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  1. The Content of Marr’s Information-Processing Framework.J. Brendan Ritchie - 2019 - Philosophical Psychology 32 (7):1078-1099.
    ABSTRACTThe seminal work of David Marr, popularized in his classic work Vision, continues to exert a major influence on both cognitive science and philosophy. The interpretation of his work also co...
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  • Commentary: Integrative Modeling and the Role of Neural Constraints.Brice Bantegnie - 2017 - Frontiers in Psychology 8.
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  • Psychology and Neuroscience: Problems of Integration.M. A. Sushchin - 2019 - Russian Journal of Philosophical Sciences 62 (1):89-105.
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  • Explaining Cognitive Phenomena with Internal Representations: A Mechanistic Perspective.Paweł Gładziejewski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):63-90.
    Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. (...)
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  • Achieving Cumulative Progress In Understanding Crime: Some Insights From the Philosophy of Science.Jacqueline Anne Sullivan - forthcoming - Psychology, Crime and Law.
    Crime is a serious social problem, but its causes are not exclusively social. There is growing consensus that explaining and preventing it requires interdisciplinary research efforts. Indeed, the landscape of contemporary criminology includes a variety of theoretical models that incorporate psychological, biological and sociological factors. These multi-disciplinary approaches, however, have yet to radically advance scientific understandings of crime and shed light on how to manage it. In this paper, using conceptual tools on offer in the philosophy of science in combination (...)
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  • Neo-Mechanistic Explanatory Integration for Cognitive Science: The Problem of Reduction Remains.Diego Azevedo Leite - 2019 - Sofia 8 (1):124-145.
    One of the central aims of the neo-mechanistic framework for the neural and cognitive sciences is to construct a pluralistic integration of scientific explanations, allowing for a weak explanatory autonomy of higher-level sciences, such as cognitive science. This integration involves understanding human cognition as information processing occurring in multi-level human neuro-cognitive mechanisms, explained by multi-level neuro-cognitive models. Strong explanatory neuro-cognitive reduction, however, poses a significant challenge to this pluralist ambition and the weak autonomy of cognitive science derived therefrom. Based on (...)
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  • Construct Stabilization and the Unity of the Mind-Brain Sciences.Jacqueline Anne Sullivan - 2016 - Philosophy of Science 83 (5):662-673.
    This paper offers a critique of an account of explanatory integration that claims that explanations of cognitive capacities by functional analyses and mechanistic explanations can be seamlessly integrated. It is shown that achieving such explanatory integration requires that the terms designating cognitive capacities in the two forms of explanation are stable but that experimental practice in the mind-brain sciences currently is not directed at achieving such stability. A positive proposal for changing experimental practice so as to promote such stability is (...)
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  • A Property Cluster Theory of Cognition.Cameron Buckner - 2015 - Philosophical Psychology 28 (3):307-336.
    Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical tests that comparative (...)
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  • Deep Learning: A Philosophical Introduction.Cameron Buckner - forthcoming - Philosophy Compass.
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  • Five Theses on Neuroeconomics.Roberto Fumagalli - 2016 - Journal of Economic Methodology 23 (1):77-96.
    Over the last decade, neuroeconomic research has attracted increasing attention by economic modellers and methodologists. In this paper, I examine five issues about neuroeconomic modelling and methodology that have recently been subject to considerable controversy. For each issue, I explicate and appraise prominent neuroeconomists' findings, focusing on those that are claimed to directly inform economic theorizing. Moreover, I assess often-made assertions concerning how neuroeconomic research putatively advances the economic modelling of choice. In doing so, I combine review and critical arguments (...)
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  • The Functional Sense of Mechanism.Justin Garson - 2013 - Philosophy of Science 80 (3):317-333.
    This article presents a distinct sense of ‘mechanism’, which I call the functional sense of mechanism. According to this sense, mechanisms serve functions, and this fact places substantive restrictions on the kinds of system activities ‘for which’ there can be a mechanism. On this view, there are no mechanisms for pathology; pathologies result from disrupting mechanisms for functions. Second, on this sense, natural selection is probably not a mechanism for evolution because it does not serve a function. After distinguishing this (...)
