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Methodological Differences in Measuring Mind Wandering Influences Willingness to Re-Engage with Cognitive Tasks
Get PDFWhat if mind wandering really isn’t all that bad, just misunderstood? There is a body of literature that often cites it as the issue in failing to perceive important events (McVay & Kane, 2009). On the other hand, there is a body of literature that cites mind wandering as a contributing factor for beneficial processes such as generating novel ideas or solutions to problems (Baird, Smallwood, & Schooler, 2011). However, the method by which data on mind wandering is measured influences the interpretations made by previous researchers regarding its benefits and detriments. Furthermore, little work has been done to assess how mind wandering influences re-engagement with a task that is being performed for extended periods of time. I focused on exploring how 1) Probe Placement influences task performance and willingness to re-engage with the task; 2) if mind wandering shares a similar cognitive mechanism as task switching by looking at performance differences between those with high versus low levels of mind wandering for a complex cognitive task (such as a task-switching paradigm) and the sustained attention to response task (SART); and 3) if ratings on scales measuring effort, difficulty, frustration, and available mental resources differ between those who mind wander and those who do not. For Study 1, I analyzed the results of 107 participants. Of those participants included in the analytic sample, 27 were randomly assigned to the interrupting probe with enjoyable music condition, 26 for the interrupting probe with unenjoyable music condition, 24 for the post probe with enjoyable music condition, and 30 for the post probe with unenjoyable music condition. Results show that the Participant Pool influenced the likelihood of mind wandering, such that those recruited from SONA Systems were 4.5 times more likely to report having high levels of mind wandering. Probe placement influenced errors of omission for SART performance. Those who received post probes were more likely to not respond to stimuli on the Go trials. Probe Placement also influenced their paid response indicating their willingness to re-engage with the task. Those in the interrupting probe group requested more money (mean = 44.83, SD = 29.82). Levels of mind wandering also impacted participants’ paid responses. Those who were labeled as having high levels of mind wandering and were assigned to the interrupting probe condition had an average reported amount to be paid of 55.50 (SD = 33.73). For the post probe group, those identified as having high levels of mind wandering had an average reported amount to be paid of 42.50 (SD = 29.61). For Study 2, I analyzed the results of 90 participants. Of those participants included in the analytic sample, 20 had been assigned to the enjoyable condition and 22 had been assigned to the not enjoyable condition for the SART task. While 25 had been assigned to the Enjoyable condition and 23 to the Not Enjoyable condition for the Task-Switching paradigm. There was a significant interaction between levels of mind wandering and congruency for task accuracy on the Task-Switching paradigm. Participants with low levels of mind wandering had an average accuracy of 0.94 (SD = 0.24) on congruent trials and an average accuracy of 0.92 (SD = 0.27) on incongruent trials. In contrast, participants with high levels of mind wandering had an average accuracy of 0.95 (SD = 0.22) on congruent trials and an average accuracy of 0.90 (SD = 0.30) for incongruent trials. Additionally, levels of mind wandering impacted accuracy and errors of commission for the SART task. Those in the Enjoyable condition with high levels of mind wandering had an average accuracy for No Go trials of 48% (x = 0.48, SD = 0.50) while those in the Enjoyable condition with low levels of mind wandering had an average accuracy of 66% (x = 0.66, SD = 0.47). Those in the Unenjoyable condition with high levels of mind wandering had an average accuracy of 57% (x = 0.57, SD = 0.50) and those in the Unenjoyable condition with low levels of mind wandering had an average accuracy of 68% (x = 0.68, SD = 0.47). Also, those with high levels of mind wandering and who were in the Enjoyment condition, an average rate of errors of commissions of 0.53 (SD = 0.50) while those with low levels of mind wandering and in the enjoyable condition had an average rate of errors of commissions of 0.34 (SD = 0.47). Furthermore, those with high levels of mind wandering who were in the unenjoyable condition had an average rate of errors of commissions of 0.43 (SD = 0.50). While those with low levels of mind wandering who were in the unenjoyable condition had an average rate of errors of commissions of 0.32 (SD = 0.47). Furthermore, I found a significant main effect of Task Type for participants’ paid responses. Those who went through the SART task requested an average dollar amount of 