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  1. On the Identity of Thought Experiments: Thought Experiments Rethought.Alisa Bokulich & Mélanie Frappier - 2017 - In Michael T. Stuart, Yiftach J. H. Fehige & James Robert Brown (eds.), The Routledge Companion to Thought Experiments. Routledge.
  • Representing Experimental Procedures Through Diagrams at CERN’s Large Hadron Collider: The Communicatory Value of Diagrammatic Representations in Collaborative Research.Koray Karaca - 2017 - Perspectives on Science 25 (2):177-203.
    In relatively recent years, quite a number of diverse case studies concerning the use of visual displays—such as graphs, diagrams, tables, pictures, drawings, etc.—in both the physical and biological sciences have been offered in the literature of the history and philosophy of science —see, e.g., Miller 1984; Lynch and Woolgar 1990; Baigrie 1996; Pauwels 2006. These case studies have shown that visual representations fulfill important functions in both the theoretical and experimental practices of science, thereby emphasizing the non-verbal dimension of (...)
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  • Of Water Drops and Atomic Nuclei: Analogies and Pursuit Worthiness in Science.Rune Nyrup - forthcoming - British Journal for the Philosophy of Science.
  • Methods of Representation as Inferential Devices.Matías Osta Vélez - 2019 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 50 (2):231-245.
    In this article I am going to reconstruct Stephen Toulmin’s procedural theory of concepts and explanations in order to develop two overlooked ideas from his philosophy of science: methods of representations and inferential techniques. I argue that these notions, when properly articulated, could be useful for shedding some light on how scientific reasoning is related to representational structures, concepts, and explanation within scientific practices. I will explore and illustrate these ideas by studying the development of the notion of instantaneous speed (...)
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  • Epistemology for Interdisciplinary Research – Shifting Philosophical Paradigms of Science.Mieke Boon & Sophie Van Baalen - 2019 - European Journal for Philosophy of Science 9 (1):16.
    In science policy, it is generally acknowledged that science-based problem-solving requires interdisciplinary research. For example, policy makers invest in funding programs such as Horizon 2020 that aim to stimulate interdisciplinary research. Yet the epistemological processes that lead to effective interdisciplinary research are poorly understood. This article aims at an epistemology for interdisciplinary research, in particular, IDR for solving ‘real-world’ problems. Focus is on the question why researchers experience cognitive and epistemic difficulties in conducting IDR. Based on a study of educational (...)
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  • What’s in It for the Historian of Science? Reflections on the Value of Philosophy of Science for History of Science.Theodore Arabatzis - 2017 - International Studies in the Philosophy of Science 31 (1):69-82.
    In this article, I explore the value of philosophy of science for history of science. I start by introducing a distinction between two ways of integrating history and philosophy of science: historical philosophy of science and philosophical history of science. I then offer a critical discussion of Imre Lakatos’s project to bring philosophy of science to bear on historical interpretation. I point out certain flaws in Lakatos’s project, which I consider indicative of what went wrong with PHS in the past. (...)
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  • From Core Cognition to Intuitive Theories: A Psychologist’s Account of Conceptual Change. [REVIEW]Christophe Heintz - 2012 - Metascience 21 (2):439-444.
  • Hidden Underdetermination: A Case Study in Classical Electrodynamics.Wolfgang Pietsch - 2012 - International Studies in the Philosophy of Science 26 (2):125-151.
    In this article, I present a case study of underdetermination in nineteenth-century electrodynamics between a pure field theory and a formulation in terms of action at a distance. A particular focus is on the question if and how this underdetermination is eventually resolved. It turns out that after a period of overt underdetermination, during which the approaches are developed separately, the two programmes are merged. On the basis of this development, I argue that the original underdetermination survives in hidden form (...)
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  • Two Styles of Reasoning in Scientific Practices: Experimental and Mathematical Traditions.Mieke Boon - 2011 - International Studies in the Philosophy of Science 25 (3):255 - 278.
