2,367 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Linking language and emotion: how emotion is understood in language comprehension, production and prediction using psycholinguistic methods

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    Emotions are an integral part of why and how we use language in everyday life. We communicate our concerns, express our woes, and share our joy through the use of non-verbal and verbal language. Yet there is a limited understanding of when and how emotional language is processed differently to neutral language, or of how emotional information facilitates or inhibits language processing. Indeed, various efforts have been made to bring back emotions into the discipline of psycholinguistics in the last decade. This can be seen in many interdisciplinary models focusing on the role played by emotion in each aspect of linguistic experience. In this thesis, I answer this call and pursue questions that remain unanswered in psycholinguistics regarding its interaction with emotion. The general trend that I am using to bring emotion into psycholinguistic research is straightforward. Where applicable and relevant, I use well-established tasks or paradigms to investigate the effects of emotional content in language processing. Hence, I focused on three main areas of language processing: comprehension, production and prediction. The first experimental chapter includes a series of experiments utilising the Modality Switching Paradigm to investigate whether sentences describing emotional states are processed differently from sentences describing cognitive states. No switching effects were found consistently in my 3 experiments. My results suggest that these distinct classes of interoceptive concepts, such as ‘thinking’ or ‘being happy’, are not processed differently from each other, suggesting that people do not switch attention between different interoceptive systems when comprehending emotional or cognitive sentences. I discuss the implications for grounded cognition theory in the embodiment literature. In my second experimental chapter, I used the Cumulative Semantic Interference Paradigm to investigate these two questions: (1) whether emotion concepts interfere with one another when repeatedly retrieved (emotion label objects), and (2) whether similar interference occurs for concrete objects that share similar valence association (emotion-laden objects). This could indicate that people use information such as valence and arousal to group objects in semantic memory. I found that interference occurs when people retrieve direct emotion labels repeatedly (e.g., “happy” and “sad”) but not when they retrieve the names of concrete objects that have similar emotion connotations (e.g., “puppy” and “rainbow”). I discuss my findings in terms of the different types of information that support representation of abstract vs. concrete concepts. In my final experimental chapter, I used the Visual World Paradigm to investigate whether the emotional state of an agent is used to inform predictions during sentence processing. I found that people do use the description of emotional state of an agent (e.g., “The boy is happy”) to predict the cause of that affective state during sentence processing (e.g., “because he was given an ice-cream”). A key result here is that people were more likely to fixate on the emotionally congruent objects (e.g., ice-cream) compared to incongruent objects (e.g., broccoli). This suggests that people rapidly and automatically inform predictions about upcoming sentence information based on the emotional state of the agent. I discuss our findings as a novel contribution to the Visual World literature. I conducted a diverse set of experiments using a range of established psycholinguistic methods to investigate the roles of emotional information in language processing. I found clear results in the eye-tracking study but inconsistent effects in both switching and interference studies. I interpret these mixed findings in the following way: emotional content does not always have effects in language processing and that effect are most likely in tasks that explicitly require participants to simulate emotion states in some way. Regardless, not only was I successful in finding some novel results by extending previous tasks, but I was also able to show that this is an avenue that can be explored more to advance the affective psycholinguistic field

    I like therefore I can, and I can therefore I like: the role of self-efficacy and affect in active inference of allostasis

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    Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input. The discrepancy between a prediction and the actual input results in prediction error. GMs then execute action policies predicted to minimize the prediction error. The free-energy principle provides a rationale for AIF by stipulating that information-processing open systems must constantly minimize their free energy (through suppressing the cumulative prediction error) to avoid decay. The theory of homeostasis and allostasis has a similar logic. Homeostatic set points are expectations of living organisms. Discrepancies between set points and actual states generate stress. For optimal functioning, organisms avoid stress by preserving homeostasis. Theories of AIF and homeostasis have recently converged, with AIF providing a formal account for homeo- and allostasis. In this paper, we present bacterial chemotaxis as molecular AIF, where mutual constraints by extero- and interoception play an essential role in controlling bacterial behavior supporting homeostasis. Extending this insight to the brain, we propose a conceptual model of the brain homeostatic GM, in which we suggest partition of the brain GM into cognitive and physiological homeostatic GMs. We outline their mutual regulation as well as their integration based on the free-energy principle. From this analysis, affect and self-efficacy emerge as the main regulators of the cognitive homeostatic GM. We suggest fatigue and depression as target neurocognitive phenomena for studying the neural mechanisms of such regulation

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    The Individual And Their World

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    Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

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    General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

    Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being

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    The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when and, to some extent, what an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited. My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies. The contributions of my dissertation are four-fold. First, I develop a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about one’s eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D

    A Philosophical and Empirical Investigation into Buddhist Economics

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    There is a growing body of literature on Buddhist economics from a philosophical perspective; however, no work to date has sought to empirically validate it as an effective economic theory at a global scale. In my paper, I draw on the long history of Buddhist metaphysics to construct an account of Buddhist ethics and then proceed to derive a set of Buddhist economic principles. I draw on the World Happiness Report’s methodology to quantitatively demonstrate the relationship between Buddhist economic principles and the psychological wellbeing of a country’s citizens, as measured through their own evaluation of their quality of life and the dispersion of those evaluations within a given country. Overall, I find that the implementation of Buddhist economic principles at a national government level is positively correlated with higher average levels of wellbeing and lower dispersion of life evaluations. However, I also find significant differences between how Buddhist principles affect wellbeing between countries with initial higher and lower rates of wellbeing dispersion
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