402,323 research outputs found

    Rationality in discovery : a study of logic, cognition, computation and neuropharmacology

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    Part I Introduction The specific problem adressed in this thesis is: what is the rational use of theory and experiment in the process of scientific discovery, in theory and in the practice of drug research for Parkinson’s disease? The thesis aims to answer the following specific questions: what is: 1) the structure of a theory?; 2) the process of scientific reasoning?; 3) the route between theory and experiment? In the first part I further discuss issues about rationality in science as introduction to part II, and I present an overview of my case-study of neuropharmacology, for which I interviewed researchers from the Groningen Pharmacy Department, as an introduction to part III. Part II Discovery In this part I discuss three theoretical models of scientific discovery according to studies in the fields of Logic, Cognition, and Computation. In those fields the structure of a theory is respectively explicated as: a set of sentences; a set of associated memory chunks; and as a computer program that can generate the observed data. Rationality in discovery is characterized by: finding axioms that imply observation sentences; heuristic search for a hypothesis, as part of problem solving, by applying memory chunks and production rules that represent skill; and finding the shortest program that generates the data, respectively. I further argue that reasoning in discovery includes logical fallacies, which are neccesary to introduce new hypotheses. I also argue that, while human subjects often make errors in hypothesis evaluation tasks from a logical perspective, these evaluations are rational given a probabilistic interpretation. Part III Neuropharmacology In this last part I discusses my case-study and a model of discovery in a practice of drug research for Parkinson’s disease. I discuss the dopamine theory of Parkinson’s disease and model its structure as a qualitative differential equation. Then I discuss the use and reasons for particular experiments to both test a drug and explore the function of the brain. I describe different kinds of problems in drug research leading to a discovery. Based on that description I distinguish three kinds of reasoning tasks in discovery, inference to: the best explanation, the best prediction and the best intervention. I further demonstrate how a part of reasoning in neuropharmacology can be computationally modeled as qualitative reasoning, and aided by a computer supported discovery system

    Multimodal Reasoning about Physical Systems

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    Abstract We present a knowledge representation and reasoning framework that integrates qualitative reasoning, qualitative simulation, numerical simulation, geometric reasoning, constraint reasoning, resolution, reasoning with abstraction levels, declarative meta-level control, and a simple form of truth maintenance. The framework is the core of PRET, a system identification program that automates the process of modeling physical systems. Introduction Models are powerful tools that are used to understand physical systems. The process of inferring an internal model from external observations of a system---often called system identification--is a routine and difficult problem faced by engineers in a variety of domains The program PaET (Bradley & Stolle 1996) automates both stages of the system identification process; its goal is to find a system of ODEs that models

    PEMBELAJARAN PENEMUAN TERBIMBING UNTUK MENINGKATKAN PENALARAN MATEMATIS SISWA PADA MATERI PROGRAM LINEAR

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    This study describes the application of guided discovery which improves students' mathematical reasoning in the Linear Program material. This study uses a qualitative approach with the type of Classroom Action Research (PTK). This PTK is implemented in Class XI TPM 3 SMKN 1 Madiun. The application of guided discovery is carried out in four stages. Introduction and Review, namely the teacher conditions students to be ready to learn by conveying learning objectives, explaining the benefits, and reminding the prerequisite material. The Open Stage, in which the teacher sets the group, then the students observe the examples, ask questions, and write down the characteristics of the concept based on the observations. Convergent Stage, where the teacher presents the problem, then students make assumptions, collect information, perform mathematical manipulation, conclude problem solving, re-examine problem solving, and present the results of the discussion. Closing and application, namely the teacher emphasizes important things, the teacher guides students to make conclusions and reflects, then the students work on the quiz. The results showed an increase in students' mathematical reasoning abilities in solving Linear Program problems. Students also gave positive responses to guided discovery learning.Keywords: guided discovery, mathematical reasoning, Linear Progra

    Qualitative–Quantitative Reasoning: Thinking Informally About Formal Things

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    Qualitative–quantitative reasoning is the way we think informally about formal or numerical phenomena. It is ubiquitous in scientific, professional and day-to-day life. Mathematicians have strong intuitions about whether a theorem is true well before a proof is found – intuition that also drives the direction of new proofs. Engineers use various approximations and can often tell where a structure will fail. In computation we deal with order of magnitude arguments in complexity theory and data science practitioners need to match problems to the appropriate neural architecture or statistical method. Even in the supermarket, we may have a pretty good idea of about how much things will cost before we get to the checkout. This paper will explore some of the different forms of QQ–reasoning through examples including the author’s own experience numerically modelling agricultural sprays and formally modelling human–computer interactions. We will see that it is often the way in which formal and mathematical results become useful and also the importance for public understanding of key issues including Covid and climate change. Despite its clear importance, it is a topic that is left to professional experience, or sheer luck. In early school years pupils may learn estimation, but in later years this form of reasoning falls into the gap between arithmetic and formal mathematics despite being more important in adult life than either. The paper is partly an introduction to some of the general features of QQ-reasoning, and partly a ‘call to arms’ for academics and educators

    Grounding osteopathic research – Introducing grounded theory

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    Over the last four decades there has been a proliferation of qualitative research into healthcare practice, including manual therapy. Grounded theory is the most widely used qualitative research methodology, and has contributed to the knowledge base of a number of healthcare professions. This Masterclass provides an introduction to grounded theory and uses a recent doctoral study into osteopathic clinical decision-making as an example to illustrate the main processes and procedures when conducting and evaluating grounded theory research. This paper highlights how grounded theory research may be of help in developing a robust and rounded evidence-base in relation to osteopathic practice. Keywords: Grounded theory, Qualitative research, Methodology, Research methods, Qualitative data analysis, Decision-making, Clinical reasoning, Osteopathy, Osteopathic medicin
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