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  1. Psa 2018.Philsci-Archive -Preprint Volume- - unknown
    These preprints were automatically compiled into a PDF from the collection of papers deposited in PhilSci-Archive in conjunction with the PSA 2018.
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  • La deriva genética como fuerza evolutiva.Ariel Jonathan Roffé - 2015 - In J. Ahumada, N. Venturelli & S. Seno Chibeni (eds.), Selección de Trabajos del IX Encuentro AFHIC y las XXV Jornadas de Epistemología e Historia de la ciencia. pp. 615-626.
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  • Historical Case Studies: The “Model Organisms” of Philosophy of Science.Samuel Schindler & Raphael Scholl - 2020 - Erkenntnis 87 (2):933-952.
    Philosophers use historical case studies to support wide-ranging claims about science. This practice is often criticized as problematic. In this paper we suggest that the function of case studies can be understood and justified by analogy to a well-established practice in biology: the investigation of model organisms. We argue that inferences based on case studies are no more problematic than inferences from model organisms to larger classes of organisms in biology. We demonstrate our view in detail by reference to a (...)
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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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  • Potential Controversies: Causation and the Hodgkin and Huxley Equations.David Evan Pence - 2017 - Philosophy of Science 84 (5):1177-1188.
    The import of Hodgkin and Huxley’s classic model of the action potential has been hotly debated in recent years, with particular controversy surrounding claims by prominent proponents of mechanistic explanation. For these authors, the Hodgkin-Huxley model is an excellent predictive tool but ultimately lacks causal/explanatory import. What is more, they claim that this is how Hodgkin and Huxley themselves saw the model. I argue that these claims rest on a problematic reading of the work. Hodgkin and Huxley’s model is both (...)
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  • The normativity of nature. Essays on Kant's Critique of Judgement.Michela Massimi - 2015 - Intellectual History Review 25 (4):464-467.
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  • Data science and molecular biology: prediction and mechanistic explanation.Ezequiel López-Rubio & Emanuele Ratti - 2021 - Synthese 198 (4):3131-3156.
    In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...)
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  • Data science and molecular biology: prediction and mechanistic explanation.Ezequiel López-Rubio & Emanuele Ratti - 2019 - Synthese (4):1-26.
    In the last few years, biologists and computer scientists have claimed that the introduction of data science techniques in molecular biology has changed the characteristics and the aims of typical outputs (i.e. models) of such a discipline. In this paper we will critically examine this claim. First, we identify the received view on models and their aims in molecular biology. Models in molecular biology are mechanistic and explanatory. Next, we identify the scope and aims of data science (machine learning in (...)
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  • The unity of neuroscience: a flat view.Arnon Levy - 2016 - Synthese 193 (12):3843-3863.
    This paper offers a novel view of unity in neuroscience. I set out by discussing problems with the classical account of unity-by-reduction, due to Oppenheim and Putnam. That view relies on a strong notion of levels, which has substantial problems. A more recent alternative, the mechanistic “mosaic” view due to Craver, does not have such problems. But I argue that the mosaic ideal of unity is too minimal, and we should, if possible, aspire for more. Relying on a number of (...)
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  • Modelling as Indirect Representation? The Lotka–Volterra Model Revisited.Tarja Knuuttila & Andrea Loettgers - 2017 - British Journal for the Philosophy of Science 68 (4):1007-1036.
    ABSTRACT Is there something specific about modelling that distinguishes it from many other theoretical endeavours? We consider Michael Weisberg’s thesis that modelling is a form of indirect representation through a close examination of the historical roots of the Lotka–Volterra model. While Weisberg discusses only Volterra’s work, we also study Lotka’s very different design of the Lotka–Volterra model. We will argue that while there are elements of indirect representation in both Volterra’s and Lotka’s modelling approaches, they are largely due to two (...)
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  • Moving parts: the natural alliance between dynamical and mechanistic modeling approaches.David Michael Kaplan - 2015 - Biology and Philosophy 30 (6):757-786.
