155 research outputs found

    The role of neurocomputational decision processes in affect-based impulsivity in borderline personality

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    Borderline personality disorder (BPD) portends heightened risk for a variety of negative life outcomes, ranging from broad impairments in social functioning to suicide. People with BPD often engage in impulsive behaviors during states of heightened negative affect. Building on preclinical work on the effect of stress on decision-making, as well as Bayesian models of affect’s role in decision-making, we propose that affect alters the balance of Pavlovian and goal-directed decision systems, favoring Pavlovian influences that heighten the pursuit of rewards. To test this account, we sought to (1) characterize effects of affect and stress on decision processes (Aim 1) and (2) quantify alterations in these decision processes among individuals prone to affect-based impulsivity using computational reinforcement learning models (Aim 2). Following an experimental manipulation of affect, participants completed a social variant of a decision-tree task . Among individuals prone to affect-based impulsivity, exposure to the negative affect induction invigorated pursuit of immediately valuable, though often suboptimal, actions. We further found that affect-based impulsivity was associated with heightened discounting of future rewards. These data provide support the account that affect-based impulsivity is associated with a heightened influence of Pavlovian cues on decision-making and with blunted effects of model-based goal-directed reasoning on decision-making. This research lends initial insights into the neurocomputational mechanisms of affect-based impulsivity in BPD.Doctor of Philosoph

    Analogical Transfer in Multi-Attribute Decision Making

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    People often must make inferences in domains with limited information. In such cases, they can leverage their knowledge from other domains to make these inferences. This knowledge transfer process is quite common, but what are the underlying mechanisms that allow us to accomplish it? Analogical reasoning may be one such mechanism. This dissertation explores the role of analogy in influencing decision-making performance when faced with a new domain. We delve into the knowledge transferred between tasks and how this influences decision-making in novel tasks. Experiment I has two conditions, and each condition has two tasks. In one condition, the two task domains are analogically related, where for example, participants make inferences first about water flow and then about heat flow. In the second condition, the domains do not share obvious similarities. For example, car efficiency and water flow. Experiment I shows that participants presented with an analogy demonstrated better performance than those without. We hypothesize that this knowledge transfer occurs in two ways: firstly, analogical mapping enhances comprehension of cue utilization in a new task; secondly, the strategy employed is transferred. In Chapter 3, we developed a machine learning technique to uncover the strategies used by participants. Our findings reveal that the best-performing strategy from the old task is typically carried over to the new task. In Chapter 4, we developed a model of analogical transfer in multi-attribute decision making. We use the ACT-R theory of cognition as a framework to model knowledge transfer by integrating a reinforcement learning model of strategy selection with a model of analogy. The simulation results showcase a similar trend of both accuracy and strategy use to the behavioral data. Finally, we critically analyze our study\u27s limitations and outline promising directions for future research, thereby paving the way for a deeper understanding of knowledge transfer mechanisms

    Distinction of Spirituality and Religiosity at the Level of Sacral Practices

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    For more than 100 years, psychologists have differentiated religiosity and spirituality at a conceptual level. Religiousness is acceptance of traditional communal religious beliefs and practices, while spiritualism is a quest for meaning and truth, a sense of connectedness with the social and natural world, and contemplations of oneself. Recent psychometric empirical evidence confirmed they are independent psychological dispositions. In this study we build up on the empirical evidence on religiosity and spirituality as different constructs by operationalizing them not just as beliefs but also as practices. We hypothesis religious beliefs should predict only religious practices, and spiritual beliefs spiritual practices. To measure beliefs, we have used 16-item subscales of questionnaire Lexical social attitudes - Serbia. Summary scores for both subscales are highly reliable (αREL = .93, αSPIR = .88). To measure practices we constructed a questionnaire for this study. On a binary scale (yes or no), participants assessed if they had done at least once in the previous year each of the ten traditional religious practices (e.g., prayed, confessed, read a holy book) and ten spiritual practices (e.g., spent time in nature, made art, wrote a diary). Reliability of summary scores is not satisfactory neither for the scale of religious practices (α = .63) nor spiritual (α = .58), so one should take caution with interpreting the results. In an online survey, 197 participants (70.0% women, Mage = 19.47, SDage = 5.43), filled questionnaires. Orthodox Christians comprised 70.1%, followed by atheists 14.2%, and 11.2% agnostics. To test the hypothesis, we derived a canonical correlation between beliefs on one side and practices on the other. The first canonical correlation (R = .66, F(4,386) = 43.00, p < .001) describes religious people (b = .98) who follow religious practices (r = .92) but decline spiritual (r = -.41). The second one (R = .38, F(1,194) = 33.95, p < .001) describes spiritual people (r = .98) who in order to find meaning and truth engage in spiritual practices (r = .91) and religious too some extent (r = .39). Results are in line with conceptualizing religiosity and spirituality as different constructs, but results eject orthogonality. Spiritualism manifests through spiritual beliefs and diverse practices that provide sense of connectedness with the social and natural world and contemplations of oneself, unlike rigid religiosity

    An exploration of executive function, its theoretical construction, and challenges encountered in its understanding and measurement: did neuropsychology get this right?

