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The cognitive basis of model-based reasoning in science

In Peter Carruthers, Stephen Stich & Michael Siegal (eds.), The Cognitive Basis of Science. New York: Cambridge University Press. pp. 133--153 (2002)

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  1. Conceptual Modelling, Combinatorial Heuristics and Ars Inveniendi: An Epistemological History (Ch 1 & 2).Tom Ritchey - manuscript
    (1) An introduction to the principles of conceptual modelling, combinatorial heuristics and epistemological history; (2) the examination of a number of perennial epistemological-methodological schemata: conceptual spaces and blending theory; ars inveniendi and ars demonstrandi; the two modes of analysis and synthesis and their relationship to ars inveniendi; taxonomies and typologies as two fundamental epistemic structures; extended cognition, cognitio symbolica and model-based reasoning; (3) Plato’s notions of conceptual spaces, conceptual blending and hypothetical-analogical models (paradeigmata); (4) Ramon Llull’s concept analysis and combinatoric (...)
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  • Updating evolutionary epistemology.Christophe Heintz - 2018 - In Kris Rutten, Stefaan Blancke & Ronald Soetaert (eds.), Perspectives on Science and Culture. West Lafayette, Indiana: Purdue University Press. pp. 195-222.
    This chapter critically analyzes evolutionary epistemology as a theoretical framework for the study of science as a historical and cultural phenomenon. As spelled out by Campbell in the 1970s, evolutionary epistemology has an ambitious goal: it aims at understanding the complex relations between bio- logical evolution, especially the biological evolution of human cognition, and the cultural evolution of scientific knowledge. It eventually aims at forming an integrated causal theory of the evolution of science, starting with the evo- lution of human (...)
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  • Conceptual change and evolutionary developmental biology.A. C. Love - 2015 - In Alan C. Love (ed.), Conceptual Change in Biology: Scientific and Philosophical Perspectives on Evolution and Development. Berlin: Springer Verlag, Boston Studies in the Philosophy of Science. pp. 1-54.
    The 1981 Dahlem conference was a catalyst for contemporary evolutionary developmental biology (Evo-devo). This introductory chapter rehearses some of the details of the history surrounding the original conference and its associated edited volume, explicates the philosophical problem of conceptual change that provided the rationale for a workshop devoted to evaluating the epistemic revisions and transformations that occurred in the interim, explores conceptual change with respect to the concept of evolutionary novelty, and highlights some of the themes and patterns in the (...)
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  • Evo-Devo as a Trading Zone.Rasmus Grønfeldt Winther - 2015 - In Alan C. Love (ed.), Conceptual Change in Biology: Scientific and Philosophical Perspectives on Evolution and Development. Berlin: Springer Verlag, Boston Studies in the Philosophy of Science.
    Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...)
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  • The Role of Imagistic Simulation in Scientific Thought Experiments.John J. Clement - 2009 - Topics in Cognitive Science 1 (4):686-710.
    Interest in thought experiments (TEs) derives from the paradox: “How can findings that carry conviction result from a new experiment conducted entirely within the head?” Historical studies have established the importance of TEs in science but have proposed disparate hypotheses concerning the source of knowledge in TEs, ranging from empiricist to rationalist accounts. This article analyzes TEs in think‐aloud protocols of scientifically trained experts to examine more fine‐grained information about their use. Some TEs appear powerful enough to discredit an existing (...)
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  • Rethinking correspondence: how the process of constructing models leads to discoveries and transfer in the bioengineering sciences.Nancy J. Nersessian & Sanjay Chandrasekharan - 2017 - Synthese 198 (Suppl 21):1-30.
    Building computational models of engineered exemplars, or prototypes, is a common practice in the bioengineering sciences. Computational models in this domain are often built in a patchwork fashion, drawing on data and bits of theory from many different domains, and in tandem with actual physical models, as the key objective is to engineer these prototypes of natural phenomena. Interestingly, such patchy model building, often combined with visualizations, whose format is open to a wide range of choice, leads to the discovery (...)
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  • Counterfactuals in science and engineering.Sanjay Chandrasekharan & Nancy J. Nersessian - 2007 - Behavioral and Brain Sciences 30 (5-6):454-455.
    The notion of mutation is applicable to the generation of novel designs and solutions in engineering and science. This suggests that engineers and scientists have to work against the biases identified in counterfactual thinking. Therefore, imagination appears a lot less rational than claimed in the target article.
