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  1. Libertarian Free Will and the Physical Indeterminism Luck Objection.Dwayne Moore - 2021 - Philosophia 50 (1):159-182.
    Libertarian free will is, roughly, the view that agents cause actions to occur or not occur: Maddy’s decision to get a beer causes her to get up off her comfortable couch to get a beer, though she almost chose not to get up. Libertarian free will notoriously faces the luck objection, according to which agential states do not determine whether an action occurs or not, so it is beyond the control of the agent, hence lucky, whether an action occurs or (...)
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  • Embodiment and cognitive neuroscience: the forgotten tales.Vicente Raja - 2022 - Phenomenology and the Cognitive Sciences 21 (3):603-623.
    In this paper, I suggest that some tales (or narratives) developed in the literature of embodied and radical embodied cognitive science can contribute to the solution of two longstanding issues in the cognitive neuroscience of perception and action. The two issues are (i) the fundamental problem of perception, or how to bridge the gap between sensations and the environment, and (ii) the fundamental problem of motor control, or how to better characterize the relationship between brain activity and behavior. In both (...)
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  • Some hallucinations are experiences of the past.Michael Barkasi - 2020 - Pacific Philosophical Quarterly 101 (3):454-488.
    When you hallucinate an object, you are not in the normal sort of concurrent causal sensory interaction with that object. It's standardly further inferred that the hallucinated object does not actually exist. But the lack of normal concurrent causal sensory interaction does not imply that there does not exist an object that is hallucinated. It might be a past‐perceived object. In this paper, I argue that this claim holds for at least some interesting cases of hallucination. Hallucinations generated by misleading (...)
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  • Learning From Success or Failure? – Positivity Biases Revisited.Tsutomu Harada - 2020 - Frontiers in Psychology 11.
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  • The Unbearable Shallow Understanding of Deep Learning.Alessio Plebe & Giorgio Grasso - 2019 - Minds and Machines 29 (4):515-553.
    This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out (...)
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  • Cultural Attachment: From Behavior to Computational Neuroscience.Wei-Jie Yap, Bobby Cheon, Ying-yi Hong & George I. Christopoulos - 2019 - Frontiers in Human Neuroscience 13:451013.
    Cultural attachment (CA) refers to processes that allow culture and its symbols to provide psychological security when facing threat. Epistemologically, whereas we currently have an adequate predictivist model of CA, it is necessary to prepare for a mechanistic approach that will not only predict, but also explain CA phenomena. Toward that direction, we here first examine the concepts and mechanisms that are the building blocks of both the prototypical maternal attachment as well as CA. Based on existing robust neuroscience models (...)
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  • Philosophie der Neurowissenschaften.Holger Lyre - 2017 - In Simon Lohse & Thomas Reydon (eds.), Grundriss Wissenschaftsphilosophie. Die Philosophien der Einzelwissenschaften. Hamburg: Meiner.
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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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  • 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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  • A Neurodynamic Model of Feature-Based Spatial Selection.Mateja Marić & Dražen Domijan - 2018 - Frontiers in Psychology 9.
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  • The Brain as an Input–Output Model of the World.Oron Shagrir - 2018 - Minds and Machines 28 (1):53-75.
    An underlying assumption in computational approaches in cognitive and brain sciences is that the nervous system is an input–output model of the world: Its input–output functions mirror certain relations in the target domains. I argue that the input–output modelling assumption plays distinct methodological and explanatory roles. Methodologically, input–output modelling serves to discover the computed function from environmental cues. Explanatorily, input–output modelling serves to account for the appropriateness of the computed function to the explanandum information-processing task. I compare very briefly the (...)
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  • Computation vs. information processing: why their difference matters to cognitive science.Gualtiero Piccinini & Andrea Scarantino - 2010 - Studies in History and Philosophy of Science Part A 41 (3):237-246.
    Since the cognitive revolution, it has become commonplace that cognition involves both computation and information processing. Is this one claim or two? Is computation the same as information processing? The two terms are often used interchangeably, but this usage masks important differences. In this paper, we distinguish information processing from computation and examine some of their mutual relations, shedding light on the role each can play in a theory of cognition. We recommend that theorists of cognition be explicit and careful (...)
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  • Bayesian reverse-engineering considered as a research strategy for cognitive science.Carlos Zednik & Frank Jäkel - 2016 - Synthese 193 (12):3951-3985.
    Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...)
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  • Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.A. David Redish, Steve Jensen, Adam Johnson & Zeb Kurth-Nelson - 2007 - Psychological Review 114 (3):784-805.
