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  1. Review of Physical Computation: A Mechanistic Account by Gualtiero Piccinini - Gualtiero Piccinini, Physical Computation: A Mechanistic Account. Oxford: Oxford University Press (2015), 313 Pp., $65.00 (Cloth). [REVIEW]Oron Shagrir - 2017 - Philosophy of Science 84 (3):604-612.
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  • Unifying the Debates: Mathematical and Non-Causal Explanations.Daniel Kostić - 2019 - Perspectives on Science 27 (1):1-6.
    In the last couple of years a few seemingly independent debates on scientific explanation have emerged, with several key questions that take different forms in different areas. For example, the questions what makes an explanation distinctly mathematical and are there any non-causal explanations in sciences sometimes take a form of the question what makes mathematical models explanatory, especially whether highly idealized models in science can be explanatory and in virtue of what they are explanatory. These questions raise further issues about (...)
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  • The Topological Realization.Daniel Kostić - 2018 - Synthese (1).
    In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the (...)
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  • Minimal Structure Explanations, Scientific Understanding and Explanatory Depth.Daniel Kostić - 2018 - Perspectives on Science (1):48-67.
    In this paper, I outline a heuristic for thinking about the relation between explanation and understanding that can be used to capture various levels of “intimacy”, between them. I argue that the level of complexity in the structure of explanation is inversely proportional to the level of intimacy between explanation and understanding, i.e. the more complexity the less intimacy. I further argue that the level of complexity in the structure of explanation also affects the explanatory depth in a similar way (...)
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  • Decoupling Topological Explanations From Mechanisms.Daniel Kostic & Kareem Khalifa - forthcoming - Philosophy of Science:1-39.
    We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. (...)
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  • Outlines of a Theory of Structural Explanations.Philippe Huneman - 2018 - Philosophical Studies 175 (3):665-702.
    This paper argues that in some explanations mathematics are playing an explanatory rather than a representational role, and that this feature unifies many types of non-causal or non-mechanistic explanations that some philosophers of science have been recently exploring under various names. After showing how mathematics can play either a representational or an explanatory role by considering two alternative explanations of a same biological pattern—“Bergmann’s rule”—I offer an example of an explanation where the bulk of the explanatory job is done by (...)
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  • Cognition Without Neural Representation: Dynamics of a Complex System.Inês Hipólito - 2022 - Frontiers in Psychology 12.
    This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience must literally exist the system being modelled. Computational models are important tools to test a theory about how the collected data has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in (...)
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  • Taming vagueness: the philosophy of network science.Gábor Elek & Eszter Babarczy - 2022 - Synthese 200 (2):1-31.
    In the last 20 years network science has become an independent scientific field. We argue that by building network models network scientists are able to tame the vagueness of propositions about complex systems and networks, that is, to make these propositions precise. This makes it possible to study important vague properties such as modularity, near-decomposability, scale-freeness or being a small world. Using an epistemic model of network science, we systematically analyse the specific nature of network models and the logic behind (...)
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  • Integrating Computation Into the Mechanistic Hierarchy in the Cognitive and Neural Sciences.Lotem Elber-Dorozko & Oron Shagrir - 2019 - Synthese 199 (Suppl 1):43-66.
    It is generally accepted that, in the cognitive and neural sciences, there are both computational and mechanistic explanations. We ask how computational explanations can integrate into the mechanistic hierarchy. The problem stems from the fact that implementation and mechanistic relations have different forms. The implementation relation, from the states of an abstract computational system to the physical, implementing states is a homomorphism mapping relation. The mechanistic relation, however, is that of part/whole; the explaining features in a mechanistic explanation are the (...)
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  • Explaining the Behaviour of Random Ecological Networks: The Stability of the Microbiome as a Case of Integrative Pluralism.Roger Deulofeu, Javier Suárez & Alberto Pérez-Cervera - 2019 - Synthese 198 (3):2003-2025.
    Explaining the behaviour of ecosystems is one of the key challenges for the biological sciences. Since 2000, new-mechanicism has been the main model to account for the nature of scientific explanation in biology. The universality of the new-mechanist view in biology has been however put into question due to the existence of explanations that account for some biological phenomena in terms of their mathematical properties (mathematical explanations). Supporters of mathematical explanation have argued that the explanation of the behaviour of ecosystems (...)
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  • The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors.Nina S. de Boer, Leon C. de Bruin, Jeroen J. G. Geurts & Gerrit Glas - 2021 - Frontiers in Psychology 12.
    Borsboom and colleagues have recently proposed a “network theory” of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an “extended” network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes (...)
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  • The Explanatory Power of Network Models.Carl F. Craver - 2016 - Philosophy of Science 83 (5):698-709.
    Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.