49.46 (SD = 34.50). For Study 3, I used all 197 participants collected from the previous two studies. An exploratory factor analysis was conducted on the post-task questions administered to each participant. A Positive Affect factor, Stress factor, GTQ factor, Mental Demand factor, and Effort factor were identified. I then ran a linear model testing the effect probe condition (Interrupting Probe vs. Post Probe), task type (SART vs. Task-Switch), Levels of Mind Wandering grouping (High vs. Low), and sample source (MTurk vs. SONA) on willingness to re-engage. There was a statistically significant main effect of levels of mind wandering. those in the interrupting probe condition with High Levels of Mind Wandering, had a higher paid response (x = 56.40, SD = 33.20). However, those with Low Levels of Mind Wandering who were in the interrupting probe condition gave an average paid response of 49.39 (SD = 32.19). The results show those with high levels of mind wandering are less willing to re-engage with the task. I then ran linear models testing the effect probe condition (Interrupting Probe vs. Post Probe), task type (SART vs. Task-Switch), and Levels of Mind Wandering grouping (High vs. Low) on the factors from the EFA. Probe placement and levels of mind wandering significantly influenced scores for the Positive Affect factor, while Probe Placement alone influenced scores on the GTQ factor. in the Interrupting Probe condition with high levels of mind wandering had an average Positive Affect score of -0.42 (SD = 0.95), while those in the Interrupting Probe condition with low levels of mind wandering had an average Positive Affect score of -0.03 (SD = 0.92). Those in the Post Probe condition with high levels of mind wandering had an average Positive Affect score of 0.24 (SD = 0.90). Finally, participants in the Post Probe condition with low levels of mind wandering had an average positive affect score of -0.02 (SD = 0.83), indicating those in the Post Probe condition had a more positive view of the cognitive task. For the GTQ factor, participants who received the mind wandering probe after the cognitive task had positive scores on the GTQ factor (x = 0.07, SD = 0.96), indicating more mental resources after completing the task, while those who received the interrupting probe had on average negative scores on the GTQ factor (x = -0.19, SD = 1.10) indicating fewer mental resources after completing the task. I also found that mind wandering affected scores for the Stress factor. Those with high levels of Mind Wandering generally had higher ratings of Stress (x = 0.51, SD = 0.86) towards the task compared to those Low levels of Mind Wandering (x = -0.25, SD = 0.97), indicating that those who had a high level of Mind Wandering found the tasks to be more demanding of their time and frustrating to engage with. The results show that participants who were labeled as High Levels of Mind Wandering generally viewed the tasks negatively and found them to be more stressful. Overall, results show that Probe Placement influences willingness to re-engage with the SART task. Additionally, those who report having high levels of mind wandering during the cognitive tasks are less willing to re-engage, report higher levels of frustration and lower levels of positivity towards the tasks
Sudan War: Retrospective on Health Care System Destruction
Get PDFThis document is a working draft.The primary objective of this report is to examine the character and scope of attacks by Sudanese belligerents against Sudan’s healthcare system from April 2023 through June 2024.Produced with the support of the Bureau of Conflict and Stabilization Operations, United States Department of State
Closure duration vs. f0 perturbation as a cue to underlying stops for the American English intervocalic flap
Get PDFResearchers found that there are five main cues for distinguishing the voicing of these intervocalic plosives, preceding vowel duration, following vowel duration, closure duration, semantic context, and f0 perturbation, also known as consonant intrinsic f0. In American English, when /t/ and /d/ occur intervocalically, they are realized as a voiced alveolar flap [ɾ] (e.g., [ɹa͡ɪɾɪŋ] writing/riding, [liɾɚ] liter/leader). The presence of these cues indicates incomplete neutralization of these forms. To address a current gap in the literature, the present study focused on American English native speakers’ perception and use of CF0 and closure duration as independent and combined informative cues for deciding voicing contrast of /t/ and /d/ when neutralized by the flap. Participants engaged in a self-administered online forced-judgement task of a pseudoword in 30 combinations of CF0 and flap closure duration to indicate the underlying representation (/hɑtɑ/ or /hɑdɑ/) of the surface production [hɑɾɑ] they perceived. This study observed that participants are more likely to select /hɑtɑ/ when the CF0 is higher and /hɑdɑ/ elsewhere. These modest findings suggest that CF0 is a useful cue for voicing distinction. The results suggest that models of spoken word recognition and speech perception ought to include CF0 as a cue