    This article outlines a philosophy of science in practice that focuses on the engineering sciences. A methodological issue is that these practices seem to be divided by two different styles of scientific reasoning, namely, causal-mechanistic and mathematical reasoning. These styles are philosophically characterized by what Kuhn called ?disciplinary matrices?. Due to distinct metaphysical background pictures and/or distinct ideas of what counts as intelligible, they entail distinct ideas of the character of phenomena and what counts as a scientific explanation. It is (...)
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  • Can an Engineer Fix an Immune System?–Rethinking Theoretical Biology.Claudio Mattiussi - 2013 - Acta Biotheoretica 61 (2):223-258.
    In an instant classic paper ; 2002: 179–182) biologist Yuri Lazebnik deplores the poor effectiveness of the approach adopted by biologists to understand and “fix” biological systems. Lazebnik suggests that to remedy this state of things biologist should take inspiration from the approach used by engineers to design, understand, and troubleshoot technological systems. In the present paper I substantiate Lazebnik’s analysis by concretely showing how to apply the engineering approach to biological problems. I use an actual example of electronic circuit (...)
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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.
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  • Two Kinds of Knowledge in Scientific Discovery.Will Bridewell & Pat Langley - 2010 - Topics in Cognitive Science 2 (1):36-52.
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  • The Dynamics of Scientific Concepts: The Relevance of Epistemic Aims and Values.Ingo Brigandt - 2012 - In Uljana Feest & Friedrich Steinle (eds.), Scientific Concepts and Investigative Practice. Berlin: de Gruyter. pp. 75–103.
    The philosophy of science that grew out of logical positivism construed scientific knowledge in terms of set of interconnected beliefs about the world, such as theories and observation statements. Nowadays science is also conceived of as a dynamic process based on the various practices of individual scientists and the institutional settings of science. Two features particularly influence the dynamics of scientific knowledge: epistemic standards and aims (e.g., assumptions about what issues are currently in need of scientific study and explanation). While (...)
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  • Peeking Inside the Black Box: A New Kind of Scientific Visualization.Michael T. Stuart & Nancy J. Nersessian - 2018 - Minds and Machines 29 (1):87-107.
    Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization (...)
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  • Human Rationality Challenges Universal Logic.Brian R. Gaines - 2010 - Logica Universalis 4 (2):163-205.
    Tarski’s conceptual analysis of the notion of logical consequence is one of the pinnacles of the process of defining the metamathematical foundations of mathematics in the tradition of his predecessors Euclid, Frege, Russell and Hilbert, and his contemporaries Carnap, Gödel, Gentzen and Turing. However, he also notes that in defining the concept of consequence “efforts were made to adhere to the common usage of the language of every day life.” This paper addresses the issue of what relationship Tarski’s analysis, and (...)
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  • Naturalising Badiou: Mathematical Ontology and Structural Realism.Fabio Gironi - unknown
    This thesis offers a naturalist revision of Alain Badiou’s philosophy. This goal is pursued through an encounter of Badiou’s mathematical ontology and theory of truth with contemporary trends in philosophy of mathematics and philosophy of science. I take issue with Badiou’s inability to elucidate the link between the empirical and the ontological, and his residual reliance on a Heideggerian project of fundamental ontology, which undermines his own immanentist principles. I will argue for both a bottom-up naturalisation of Badiou’s philosophical approach (...)
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  • Why Cognitive Science Needs Philosophy and Vice Versa.Paul Thagard - 2009 - Topics in Cognitive Science 1 (2):237-254.
    Contrary to common views that philosophy is extraneous to cognitive science, this paper argues that philosophy has a crucial role to play in cognitive science with respect to generality and normativity. General questions include the nature of theories and explanations, the role of computer simulation in cognitive theorizing, and the relations among the different fields of cognitive science. Normative questions include whether human thinking should be Bayesian, whether decision making should maximize expected utility, and how norms should be established. These (...)
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  • How Do Models Give Us Knowledge? The Case of Carnot’s Ideal Heat Engine.Tarja Knuuttila & Mieke Boon - 2011 - European Journal for Philosophy of Science 1 (3):309-334.
