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Investigating the Impact of Bilingual Experience and Language Use Context on Cognitive Control in an Auditory Stroop Task
No full textBilinguals are often reported to outperform monolinguals on executive control tasks, yet recent work suggests that any bilingual adaptations depend on how, and in which contexts, individuals use their two languages. The present dissertation tested whether specific dimensions of bilingual experiences (proficiency balance and daily interaction situations) predict the way listeners engage proactive (anticipatory) versus reactive (stimulus-driven) control when resolving non-linguistic conflict. Sixty young adults completed a non-linguistic auditory Stroop task that manipulated conflict expectancy both list-wide and item-specifically. Group-level analysis replicated classic proportion-congruency effects, but not the predicted proactive control signature, and transfer cost indexing reactive control was not observed. Crucially, multivariate models revealed that these effects masked systematic individual differences. More balanced bilinguals and those who navigate high-control settings showed (a) greater trial-to-trial variability, and (b) uniformly small interference across items and blocks - consistent with a generalized, proactive control mode. Task order did not influence any interference or variability measure, suggesting that the auditory Stroop paradigm is resilient to transient cognitive-fatigue effects. Findings refine the Adaptive Control Hypothesis by showing that proactive mechanisms emerge primarily in bilinguals who are both highly balanced and habitually manage language choice, whereas reactive strategies persist when linguistic demands are lower. Methodologically, they demonstrate that a nonlinguistic auditory Stroop task with proportion congruency manipulations sensitively captures experience-dependent differences overlooked by visual paradigms. Implications for measuring bilingualism and modeling control dynamics across sensory modalities are discussed
Bilingual Inconsistency Effect
No full textOne of the leading models of discourse processing states that the activation of information within a text occurs through an automatic spread of activation between associated concepts, known as cohort-based retrieval. Moreover, previous studies that have investigated bilingual reading comprehension have found that certain processes that support the construction of quality mental representations of discourse are less efficient in a bilingual\u27s non-dominant language (L2) compared to their dominant language (L1). The current study investigated cohort-based retrieval in the L1 and the L2 using a sample of 44 highly proficient, college-aged Spanish-English bilinguals that read narrative passages all in their L1 or L2 and that did or did not contain inconsistent information. We failed to find any evidence of cohort-based retrieval in the earliest stages of textual processing but did see a trend in a later stage measure of processing such that when the bilingual participants read in their L2, total reading times were slower on the inconsistent versions of the passages compared to the consistent versions. All in all, we share evidence to refute the current description of cohort-based retrieval as an initial stage of processing and to replicate previous findings that bilinguals are less efficient at resolving inconsistent information when processing text in their L2
A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification
No full textThis thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that\u27s speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then reorders them based on how the user prioritizes different traits. By doing this, it helps reduce guesswork and streamlines the design process. The app was trained using a carefully built dataset that reflects realistic actuator performance profiles across common engineering tasks. By combining prediction and trade-off evaluation into one platform, this system doesn\u27t just make recommendations - it helps engineers explore options, visualize differences, and make choices that align with their design goals. Overall, it aims to make early-stage decisions easier, clearer, and more aligned with what users actually care about
Kernel Density Estimation and Convolution
No full textKernel Density Estimation (KDE) is a widely used technique for estimating the probability density function of a random variable. In this study, we revisit KDE through the lens of convolution and extend this perspective to special cases such as positive, bounded and heavy tailed random variables. Building on this foundation, we propose a novel simulation-based density estimation method that generates new data by adding noise to observed values and then smoothing the resulting histogram using splines. A minor adjustment to natural cubic splines is required to ensure nonnegative estimates. The noise is drawn from a class of bounded polynomial kernel densities obtained via convolution of uniform random variables, with the smoothing parameter naturally defined by the support bound. A practical choice for this parameter is determined by a percentile of the neighboring distances among sorted data. The proposed method offers enhanced flexibility for handling variables with specific support constraints (e.g., positive, bounded and heavy tailed) through simple transformations, and numerical studies demonstrate its competitive or superior performance compared to standard KDE across various scenarios
Colostomy Care: EP
No full textObjective: This capstone project aimed to improve knowledge and self-efficacy in colostomy care through a structured educational booklet.
Methods: Six adult inpatient participants at The Hospitals of Providence Memorial Campus received the booklet and completed pre- and post-surveys.
