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  1. 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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  • Deep Learning: A Philosophical Introduction.Cameron Buckner - forthcoming - Philosophy Compass.
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  • The Directionality of Distinctively Mathematical Explanations.Carl F. Craver & Mark Povich - 2017 - Studies in History and Philosophy of Science Part A 63:31-38.
    In “What Makes a Scientific Explanation Distinctively Mathematical?” (2013b), Lange uses several compelling examples to argue that certain explanations for natural phenomena appeal primarily to mathematical, rather than natural, facts. In such explanations, the core explanatory facts are modally stronger than facts about causation, regularity, and other natural relations. We show that Lange's account of distinctively mathematical explanation is flawed in that it fails to account for the implicit directionality in each of his examples. This inadequacy is remediable in each (...)
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  • The Search of “Canonical” Explanations for the Cerebral Cortex.Alessio Plebe - 2018 - History and Philosophy of the Life Sciences 40 (3):40.
    This paper addresses a fundamental line of research in neuroscience: the identification of a putative neural processing core of the cerebral cortex, often claimed to be “canonical”. This “canonical” core would be shared by the entire cortex, and would explain why it is so powerful and diversified in tasks and functions, yet so uniform in architecture. The purpose of this paper is to analyze the search for canonical explanations over the past 40 years, discussing the theoretical frameworks informing this research. (...)
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  • First Principles in the Life Sciences: The Free-Energy Principle, Organicism, and Mechanism.Matteo Colombo & Cory Wright - forthcoming - Synthese:1-26.
    The free-energy principle claims that biological systems behave adaptively maintaining their physical integrity only if they minimize the free energy of their sensory states. Originally proposed to account for perception, learning, and action, the free-energy principle has been applied to the evolution, development, morphology, and function of the brain, and has been called a “postulate,” a “mandatory principle,” and an “imperative.” While it might afford a theoretical foundation for understanding the complex relationship between physical environment, life, and mind, its epistemic (...)
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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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  • Wiring Optimization Explanation in Neuroscience: What is Special About It?Sergio Daniel Barberis - 2019 - Theoria : An International Journal for Theory, History and Fundations of Science 1 (34):89-110.
    This paper examines the explanatory distinctness of wiring optimization models in neuroscience. Wiring optimization models aim to represent the organizational features of neural and brain systems as optimal (or near-optimal) solutions to wiring optimization problems. My claim is that that wiring optimization models provide design explanations. In particular, they support ideal interventions on the decision variables of the relevant design problem and assess the impact of such interventions on the viability of the target system.
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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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  • Manipulation is Key: On Why Non-Mechanistic Explanations in the Cognitive Sciences Also Describe Relations of Manipulation and Control.Lotem Elber-Dorozko - 2018 - Synthese 195 (12):5319-5337.
    A popular view presents explanations in the cognitive sciences as causal or mechanistic and argues that an important feature of such explanations is that they allow us to manipulate and control the explanandum phenomena. Nonetheless, whether there can be explanations in the cognitive sciences that are neither causal nor mechanistic is still under debate. Another prominent view suggests that both causal and non-causal relations of counterfactual dependence can be explanatory, but this view is open to the criticism that it is (...)
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  • An Efficient Coding Approach to the Debate on Grounded Cognition.Abel Wajnerman Paz - 2018 - Synthese 195 (12):5245-5269.
    The debate between the amodal and the grounded views of cognition seems to be stuck. Their only substantial disagreement is about the vehicle or format of concepts. Amodal theorists reject the grounded claim that concepts are couched in the same modality-specific format as representations in sensory systems. The problem is that there is no clear characterization of format or its neural correlate. In order to make the disagreement empirically meaningful and move forward in the discussion we need a neurocognitive criterion (...)
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  • A Mechanistic Perspective on Canonical Neural Computation.Abel Wajnerman Paz - 2017 - Philosophical Psychology 30 (3):209-230.
    Although it has been argued that mechanistic explanation is compatible with abstraction, there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and (...)
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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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