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  • Psychoneural Reduction: A Perspective From Neural Circuits.David Parker - 2019 - Biology and Philosophy 34 (4):44.
    Psychoneural reduction has been debated extensively in the philosophy of neuroscience. In this article I will evaluate metascientific approaches that claim direct molecular and cellular explanations of cognitive functions. I will initially consider the issues involved in linking cellular properties to behaviour from the general perspective of neural circuits. These circuits that integrate the molecular and cellular components underlying cognition and behaviour, making consideration of circuit properties relevant to reductionist debates. I will then apply this general perspective to specific systems (...)
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  • Model-Based Cognitive Neuroscience: Multifield Mechanistic Integration in Practice.Mark Povich - forthcoming - Theory & Psychology.
    Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function (e.g., Weiskopf, 2011). In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function (e.g., Piccinini & Craver, 2011; Povich, 2015). In this paper, I show how model-based cognitive neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates (...)
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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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  • Is the Next Frontier in Neuroscience a Decade of the Mind?Jacqueline Anne Sullivan - 2014 - In Charles Wolfe (ed.), Brain Theory: Essays in Critical Neurophilosophy. Palgrave MacMillan.
    In 2007, ten world-renowned neuroscientists proposed “A Decade of the Mind Initiative.” The contention was that, despite the successes of the Decade of the Brain, “a fundamental understanding of how the brain gives rise to the mind [was] still lacking” (2007, 1321). The primary aims of the decade of the mind were “to build on the progress of the recent Decade of the Brain (1990-99)” by focusing on “four broad but intertwined areas” of research, including: healing and protecting, understanding, enriching, (...)
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  • Neuroscientific Kinds Through the Lens of Scientific Practice.Jacqueline Anne Sullivan - 2016 - In Catherine Kendig (ed.), Natural Kinds and Classification in Scientific Practice. Routledge. pp. 47-56.
    In this chapter, I argue that scientific practice in the neurosciences of cognition is not conducive to the discovery of natural kinds of cognitive capacities. The “neurosciences of cognition” include cognitive neuroscience and cognitive neurobiology, two research areas that aim to understand how the brain gives rise to cognition and behavior. Some philosophers of neuroscience have claimed that explanatory progress in these research areas ultimately will result in the discovery of the underlying mechanisms of cognitive capacities. Once such mechanistic understanding (...)
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  • Function, Selection, and Construction in the Brain.Justin Garson - 2012 - Synthese 189 (3):451-481.
    A common misunderstanding of the selected effects theory of function is that natural selection operating over an evolutionary time scale is the only functionbestowing process in the natural world. This construal of the selected effects theory conflicts with the existence and ubiquity of neurobiological functions that are evolutionary novel, such as structures underlying reading ability. This conflict has suggested to some that, while the selected effects theory may be relevant to some areas of evolutionary biology, its relevance to neuroscience is (...)
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  • Unification Strategies in Cognitive Science.Marcin Miłkowski - 2016 - Studies in Logic, Grammar and Rhetoric 48 (1):13–33.
    Cognitive science is an interdisciplinary conglomerate of various research fields and disciplines, which increases the risk of fragmentation of cognitive theories. However, while most previous work has focused on theoretical integration, some kinds of integration may turn out to be monstrous, or result in superficially lumped and unrelated bodies of knowledge. In this paper, I distinguish theoretical integration from theoretical unification, and propose some analyses of theoretical unification dimensions. Moreover, two research strategies that are supposed to lead to unification are (...)
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  • Long-Term Potentiation: One Kind or Many?Jacqueline Sullivan - 2016 - In Eppur Si Muove: Doing History and Philosophy of Science with Peter Machamer, A Collection of Essays in Honor of Peter Machamer. Springer Verlag. pp. 127-140.
    Do neurobiologists aim to discover natural kinds? I address this question in this chapter via a critical analysis of classification practices operative across the 43-year history of research on long-term potentiation (LTP). I argue that this 43-year history supports the idea that the structure of scientific practice surrounding LTP research has remained an obstacle to the discovery of natural kinds.