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  • Computationalism, The Church–Turing Thesis, and the Church–Turing Fallacy.Gualtiero Piccinini - 2007 - Synthese 154 (1):97-120.
    The Church–Turing Thesis (CTT) is often employed in arguments for computationalism. I scrutinize the most prominent of such arguments in light of recent work on CTT and argue that they are unsound. Although CTT does nothing to support computationalism, it is not irrelevant to it. By eliminating misunderstandings about the relationship between CTT and computationalism, we deepen our appreciation of computationalism as an empirical hypothesis.
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  • (2 other versions)The Rise of Cognitive Science in the 20th Century.Carrie Figdor - 2017 - In Amy Kind (ed.), Philosophy of Mind in the Twentieth and Twenty-First Centuries: The History of the Philosophy of Mind, Volume 6. New York: Routledge. pp. 280-302.
    This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.
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  • The Ontic Account of Scientific Explanation.Carl F. Craver - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 27-52.
    According to one large family of views, scientific explanations explain a phenomenon (such as an event or a regularity) by subsuming it under a general representation, model, prototype, or schema (see Bechtel, W., & Abrahamsen, A. (2005). Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421–441; Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge: MIT Press; Darden (2006); Hempel, C. G. (1965). Aspects of scientific (...)
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  • The cognitive neuroscience revolution.Worth Boone & Gualtiero Piccinini - 2016 - Synthese 193 (5):1509-1534.
    We outline a framework of multilevel neurocognitive mechanisms that incorporates representation and computation. We argue that paradigmatic explanations in cognitive neuroscience fit this framework and thus that cognitive neuroscience constitutes a revolutionary break from traditional cognitive science. Whereas traditional cognitive scientific explanations were supposed to be distinct and autonomous from mechanistic explanations, neurocognitive explanations aim to be mechanistic through and through. Neurocognitive explanations aim to integrate computational and representational functions and structures across multiple levels of organization in order to explain (...)
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  • (1 other version)Cognitive Science.Thagard Paul - forthcoming - Stanford Encyclopedia of Philosophy.
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  • Cortical and basal ganglia contributions to habit learning and automaticity.F. Gregory Ashby, Benjamin O. Turner & Jon C. Horvitz - 2010 - Trends in Cognitive Sciences 14 (5):208.
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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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  • (1 other version)The self as a system of multilevel interacting mechanisms.Paul Thagard - 2012 - Philosophical Psychology (2):1-19.
    This paper proposes an account of the self as a multilevel system consisting of social, individual, neural, and molecular mechanisms. It argues that the functioning of the self depends on causal relations between mechanisms operating at different levels. In place of reductionist and holistic approaches to cognitive science, I advocate a method of multilevel interacting mechanisms. This method is illustrated by showing how self-concepts operate at several different levels.
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  • Explanation and description in computational neuroscience.David Michael Kaplan - 2011 - Synthese 183 (3):339-373.
    The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...)
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  • The Explanatory Force of Dynamical and Mathematical Models in Neuroscience: A Mechanistic Perspective.David Michael Kaplan & Carl F. Craver - 2011 - Philosophy of Science 78 (4):601-627.
    We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...)
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  • Predictive coding explains binocular rivalry: an epistemological review.Jakob Hohwy, Andreas Roepstorff & Karl Friston - 2008 - Cognition 108 (3):687-701.
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  • Explaining Economic Crises: Are There Collective Representations?Paul Thagard - 2010 - Episteme 7 (3):266-283.
    This paper uses the economic crisis of 2008 as a case study to examine the explanatory validity of collective mental representations. Distinguished economists such as Paul Krugman and Joseph Stiglitz attribute collective beliefs, desires, intentions, and emotions to organizations such as banks and governments. I argue that the most plausible interpretation of these attributions is that they are metaphorical pointers to a complex of multilevel social, psychological, and neural mechanisms. This interpretation also applies to collective knowledge in science: scientific communities (...)
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  • (1 other version)Cognitive science.Paul Thagard - 2008 - Stanford Encyclopedia of Philosophy.
    Cognitive science is the interdisciplinary investigation of mind and intelligence, embracing psychology, neuroscience, anthropology, artificial intelligence, and philosophy. There are many important philosophical questions related to this investigation, but this short chapter will focus on the following three. What is the nature of the explanations and theories developed in cognitive science? What are the relations among the five disciplines that comprise cognitive science? What are the implications of cognitive science research for general issues in the philosophy of science? I will (...)
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  • Computational modeling vs. computational explanation: Is everything a Turing machine, and does it matter to the philosophy of mind?Gualtiero Piccinini - 2007 - Australasian Journal of Philosophy 85 (1):93 – 115.