District Leaders’ Perceptions of Organizational Learning in the Area of Literacy
Get PDFResearch in the area of organizational learning has focused upon division leaders and the contexts that drive policy, reform, and professional learning in the area of literacy. The purpose of this case study was to examine the ways and means through which district leaders perceived their experiences within an organizational learning context of a Literacy Academy, implemented at the district level prior to COVID-19. The researcher used the methodologies of case study research and collected data through individual interviews of Literacy Academy members, with a storyline strategy activity. This study adds to existing knowledge about the collaborative roles and positioning of district leaders as they engaged in an organizational learning process, serving as agents of change regarding literacy initiatives, during the post-COVID-19 era
The complete mitochondrial genome of Meller’s mongoose (Rhynchogale melleri)
Get PDFMeller’s mongoose (Rhynchogale melleri) is a member of the family Herpestidae (Mammalia: Carnivora) and the sole species in the genus Rhynchogale. It is primarily found in savannas and open woodlands of eastern sub-Saharan Africa. Here, we report the first complete mitochondrial genome for a female Meller’s mongoose collected in Tanzania, generated using a genome-skimming approach. The mitogenome had a final length of 16,644 bp and a total of 37 annotated genes. Phylogenetic analysis validated the placement of this species in the herpestid subfamily Herpestinae. Ultimately, the outcomes of this research offer a genetic foundation for future studies of Meller’s mongoose
EXPERIMENTAL STUDY OF THE IMPORTANCE OF DATA FOR MACHINE LEARNING-BASED BREAST CANCER OUTCOME PREDICTION
Get PDFEXPERIMENTAL STUDY OF THE IMPORTANCE OF DATA FOR MACHINE LEARNING-BASED BREAST CANCER OUTCOME PREDICTIONWid Yamani, Ph.D. George Mason University, 2024 Dissertation Director: Dr. Janusz Wojtusiak Researchers have used various large-scale datasets to develop and validate predictive models in breast cancer outcome prediction. However, a notable gap exists due to the lack of a systematic comparison among these datasets regarding predictive performance, feature availability, and suitability for different analytical objectives. While each dataset has unique strengths and limitations, no comprehensive studies evaluate how these differences impact model performance, particularly across diverse timeframes, survival, and recurrence outcomes. This gap limits researchers in making informed choices about the most appropriate dataset for specific research questions.Effective modeling and prediction of breast cancer outcomes (such as cancer survival and recurrence) rely on the dataset's quality, the pre-processing techniques used to clean and transform data, and the choice of predictive models. Therefore, selecting a suitable dataset and identifying relevant variables are as crucial as the choice of the model itself. This thesis addresses this gap by systematically comparing five prominent datasets for predicting breast cancer outcomes. This dissertation compares five datasets—SEER Research 8, SEER Research 17, SEER Research Plus, SEER-Medicare, and Medicare Claims data—focusing on breast cancer survival and recurrence. It evaluates the predictive performance of each dataset using supervised machine learning methods, including logistic regression, random forest, and gradient boosting. The models were tested on metrics such as AUC, accuracy, recall, and precision, with gradient boosting delivering the most accurate results. The findings indicate that SEER-Medicare, which integrates cancer registry data with three years of retrospective claims, outperformed the other datasets, achieving AUCs of 0.891 for 5-year survival and 0.942 for 10-year survival. This dataset's inclusion of comprehensive health information, including pre-existing conditions and other claims data, makes it particularly valuable for outcome prediction. However, a drawback of SEER-Medicare is that it primarily includes patients aged 65 and older, as it is based on Medicare data. This limitation reduces its suitability for predicting outcomes in younger breast cancer patients, a significant subgroup with distinct risk factors and treatment responses. SEER Research Plus ranked second, offering data on patient demographics, breast cancer characteristics, staging, outcomes, and treatment, with AUC values of 0.877, 0.901, and 0.937 for 5-year, 10-year, and 15-year survival, respectively. SEER Research 17 and SEER Research 8 include patient demographics, breast cancer characteristics, and staging information but lack treatment details. SEER Research 17, which covers a larger population with more variables, yielded AUC values of 0.870 for 5-year survival, 0.897 for 10-year survival, and 0.920 for 15-year survival. SEER Research 8, which covers a smaller population over a more extended period, yielded slightly lower AUC values of 0.857, 0.868, and 0.880 for 5-year, 10-year, and 15-year survival, respectively. Results indicate that including treatment and additional variables significantly enhances prediction accuracy while the data size is less critical. This thesis is the first study that compares SEER datasets and provides a groundbreaking, comprehensive evaluation of these datasets, providing crucial insights into how data characteristics influence breast cancer outcome modeling