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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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  • Neural Correlates Without Reduction: The Case of the Critical Period.Muhammad Ali Khalidi - forthcoming - Synthese:1-13.
    Researchers in the cognitive sciences often seek neural correlates of psychological constructs. In this paper, I argue that even when these correlates are discovered, they do not always lead to reductive outcomes. To this end, I examine the psychological construct of a critical period and briefly describe research identifying its neural correlates. Although the critical period is correlated with certain neural mechanisms, this does not imply that there is a reductionist relationship between this psychological construct and its neural correlates. Instead, (...)
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  • Integrating Cognitive (Neuro)Science Using Mechanisms.Marcin Miłkowski - 2016 - Avant: Trends in Interdisciplinary Studies (2):45-67.
    In this paper, an account of theoretical integration in cognitive (neuro)science from the mechanistic perspective is defended. It is argued that mechanistic patterns of integration can be better understood in terms of constraints on representations of mechanisms, not just on the space of possible mechanisms, as previous accounts of integration had it. This way, integration can be analyzed in more detail with the help of constraintsatisfaction account of coherence between scientific representations. In particular, the account has resources to talk of (...)
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  • A Deflationary Account of Mental Representation (Draft).Frances Egan - manuscript
    Among the cognitive capacities of evolved creatures is the capacity to represent. Theories in cognitive neuroscience typically explain our manifest representational capacities by positing internal representations, but there is little agreement about how these representations function, especially with the relatively recent proliferation of connectionist, dynamical, embodied, and enactive approaches to cognition. In this talk I sketch an account of the nature and function of representation in cognitive neuroscience that couples a realist construal of representational vehicles with a pragmatic account of (...)
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  • Judging Mechanistic Neuroscience: A Preliminary Conceptual-Analytic Framework for Evaluating Scientific Evidence in the Courtroom.Jacqueline Anne Sullivan & Emily Baron - 2018 - Psychology, Crime and Law (00):00-00.
    The use of neuroscientific evidence in criminal trials has been steadily increasing. Despite progress made in recent decades in understanding the mechanisms of psychological and behavioral functioning, neuroscience is still in an early stage of development and its potential for influencing legal decision-making is highly contentious. Scholars disagree about whether or how neuroscientific evidence might impact prescriptions of criminal culpability, particularly in instances in which evidence of an accused’s history of mental illness or brain abnormality is offered to support a (...)
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  • Functional Analyses, Mechanistic Explanations, and Explanatory Tradeoffs.Sergio Daniel Barberis - 2013 - Journal of Cognitive Science 14:229-251.
    Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...)
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  • Making Too Many Enemies: Hutto and Myin’s Attack on Computationalism.Jesse Kuokkanen & Anna-Mari Rusanen - 2018 - Philosophical Explorations 21 (2):282-294.
    We analyse Hutto & Myin's three arguments against computationalism [Hutto, D., E. Myin, A. Peeters, and F. Zahnoun. Forthcoming. “The Cognitive Basis of Computation: Putting Computation In Its Place.” In The Routledge Handbook of the Computational Mind, edited by M. Sprevak, and M. Colombo. London: Routledge.; Hutto, D., and E. Myin. 2012. Radicalizing Enactivism: Basic Minds Without Content. Cambridge, MA: MIT Press; Hutto, D., and E. Myin. 2017. Evolving Enactivism: Basic Minds Meet Content. Cambridge, MA: MIT Press]. The Hard Problem (...)
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  • Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  • Pluralistic Mechanism.Abel Wajnerman Paz - 2017 - Theoria : An International Journal for Theory, History and Fundations of Science 32 (2).
    An argument recently proposed by Chirimuuta seems to motivate the rejection of the claims that every neurocognitive phenomenon can have a mechanistic explanation and that every neurocognitive explanation is mechanistic. In this paper, I focus on efficient coding models involving the so-called “canonical neural computations” and argue that although they imply some form of pluralism, they are compatible with two mechanistic generalizations: all neurocognitive explanations are mechanistic; and all neurocognitive phenomena that have an explanation have a purely mechanistic explanation.