    According to pancomputationalism, everything is a computing system. In this paper, I distinguish between different varieties of pancomputationalism. I find that although some varieties are more plausible than others, only the strongest variety is relevant to the philosophy of mind, but only the most trivial varieties are true. As a side effect of this exercise, I offer a clarified distinction between computational modelling and computational explanation.<br><br>.
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  • Kinds of behaviour.Robert Aunger & Valerie Curtis - 2008 - Biology and Philosophy 23 (3):317-345.
    Sciences able to identify appropriate analytical units for their domain, their natural kinds, have tended to be more progressive. In the biological sciences, evolutionary natural kinds are adaptations that can be identified by their common history of selection for some function. Human brains are the product of an evolutionary history of selection for component systems which produced behaviours that gave adaptive advantage to their hosts. These structures, behaviour production systems, are the natural kinds that psychology seeks. We argue these can (...)
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  • Neural representations unobserved—or: a dilemma for the cognitive neuroscience revolution.Marco Facchin - 2023 - Synthese 203 (1):1-42.
    Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of observation. (...)
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  • (2 other versions)Scientonomy and the sociotechnical domain.Paul E. Patton (ed.) - 2021 - Willmington, Delaware: Vernon Press.
    The sociotechnical domain is the realm of scientists, the communities and institutions they form, and the tools and instruments they use to create, disseminate, and preserve knowledge. This paper reviews current scientonomic theory concerning this domain. A core scientonomic concept is that of an epistemic agent. Generally, an agent is an entity capable of intentional action—action that has content or meaning due to its purposeful direction towards a goal. An epistemic agent is one whose actions are the taking of epistemic (...)
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  • A nonreductive physicalist libertarian free will.Dwayne Moore - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    Libertarian free will is, roughly, the view that the same agential states can cause different possible actions. Nonreductive physicalism is, roughly, the view that mental states cause actions to occur, while these actions also have sufficient physical causes. Though libertarian free will and nonreductive physicalism have overlapping subject matter, and while libertarian free will is currently trending at the same time as nonreductive physicalism is a dominant metaphysical posture, there are few sustained expositions of a nonreductive physicalist model of libertarian (...)
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  • Reinforcement learning: A brief guide for philosophers of mind.Julia Haas - 2022 - Philosophy Compass 17 (9):e12865.
    In this opinionated review, I draw attention to some of the contributions reinforcement learning can make to questions in the philosophy of mind. In particular, I highlight reinforcement learning's foundational emphasis on the role of reward in agent learning, and canvass two ways in which the framework may advance our understanding of perception and motivation.
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  • Teleosemantics and the free energy principle.Stephen Francis Mann & Ross Pain - 2022 - Biology and Philosophy 37 (4):1-25.
    The free energy principle is notoriously difficult to understand. In this paper, we relate the principle to a framework that philosophers of biology are familiar with: Ruth Millikan’s teleosemantics. We argue that: systems that minimise free energy are systems with a proper function; and Karl Friston’s notion of implicit modelling can be understood in terms of Millikan’s notion of mapping relations. Our analysis reveals some surprising formal similarities between the two frameworks, and suggests interesting lines of future research. We hope (...)
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  • The evaluative mind.Julia Haas - forthcoming - In Mind Design III.
    I propose that the successes and contributions of reinforcement learning urge us to see the mind in a new light, namely, to recognise that the mind is fundamentally evaluative in nature.
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  • Free energy: a user’s guide.Stephen Francis Mann, Ross Pain & Michael D. Kirchhoff - 2022 - Biology and Philosophy 37 (4):1-35.
    Over the last fifteen years, an ambitious explanatory framework has been proposed to unify explanations across biology and cognitive science. Active inference, whose most famous tenet is the free energy principle, has inspired excitement and confusion in equal measure. Here, we lay the ground for proper critical analysis of active inference, in three ways. First, we give simplified versions of its core mathematical models. Second, we outline the historical development of active inference and its relationship to other theoretical approaches. Third, (...)
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  • Enactivism and predictive processing: A non-representational view.Michael David Kirchhoff & Ian Robertson - 2018 - Philosophical Explorations 21 (2):264-281.
    This paper starts by considering an argument for thinking that predictive processing (PP) is representational. This argument suggests that the Kullback–Leibler (KL)-divergence provides an accessible measure of misrepresentation, and therefore, a measure of representational content in hierarchical Bayesian inference. The paper then argues that while the KL-divergence is a measure of information, it does not establish a sufficient measure of representational content. We argue that this follows from the fact that the KL-divergence is a measure of relative entropy, which can (...)