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  • Building to Discover: A Common Coding Model.Sanjay Chandrasekharan - 2009 - Cognitive Science 33 (6):1059-1086.
    I present a case study of scientific discovery, where building two functional and behavioral approximations of neurons, one physical and the other computational, led to conceptual and implementation breakthroughs in a neural engineering laboratory. Such building of external systems that mimic target phenomena, and the use of these external systems to generate novel concepts and control structures, is a standard strategy in the new engineering sciences. I develop a model of the cognitive mechanism that connects such built external systems with (...)
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  • Building Cognition: The Construction of Computational Representations for Scientific Discovery.Sanjay Chandrasekharan & Nancy J. Nersessian - 2015 - Cognitive Science 39 (8):1727-1763.
    Novel computational representations, such as simulation models of complex systems and video games for scientific discovery, are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that (...)
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  • Argumentation in Science Education: A Model-based Framework.Florian Böttcher & Anke Meisert - 2011 - Science & Education 20 (2):103-140.
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  • Inflating the social aspects of cognitive structural realism.Majid D. Beni - 2021 - European Journal for Philosophy of Science 11 (3):1-18.
    Inspired by Ronald Giere’s cognitive approach to scientific models, Cognitive Structural Realism has presented a naturalist account of scientific representation. CSR characterises the structure of theories in terms of cognitive structures. These are informational structures embodied in the brains of scientists. CSR accounts for scientific representation in terms of the dynamical relationship between the organism and its environment. The proposal has been criticised on account of its negligence of social aspects of scientific practice. The present paper aims to chart out (...)
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  • Interdisciplinarity in the Making: Models and Methods in Frontier Science.Nancy J. Nersessian - 2022 - Cambridge, MA: MIT.
    A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering (...)
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  • Connections between pedagogical and epistemological constructivism: Questions for teaching and research in chemistry. [REVIEW]Donald J. Wink - 2006 - Foundations of Chemistry 8 (2):111-151.
    The rich and ongoing debate about constructivism in chemistry education includes questions about the relationship, for better or worse, between applications of the theory in pedagogy and in epistemology. This paper presents an examination of the potential to use connections of epistemological and pedagogical constructivism to one another. It examines connections linked to the content, processes, and premises of science with a goal of prompting further research in these areas.
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  • Mechanistic explanation at the limit.Jonathan Waskan - 2011 - Synthese 183 (3):389-408.
    Resurgent interest in both mechanistic and counterfactual theories of explanation has led to a fair amount of discussion regarding the relative merits of these two approaches. James Woodward is currently the pre-eminent counterfactual theorist, and he criticizes the mechanists on the following grounds: Unless mechanists about explanation invoke counterfactuals, they cannot make sense of claims about causal interactions between mechanism parts or of causal explanations put forward absent knowledge of productive mechanisms. He claims that these shortfalls can be offset if (...)
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  • Exploring students’ epistemological knowledge of models and modelling in science:results from a teaching/learning experience on climate change.Giulia Tasquier, Olivia Levrini & Justin Dillon - 2016 - International Journal of Science Education 38 (4):539-563.
    The scientific community has been debating climate change for over two decades. In the light of certain arguments put forward by the aforesaid community, the EU has recommended a set of innovative reforms to science teaching such as incorporating environmental issues into the scientific curriculum, thereby helping to make schools a place of civic education. However, despite these European recommendations, relatively little emphasis is still given to climate change within science curricula. Climate change, although potentially engaging for students, is a (...)
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  • School Chemistry: The Need for Transgression.Vicente Talanquer - 2013 - Science & Education 22 (7):1757-1773.
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  • Galileo and the Mountains of the Moon: Analogical Reasoning, Models and Metaphors in Scientific Discovery.Marta Spranzi - 2004 - Journal of Cognition and Culture 4 (3-4):451-483.
    This paper is about the use of analogical reasoning, models and metaphors in Galileo's discovery of the mountains of the moon, which he describes in the Starry Messenger, a short but groundbreaking treatise published in 1610. On the basis of the observations of the Moon he has made with the newly invented telescope, Galileo shows that the Moon has mountains and that therefore it shares the same solid, opaque and rugged nature of the Earth. I will first reconstruct Galileo's reasoning, (...)
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  • The strong program in embodied cognitive science.Guilherme Sanches de Oliveira - 2023 - Phenomenology and the Cognitive Sciences 22 (4):841-865.