    Our concern is in explaining how and why models give us useful knowledge. We argue that if we are to understand how models function in the actual scientific practice the representational approach to models proves either misleading or too minimal. We propose turning from the representational approach to the artefactual, which implies also a new unit of analysis: the activity of modelling. Modelling, we suggest, could be approached as a specific practice in which concrete artefacts, i.e., models, are constructed with (...)
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  • Deflationary Representation, Inference, and Practice.Mauricio Suárez - 2015 - Studies in History and Philosophy of Science Part A 49:36-47.
    This paper defends the deflationary character of two recent views regarding scientific representation, namely RIG Hughes’ DDI model and the inferential conception. It is first argued that these views’ deflationism is akin to the homonymous position in discussions regarding the nature of truth. There, we are invited to consider the platitudes that the predicate “true” obeys at the level of practice, disregarding any deeper, or more substantive, account of its nature. More generally, for any concept X, a deflationary approach is (...)
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  • Prototypical Reasoning About Species and the Species Problem.Yuichi Amitani - 2015 - Biological Theory 10 (4):289-300.
    The species problem is often described as the abundance of conflicting definitions of _species_, such as the biological species concept and phylogenetic species concepts. But biologists understand the notion of species in a non-definitional as well as a definitional way. In this article I argue that when they understand _species_ without a definition in their mind, their understanding is often mediated by the notion of _good species_, or prototypical species, as the idea of ``prototype'' is explicated in cognitive psychology. This (...)
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  • Outline of a Dynamical Inferential Conception of the Application of Mathematics.Tim Räz & Tilman Sauer - 2015 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 49:57-72.
    We outline a framework for analyzing episodes from the history of science in which the application of mathematics plays a constitutive role in the conceptual development of empirical sciences. Our starting point is the inferential conception of the application of mathematics, recently advanced by Bueno and Colyvan. We identify and discuss some systematic problems of this approach. We propose refinements of the inferential conception based on theoretical considerations and on the basis of a historical case study. We demonstrate the usefulness (...)
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  • On the Poietic Character of Technology.Federica Russo - 2016 - Humana Mente 9 (30).
    Large part of contemporary science is in fact technoscience, in the sense that it crucially depends on several technologies for the generation, collection, and analysis of data. This prompts a re-examination of the relations between science and technologies. In this essay, I advance the view that we’d better move beyond the ‘subordination view’ and the ‘instrumental’ view. The first aims to establish the primacy of science over technology, and the second uses technology instrumentally to support a realist position about theoretical (...)
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  • Constraint‐Based Reasoning for Search and Explanation: Strategies for Understanding Variation and Patterns in Biology.Sara Green & Nicholaos Jones - 2016 - Dialectica 70 (3):343-374.
    Life scientists increasingly rely upon abstraction-based modeling and reasoning strategies for understanding biological phenomena. We introduce the notion of constraint-based reasoning as a fruitful tool for conceptualizing some of these developments. One important role of mathematical abstractions is to impose formal constraints on a search space for possible hypotheses and thereby guide the search for plausible causal models. Formal constraints are, however, not only tools for biological explanations but can be explanatory by virtue of clarifying general dependency-relations and patterning between (...)
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  • Scientific Discovery Through Fictionally Modelling Reality.Fiora Salis - 2018 - Topoi:1-11.
    How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development of models (...)
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  • Reconsidering the Carnap-Kuhn Connection.Jonathan Y. Tsou - 2015 - In Kuhn’s Structure of Scientific Revolutions - 50 Years On. Springer Verlag.
    Recently, some philosophers of science (e.g., Gürol Irzik, Michael Friedman) have challenged the ‘received view’ on the relationship between Rudolf Carnap and Thomas Kuhn, suggesting that there is a close affinity (rather than opposition) between their philosophical views. In support of this argument, these authors cite Carnap and Kuhn’s similar views on incommensurability, theory-choice, and scientific revolutions. Against this revisionist view, I argue that the philosophical relationship between Carnap and Kuhn should be regarded as opposed rather than complementary. In particular, (...)