ESSAYS ON CRYPTOCURRENCIES: EXPERIMENTS MEASURING ATTITUDES, USES, AND MECHANISMS
No full textThis work is embargoed by the author and will not be publicly available until May 2026.This dissertation focuses on various aspects of cryptocurrencies, from the individual user’s side to specific mechanism designs. There are three main aspects of cryptocurrency markets that I study, consisting of nonowners, who may be open to using cryptocurrencies in the future; current owners of cryptocurrencies, who use various institutions to achieve profits; and cryptocurrency mechanisms themselves, which provide avenues for users to engage in cryptocurrency transactions. To understand these aspects, I conduct a variety of experiments analyzing individuals’ current perceptions of and willingness to own cryptocurrencies, as well as the mechanisms to trade, hold, and invest in cryptocurrencies.Chapter One, co-authored with Johanna Mollerstrom, examines how different factors impact an individual's willingness to own cryptocurrencies. We analyze this through an online survey experiment with a diverse population and a laboratory experiment with incentivized measures and a smaller sample. We first find that cryptocurrency owners are young males with a high tolerance for risk. Secondly, to understand how a lack of knowledge is a barrier to cryptocurrency use, we provide participants in our survey with varying information treatments concerning cryptocurrencies and evaluate their willingness to own cryptocurrencies after receiving the information. Our results indicate that information conveying the ease of use of cryptocurrencies, or the security features of cryptocurrencies is effective at increasing nonowners’ willingness to own cryptocurrencies. Treatments varying the source of the information, however, show no difference, indicating that participants care more about the content than the source of the information. In our related laboratory experiment, we use the same measures as the survey but conduct an incentivized version, where participants earn bonus rewards in Bitcoin in the form of a paper wallet. We also measure whether receiving a small amount of cryptocurrencies can increase the willingness to own them in the future and find support for this. Chapter Two is a continuation of the laboratory experiment co-authored with Johanna Mollerstrom that looks at cryptocurrency use outside of the willingness to own them. In the laboratory setting, we conduct simple trust and risk games to determine how behavior is affected by the introduction of cryptocurrencies. Individual perceptions of cryptocurrencies can influence their adoption but could also impact actions with the currency once they are owned. We find evidence that participants act differently when interacting with Bitcoin compared to dollars. Participants engage in less risky behavior when playing with Bitcoin and are particularly risk-averse when they receive an initial bonus of cryptocurrencies. This indicates that mechanisms designed for interacting with cryptocurrencies should consider the difference in behavior that individuals have when using cryptocurrency assets instead of money. Chapter Three moves from individual use to the evaluation of a popular cryptocurrency trading mechanism. Co-authored with Kevin McCabe, Aleksander Psurek, and Nalin Bhatt, we conduct agent-based model simulations evaluating the efficiency of Automated Market Makers (AMMs). AMMs are a method of trading cryptocurrencies in a decentralized manner. This work helps us understand the current market and use of AMMs and determine the parameters and conditions that lead to inefficient transactions. Through simulation experiments, we model a decentralized finance system where AMMs provide trading opportunities to individual agents. We specifically analyze arbitrage agents, who are traders that use token price differences in various markets to make trades, earning profits and theoretically equilibrating the prices of cryptocurrencies across markets as they do so. By nature of their incentives, arbitrageurs make no-risk profits, making it an attractive option for cryptocurrency traders. We provide the first analysis of arbitrage agents using simulations and explore how their behavior can influence AMMs’ prices and volatility. We find that AMMs with lower liquidity depths experience large price fluctuations away from tokens’ external market prices, and that AMMs without active arbitrageurs lead to long-standing price misalignment. These results indicate that liquidity, arbitrage activity, and additional features should be considered when using and creating AMMs.2026-05-1
