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  1. Philosophy of Developmental Biology.Marcel Weber - 2022 - Cambridge: Cambridge University Press.
    The history of developmental biology is interwoven with debates as to whether mechanistic explanations of development are possible or whether alternative explanatory principles or even vital forces need to be assumed. In particular, the demonstrated ability of embryonic cells to tune their developmental fate precisely to their relative position and the overall size of the embryo was once thought to be inexplicable in mechanistic terms. Taking a causal perspective, this Element examines to what extent and how developmental biology, having turned (...)
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  • Should explanation be a guide to ground?Alexander Skiles & Kelly Trogdon - 2021 - Philosophical Studies 178 (12):4083-4098.
    Grounding and explanation are said to be intimately connected. Some even maintain that grounding just is a form of explanation. But grounding and explanation also seem importantly different—on the face of it, the former is ‘worldy’ or ‘objective’ while the latter isn’t. In this paper, we develop and respond to an argument to the effect that there is no way to fruitfully address this tension that retains orthodox views about grounding and explanation but doesn’t undermine a central piece of methodology, (...)
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  • Grounding-Mechanical Explanation.Kelly Trogdon - 2018 - Philosophical Studies 175 (6):1289-1309.
    Characterization of a form of explanation involving grounding on the model of mechanistic causal explanation.
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  • Mapping the Continuum of Research Strategies.Matthew Baxendale - 2019 - Synthese 196 (11):4711-4733.
    Contemporary philosophy of science has seen a growing trend towards a focus on scientific practice over the epistemic outputs that such practices produce. This practice-oriented approach has yielded a clearer understanding of how reductive research strategies play a central role in contemporary scientific inquiry. In parallel, a growing body of work has sought to explore the role of non-reductive, or systems-level, research strategies. As a result, the relationship between reductive and non-reductive scientific practices is becoming of increased importance. In this (...)
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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.
  • Breaking Explanatory Boundaries: Flexible Borders and Plastic Minds.Michael David Kirchhoff & Russell Meyer - forthcoming - Phenomenology and the Cognitive Sciences:1-20.
    In this paper, we offer reasons to justify the explanatory credentials of dynamical modeling in the context of the metaplasticity thesis, located within a larger grouping of views known as 4E Cognition. Our focus is on showing that dynamicism is consistent with interventionism, and therefore with a difference-making account at the scale of system topologies that makes sui generis explanatory differences to the overall behavior of a cognitive system. In so doing, we provide a general overview of the interventionist approach. (...)
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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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  • Functional Analysis and Mechanistic Explanation.David Barrett - 2014 - Synthese 191 (12):2695-2714.
    Piccinini and Craver (Synthese 183:283–311, 2011) argue for the surprising view that psychological explanation, properly understood, is a species of mechanistic explanation. This contrasts with the ‘received view’ (due, primarily, to Cummins and Fodor) which maintains a sharp distinction between psychological explanation and mechanistic explanation. The former is typically construed as functional analysis, the analysis of some psychological capacity into an organized series of subcapacities without specifying any of the structural features that underlie the explanandum capacity. The latter idea, of (...)
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  • Constructing a Philosophy of Science of Cognitive Science.William Bechtel - 2009 - Topics in Cognitive Science 1 (3):548-569.
    Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...)
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  • Abstract Concepts Require Concrete Models: Why Cognitive Scientists Have Not Yet Embraced Nonlinearly Coupled, Dynamical, Self-Organized Critical, Synergistic, Scale-Free, Exquisitely Context-Sensitive, Interaction-Dominant, Multifractal, Interdependent Brain-Body-Niche Systems.Eric-Jan Wagenmakers, Han L. J. van der Maas & Simon Farrell - 2012 - Topics in Cognitive Science 4 (1):87-93.
    After more than 15 years of study, the 1/f noise or complex-systems approach to cognitive science has delivered promises of progress, colorful verbiage, and statistical analyses of phenomena whose relevance for cognition remains unclear. What the complex-systems approach has arguably failed to deliver are concrete insights about how people perceive, think, decide, and act. Without formal models that implement the proposed abstract concepts, the complex-systems approach to cognitive science runs the danger of becoming a philosophical exercise in futility. The complex-systems (...)
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  • The Explanatory Role of Computation in Cognitive Science.Nir Fresco - 2012 - Minds and Machines 22 (4):353-380.
    Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...)
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  • Toward Mechanism 2.1: A Dynamic Causal Approach.Wei Fang - 2021 - Philosophy of Science 88 (5):796-809.
    I propose a dynamic causal approach to characterizing the notion of a mechanism. Levy and Bechtel, among others, have pointed out several critical limitations of the new mechanical philosophy, and pointed in a new direction to extend this philosophy. Nevertheless, they have not fully fleshed out what that extended philosophy would look like. Based on a closer look at neuroscientific practice, I propose that a mechanism is a dynamic causal system that involves various components interacting, typically nonlinearly, with one another (...)
