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  1. A Competence Framework for Artificial Intelligence Research.Lisa Miracchi - 2019 - Philosophical Psychology 32 (5):588-633.
    ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...)
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  • Syntax, Semantics, and Computer Programs.William J. Rapaport - forthcoming - Philosophy and Technology:1-13.
    Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.
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  • Bodily Skill and Internal Representation in Sensorimotor Perception.David Silverman - 2018 - Phenomenology and the Cognitive Sciences 17 (1):157-173.
    The sensorimotor theory of perceptual experience claims that perception is constituted by bodily interaction with the environment, drawing on practical knowledge of the systematic ways that sensory inputs are disposed to change as a result of movement. Despite the theory’s associations with enactivism, it is sometimes claimed that the appeal to ‘knowledge’ means that the theory is committed to giving an essential theoretical role to internal representation, and therefore to a form of orthodox cognitive science. This paper defends the role (...)
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  • Vanilla PP for Philosophers: A Primer on Predictive Processing.Wanja Wiese & Thomas Metzinger - 2017 - Philosophy and Predictive Processing.
    The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. -/- The features described here (...)
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  • The Nature and Function of Content in Computational Models.Frances Egan - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge.
    Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature (...)
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  • Representation in Cognitive Science.Nicholas Shea - 2018 - Oxford University Press.
    How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.
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  • Just How Conservative is Conservative Predictive Processing?Paweł Gładziejewski - 2017 - Hybris. Revista de Filosofía 38:98-122.
    Predictive Processing (PP) framework construes perception and action (and perhaps other cognitive phenomena) as a matter of minimizing prediction error, i.e. the mismatch between the sensory input and sensory predictions generated by a hierarchically organized statistical model. There is a question of how PP fits into the debate between traditional, neurocentric and representation-heavy approaches in cognitive science and those approaches that see cognition as embodied, environmentally embedded, extended and (largely) representation-free. In the present paper, I aim to investigate and clarify (...)
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  • Predictive Minds and Small-Scale Models: Kenneth Craik’s Contribution to Cognitive Science.Daniel Williams - 2018 - Philosophical Explorations 21 (2):245-263.
    I identify three lessons from Kenneth Craik’s landmark book “The Nature of Explanation” for contemporary debates surrounding the existence, extent, and nature of mental representation: first, an account of mental representations as neural structures that function analogously to public models; second, an appreciation of prediction as the central component of intelligence in demand of such models; and third, a metaphor for understanding the brain as an engineer, not a scientist. I then relate these insights to discussions surrounding the representational status (...)
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  • Representation and Mental Representation.Robert D. Rupert - 2018 - Philosophical Explorations 21 (2):204-225.
    This paper engages critically with anti-representationalist arguments pressed by prominent enactivists and their allies. The arguments in question are meant to show that the “as-such” and “job-description” problems constitute insurmountable challenges to causal-informational theories of mental content. In response to these challenges, a positive account of what makes a physical or computational structure a mental representation is proposed; the positive account is inspired partly by Dretske’s views about content and partly by the role of mental representations in contemporary cognitive scientific (...)
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  • Explaining Representation: A Reply to Matthen.Frances Egan - 2014 - Philosophical Studies 170 (1):137-142.
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  • Competence to Know.Lisa Miracchi - 2015 - Philosophical Studies 172 (1):29-56.
    I argue against traditional virtue epistemology on which knowledge is a success due to a competence to believe truly, by revealing an in-principle problem with the traditional virtue epistemologist’s explanation of Gettier cases. The argument eliminates one of the last plausible explanation of Gettier cases, and so of knowledge, in terms of non-factive mental states and non-mental conditions. I then I develop and defend a different kind of virtue epistemology, on which knowledge is an exercise of a competence to know. (...)
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  • The Fallacy of the Homuncular Fallacy.Carrie Figdor - 2018 - Belgrade Philosophical Annual 31:41-56.
    A leading theoretical framework for naturalistic explanation of mind holds that we explain the mind by positing progressively "stupider" capacities ("homunculi") until the mind is "discharged" by means of capacities that are not intelligent at all. The so-called homuncular fallacy involves violating this procedure by positing the same capacities at subpersonal levels. I argue that the homuncular fallacy is not a fallacy, and that modern-day homunculi are idle posits. I propose an alternative view of what naturalism requires that reflects how (...)
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  • Toward a Mature Science of Consciousness.Wanja Wiese - 2018 - Frontiers in Psychology 9.
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  • Structural Representations: Causally Relevant and Different From Detectors.Paweł Gładziejewski & Marcin Miłkowski - 2017 - Biology and Philosophy 32 (3):337-355.
    This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using (...)
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  • Towards a Cognitive Neuroscience of Intentionality.Alex Morgan & Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):119-139.
    We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we focus on views that attempt to render such ascriptions naturalistic by construing them in a deflationary or merely pragmatic way. We then contrast these views with staunchly realist views that attempt to naturalize intentionality by developing theories of content for representations in terms of information and biological function. (...)
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  • Explaining Representation: A Reply to Matthen.Frances Egan - 2013 - Philosophical Studies (1):1-6.
    Mohan Matthen has failed to understand the position I develop and defend in “How to Think about Mental Content.” No doubt some of the fault lies with my exposition, though Matthen often misconstrues passages that are clear in context. He construes clarifications and elaborations of my argument to be “concessions.” Rather than dwell too much on specific misunderstandings of my explanatory project and its attendant claims, I will focus on the main points of disagreement.RepresentationalismMy project in the paper is to (...)
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  • Predictive Processing and the Representation Wars.Daniel Williams - 2018 - Minds and Machines 28 (1):141-172.
    Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based (...)
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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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  • Predictive Processing and the Representation Wars: A Victory for the Eliminativist.Adrian Downey - 2018 - Synthese 195 (12):5115-5139.
    In this paper I argue that, by combining eliminativist and fictionalist approaches toward the sub-personal representational posits of predictive processing, we arrive at an empirically robust and yet metaphysically innocuous cognitive scientific framework. I begin the paper by providing a non-representational account of the five key posits of predictive processing. Then, I motivate a fictionalist approach toward the remaining indispensable representational posits of predictive processing, and explain how representation can play an epistemologically indispensable role within predictive processing explanations without thereby (...)
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  • Generative Explanation in Cognitive Science and the Hard Problem of Consciousness.Lisa Miracchi - 2017 - Philosophical Perspectives 31 (1):267-291.
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  • Pragmatism and the Predictive Mind.Daniel Williams - 2018 - Phenomenology and the Cognitive Sciences 17 (5):835-859.
    Predictive processing and its apparent commitment to explaining cognition in terms of Bayesian inference over hierarchical generative models seems to flatly contradict the pragmatist conception of mind and experience. Against this, I argue that this appearance results from philosophical overlays at odd with the science itself, and that the two frameworks are in fact well-poised for mutually beneficial theoretical exchange. Specifically, I argue: first, that predictive processing illuminates pragmatism’s commitment to both the primacy of pragmatic coping in accounts of the (...)
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  • Neural Representations Observed.Eric Thomson & Gualtiero Piccinini - 2018 - Minds and Machines 28 (1):191-235.
    The historical debate on representation in cognitive science and neuroscience construes representations as theoretical posits and discusses the degree to which we have reason to posit them. We reject the premise of that debate. We argue that experimental neuroscientists routinely observe and manipulate neural representations in their laboratory. Therefore, neural representations are as real as neurons, action potentials, or any other well-established entities in our ontology.
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