    A popular trend in the sciences of the mind is to understand cognition as embodied, embedded, enactive, ecological, and so on. While some of the work under the label of “embodied cognition” takes for granted key commitments of traditional cognitive science, other projects coincide in treating embodiment as the starting point for an entirely different way of investigating all of cognition. Focusing on the latter, this paper discusses how embodied cognitive science can be made more reflexive and more sensitive to (...)
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  • The instrumentalist’s new clothes.Darrell Patrick Rowbottom - 2011 - Philosophy of Science 78 (5):1200-1211.
    This paper develops a new version of instrumentalism, in light of progress in the realism debate in recent decades, and thereby defends the view that instrumentalism remains a viable philosophical position on science. The key idea is that talk of unobservable objects should be taken literally only when those objects are assigned properties (or described in terms of analogies involving things) with which we are experientially (or otherwise) acquainted. This is derivative from the instrumentalist tradition in so far as the (...)
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  • The Instrumentalist's New Clothes.Darrell Patrick Rowbottom - 2011 - Philosophy of Science 78 (5):1200-1211.
    This article develops a new version of instrumentalism, in light of progress in the realism debate in recent decades, and thereby defends the view that instrumentalism remains a viable philosophical position on science. The key idea is that talk of unobservable objects should be taken literally only when those objects are assigned properties with which we are experientially acquainted. This is derivative from the instrumentalist tradition insofar as the distinction between unobservable and observable is taken to have significance with respect (...)
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  • Models in Biology and Physics: What’s the Difference?Darrell Patrick Rowbottom - 2009 - Foundations of Science 14 (4):281-294.
    In Making Sense of Life , Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view of epistemology and cognitive (...)
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  • Models in Biology and Physics: What’s the Difference?Darrell Patrick Rowbottom - 2009 - Foundations of Science 14 (4):281-294.
    In Making Sense of Life, Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view of epistemology and cognitive science. (...)
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  • ‘Models of’ and ‘Models for’: On the Relation between Mechanistic Models and Experimental Strategies in Molecular Biology.Emanuele Ratti - 2018 - British Journal for the Philosophy of Science (2):773-797.
    Molecular biologists exploit information conveyed by mechanistic models for experimental purposes. In this article, I make sense of this aspect of biological practice by developing Keller’s idea of the distinction between ‘models of’ and ‘models for’. ‘Models of (phenomena)’ should be understood as models representing phenomena and are valuable if they explain phenomena. ‘Models for (manipulating phenomena)’ are new types of material manipulations and are important not because of their explanatory force, but because of the interventionist strategies they afford. This (...)
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  • The distribution of representation.Lisa M. Osbeck & Nancy J. Nersessian - 2006 - Journal for the Theory of Social Behaviour 36 (2):141–160.
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  • Model‐Based Reasoning in Distributed Cognitive Systems.Nancy J. Nersessian - 2006 - Philosophy of Science 73 (5):699-709.
    This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These "model systems" are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary cognitive science that construes (...)
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  • How Do Engineering Scientists Think? Model‐Based Simulation in Biomedical Engineering Research Laboratories.Nancy J. Nersessian - 2009 - Topics in Cognitive Science 1 (4):730-757.
    Designing, building, and experimenting with physical simulation models are central problem‐solving practices in the engineering sciences. Model‐based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of “distributed cognition” to interpret data collected in ethnographic and cognitive‐historical studies (...)
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  • Reasoning, rationality, and representation.Wade Munroe - 2020 - Synthese 198 (9):8323-8345.
    Recently, a cottage industry has formed with the goal of analyzing reasoning. The relevant notion of reasoning in which philosophers are expressly interested is fixed through an epistemic functional description: reasoning—whatever it is—is our personal-level, rationally evaluable means of meeting our rational requirements through managing and updating our attitudes. Roughly, the dominant view in the extant literature as developed by Paul Boghossian, John Broome, and others is that reasoning is a rule-governed operation over propositional attitudes that results in a change (...)
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  • Imagination and insight: a new acount of the content of thought experiments.Letitia Meynell - 2014 - Synthese 191 (17):4149-4168.