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  • Synthetic Modeling and Mechanistic Account: Material Recombination and Beyond.Tarja Knuuttila & Andrea Loettgers - 2013 - Philosophy of Science 80 (5):874-885.
    Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic modeling as (...)
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  • Constraints on Localization and Decomposition as Explanatory Strategies in the Biological Sciences.Michael Silberstein & Anthony Chemero - 2013 - Philosophy of Science 80 (5):958-970.
    Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic explanation (...)
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  • Dynamical Systems Theory in Cognitive Science and Neuroscience.Luis H. Favela - 2020 - Philosophy Compass 15 (8).
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  • The Multiplicity of Explanation in Cognitive Science.Raoul Gervais - 2021 - Foundations of Science 26 (4):1089–1104.
    In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to achieve the latter.
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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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  • The Dynamical Renaissance in Neuroscience.Luis H. Favela - 2020 - Synthese 199 (1-2):2103-2127.
    Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...)
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  • Introduction: Philosophy in and Philosophy of Cognitive Science.Andrew Brook - 2009 - Topics in Cognitive Science 1 (2):216-230.
    Despite being there from the beginning, philosophical approaches have never had a settled place in cognitive research and few cognitive researchers not trained in philosophy have a clear sense of what its role has been or should be. We distinguish philosophy in cognitive research and philosophy of cognitive research. Concerning philosophy in cognitive research, after exploring some standard reactions to this work by nonphilosophers, we will pay particular attention to the methods that philosophers use. Being neither experimental nor computational, they (...)
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  • From Symbols to Icons: The Return of Resemblance in the Cognitive Neuroscience Revolution.Daniel Williams & Lincoln Colling - 2018 - Synthese 195 (5):1941-1967.
    We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...)
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  • A Cautionary Contribution to the Philosophy of Explanation in the Cognitive Neurosciences.A. Venturelli - 2016 - Minds and Machines 26 (3):259-285.
    I propose a cautionary assessment of the recent debate concerning the impact of the dynamical approach on philosophical accounts of scientific explanation in the cognitive sciences and, particularly, the cognitive neurosciences. I criticize the dominant mechanistic philosophy of explanation, pointing out a number of its negative consequences: In particular, that it doesn’t do justice to the field’s diversity and stage of development, and that it fosters misguided interpretations of dynamical models’ contribution. In order to support these arguments, I analyze a (...)
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  • Embodied Decisions and the Predictive Brain.Christopher Burr - 2016 - Dissertation, University of Bristol
    Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...)
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  • A Properly Embodied Self Within a Naturalistic, Bottom-Up and Systemic-Relational Framework.Tiziana Vistarini Massimo Marraffa - 2019 - Humana Mente 12 (36).
    In this article a neo-Jamesian approach to the self is developed within a naturalistic, bottom-up, and systemic-relational framework. In this approach, consciousness of the body as one’s own body is a necessary precondition of self-consciousness as psychological self-awareness, and hence of a socially and historically situated narrative self. Thus we take on board the criticism of those accounts of the narrative self that pay little attention to embodiment, or go to the extreme of stating that the narrative self is abstract (...)
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  • A Verisimilitudinarian Analysis of the Linda Paradox.Gustavo Cevolani, Vincenzo Crupi & Roberto Festa - 2012 - VII Conference of the Spanish Society for Logic, Methodology and Philosphy of Science.
    The Linda paradox is a key topic in current debates on the rationality of human reasoning and its limitations. We present a novel analysis of this paradox, based on the notion of verisimilitude as studied in the philosophy of science. The comparison with an alternative analysis based on probabilistic confirmation suggests how to overcome some problems of our account by introducing an adequately defined notion of verisimilitudinarian confirmation.
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  • From Wide Cognition to Mechanisms: A Silent Revolution.Marcin Miłkowski, Robert Clowes, Zuzanna Rucińska, Aleksandra Przegalińska, Tadeusz Zawidzki, Joel Krueger, Adam Gies, Marek McGann, Łukasz Afeltowicz, Witold Wachowski, Fredrik Stjernberg, Victor Loughlin & Mateusz Hohol - 2018 - Frontiers in Psychology 9.
    In this paper, we argue that several recent ‘wide’ perspectives on cognition (embodied, embedded, extended, enactive, and distributed) are only partially relevant to the study of cognition. While these wide accounts override traditional methodological individualism, the study of cognition has already progressed beyond these proposed perspectives towards building integrated explanations of the mechanisms involved, including not only internal submechanisms but also interactions with others, groups, cognitive artifacts, and their environment. The claim is substantiated with reference to recent developments in the (...)
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  • The Nonmechanistic Option: Defending Dynamical Explanation.Russell Meyer - 2018 - British Journal for the Philosophy of Science:0-0.