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  • The Philosophy of Computer Science.Raymond Turner - 2013 - Stanford Encyclopedia of Philosophy.
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  • Emotions, Concepts and the Indeterminacy of Natural Kinds.Henry Taylor - forthcoming - Synthese.
    A central question for philosophical psychology is which mental faculties form natural kinds. There is hot debate over the kind status of faculties as diverse as consciousness, seeing, concepts, emotions, constancy and the senses. In this paper, I take emotions and concepts as my main focus, and argue that questions over the kind status of these faculties are complicated by the undeservedly overlooked fact that natural kinds are indeterminate in certain ways. I will show that indeterminacy issues have led to (...)
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  • The Relationship Between Psychological Capacities and Neurobiological Activities.Gregory Johnson - 2012 - European Journal for Philosophy of Science 2 (3):453-480.
    This paper addresses the relationship between psychological capacities, as they are understood within cognitive psychology, and neurobiological activities. First, Lycan’s (1987) account of this relationship is examined and certain problems with his account are explained. According to Lycan, psychological capacities occupy a higher level than neurobiological activities in a hierarchy of levels of nature, and psychological entities can be decomposed into neurobiological entities. After discussing some problems with Lycan’s account, a similar, more recent account built around levels of mechanisms is (...)
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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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  • Evaluating Artificial Models of Cognition.Marcin Miłkowski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...)
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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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  • Mechanism or Bust? Explanation in Psychology.Lawrence A. Shapiro - 2017 - British Journal for the Philosophy of Science 68 (4):1037-1059.
    ABSTRACT Proponents of mechanistic explanation have recently suggested that all explanation in the cognitive sciences is mechanistic, even functional explanation. This last claim is surprising, for functional explanation has traditionally been conceived as autonomous from the structural details that mechanistic explanations emphasize. I argue that functional explanation remains autonomous from mechanistic explanation, but not for reasons commonly associated with the phenomenon of multiple realizability. 1Introduction 2Mechanistic Explanation: A Quick Primer 3Functional Explanation: An Example 4Autonomy as Lack of Constraint 5The Price (...)
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  • Localization and Intrinsic Function.Charles Rathkopf - 2013 - Philosophy of Science 80 (1):1-21.
    This paper describes one style of functional analysis commonly used in the neurosciences called task-bound functional analysis. The concept of function invoked by this style of analysis is distinctive in virtue of the dependence relations it bears to transient environmental properties. It is argued that task-bound functional analysis cannot explain the presence of structural properties in nervous systems. An alternative concept of neural function is introduced that draws on the theoretical neuroscience literature, and an argument is given to show that (...)
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  • A Property Cluster Theory of Cognition.Cameron Buckner - 2013 - Philosophical Psychology (3):1-30.
    Our prominent definitions of cognition are too vague and lack empirical grounding. They have not kept up with recent developments, and cannot bear the weight placed on them across many different debates. I here articulate and defend a more adequate theory. On this theory, behaviors under the control of cognition tend to display a cluster of characteristic properties, a cluster which tends to be absent from behaviors produced by non-cognitive processes. This cluster is reverse-engineered from the empirical tests that comparative (...)
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  • Functional Individuation, Mechanistic Implementation: The Proper Way of Seeing the Mechanistic View of Concrete Computation.Dimitri Coelho Mollo - 2018 - Synthese 195 (8):3477-3497.
    I examine a major objection to the mechanistic view of concrete computation, stemming from an apparent tension between the abstract nature of computational explanation and the tenets of the mechanistic framework: while computational explanation is medium-independent, the mechanistic framework insists on the importance of providing some degree of structural detail about the systems target of the explanation. I show that a common reply to the objection, i.e. that mechanistic explanation of computational systems involves only weak structural constraints, is not enough (...)
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  • Un Análisis Crítico de la Concepción Mecanicista de la Explicación.Sergio Daniel Barberis - 2012 - Revista Latinoamericana de Filosofia 38 (2):233-265.
