congenital birth anomalies,
pectus excavatum, developmental psychology, psychoanalysis, and identity crises. The
following provides background and an annotated bibliography to establish whether there
is a causal relationship between individuals born with congenital birth anomalies,
specifically pectus excavatum (PE), and the impact it has to identity development during
adolescence. This work is important to me because I was born with pectus excavatum,
and I believe a causal relationship does exist. While I claim the causal relationship does
exist, I should note this is conjecture and anecdotally based on 7 years of interacting with
patients in both a clinical and personal setting. The conversations are reflective of
discussions that have taken place about having been born with pectus excavatum and how
the condition has impacted our lives.
ContributorsMihuc, Michael (Author) / Maienschein, Jane (Thesis advisor) / Ellison, Karin (Committee member) / Gur-Arie, Rachel (Committee member) / Arizona State University (Publisher)
Created2023
Description
The purpose of this project was to discuss the physiological effects of isolation on the human body and how the body adapts. Through reviewing stories and studies of social and perceptual isolation, the adaptations of the human mind are detailed. This project explores the experiences of prisoners, sensory deprivation tanks, cave explorations, as well as studies involving monkeys and carpenter ants. The adaptations witnessed include hallucinations, increased mortality, anxiety, agitation, altered sense of time, delayed response, and lowered blood pressure. Knowing the factors surrounding the isolation experience is crucial to understand the presenting adaptation methods. These factors include duration, voluntary or involuntary participation, mental strength, and the restriction level of the isolation.
DISCLAIMER: Due to the unexpected COVID-19 pandemic, the attached podcast is a draft recording in lieu of the final recording
DISCLAIMER: Due to the unexpected COVID-19 pandemic, the attached podcast is a draft recording in lieu of the final recording
ContributorsSidhu, Nimrit (Co-author) / Deacon, Hannah (Co-author) / Hyatt, JP (Thesis director) / Kingsbury, Jeffrey (Committee member) / School of Social Work (Contributor) / College of Health Solutions (Contributor) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05