    This paper demonstrates that nonmechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat nonmechanistic, dynamical explanation. Using the examples of Haken et al.’s ([1985]) HKB model of bimanual coordination, and Thelen (...)
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  • Functional Kinds: A Skeptical Look.Cameron Buckner - 2015 - Synthese 192 (12):3915-3942.
    The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological approaches as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has (...)
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  • Radical Embodied Cognitive Neuroscience: Addressing “Grand Challenges” of the Mind Sciences.Luis H. Favela - 2014 - Frontiers in Human Neuroscience 8:01-10.
    It is becoming ever more accepted that investigations of mind span the brain, body, and environment. To broaden the scope of what is relevant in such investigations is to increase the amount of data scientists must reckon with. Thus, a major challenge facing scientists who study the mind is how to make big data intelligible both within and between fields. One way to face this challenge is to structure the data within a framework and to make it intelligible by means (...)
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  • Varieties of Difference-Makers: Considerations on Chirimuuta’s Approach to Non-Causal Explanation in Neuroscience.Abel Wajnerman Paz - 2019 - Manuscrito 42 (1):91-119.
    Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal difference-makers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. The problem is (...)
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  • The Structure of Sensorimotor Explanation.Alfredo Vernazzani - 2018 - Synthese (11):4527-4553.
    The sensorimotor theory of vision and visual consciousness is often described as a radical alternative to the computational and connectionist orthodoxy in the study of visual perception. However, it is far from clear whether the theory represents a significant departure from orthodox approaches or whether it is an enrichment of it. In this study, I tackle this issue by focusing on the explanatory structure of the sensorimotor theory. I argue that the standard formulation of the theory subscribes to the same (...)
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  • Sculpting the Space of Actions. Explaining Human Action by Integrating Intentions and Mechanisms.Machiel Keestra - 2014 - Dissertation, University of Amsterdam
    How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...)
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  • Intentional Models as Essential Scientific Tools.Eric Hochstein - 2013 - International Studies in the Philosophy of Science 27 (2):199-217.
    In this article, I argue that the use of scientific models that attribute intentional content to complex systems bears a striking similarity to the way in which statistical descriptions are used. To demonstrate this, I compare and contrast an intentional model with a statistical model, and argue that key similarities between the two give us compelling reasons to consider both as a type of phenomenological model. I then demonstrate how intentional descriptions play an important role in scientific methodology as a (...)
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  • Recent Work in The Philosophy of Biology.Christopher J. Austin - 2017 - Analysis 77 (2):anx032.
    The biological sciences have always proven a fertile ground for philosophical analysis, one from which has grown a rich tradition stemming from Aristotle and flowering with Darwin. And although contemporary philosophy is increasingly becoming conceptually entwined with the study of the empirical sciences with the data of the latter now being regularly utilised in the establishment and defence of the frameworks of the former, a practice especially prominent in the philosophy of physics, the development of that tradition hasn’t received the (...)
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  • Extended Mechanistic Explanations: Expanding the Current Mechanistic Conception to Include More Complex Biological Systems.Sarah M. Roe & Bert Baumgaertner - 2017 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 48 (4):517-534.
    Mechanistic accounts of explanation have recently found popularity within philosophy of science. Presently, we introduce the idea of an extended mechanistic explanation, which makes explicit room for the role of environment in explanation. After delineating Craver and Bechtel’s account, we argue this suggestion is not sufficiently robust when we take seriously the mechanistic environment and modeling practices involved in studying contemporary complex biological systems. Our goal is to extend the already profitable mechanistic picture by pointing out the importance of the (...)
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  • The Role of Supervenience and Constitution in Neuroscientific Research.Jens Harbecke - 2014 - Synthese 191 (5):1-19.
    This paper is concerned with the notions of supervenience and mechanistic constitution as they have been discussed in the philosophy of neuroscience. Since both notions essentially involve specific dependence and determination relations among properties and sets of properties, the question arises whether the notions are systematically connected and how they connect to science. In a first step, some definitions of supervenience and mechanistic constitution are presented and tested for logical independence. Afterwards, certain assumptions fundamental to neuroscientific inquiry are made explicit (...)
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  • Investigating Neural Representations: The Tale of Place Cells.William Bechtel - 2016 - Synthese 193 (5):1287-1321.
    While neuroscientists often characterize brain activity as representational, many philosophers have construed these accounts as just theorists’ glosses on the mechanism. Moreover, philosophical discussions commonly focus on finished accounts of explanation, not research in progress. I adopt a different perspective, considering how characterizations of neural activity as representational contributes to the development of mechanistic accounts, guiding the investigations neuroscientists pursue as they work from an initial proposal to a more detailed understanding of a mechanism. I develop one illustrative example involving (...)