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  • Mechanistic Models and the Explanatory Limits of Machine Learning.Emanuele Ratti & Ezequiel López-Rubio - unknown
    We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates (...)
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  • Discovery of Causal Mechanisms: Oxidative Phosphorylation and the Calvin-Benson Cycle.Raphael Scholl & Kärin Nickelsen - unknown
    We investigate the context of discovery of two significant achievements of 20th century biochemistry: the chemiosmotic mechanism of oxidative phosphorylation and the dark reaction of photosynthesis. The pursuit of these problems involved discovery strategies such as the transfer, recombination and reversal of previous causal and mechanistic knowledge in biochemistry. We study the operation and scope of these strategies by careful historical analysis, reaching a number of systematic conclusions: 1) Even basic strategies can illuminate "hard cases" of scientific discovery that go (...)
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  • Constructing Diagrams to Understand Phenomena and Mechanisms.Benjamin Sheredos & William Bechtel - manuscript
    Biologists often hypothesize mechanisms to explai phenomena. Our interest is how their understanding of the phenomena and mechanisms develops as they construct diagrams to communicate their claims. We present two case studies in which scientists integrate various data to create a single diagram to communicate their major conclusions in a research publication. In both cases, the history of revisions suggests that scientists' initial drafts encode biases and oversights that are only gradually overcome through prolonged, reflective re-design. To account for this, (...)
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  • Beyond Cognitive Myopia: A Patchwork Approach to the Concept of Neural Function.Philipp Haueis - 2018 - Synthese 195 (12):5373-5402.
    In this paper, I argue that looking at the concept of neural function through the lens of cognition alone risks cognitive myopia: it leads neuroscientists to focus only on mechanisms with cognitive functions that process behaviorally relevant information when conceptualizing “neural function”. Cognitive myopia tempts researchers to neglect neural mechanisms with noncognitive functions which do not process behaviorally relevant information but maintain and repair neural and other systems of the body. Cognitive myopia similarly affects philosophy of neuroscience because scholars overlook (...)
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  • On the Status and Role of Instrumental Images in Contemporary Science: Some Epistemological Issues.Hermínio Martins - 2014 - Scientiae Studia 12 (SPE):11-36.
    The controversy over imageless thought versus picture thinking , with the recent reconsideration of model-based reasoning in the physical sciences is briefly examined. The main focus of the article is on the role of instrumentally elicited images in the sciences, especially in the physical sciences, with special reference to optics, experimental particle physics and observational astronomy, against the background of the civilization of digital images, though to some degree every scientific discipline is implicated. Imaging, today chiefly in the mode of (...)
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  • Critical Thinking and Informal Logic: Neuropsychological Perspectives.Paul Thagard - 2011 - Informal Logic 31 (3):152-170.
    This article challenges the common view that improvements in critical thinking are best pursued by investigations in informal logic. From the perspective of research in psychology and neuroscience, hu-man inference is a process that is multimodal, parallel, and often emo-tional, which makes it unlike the linguistic, serial, and narrowly cog-nitive structure of arguments. At-tempts to improve inferential prac-tice need to consider psychological error tendencies, which are patterns of thinking that are natural for peo-ple but frequently lead to mistakes in judgment. (...)
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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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  • Towards a Dual Process Epistemology of Imagination.Michael T. Stuart - 2019 - Synthese:1-22.
    Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes (...)
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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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  • 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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  • Mapping the Brain's Metaphor Circuitry: Metaphorical Thought in Everyday Reason.George Lakoff - 2014 - Frontiers in Human Neuroscience 8.
  • The Cognitive Integration of Scientific Instruments: Information, Situated Cognition, and Scientific Practice.Richard Heersmink - 2016 - Phenomenology and the Cognitive Sciences 15 (4):1-21.