Writing Faculty Perception and Application of Linguistic Justice Principles
Get PDFWriting studies in the United States has a long history of advocating for linguistic equity, starting with the passing of the Students’ Right to Their Own Language (SRTOL) in 1974 and more recently in the form of translingualism (Lu & Horner, 2013) and anti-racist writing (Baker-Bell, 2020) theoretical frameworks and pedagogies. Alongside this, the reality of larger numbers of language diverse students enrolling in US universities and colleges has motivated writing programs at many institutions to launch linguistic justice initiatives. However, the reach and impact of such programs has not been studied. This dissertation presents a mixed-methods study that measures to what degree writing faculty in a composition program at a large public university have taken up the concept of linguistic justice and describes factors that influence the operationalization of linguistic justice in their teaching practices. Survey, interview, and document data were combined and analyzed using activity theory (Engeström, 2000) as a way to describe the linguistic justice program as a dynamic system in all its complexity with all its internal tensions. This analysis shows that training and faculty working groups have been effective in helping faculty understand and adopt a language equity stance; however, it has been hard for faculty to operationalize that stance in their teaching. Identified contradictions within the activity system, including a large number of learning outcomes in sequenced courses, concern over student needs and expectations, difficulty operationalizing theory into practice, and faculty agency in light of changing political environments serve as focal points for modification of the composition program’s linguistic justice initiative. The findings and methodology of this study can serve as a model for other higher education institutions looking for ways to include and advance linguistic justice within their writing programs
Multigrid Algorithms and Software for PDEs and Optimization
Get PDFPartial differential equations (PDEs) require specialized tools that need to be built to accommodatevariables specific to each PDE. This results in a collection of bespoke solvers at the expense of time and adaptability. The intent of this research is to develop methods to solve PDEs that allow for flexibility and broad applicability, and that maintain a transparent academic record that can be accessible when developing future PDE solvers. Two projects were undertaken in pursuit of these goals. The first developed a multigrid preconditioner for fractional PDE-constrained optimization problems. The second developed a PDE Library to serve as a toolkit that adheres to the Ideals of Generality, Flexibility, and Transparency while maintaining the academic record. By keeping the Ideals of Generality, Flexibility, and Transparency in mind, we could address the issue of current methods being specialized and opaque. Our toolkit evaluated the strengths and weaknesses of existing PDE solvers and structural analysis software with the intent of identifying which characteristics to apply to an academically open source tool that maintains the academic record. Throughout this research, we found that following these principles led us down paths that allow us to solve problems that have been difficult to solve in the past and we were able to develop methods that would be applicable to a wide range of PDEs with the consequence that future work would have a ready foundation to move forward
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 5868.01
Get PDF“Context. Observing and analyzing transiting planets around host stars is crucial to our understanding of the formation and evolution of planetary systems. Through the Transiting
Exoplanet Survey Satellite (TESS) mission, TESS Object of Interest (TOI) 5868.01 was identified as a possible exoplanet around host star TOI 5868.
Aims. The focus of this paper is validating the predicted transit that may lead to the identification of TOI 5868.01. We compared ground-based data with predicted data from the TESS mission to confirm that TOI 5868.01 is an exoplanet.
Methods. We created TESS light curves using Python’s Jupyter Notebook. We then plate-solved images taken at the George Mason University Observatory from June 6, 2024 to June 7, 2024 to conduct multi-aperture photometry through AstroImageJ (AIJ). Using the data collected, we created a ground-based light curve and Near Eclipsing Binary (NEB) analysis.
Results. We concluded that the chance of TOI 5868.01 being an exoplanet is high, as the processed data was similar to the predicted data and was statistically significant. However, more future work is needed to validate that TOI 5868.01 is an exoplanet as we were not able to completely rule out the possibility of a false positive.