Results: Participants had a 7.37% increase in scores, with statistically significant improvements in knowledge and confidence (p \u3c .001). Patients reported the material was easy to understand and helpful.
Discussion: The project highlights the OT\u27s role in education and emotional support during post-operative recovery. The booklet is a sustainable, low-cost tool that can be replicated across diverse clinical settings.https://scholarworks.utep.edu/otcapstones/1007/thumbnail.jp
Mood Check: Screening for Better Outcomes
No full textBackground: Bipolar disorder is a life-long, disabling mental health condition that affects people universally, without discrimination. Its symptoms often overlap with those of depression, making accurate diagnosis particularly challenging. As a result, many individuals receive inappropriate treatment, leading to poorer health outcomes and a prolonged path to effective management.
Problem: Studies show that up to 70% of people with bipolar disorder are initially misdiagnosed, resulting in a delay of 5 to 10 years between the onset of symptoms and an accurate diagnosis, which leads to poorer health outcomes
Intervention: This project aims to enhance the diagnostic accuracy of Bipolar I disorder by implementing the Rapid Mood Screener (RMS) for patients presenting with depressive symptoms. The RMS offers a substantial advantage, with an 88% sensitivity, 80% specificity, and 84% overall accuracy in differentiating between depression and bipolar disorder. The screening tool was administered in a behavioral health acute inpatient setting for patients endorsing depressive symptoms during their initial visits.
Results: The implementation of the RMS screening tool into practice proved effective in enhancing the accuracy of Bipolar I disorder diagnosis.
Conclusion: This quality improvement (QI) project demonstrated the potential of the RMS to improve diagnostic accuracy for Bipolar I disorder in patients presenting with depressive symptoms. By distinguishing between depression and bipolar disorder more effectively, the RMS can help reduce misdiagnoses and promote timely and appropriate treatment
How to Deal with High-Impact Low-Probability Events: Theoretical Explanation of the Empirically Successful Fuzzy-Like Technique
No full textWhen making decisions, it is important to take into account high-impact low-probability events. For such events, traditional probability-based approach -- which considers the product of the probability p that this event happens and the probability P that a randomly selected building will be destroyed -- often underestimates risks. Available data has lead to an empirical table that provides a more adequate risk estimate. Most of the entries in this table correspond to the fuzzy-like formula min(p,P). This paper explains this empirical result. Specifically, it explains both the effectiveness of the min formula -- and also explains deviations from this formula
A Natural Extension of F-Transform to Triangular and Triangulated Domains Necessitates the Use of Triangular Membership Functions
No full textIn many practical situations when we process 1-D data, the method of F-transform turned out to be very useful. In this method, we can use either triangular membership functions or more complex ones. Because this method has been so successful in 1-D applications, a natural idea is to extend it to functions defined on 2-D and higher-dimensional domains -- e.g., to images. This method allows natural generalization to rectangular domains, where it indeed turned out to be very effective. A recent paper showed that it can extended to more general domains -- e.g., to triangular domains and to more general domains that are divided into triangular domains by triangulation. Interestingly, while all 1-D membership functions can be extended to the rectangular domains, the current extension to triangular and more general domains was produced only for triangular membership functions. In this paper, we show that this restriction is not accidental: a natural extension of F-transform to triangular domains is only possible for triangular membership functions. This may explain why such membership functions are often very effective
From Machine Learning to Human Learning: What Can Pedagogy Learn from AI Successes
No full textMany machine learning techniques -- including many techniques behind the current AI-based boom in machine learning -- come from the analysis of successful human learning strategies (and researchers expect that other human learning experiences can lead to even more effective AI-based systems). At this moment, so much experience have been accumulated in AI-based machine learning that it is time to start the analysis in the opposite direction -- to see what can human-based pedagogy learn from AI successes. In this chapter, we provide the first results of such an analysis -- some of which go somewhat against the current pedagogical wisdom
Why um and u*log(u) Are the Most Effective Nonlinear Functions in Fuzzy Clustering: Theoretical Explanation of the Empirical Fact
No full textIn fuzzy clustering, we need to have non-linear functions of the membership degrees. Different nonlinear functions have been tried. Empirical evidence shows that for fuzzy clustering, the most effective nonlinear functions are um and u*log(u). In this paper, we provide a theoretical explanation for this empirical fact