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  • Discovering Brain Mechanisms Using Network Analysis and Causal Modeling.Matteo Colombo & Naftali Weinberger - 2018 - Minds and Machines 28 (2):265-286.
    Mechanist philosophers have examined several strategies scientists use for discovering causal mechanisms in neuroscience. Findings about the anatomical organization of the brain play a central role in several such strategies. Little attention has been paid, however, to the use of network analysis and causal modeling techniques for mechanism discovery. In particular, mechanist philosophers have not explored whether and how these strategies incorporate information about the anatomical organization of the brain. This paper clarifies these issues in the light of the distinction (...)
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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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  • When Philosophy (of Science) Meets Formal Methods: A Citation Analysis of Early Approaches Between Research Fields.Guido Bonino, Paolo Maffezioli, Eugenio Petrovich & Paolo Tripodi - 2022 - Synthese 200 (2).
    The article investigates what happens when philosophy meets and begins to establish connections with two formal research methods such as game theory and network science. We use citation analysis to identify, among the articles published in Synthese and Philosophy of Science between 1985 and 2021, those that cite the specialistic literature in game theory and network science. Then, we investigate the structure of the two corpora thus identified by bibliographic coupling and divide them into clusters of related papers by automatic (...)
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  • On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists.Maxwell A. Bertolero & Danielle S. Bassett - 2020 - Topics in Cognitive Science 12 (4):1272-1293.
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  • New Mechanistic Philosophy and the Scientific Prospects of Code Biology.Majid Davoody Beni - 2019 - Biosemiotics 12 (2):197-211.
    Marcello Barbieri has presented code biology as an alternative to the Peircean approach to biosemiotics. Some critics questioned the viability of code biology on grounds that Barbieri’s conception of science is limited. It has been argued that code biology’s mechanistic tendency is the cause of the allegedly limited conception of science. In this paper, I evaluate the scientific viability of the code model from the perspective of scientific realism in the philosophy of science. To be more precise, I draw on (...)
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  • Topological Explanations: An Opinionated Appraisal.Daniel Kostić - forthcoming - In I. Lawler, E. Shech & K. Khalifa (eds.), Scientific Understanding and Representation: Modeling in the Physical Sciences.
    This chapter provides a systematic overview of topological explanations in the philosophy of science literature. It does so by presenting an account of topological explanation that I (Kostić and Khalifa 2021; Kostić 2020a; 2020b; 2018) have developed in other publications and then comparing this account to other accounts of topological explanation. Finally, this appraisal is opinionated because it highlights some problems in alternative accounts of topological explanations, and also it outlines responses to some of the main criticisms raised by the (...)
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  • Models and Mechanisms in Network Neuroscience.Carlos Zednik - 2018 - Philosophical Psychology 32 (1):23-51.
    This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal (...)
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  • Near-Decomposability and the Timescale Relativity of Causal Representations.Naftali Weinberger - 2020 - Philosophy of Science 87 (5):841-856.
    A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has (...)
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  • Network Modularity as a Foundation for Neural Reuse.Matthew L. Stanley, Bryce Gessell & Felipe De Brigard - 2019 - Philosophy of Science 86 (1):23-46.
    The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network (...)
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  • Using Network Models in Person-Centered Care in Psychiatry: How Perspectivism Could Help To Draw Boundaries.Nina de Boer, Daniel Kostić, Marcos Ross, Leon de Bruin & Gerrit Glas - 2022 - Frontiers in Psychiatry, Section Psychopathology 13 (925187).
    In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we can make more explicit (...)
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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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  • General Theory of Topological Explanations and Explanatory Asymmetry.Daniel Kostic - 2020 - Philosophical Transactions of the Royal Society B: Biological Sciences 375 (1796):1-8.
    In this paper, I present a general theory of topological explanations, and illustrate its fruitfulness by showing how it accounts for explanatory asymmetry. My argument is developed in three steps. In the first step, I show what it is for some topological property A to explain some physical or dynamical property B. Based on that, I derive three key criteria of successful topological explanations: a criterion concerning the facticity of topological explanations, i.e. what makes it true of a particular system; (...)
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  • Psychoneural Isomorphism: From Metaphysics to Robustness.Alfredo Vernazzani - 2020 - In Marco Viola & Fabrizio Calzavarini (eds.), Neural Mechanisms: New Challenges in the Philosophy of Neuroscience. Springer.
    At the beginning of the 20th century, Gestalt psychologists put forward the concept of psychoneural isomorphism, which was meant to replace Fechner’s obscure notion of psychophysical parallelism and provide a heuristics that may facilitate the search for the neural correlates of the mind. However, the concept has generated much confusion in the debate, and today its role is still unclear. In this contribution, I will attempt a little conceptual spadework in clarifying the concept of psychoneural isomorphism, focusing exclusively on conscious (...)
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