    This paper motivates, explains, and defends a new account of the content of thought experiments. I begin by briefly surveying and critiquing three influential accounts of thought experiments: James Robert Brown’s Platonist account, John Norton’s deflationist account that treats them as picturesque arguments, and a cluster of views that I group together as mental model accounts. I use this analysis to motivate a set of six desiderata for a new approach. I propose that we treat thought experiments primarily as aesthetic (...)
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  • What makes interdisciplinarity difficult? Some consequences of domain specificity in interdisciplinary practice.Miles MacLeod - 2018 - Synthese 195 (2):697-720.
    Research on interdisciplinary science has for the most part concentrated on the institutional obstacles that discourage or hamper interdisciplinary work, with the expectation that interdisciplinary interaction can be improved through institutional reform strategies such as through reform of peer review systems. However institutional obstacles are not the only ones that confront interdisciplinary work. The design of policy strategies would benefit from more detailed investigation into the particular cognitive constraints, including the methodological and conceptual barriers, which also confront attempts to work (...)
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  • Modeling complexity: cognitive constraints and computational model-building in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2018 - History and Philosophy of the Life Sciences 40 (1):17.
    Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to (...)
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  • Coupling simulation and experiment: The bimodal strategy in integrative systems biology.Miles MacLeod & Nancy J. Nersessian - 2013 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4a):572-584.
    The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling (...)
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  • Inference to the Best explanation.Peter Lipton - 2004 - In Martin Curd & Stathis Psillos (eds.), The Routledge Companion to Philosophy of Science. Routledge. pp. 193.
    Science depends on judgments of the bearing of evidence on theory. Scientists must judge whether an observation or the result of an experiment supports, disconfirms, or is simply irrelevant to a given hypothesis. Similarly, scientists may judge that, given all the available evidence, a hypothesis ought to be accepted as correct or nearly so, rejected as false, or neither. Occasionally, these evidential judgments can be made on deductive grounds. If an experimental result strictly contradicts a hypothesis, then the truth of (...)
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  • Science and Religion as Languages: Understanding the Science–Religion Relationship Using Metaphors, Analogies, and Models.Amy H. Lee - 2019 - Zygon 54 (4):880-908.
    Many scholars often use the terms “metaphors,” “analogies,” and “models” interchangeably and inadvertently overlook the uniqueness of each word. According to recent cognitive studies, the three terms involve distinct cognitive processes using features from a familiar concept and applying them to an abstract, complicated concept. In the field of science and religion, there have been various objects or ideas used as metaphors, analogies, or models to describe the science–religion relationship. Although these heuristic tools provided some understanding of the complex interaction, (...)
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  • Varieties of noise: Analogical reasoning in synthetic biology.Tarja Knuuttila & Andrea Loettgers - 2014 - Studies in History and Philosophy of Science Part A 48:76-88.
    The picture of synthetic biology as a kind of engineering science has largely created the public understanding of this novel field, covering both its promises and risks. In this paper, we will argue that the actual situation is more nuanced and complex. Synthetic biology is a highly interdisciplinary field of research located at the interface of physics, chemistry, biology, and computational science. All of these fields provide concepts, metaphors, mathematical tools, and models, which are typically utilized by synthetic biologists by (...)
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  • Cognitive structural realism: A radical solution to the problem of scientific representation.Max Jones - 2020 - Philosophical Psychology 33 (5):772-775.
    Volume 33, Issue 5, July 2020, Page 772-775.
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  • Maps, languages, and manguages: Rival cognitive architectures?Kent Johnson - 2015 - Philosophical Psychology 28 (6):815-836.
    Provided we agree about the thing, it is needless to dispute about the terms. —David Hume, A treatise of human nature, Book 1, section VIIMap-like representations are frequently invoked as an alternative type of representational vehicle to a language of thought. This view presupposes that map-systems and languages form legitimate natural kinds of cognitive representational systems. I argue that they do not, because the collections of features that might be taken as characteristic of maps or languages do not themselves provide (...)
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  • A new framework for teaching scientific reasoning to students from application-oriented sciences.Wybo Houkes & Krist Vaesen - 2021 - European Journal for Philosophy of Science 11 (2):1-16.
    About three decades ago, the late Ronald Giere introduced a new framework for teaching scientific reasoning to science students. Giere’s framework presents a model-based alternative to the traditional statement approach—in which scientific inferences are reconstructed as explicit arguments, composed of (single-sentence) premises and a conclusion. Subsequent research in science education has shown that model-based approaches are particularly effective in teaching science students how to understand and evaluate scientific reasoning. One limitation of Giere’s framework, however, is that it covers only one (...)