    Researchers in the biological and biomedical sciences, particularly those working in laboratories, use a variety of artifacts to help them perform their cognitive tasks. This paper analyses the relationship between researchers and cognitive artifacts in terms of integration. It first distinguishes different categories of cognitive artifacts used in biological practice on the basis of their informational properties. This results in a novel classification of scientific instruments, conducive to an analysis of the cognitive interactions between researchers and artifacts. It then uses (...)
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  • Can You Visualize Theory? On the Use of Visual Thinking in Theory Pictures, Theorizing Diagrams, and Visual Sketches.Richard Swedberg - 2016 - Sociological Theory 34 (3):250-275.
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  • Models as Relational Categories.Tommi Kokkonen - 2017 - Science & Education 26 (7-9):777-798.
    Model-based learning has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, (...)
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  • Model-Based Science: Diverse Perspectives, Little Cross-Disciplinary Dialogue.Guilherme Sanches de Oliveira - 2018 - Metascience 27 (3):453-456.
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  • Stellan Ohlsson: Deep Learning: How the Mind Overrides Experience.Carol L. Smith - 2012 - Science & Education 21 (9):1381-1392.
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  • Theoretical Terms Without Analytic Truths.Michael Strevens - 2012 - Philosophical Studies 160 (1):167-190.
    When new theoretical terms are introduced into scientific discourse, prevailing accounts imply, analytic or semantic truths come along with them, by way of either definitions or reference-fixing descriptions. But there appear to be few or no analytic truths in scientific theory, which suggests that the prevailing accounts are mistaken. This paper looks to research on the psychology of natural kind concepts to suggest a new account of the introduction of theoretical terms that avoids both definition and reference-fixing description. At the (...)
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  • The Cognitive Science of Feynmen.Sanjay Chandrasekharan - 2013 - Metascience 22 (3):647-652.
  • The AHA! Experience: Creativity Through Emergent Binding in Neural Networks.Paul Thagard & Terrence C. Stewart - 2011 - Cognitive Science 35 (1):1-33.
    Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support (...)
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  • What Can Be Learned From a Laboratory Model of Conceptual Change? Descriptive Findings and Methodological Issues.Stellan Ohlsson & David G. Cosejo - 2014 - Science & Education 23 (7):1485-1504.
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  • Representing the Electromagnetic Field: How Maxwell’s Mathematics Empowered Faraday’s Field Theory.Ryan D. Tweney - 2011 - Science & Education 20 (7-8):687-700.
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  • Overcoming the Limits of Quantification by Visualization.Isabella Sarto-Jackson & Richard R. Nelson - 2015 - Biological Theory 10 (3):253-262.
    Biological sciences have strived to adopt the conceptual framework of physics and have become increasingly quantitatively oriented, aiming to refute the assertion that biology appears unquantifiable, unpredictable, and messy. But despite all effort, biology is characterized by a paucity of quantitative statements with universal applications. Nonetheless, many biological disciplines—most notably molecular biology—have experienced an ascendancy over the last 50 years. The underlying core concepts and ideas permeate and inform many neighboring disciplines. This surprising success is probably not so much attributable (...)
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  • The Interplay of Psychology and Mathematics Education: From the Attraction of Psychology to the Discovery of the Social.Karen François, Kathleen Coessens & Jean Paul Van Bendegem - 2012 - Journal of Philosophy of Education 46 (3):370-385.
    It is a rather safe statement to claim that the social dimensions of the scientific process are accepted in a fair share of studies in the philosophy of science. It is a somewhat safe statement to claim that the social dimensions are now seen as an essential element in the understanding of what human cognition is and how it functions. But it would be a rather unsafe statement to claim that the social is fully accepted in the philosophy of mathematics. (...)
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  • Argumentation in Science Education: A Model-Based Framework.Florian Böttcher & Anke Meisert - 2011 - Science & Education 20 (2):103-140.
  • Knowledge Building Expertise: Nanomodellers’ Education as an Example.Suvi Tala - 2013 - Science & Education 22 (6):1323-1346.