3,258 research outputs found

    Actual Causation in CP-logic

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    Given a causal model of some domain and a particular story that has taken place in this domain, the problem of actual causation is deciding which of the possible causes for some effect actually caused it. One of the most influential approaches to this problem has been developed by Halpern and Pearl in the context of structural models. In this paper, I argue that this is actually not the best setting for studying this problem. As an alternative, I offer the probabilistic logic programming language of CP-logic. Unlike structural models, CP-logic incorporates the deviant/default distinction that is generally considered an important aspect of actual causation, and it has an explicitly dynamic semantics, which helps to formalize the stories that serve as input to an actual causation problem

    Motion as manipulation: Implementation of motion and force analogies by event-file binding and action planning\ud

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    Tool improvisation analogies are a special case of motion and force analogies that appear to be implemented pre-conceptually, in many species, by event-file binding and action planning. A detailed reconstruction of the analogical reasoning steps involved in Rutherford's and Bohr's development of the first quantized-orbit model of atomic structure is used to show that human motion and force analogies generally can be implemented by the event-file binding and action planning mechanism. Predictions that distinguish this model from competing concept-level models of analogy are discussed, available data pertaining to them are reviewed, and further experimental tests are proposed

    A DSEL for Studying and Explaining Causation

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    We present a domain-specific embedded language (DSEL) in Haskell that supports the philosophical study and practical explanation of causation. The language provides constructs for modeling situations comprised of events and functions for reliably determining the complex causal relationships that emerge between these events. It enables the creation of visual explanations of these causal relationships and a means to systematically generate alternative, related scenarios, along with corresponding outcomes and causes. The DSEL is based on neuron diagrams, a visual notation that is well established in practice and has been successfully employed for causation explanation and research. In addition to its immediate applicability by users of neuron diagrams, the DSEL is extensible, allowing causation experts to extend the notation to introduce special-purpose causation constructs. The DSEL also extends the notation of neuron diagrams to operate over non-boolean values, improving its expressiveness and offering new possibilities for causation research and its applications.Comment: In Proceedings DSL 2011, arXiv:1109.032

    Actual causation and the art of modeling

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    We look more carefully at the modeling of causality using structural equations. It is clear that the structural equations can have a major impact on the conclusions we draw about causality. In particular, the choice of variables and their values can also have a significant impact on causality. These choices are, to some extent, subjective. We consider what counts as an appropriate choice. More generally, we consider what makes a model an appropriate model, especially if we want to take defaults into account, as was argued is necessary in recent work.Comment: In Heuristics, Probability and Causality: A Tribute to Judea Pearl (editors, R. Dechter, H. Geffner, and J. Y. Halpern), College Publications, 2010, pp. 383-40

    Development of the Neuron Assessment for Measuring Biology Students’ Use of Experimental Design Concepts and Representations

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    Researchers, instructors, and funding bodies in biology education are unanimous about the importance of developing students’ competence in experimental design. Despite this, only limited measures are available for assessing such competence development, especially in the areas of molecular and cellular biology. Also, existing assessments do not measure how well students use standard symbolism to visualize biological experiments. We propose an assessment-design process that 1) provides background knowledge and questions for developers of new “experimentation assessments,” 2) elicits practices of representing experiments with conventional symbol systems, 3) determines how well the assessment reveals expert knowledge, and 4) determines how well the instrument exposes student knowledge and difficulties. To illustrate this process, we developed the Neuron Assessment and coded responses from a scientist and four undergraduate students using the Rubric for Experimental Design and the Concept-Reasoning Mode of representation (CRM) model. Some students demonstrated sound knowledge of concepts and representations. Other students demonstrated difficulty with depicting treatment and control group data or variability in experimental outcomes. Our process, which incorporates an authentic research situation that discriminates levels of visualization and experimentation abilities, shows potential for informing assessment design in other disciplines

    Diagnosing undergraduate biology students\u27 experimental design knowledge and difficulties

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    Experimental design is an important component of undergraduate biology education as it generates knowledge of biology. This dissertation addresses the challenge undergraduate educators face for assessing knowledge of experimental design in biology by examining knowledge of, and difficulties with, experimental design in the context of first-year undergraduate biology students at Purdue. The first chapter reviews several recent reports that highlight the necessity to increase understanding of the experimental research process as a core scientific ability (for e.g., AAAS, 2011; AAMC-HHMI, 2009; NRC, 2007). Despite its importance, there is limited information about what students actually learn from designing experiments. In the second chapter, the development and validation of a Rubric for Experimental Design (RED) was informed by a literature review and empirical analysis of thousands of undergraduate biology students\u27 responses to three published assessments. The RED is a useful probe for five major areas of experimental design abilities: the variable properties of an experimental subject; the manipulated variables; measurement of outcomes; accounting for variability; and the scope of inference appropriate for experimental findings. The third chapter presents an original \u27Neuron Assessment\u27 based on a current research problem related to a disease caused by defective movement of mitochondria in neurons. This assessment provides necessary background information and figures to examine knowledge of experiments through representations and experimental design concepts. A case study method was conducted with oral interviews to investigate interactions among three factors, conceptual knowledge (C), reasoning skills (R) and modes of representation (M). Findings indicate the usefulness of the \u27Neuron Assessment\u27 to probe knowledge and difficulties in areas characterized by RED. The fourth chapter examines evidence from the case study participants\u27 written responses to paper and pencil tests to validate the \u27Neuron Assessment\u27 as a diagnostic tool for the RED areas. In comparison to the published assessments that formed the basis for development of RED, findings with the \u27Neuron Assessment\u27 provide strong evidence for its validity as a probe to distinguish expert and student knowledge from difficulties with experimentation concepts and representations. In summary, a mixed methods approach was used to characterize undergraduate biology students\u27 knowledge and difficulties with experimental design. Findings from this dissertation illuminate knowledge of experimental design at the undergraduate level and open up several new avenues for improved teaching and research on how to evaluate learning about the experimental basis for understanding biological phenomena
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