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  • Tensions Between Learning Models and Engaging in Modeling.Candice Guy-Gaytán, Julia S. Gouvea, Chris Griesemer & Cynthia Passmore - 2019 - Science & Education 28 (8):843-864.
    The ability to develop and use models to explain phenomena is a key component of the Next Generation Science Standards, and without examples of what modeling instruction looks like in the reality of classrooms, it will be difficult for us as a field to understand how to move forward in designing curricula that foreground the practice in ways that align with the epistemic commitments of modeling. In this article, we illustrate examples drawn from a model-based curriculum development project to problematize (...)
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  • Resources for Research on Analogy: A Multi-disciplinary Guide.Marcello Guarini, Amy Butchart, Paul Simard Smith & Andrei Moldovan - 2009 - Informal Logic 29 (2):84-197.
    Work on analogy has been done from a number of disciplinary perspectives throughout the history of Western thought. This work is a multidisciplinary guide to theorizing about analogy. It contains 1,406 references, primarily to journal articles and monographs, and primarily to English language material. classical through to contemporary sources are included. The work is classified into eight different sections (with a number of subsections). A brief introduction to each section is provided. Keywords and key expressions of importance to research on (...)
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  • Science and common sense: perspectives from philosophy and science education.Sara Green - 2019 - Synthese 196 (3):795-818.
    This paper explores the relation between scientific knowledge and common sense intuitions as a complement to Hoyningen-Huene’s account of systematicity. On one hand, Hoyningen-Huene embraces continuity between these in his characterization of scientific knowledge as an extension of everyday knowledge, distinguished by an increase in systematicity. On the other, he argues that scientific knowledge often comes to deviate from common sense as science develops. Specifically, he argues that a departure from common sense is a price we may have to pay (...)
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  • Two Views About Explicitly Teaching Nature of Science.Richard A. Duschl & Richard Grandy - 2013 - Science & Education 22 (9):2109-2139.
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  • Cognitive science.Paul Thagard - 2008 - Stanford Encyclopedia of Philosophy.
    Cognitive science is the interdisciplinary investigation of mind and intelligence, embracing psychology, neuroscience, anthropology, artificial intelligence, and philosophy. There are many important philosophical questions related to this investigation, but this short chapter will focus on the following three. What is the nature of the explanations and theories developed in cognitive science? What are the relations among the five disciplines that comprise cognitive science? What are the implications of cognitive science research for general issues in the philosophy of science? I will (...)
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  • Modeling as a teaching learning process for understanding materials: A case study in primary education.Andrés Acher, María Arcà & Neus Sanmartí - 2007 - Science Education 91 (3):398-418.
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  • Interpreting scientific and engineering practices: Integrating the cognitive, social, and cultural dimensions.N. J. Nersessian - 2005 - In M. Gorman, R. Tweney, D. Gooding & A. Kincannon (eds.), Scientific and Technological Thinking. Erlbaum. pp. 17--56.
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  • Modal Rationalism and Constructive Realism: Models and Their Modality.William Kallfelz - 2010
    I present a case for a rapprochement between aspects of rationalism and scientific realism, by way of a general framework employing modal epistemology and elements of 2-dimensional semantics (2DS). My overall argument strategy is meta-inductive: The bulk of this paper establishes a “base case,” i.e., a concretely constructive example by which I demonstrate this linkage. The base case or constructive example acts as the exemplar for generating, in a constructively ‘bottom-up’ fashion, a more generally rigorous case for rationalism-realism qua modal (...)
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  • Mechanisms and psychological explanation.Cory Wright & William Bechtel - 2007 - In Paul Thagard (ed.), Handbook of the Philosophy of Psychology and Cognitive Science. Elsevier.
    As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...)
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  • Thought experiments and mental simulations.John Zeimbekis - 2011 - In Katerina Ierodiakonou & Sophie Roux (eds.), Thought Experiments in Methodological and Historical Contexts. Brill.
    Thought experiments have a mysterious way of informing us about the world, apparently without examining it, yet with a great degree of certainty. It is tempting to try to explain this capacity by making use of the idea that in thought experiments, the mind somehow simulates the processes about which it reaches conclusions. Here, I test this idea. I argue that when they predict the outcomes of hypothetical physical situations, thought experiments cannot simulate physical processes. They use mental models, which (...)
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