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  • Content and misrepresentation in hierarchical generative models.Alex Kiefer & Jakob Hohwy - 2018 - Synthese 195 (6):2387-2415.
    In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing that it (...)
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  • (1 other version)The self as a system of multilevel interacting mechanisms.Paul Thagard - 2014 - Philosophical Psychology 27 (2):145-163.
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  • A statistical mechanical problem?Tommaso Costa & Mario Ferraro - 2014 - Frontiers in Psychology 5.
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  • Networks in Cognitive Science.Andrea Baronchelli, Ramon Ferrer-I.-Cancho, Romualdo Pastor-Satorras, Nick Chater & Morten H. Christiansen - 2013 - Trends in Cognitive Sciences 17 (7):348-360.
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  • Minimal models and canonical neural computations: the distinctness of computational explanation in neuroscience.M. Chirimuuta - 2014 - Synthese 191 (2):127-153.
    In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...)
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  • The Resilience of Computationalism.Gualtiero Piccinini - 2010 - Philosophy of Science 77 (5):852-861.
    Roughly speaking, computationalism says that cognition is computation, or that cognitive phenomena are explained by the agent‘s computations. The cognitive processes and behavior of agents are the explanandum. The computations performed by the agents‘ cognitive systems are the proposed explanans. Since the cognitive systems of biological organisms are their nervous 1 systems (plus or minus a bit), we may say that according to computationalism, the cognitive processes and behavior of organisms are explained by neural computations. Some people might prefer to (...)
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  • The first computational theory of mind and brain: A close look at McCulloch and Pitts' Logical Calculus of Ideas Immanent in Nervous Activity.Gualtiero Piccinini - 2004 - Synthese 141 (2):175-215.
    Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in (...)
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  • Computational explanation in neuroscience.Gualtiero Piccinini - 2006 - Synthese 153 (3):343-353.
    According to some philosophers, computational explanation is proprietary
    to psychology—it does not belong in neuroscience. But neuroscientists routinely offer computational explanations of cognitive phenomena. In fact, computational explanation was initially imported from computability theory into the science of mind by neuroscientists, who justified this move on neurophysiological grounds. Establishing the legitimacy and importance of computational explanation in neuroscience is one thing; shedding light on it is another. I raise some philosophical questions pertaining to computational explanation and outline some promising answers that (...)
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  • Explanation in Neurobiology: An Interventionist Perspective.James Woodward - unknown
    This paper employs an interventionist framework to elucidate some issues having to do with explanation in neurobiology and with the differences between mechanistic and non-mechanistic explanations.
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  • The scope and limits of a mechanistic view of computational explanation.Maria Serban - 2015 - Synthese 192 (10):3371-3396.
    An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience as a subspecies (...)
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  • Empathy and Instinct: Cognitive Neuroscience and Folk Psychology.Anne Jaap Jacobson - 2009 - Inquiry: An Interdisciplinary Journal of Philosophy 52 (5):467-482.
    Might we have an instinctive tendency to perform helpful actions? This paper explores a model under development in cognitive neuroscience that enables us to understand what instinctive, helpful actions might look like. The account that emerges puts some pressure on key concepts in the philosophical understanding of folk psychology. In developing the contrast, a notion of embodied beliefs is introduced; it arguably fits folk conceptions better than philosophical ones. One upshot is that Humean insights into the role of empathy and (...)
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  • Why there are no good arguments for any interesting version of determinism.Mark Balaguer - 2009 - Synthese 168 (1):1 - 21.
    This paper considers the empirical evidence that we currently have for various kinds of determinism that might be relevant to the thesis that human beings possess libertarian free will. Libertarianism requires a very strong version of indeterminism, so it can be refuted not just by universal determinism, but by some much weaker theses as well. However, it is argued that at present, we have no good reason to believe even these weak deterministic views and, hence, no good reason—at least from (...)
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  • Innateness and the brain.Steven R. Quartz - 2003 - Biology and Philosophy 18 (1):13-40.
    The philosophical innateness debate has long relied onpsychological evidence. For a century, however, a parallel debate hastaken place within neuroscience. In this paper, I consider theimplications of this neuroscience debate for the philosophicalinnateness debate. By combining the tools of theoretical neurobiologyand learning theory, I introduce the ``problem of development'' that alladaptive systems must solve, and suggest how responses to this problemcan demarcate a number of innateness proposals. From this perspective, Isuggest that the majority of natural systems are in fact innate. (...)
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