    Recently, it has been provocatively claimed that dynamical modeling approaches signal the emergence of a new explanatory framework distinct from that of mechanistic explanation. This paper rejects this proposal and argues that dynamical explanations are fully compatible with, even naturally construed as, instances of mechanistic explanations. Specifically, it is argued that the mathematical framework of dynamics provides a powerful descriptive scheme for revealing temporal features of activities in mechanisms and plays an explanatory role to the extent it is deployed for (...)
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  • “You've Got to Work on This Axon”: J. Z. Young and Squid Giant Axon Preparations in 20th‐Century Neurobiology.Kathryn Maxson Jones - 2022 - Berichte Zur Wissenschaftsgeschichte 45 (3):317-331.
    Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 317-331, September 2022.
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  • “You've Got to Work on This Axon”: J. Z. Young and Squid Giant Axon Preparations in 20th‐Century Neurobiology.Kathryn Maxson Jones - 2022 - Berichte Zur Wissenschaftsgeschichte 45 (3):317-331.
    Berichte zur Wissenschaftsgeschichte, Volume 45, Issue 3, Page 317-331, September 2022.
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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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  • Design principles and mechanistic explanation.Wei Fang - 2022 - History and Philosophy of the Life Sciences 44 (4):1-23.
    In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all (...)
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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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  • Models, Mechanisms, and Coherence.Matteo Colombo, Stephan Hartmann & Robert van Iersel - 2015 - British Journal for the Philosophy of Science 66 (1):181-212.
    Life-science phenomena are often explained by specifying the mechanisms that bring them about. The new mechanistic philosophers have done much to substantiate this claim and to provide us with a better understanding of what mechanisms are and how they explain. Although there is disagreement among current mechanists on various issues, they share a common core position and a seeming commitment to some form of scientific realism. But is such a commitment necessary? Is it the best way to go about mechanistic (...)
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  • Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience.M. Chirimuuta - 2014 - Synthese 191 (2):127-153.
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...)
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  • Explanation in Computational Neuroscience: Causal and Non-causal.M. Chirimuuta - 2018 - British Journal for the Philosophy of Science 69 (3):849-880.
    This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...)
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  • Mechanisms and the problem of abstract models.Natalia Carrillo & Tarja Knuuttila - 2023 - European Journal for Philosophy of Science 13 (3):1-19.
    New mechanical philosophy posits that explanations in the life sciences involve the decomposition of a system into its entities and their respective activities and organization that are responsible for the explanandum phenomenon. This mechanistic account of explanation has proven problematic in its application to mathematical models, leading the mechanists to suggest different ways of aligning abstract models with the mechanist program. Initially, the discussion centered on whether the Hodgkin-Huxley model is explanatory. Network models provided another complication, as they apply to (...)
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  • Getting over Atomism: Functional Decomposition in Complex Neural Systems.Daniel C. Burnston - 2021 - British Journal for the Philosophy of Science 72 (3):743-772.
    Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of decomposition, (...)
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  • Systems biology and the integration of mechanistic explanation and mathematical explanation.Ingo Brigandt - 2013 - Studies in History and Philosophy of Biological and Biomedical Sciences 44 (4):477-492.
    The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical (...)
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  • The cognitive neuroscience revolution.Worth Boone & Gualtiero Piccinini - 2016 - Synthese 193 (5):1509-1534.
    We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...)
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  • Mechanistic Abstraction.Worth Boone & Gualtiero Piccinini - 2016 - Philosophy of Science 83 (5):686-697.
    We provide an explicit taxonomy of legitimate kinds of abstraction within constitutive explanation. We argue that abstraction is an inherent aspect of adequate mechanistic explanation. Mechanistic explanations—even ideally complete ones—typically involve many kinds of abstraction and therefore do not require maximal detail. Some kinds of abstraction play the ontic role of identifying the specific complex components, subsets of causal powers, and organizational relations that produce a suitably general phenomenon. Therefore, abstract constitutive explanations are both legitimate and mechanistic.
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  • Mechanism, autonomy and biological explanation.Leonardo Bich & William Bechtel - 2021 - Biology and Philosophy 36 (6):1-27.
    The new mechanists and the autonomy approach both aim to account for how biological phenomena are explained. One identifies appeals to how components of a mechanism are organized so that their activities produce a phenomenon. The other directs attention towards the whole organism and focuses on how it achieves self-maintenance. This paper discusses challenges each confronts and how each could benefit from collaboration with the other: the new mechanistic framework can gain by taking into account what happens outside individual mechanisms, (...)