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  • Statistical Inference and the Replication Crisis.Lincoln J. Colling & Dénes Szűcs - 2021 - Review of Philosophy and Psychology 12 (1):121-147.
    The replication crisis has prompted many to call for statistical reform within the psychological sciences. Here we examine issues within Frequentist statistics that may have led to the replication crisis, and we examine the alternative—Bayesian statistics—that many have suggested as a replacement. The Frequentist approach and the Bayesian approach offer radically different perspectives on evidence and inference with the Frequentist approach prioritising error control and the Bayesian approach offering a formal method for quantifying the relative strength of evidence for hypotheses. (...)
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  • Towards to An Explanation for Conceptual Change: A Mechanistic Alternative.Anna-Mari Rusanen - 2014 - Science & Education 23 (7):1413-1425.
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  • Breaking Explanatory Boundaries: Flexible Borders and Plastic Minds.Michael D. Kirchhoff & Russell Meyer - 2019 - Phenomenology and the Cognitive Sciences 18 (1):185-204.
    In this paper, we offer reasons to justify the explanatory credentials of dynamical modeling in the context of the metaplasticity thesis, located within a larger grouping of views known as 4E Cognition. Our focus is on showing that dynamicism is consistent with interventionism, and therefore with a difference-making account at the scale of system topologies that makes sui generis explanatory differences to the overall behavior of a cognitive system. In so doing, we provide a general overview of the interventionist approach. (...)
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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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  • New Mechanistic Explanation and the Need for Explanatory Constraints.L. R. Franklin-Hall - 2016 - In Ken Aizawa & Carl Gillett (eds.), Scientific Composition and Metaphysical Ground. Palgrave. pp. 41-74.
    This paper critiques the new mechanistic explanatory program on grounds that, even when applied to the kinds of examples that it was originally designed to treat, it does not distinguish correct explanations from those that blunder. First, I offer a systematization of the explanatory account, one according to which explanations are mechanistic models that satisfy three desiderata: they must 1) represent causal relations, 2) describe the proper parts, and 3) depict the system at the right ‘level.’ Second, I argue that (...)
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  • A Prototypical Conceptualization of Mechanisms.Bryon Cunningham - 2021 - Studies in History and Philosophy of Science Part A 85:79-91.
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  • The Non-Mechanistic Option: Defending Dynamical Explanations.Russell Meyer - 2020 - British Journal for the Philosophy of Science 71 (3):959-985.
    This article demonstrates that non-mechanistic, dynamical explanations are a viable approach to explanation in the special sciences. The claim that dynamical models can be explanatory without reference to mechanisms has previously been met with three lines of criticism from mechanists: the causal relevance concern, the genuine laws concern, and the charge of predictivism. I argue, however, that these mechanist criticisms fail to defeat non-mechanistic, dynamical explanation. Using the examples of Haken et al.’s model of bimanual coordination, and Thelen et al.’s (...)
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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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  • Mechanistic Explanation in Engineering Science.Dingmar van Eck - 2015 - European Journal for Philosophy of Science 5 (3):349-375.
    In this paper I apply the mechanistic account of explanation to engineering science. I discuss two ways in which this extension offers further development of the mechanistic view. First, functional individuation of mechanisms in engineering science proceeds by means of two distinct sub types of role function, behavior function and effect function, rather than role function simpliciter. Second, it offers refined assessment of the explanatory power of mechanistic explanations. It is argued that in the context of malfunction explanations of technical (...)
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  • Mechanistic and Non-Mechanistic Varieties of Dynamical Models in Cognitive Science: Explanatory Power, Understanding, and the ‘Mere Description’ Worry.Raoul Gervais - 2015 - Synthese 192 (1):43-66.
    In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents (...)
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  • Entrainment and Motor Emulation Approaches to Joint Action: Alternatives or Complementary Approaches?Lincoln J. Colling & Kellie Williamson - 2014 - Frontiers in Human Neuroscience 8.
  • A Philosophical Perspective on Evolutionary Systems Biology.Maureen A. O’Malley, Orkun S. Soyer & Mark L. Siegal - 2015 - Biological Theory 10 (1):6-17.
    Evolutionary systems biology is an emerging hybrid approach that integrates methods, models, and data from evolutionary and systems biology. Drawing on themes that arose at a cross-disciplinary meeting on ESB in 2013, we discuss in detail some of the explanatory friction that arises in the interaction between evolutionary and systems biology. These tensions appear because of different modeling approaches, diverse explanatory aims and strategies, and divergent views about the scope of the evolutionary synthesis. We locate these discussions in the context (...)
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  • Rethinking the Explanatory Power of Dynamical Models in Cognitive Science.Dingmar van Eck - 2018 - Philosophical Psychology 31 (8):1131-1161.
    ABSTRACTIn this paper I offer an interventionist perspective on the explanatory structure and explanatory power of dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical (...)
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