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    Section A argued for the importance of cognitive models in providing a theoretical foundation for complex neuropsychological constructs such as ‘executive function’ (EF). It consisted of a narrative review of 29 existing cognitive models of EF, which were reviewed, critiqued, and then integrated into a novel, unified model of EF. This unified account brought together the affective, motivational and attentional processes involved in goal-driven behaviour. Clinical implications were discussed, alongside recommendations for future research in this area. Section B applied a content analysis to systematically examine the ways that EF is described, explained and understood by currently available neuropsychological assessment measures and textbooks, and evaluate these in accordance with current evidence on EF. A total of 29 texts were included. Categories were derived from the current evidence base, including the ‘unified model’ of EF presented in Section A, as well as inductively from the texts. Results suggested that the majority of assessments and textbooks were unlikely to provide such an integrated account, however, there were exceptions. New leads for further theoretical development, and clinical implications were discusse

    Causal induction in time

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    Causes require time to propagate their effects. We can see stars at night because of the light they emitted hundreds of years ago. We can smell the fragrant aroma of baking bread because heat gradually changed the structure of the food, emitting particles that traveled on the breeze. In this thesis, I investigate how people use temporal information to make causal inferences. I propose a rational framework for causal induction based on continuous-time evidence, examine human performance in passive and active continuous-time causal learning tasks, and develop bounded rational accounts that can offer explanations for human causal judgments and intervention strategies. Chapters 2 and 3 review previous theoretical frameworks on causal induction, and empirical work on the role of time in causal induction, respectively. Chapter 4 develops a rational framework for processing temporal evidence. It provides an explanation for how delays shape human causal induction and accounts for human causal judgments across seven different temporal causal learning tasks. Chapter 5 and Chapter 6 test how people passively or actively learn causal structures based on events unfolding in real time. I found people are capable temporal causal learners who successfully identify structures that involve generative and preventative relationships, as well as acyclic and cyclic connections. Nevertheless, the computational demands of normative learning could easily exceed human capacity. People’s causal judgments align better with an algorithm that approximates the normative solution via a simulation and local summary statistics scheme, suggesting the reliance on structurally local computation and temporally local evidence. People’s intervention decisions align better with a resource-rational model that emphasizes a balance between expected information and expected inferential complexity when choosing interventions. Chapter 7 shows that when given a limited period of observation, people not only focus on existing data, but also consider future possibilities, relying on extrapolated data to make inferences. This demonstrates the unique “continuing” feature of time, and how generalization plays a role in the utilization of temporal information. Chapter 8 synthesizes the findings of this thesis and proposes future research directions of causal learning in temporal contexts

    Planning and Policy Improvement

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    MuZero is currently the most successful general reinforcement learning algorithm, achieving the state of the art on Go, chess, shogi, and Atari. We want to help MuZero to be successful in even more domains. Towards that, we do three steps: 1) We identify MuZero's problems on stochastic environments and provide ways to model enough information to support causally correct planning. 2) We develop a strong baseline agent on Atari. This agent, named Muesli, matches the state of the art on Atari, even without deep search. The conducted ablations inform us about the importance of model learning, deep search, large networks, and regularized policy optimization. 3) Because MuZero's tree search is very helpful on Go and chess, we use the principle of policy improvement to design search algorithms with even better properties. The new algorithms, named Gumbel AlphaZero and Gumbel MuZero, match the state of the art on Go, chess, and Atari, and significantly improve prior performance when planning with few simulations

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Satisficing: Integrating two traditions

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    Understanding Human Choices as Computationally Rational Processes

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    Risky choice involves deciding between gambles that can differ in the probability and value of outcomes. This thesis exposes the cognitive processes that underpin risky choice in humans. The approach taken involves the use of deep neural networks and reinforcement learning to discover policies that are adaptive to distributions of risky choice problems. Risky choice has been extensively studied for hundreds of years and in the modern era many phenomena have been reported. Sometimes these phenomena are explained away as ``irrationalities'' or ``biases''. This thesis uses computational methods to demonstrate that apparently irrational risky choice can be ecological rational and sometimes rational given cognitive bounds. Moreover it does so for a broader range of risky choice problems than has so far been investigated. These include both contextual choice problems and the fourfold pattern of risky choice. The implications for future work are discussed. The results show that (1) context effects (including attraction, compromise and similarity) can emerge from an optimal (rational) ``classifier'' that chooses the option with the highest expected value; (2) the new model could predict context effects, as for people, when the representation format encourages attribute comparisons; (3) the new model approximates a bounded optimal cognitive policy and makes quantitative predictions that correspond well to evidence about human contextual choice; (4) an alternative explanation that a wide range of risky choice phenomena emerge from boundedly optimal adaptation of a decision making agent to processing constraints. In each study, the model is not pre-programmed to process all information but learns to process only that information that helps it maximize utility. We argue that the models provide evidence that apparently irrational risky choices are emergent consequences of processes that prefer higher value (rational) policies or classifiers. My thesis is that a number of models offer novel and rational explanations for a broad range of phenomena exhibited by people making choice under risk. I demonstrate that apparent cognitive biases can emerge from computational rational processing. Furthermore, I propose a unifying framework for modelling risky choice phenomena. Deep reinforcement learning has the potential to help discriminate between various explanations because it provides a means of computing computationally rational policies given both ecological and cognitive bounds

    Time-Situated Metacognitive Agency and Other Aspects of Commonsense Reasoning

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    Much research in commonsense reasoning (CSR) involves use of external representations of an agent's reasoning, based on compelling features of classical logic. However, these advantages come with severe costs, including: omniscience, consistency, static semantics, frozen deadlines, lack of self-knowledge, and lack of expressive power to represent the reasoning of others. Active logic was developed to address many of these, but work to date still leaves serious gaps. The present work focuses on major extensions of active logic to deal with self-knowledge, and their implementation into a newly-developed automated reasoner for commonsense active logic. Dealing with self-knowledge has been designed and implemented in the reasoner via a new treatment of quotation as a form of nesting. More sophisticated varieties of nesting, particularly quasi-quotation mechanisms, have also been developed to extend the basic form of quotation. Active logic and the reasoner are applied to classical issues in CSR, including a treatment of one agent having the knowledge and inferential mechanisms to reason about another's time-situated reasoning
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