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  • Dosis sola facit venenum: reconceptualising biological realism.Majid D. Beni - 2022 - Biology and Philosophy 37 (6):1-18.
    Richard Levins’s (Am Sci 54(4):421–431, 1966) paper sets a landmark for the significance of scientific model-making in biology. Colombo and Palacios (Biol Philos 36(5):1–26. 10.1007/s10539-021-09818-x, 2021) have recently built their critique of the explanatory power of the Free Energy Principle on Levins’s insight into the relationship between generality, realism, and precision. This paper addresses the issue of the plausibility of biological explanations that are grounded in the Free Energy Principle (FEP) and deals with the question of the realist fortitude of (...)
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  • Explaining features of fine-grained phenomena using abstract analyses of phenomena and mechanisms: two examples from chronobiology.William Bechtel - 2017 - Synthese 198 (Suppl 24):1-23.
    Explanations of biological phenomena such as cell division, protein synthesis or circadian rhythms commonly take the form of models of the responsible mechanisms. Recently philosophers of science have attempted to analyze this practice, presenting mechanisms as organized collections of parts performing operations that together produce the phenomenon. But in some cases what researchers seek to explain is not a general phenomenon, but a specific feature of a more fine-grained phenomenon. In some of these cases, it is not the model of (...)
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  • Abduction and Composition.Ken Aizawa & Drew B. Headley - 2022 - Philosophy of Science 89 (2):268-82.
    Some New Mechanists have proposed that claims of compositional relations are justified by combining the results of top-down and bottom-up interlevel interventions. But what do scientists do when they can perform, say, a cellular intervention, but not a subcellular detection? In such cases, paired interlevel interventions are unavailable. We propose that scientists use abduction and we illustrate its use through a case study of the ionic theory of resting and action potentials.
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  • Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences.P.-A. Braillard and C. Malaterre (ed.) - 2015 - Springer.
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  • Evolutionary Developmental Biology and the Limits of Philosophical Accounts of Mechanistic Explanation.Ingo Brigandt - 2015 - In P.-A. Braillard & C. Malaterre (eds.), Explanation in Biology: An Enquiry into the Diversity of Explanatory Patterns in the Life Sciences. Springer. pp. 135-173.
    Evolutionary developmental biology (evo-devo) is considered a ‘mechanistic science,’ in that it causally explains morphological evolution in terms of changes in developmental mechanisms. Evo-devo is also an interdisciplinary and integrative approach, as its explanations use contributions from many fields and pertain to different levels of organismal organization. Philosophical accounts of mechanistic explanation are currently highly prominent, and have been particularly able to capture the integrative nature of multifield and multilevel explanations. However, I argue that evo-devo demonstrates the need for a (...)
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  • Integrating Philosophy of Understanding with the Cognitive Sciences.Kareem Khalifa, Farhan Islam, J. P. Gamboa, Daniel Wilkenfeld & Daniel Kostić - 2022 - Frontiers in Systems Neuroscience 16.
    We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.
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  • Connectomes as constitutively epistemic objects: critical perspectives on modeling in current neuroanatomy.Philipp Haueis & Jan Slaby - 2017 - In Progress in Brain Research Vol 233: The Making and Use of Animal Models in Neuroscience and Psychiatry. Amsterdam: pp. 149–177.
    in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams (...)
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  • An Artifactual Perspective on Idealization: Constant Capacitance and the Hodgkin and Huxley Model.Natalia Carrillo & Tarja Knuuttila - 2021 - In Alejandro Cassini & Juan Redmond (eds.), Models and Idealizations in Science: Fictional and Artefactual Approaches. Cham: Springer.
    There are two traditions of thinking about idealization offering almost opposite views on their functioning and epistemic status. While one tradition views idealizations as epistemic deficiencies, the other one highlights the epistemic benefits of idealization. Both of these, however, identify idealization with misrepresentation. In this article, we instead approach idealization from the artifactual perspective, comparing it to the distortion-to-reality accounts of idealization, and exemplifying it through the case of the Hodgkin and Huxley model of nerve impulse. From the artifactual perspective, (...)
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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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