    En este trabajo me propongo desarrollar un estudio crítico de la concepción mecanicista de la explicación científica. En primer lugar, argumento que la caracterización mecanicista de los modelos fenoménicos (no explicativos) es inadecuada, pues no ofrece un análisis aceptable de los conceptos de modelo científico y similitud, que son fundamentales para la propuesta. En segundo lugar, sostengo que la caracterización de los modelos mecanicistas (explicativos) es igualmente inadecuada, pues los análisis disponibles de la relación explicativa de relevancia constitutiva implican una (...)
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  • Are More Details Better? On the Norms of Completeness for Mechanistic Explanations.Carl F. Craver & David M. Kaplan - forthcoming - British Journal for the Philosophy of Science:axy015.
    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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  • 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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  • 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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  • Fuzziness in the Mind: Can Perception Be Unconscious?Henry Taylor - forthcoming - Philosophy and Phenomenological Research.
    Philosophy and Phenomenological Research, EarlyView.
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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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  • Conceptual Reconstruction and Epistemic Import: Allosteric Mechanistic Explanations as a Unified Theory-Net.Karina Alleva, José Díez & Lucía Federico - 2017 - Crítica. Revista Hispanoamericana de Filosofía 49 (146):5-36.
    The goal of this article is to show that formal analysis and reconstructions may be useful to discuss and shed light on substantive meta-theoretical issues. We proceed here by exemplification, analysing and reconstructing as a case study a paradigmatic biochemical theory, the Monod-Wyman-Changeux theory of allosterism, and applying the reconstruction to the discussion of some issues raised by prominent representatives of the new mechanist philosophy. We conclude that our study shows that at least in this case mechanicism and more traditional (...)
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  • Mechanistic Explanation Without the Ontic Conception.Cory D. Wright - 2012 - European Journal of Philosophy of Science 2 (3):375-394.
    The ontic conception of scientific explanation has been constructed and motivated on the basis of a putative lexical ambiguity in the term explanation. I raise a puzzle for this ambiguity claim, and then give a deflationary solution under which all ontically-rendered talk of explanation is merely elliptical; what it is elliptical for is a view of scientific explanation that altogether avoids the ontic conception. This result has revisionary consequences for New Mechanists and other philosophers of science, many of whom have (...)
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  • Powerful Qualities, Phenomenal Concepts, and the New Challenge to Physicalism.Henry Taylor - 2018 - Australasian Journal of Philosophy 96 (1):53-66.
    Defenders of the phenomenal concept strategy have to explain how both physical and phenomenal concepts provide a substantive grasp on the nature of their referents, whilst referring to the very same experience. This is the ‘new challenge’ to physicalism. In this paper, I argue that if the physicalist adopts the powerful qualities ontology of properties then a new and powerful version of the phenomenal concept strategy can be developed, which answers the new challenge.
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  • Coordinated Pluralism as a Means to Facilitate Integrative Taxonomies of Cognition.Jacqueline Anne Sullivan - 2017 - Philosophical Explorations 20 (2):129-145.
    The past decade has witnessed a growing awareness of conceptual and methodological hurdles within psychology and neuroscience that must be addressed for taxonomic and explanatory progress in understanding psychological functions to be possible. In this paper, I evaluate several recent knowledge-building initiatives aimed at overcoming these obstacles. I argue that while each initiative offers important insights about how to facilitate taxonomic and explanatory progress in psychology and neuroscience, only a “coordinated pluralism” that incorporates positive aspects of each initiative will have (...)
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  • The Journey From Discovery to Scientific Change: Scientific Communities, Shared Models, and Specialised Vocabulary.Sarah M. Roe - 2017 - International Studies in the Philosophy of Science 31 (1):47-67.
    Scientific communities as social groupings and the role that such communities play in scientific change and the production of scientific knowledge is currently under debate. I examine theory change as a complex social interaction among individual scientists and the scientific community, and argue that individuals will be motivated to adopt a more radical or innovative attitude when confronted with striking similarities between model systems and a more robust understanding of specialised vocabulary. Two case studies from the biological sciences, Barbara McClintock (...)
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  • Neural Computation and the Computational Theory of Cognition.Gualtiero Piccinini & Sonya Bahar - 2013 - Cognitive Science 37 (3):453-488.
    We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous (...)
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