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- All Subjects: psychology
- All Subjects: Machine Learning
- Creators: Barrett, The Honors College
Description
While acceptance towards same-sex marriage is gradually increasing, same-sex marriage is banned in many states within the United States. Laws that prohibit same-sex couples from marrying have been shown to increase feelings of depression, exclusion, and stigma for same-sex attracted individuals. The intention of this study was to explore the effect both pro- and anti-same-sex marriage advertisements have on heterosexual individuals' implicit attitudes towards same-sex couples. It was predicted that exposure to anti-same-sex advertisements would lead to viewing same-sex couples as more unpleasant and heterosexual couples as being more pleasant. However, heterosexual participants who viewed anti-same-sex marriage ads were more likely to rate heterosexual couples as being unpleasant and same-sex couples as pleasant. It is theorized that viewing anti-same-sex marriage advertisements led heterosexual individuals to report heterosexual stimuli as being more unpleasant compared to same-sex stimuli as a form of defensive processing.
ContributorsWalsh, Theodora Michelle (Author) / Newman, Matt (Thesis advisor) / Hall, Deborah (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2013
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
Interest in health and wellness has significantly increased in today's society. Living a healthy and active lifestyle is suggested to promote overall physical and psychological well-being. This study explored the effects of wearing a Fitbit Zip activity monitor and the impact of expressing mindfulness on levels of physical activity. It was predicted that expressing mindfulness, as measured by the use of present-tense language during the daily emotional writing task, would moderate the relationship between wearing a Fitbit Zip activity monitor and change in physical activity. Specifically, it was hypothesized daily monitoring would only lead to increased activity among those higher in mindful language. Over the course of five days, participants were asked to wear a Fitbit Zip and to complete a daily questionnaire and writing task at the end of each evening. On the last day of the study, participants completed a follow-up assessment, which suggested that the combination of wearing a Fitbit Zip activity monitor and expressing more mindfulness throughout the week increased levels of physical activity. An important issue for future research is to conduct this study for a longer period of time in order to get more variability in the data. However, despite the limitations of the design, these findings suggest that activity monitoring may be a promising way to promote healthy lifestyle change.
ContributorsTarachiu, Viorela (Author) / Newman, Matt L. (Thesis advisor) / Hall, Deborah (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2014
Description
A sample of 193 participants viewed one of six variations of an eyewitness giving mock testimony. Each participant viewed testimony, which varied by level of emotion (none, moderate, or high) and frame (waist-up or head only). Participants then rated the witness using the Brodsky Witness Credibility Scale and the Reyson Likability Scale. A set of ANOVA's was performed revealing an effect of emotion level on both credibility and likability. Emotion level was found to influence participant judgments of poise, however, to a lesser degree than judgments of credibility and likability. These results suggest that attorneys may want to avoid the use of videoconferencing with certain types of witnesses where testimony may be highly emotional.
ContributorsHavener, Shannon (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Committee member) / Hall, Deborah (Committee member) / Arizona State University (Publisher)
Created2014
Description
The purpose of this study was to examine mock jurors’ decision making in insanity cases. Specific instructions (vs. typical) juror instructions were tested to increase jurors’ comprehension of verdict options and reduce the effects of preexisting attitudes and reliance on cognitively biased thought processes in their legal decision making.
The specific instructions in this study were inspired by Fuzzy Trace Theory, which holds that simple language and visual aids that convey the ‘gist’ of complex information can help people make better decisions (Reyna & Brainerd, 1995). Participants (N= 496) were randomly assigned to one of two juror instruction conditions (specific vs. typical). All participants read a 10-page insanity defense case vignette, and were tasked with reaching a verdict. They were provided with 5 verdict options: Not Guilty, Guilty, and three different insanity options (Not Guilty by Reason of Insanity, Guilty but Mentally Ill, Guilty Except Insane). Results supported the hypothesis that jurors who received specific (vs. typical) instructions would comprehend more information about the available verdicts, and would be more likely to choose an insanity defense verdict. As expected, jurors’ preexisting attitudes toward the insanity defense influenced their verdicts. Although it was hypothesized that increasing jurors’ understanding would result in them relying less on their attitudes and motivated reasoning processes in reaching their legal judgments, the evidence did not support this. Results suggest more specific instructions that includes information about outcomes is preferred by jurors, and that they are better able to understand and perform their duties when provided with more useful information. However, further research is needed to identify methods for helping jurors rely less on biased reasoning processes in their legal judgments.
