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  1. 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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  • 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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  • Blobel and Sabatini’s “Beautiful Idea”: Visual Representations of the Conception and Refinement of the Signal Hypothesis.Michelle Lynne LaBonte - 2017 - Journal of the History of Biology 50 (4):797-833.
    In 1971, Günter Blobel and David Sabatini proposed a novel and quite speculative schematic model to describe how proteins might reach the proper cellular location. According to their proposal, proteins destined to be secreted from the cell contain a “signal” to direct their release. Despite the fact that Blobel and Sabatini presented their signal hypothesis as a “beautiful idea” not grounded in experimental evidence, they received criticism from other scientists who opposed such speculation. Following the publication of the 1971 model, (...)
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  • Why one model is never enough: a defense of explanatory holism.Hochstein Eric - 2017 - Biology and Philosophy 32 (6):1105-1125.
    Traditionally, a scientific model is thought to provide a good scientific explanation to the extent that it satisfies certain scientific goals that are thought to be constitutive of explanation. Problems arise when we realize that individual scientific models cannot simultaneously satisfy all the scientific goals typically associated with explanation. A given model’s ability to satisfy some goals must always come at the expense of satisfying others. This has resulted in philosophical disputes regarding which of these goals are in fact necessary (...)
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  • One mechanism, many models: a distributed theory of mechanistic explanation.Eric Hochstein - 2016 - Synthese 193 (5):1387-1407.
    There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...)
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  • Categorizing the Mental.Eric Hochstein - 2016 - Philosophical Quarterly 66 (265):745-759.
    A common view in the philosophy of mind and philosophy of psychology is that there is an ideally correct way of categorizing the structures and operations of the mind, and that the goal of neuroscience and psychology is to find this correct categorizational scheme. Categories which cannot find a place within this correct framework ought to be eliminated from scientific practice. In this paper, I argue that this general idea runs counter to productive scientific practices. Such a view ignores the (...)
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  • ‘Extreme’ organisms and the problem of generalization: interpreting the Krogh principle.Sara Green, Michael R. Dietrich, Sabina Leonelli & Rachel A. Ankeny - 2018 - History and Philosophy of the Life Sciences 40 (4):65.
    Many biologists appeal to the so-called Krogh principle when justifying their choice of experimental organisms. The principle states that “for a large number of problems there will be some animal of choice, or a few such animals, on which it can be most conveniently studied”. Despite its popularity, the principle is often critiqued for implying unwarranted generalizations from optimal models. We argue that the Krogh principle should be interpreted in relation to the historical and scientific contexts in which it has (...)
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  • Visualizing Scientific Inference.David C. Gooding - 2010 - Topics in Cognitive Science 2 (1):15-35.
    The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implications of this repertoire for understanding scientists as cognitive agents.
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  • Are More Details Better? On the Norms of Completeness for Mechanistic Explanations.Carl F. Craver & David M. Kaplan - 2020 - British Journal for the Philosophy of Science 71 (1):287-319.
    Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...)
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