The specific instructions in this study were inspired by Fuzzy Trace Theory, which holds that simple language and visual aids that convey the ‘gist’ of complex information can help people make better decisions (Reyna & Brainerd, 1995). Participants (N= 496) were randomly assigned to one of two juror instruction conditions (specific vs. typical). All participants read a 10-page insanity defense case vignette, and were tasked with reaching a verdict. They were provided with 5 verdict options: Not Guilty, Guilty, and three different insanity options (Not Guilty by Reason of Insanity, Guilty but Mentally Ill, Guilty Except Insane). Results supported the hypothesis that jurors who received specific (vs. typical) instructions would comprehend more information about the available verdicts, and would be more likely to choose an insanity defense verdict. As expected, jurors’ preexisting attitudes toward the insanity defense influenced their verdicts. Although it was hypothesized that increasing jurors’ understanding would result in them relying less on their attitudes and motivated reasoning processes in reaching their legal judgments, the evidence did not support this. Results suggest more specific instructions that includes information about outcomes is preferred by jurors, and that they are better able to understand and perform their duties when provided with more useful information. However, further research is needed to identify methods for helping jurors rely less on biased reasoning processes in their legal judgments.
ContributorsHight, Morgan Leigh (Author) / Neal, Tess M (Thesis advisor) / Schweitzer, Nicholas (Committee member) / Salerno, Jessica (Committee member) / Arizona State University (Publisher)
Created2018
Description
This paper explores how changing the color of chocolate can affect its perceived taste. While color psychology and its effects on food industry marketing are widely studied, this experiment focuses on blue, red, green, and purple striped chocolates. The study conducted for this paper focuses on these four colors based on their utilization in previously conducted experiments. Each color of chocolate involved 25 participants, for a total of 100 total individuals, who each taste tested one piece and immediately filled out a survey. The survey asked demographic questions, colored chocolate preferences, and questions ranking the chocolate's appeal. While the outcome showed that blue, green, red, and purple was indeed the order of appealing colors, the study results indicate the participants' color preferences did not affect their perceived taste of the chocolate they sampled. Rather, their preference was based on experiences they associated with the color of the chocolate they tasted.
ContributorsChan, Sydney (Author) / Gray, Nancy (Thesis director) / Giard, Jacques (Committee member) / Barrett, The Honors College (Contributor)
Created2018-05
Leveraging Machine Learning and Wireless Sensing for Robot Localization - Location Variance Analysis
Description
Object localization is used to determine the location of a device, an important aspect of applications ranging from autonomous driving to augmented reality. Commonly-used localization techniques include global positioning systems (GPS), simultaneous localization and mapping (SLAM), and positional tracking, but all of these methodologies have drawbacks, especially in high traffic indoor or urban environments. Using recent improvements in the field of machine learning, this project proposes a new method of localization using networks with several wireless transceivers and implemented without heavy computational loads or high costs. This project aims to build a proof-of-concept prototype and demonstrate that the proposed technique is feasible and accurate.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
Modern communication networks heavily depend upon an estimate of the communication channel, which represents the distortions that a transmitted signal takes as it moves towards a receiver. A channel can become quite complicated due to signal reflections, delays, and other undesirable effects and, as a result, varies significantly with each different location. This localization system seeks to take advantage of this distinctness by feeding channel information into a machine learning algorithm, which will be trained to associate channels with their respective locations. A device in need of localization would then only need to calculate a channel estimate and pose it to this algorithm to obtain its location.
As an additional step, the effect of location noise is investigated in this report. Once the localization system described above demonstrates promising results, the team demonstrates that the system is robust to noise on its location labels. In doing so, the team demonstrates that this system could be implemented in a continued learning environment, in which some user agents report their estimated (noisy) location over a wireless communication network, such that the model can be implemented in an environment without extensive data collection prior to release.
ContributorsChang, Roger (Co-author) / Kann, Trevor (Co-author) / Alkhateeb, Ahmed (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The purpose of this project is to raise awareness for children with social anxiety. As a book directed to children around the age of 12, it will give them a character they can relate to, so they can feel less alone. Throughout the story, the main character experiences symptoms of social anxiety and is subject to events that exacerbate those symptoms. Despite her challenges, the main character is able to effectively cope with her social anxiety through her own hard work, and help from her family members, teachers, and peers. The intent is to show children with social anxiety that, contrary to what their disorder makes them feel, they are special and have the capacity to develop skills that are relevant to their talents and interests, and overcome their fears. They should know that parents, teachers, and peers will be there to help and support them and will not judge them as harshly as they suspect. The supporting characters in this story show how a strong support base can influence the success of children with social anxiety. By the end of the story, the main character still has social anxiety, but has gained confidence and her symptoms are less severe. This illustrates that, although social anxiety cannot simply be overcome—that is, it doesn’t go away completely—it can be effectively managed with assistance from close others, and perseverance.
ContributorsDillard, Bethlehem (Author) / Lewis, Stephen (Thesis director) / Gaffney, Cynthia (Committee member) / School of Social and Behavioral Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05