19 found
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  1. Abnormalities in the awareness of action.Sarah-Jayne Blakemore, Daniel M. Wolpert & Christopher D. Frith - 2002 - Trends in Cognitive Sciences 6 (6):237-242.
  2. Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action.Christopher D. Frith, S. J. Blakemore & D. Wolpert - 2000 - Brain Research Reviews 31 (2):357-363.
  3.  88
    Internal models in the cerebellum.Daniel M. Wolpert, R. Chris Miall & Mitsuo Kawato - 1998 - Trends in Cognitive Sciences 2 (9):338-347.
  4.  97
    Bayesian decision theory in sensorimotor control.Konrad P. Körding & Daniel M. Wolpert - 2006 - Trends in Cognitive Sciences 10 (7):319-326.
  5.  7
    Implications of computer science theory for the simulation hypothesis.David Wolpert - manuscript
    The simulation hypothesis has recently excited renewed interest, especially in the physics and philosophy communities. However, the hypothesis specifically concerns {computers} that simulate physical universes, which means that to properly investigate it we need to couple computer science theory with physics. Here I do this by exploiting the physical Church-Turing thesis. This allows me to introduce a preliminary investigation of some of the computer science theoretic aspects of the simulation hypothesis. In particular, building on Kleene's second recursion theorem, I prove (...)
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  6. Implications of computer science theory for the simulation hypothesis.David Wolpert - manuscript
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  7.  21
    The Implications of the No-Free-Lunch Theorems for Meta-induction.David H. Wolpert - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (3):421-432.
    The important recent book by Schurz ( 2019 ) appreciates that the no-free-lunch theorems (NFL) have major implications for the problem of (meta) induction. Here I review the NFL theorems, emphasizing that they do not only concern the case where there is a uniform prior—they prove that there are “as many priors” (loosely speaking) for which any induction algorithm _A_ out-generalizes some induction algorithm _B_ as vice-versa. Importantly though, in addition to the NFL theorems, there are many _free lunch_ theorems. (...)
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  8. The Lack of A Priori Distinctions Between Learning Algorithms.David H. Wolpert - 1996 - Neural Computation 8 (7):1341–1390.
    This is the first of two papers that use off-training set (OTS) error to investigate the assumption-free relationship between learning algorithms. This first paper discusses the senses in which there are no a priori distinctions between learning algorithms. (The second paper discusses the senses in which there are such distinctions.) In this first paper it is shown, loosely speaking, that for any two algorithms A and B, there are “as many” targets (or priors over targets) for which A has lower (...)
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  9.  21
    Memory Systems, the Epistemic Arrow of Time, and the Second Law.David H. Wolpert & Jens Kipper - 2024 - Entropy 26 (2).
    The epistemic arrow of time is the fact that our knowledge of the past seems to be both of a different kind and more detailed than our knowledge of the future. Just like with the other arrows of time, it has often been speculated that the epistemic arrow arises due to the second law of thermodynamics. In this paper, we investigate the epistemic arrow of time using a fully formal framework. We begin by defining a memory system as any physical (...)
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  10.  19
    A Stochastic Model of Mathematics and Science.David H. Wolpert & David B. Kinney - 2024 - Foundations of Physics 54 (2):1-67.
    We introduce a framework that can be used to model both mathematics and human reasoning about mathematics. This framework involves stochastic mathematical systems (SMSs), which are stochastic processes that generate pairs of questions and associated answers (with no explicit referents). We use the SMS framework to define normative conditions for mathematical reasoning, by defining a “calibration” relation between a pair of SMSs. The first SMS is the human reasoner, and the second is an “oracle” SMS that can be interpreted as (...)
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  11. Forward models.D. M. Wolpert & J. R. Flanagan - 2009 - In Bayne Tim, Cleeremans Axel & Wilken Patrick (eds.), The Oxford Companion to Consciousness. Oxford University Press. pp. 294--296.
  12. The lesson of Newcomb’s paradox.David H. Wolpert & Gregory Benford - 2013 - Synthese 190 (9):1637-1646.
    In Newcomb’s paradox you can choose to receive either the contents of a particular closed box, or the contents of both that closed box and another one. Before you choose though, an antagonist uses a prediction algorithm to accurately deduce your choice, and uses that deduction to fill the two boxes. The way they do this guarantees that you made the wrong choice. Newcomb’s paradox is that game theory’s expected utility and dominance principles appear to provide conflicting recommendations for what (...)
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  13.  65
    Using self‐dissimilarity to quantify complexity.David H. Wolpert & William Macready - 2007 - Complexity 12 (3):77-85.
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  14.  27
    The Neuroscience of Social Interaction: Decoding, Influencing, and Imitating the Actions of Others.Christopher D. Frith & Daniel Wolpert (eds.) - 2004 - Oxford University Press UK.
    Humans, like other primates, are intensely social creatures. One of the major functions of our brains must be to enable us to be as skilful in social interactions as we are in our interactions with the physical world. Furthermore, any differences between human brains and those of our nearest relatives, the great apes, are likely to be linked to our unique achievements in social interaction and communication rather than our motor or perceptual skills. Unique to humans is the ability to (...)
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  15.  23
    What makes an optimization problem hand?William G. Macready & David H. Wolpert - 1996 - Complexity 1 (5):40-46.
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  16.  19
    Christopher D. Frith and.Daniel M. Wolpert - 2005 - Journal of Consciousness Studies 12 (2):90-5.
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  17.  25
    Motor learning models.Daniel M. Wolpert & Zoubin Ghahramani - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
  18. Sensorimotor learning.D. M. Wolpert & J. R. Flanagan - 2002 - In M. Arbib (ed.), The Handbook of Brain Theory and Neural Networks. MIT Press. pp. 1020--1023.
     
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  19.  15
    Theories of Knowledge and Theories of Everything.David H. Wolpert - 2018 - In Wuppuluri Shyam & Francisco Antonio Dorio (eds.), The Map and the Territory: Exploring the Foundations of Science, Thought and Reality. Springer. pp. 165-184.
    There are four types of information an agent can have concerning the state of the universe: information acquired via observation, via control, via prediction, or via retrodiction, i.e., memory. Each of these four types of information appear to rely on a different kind of physical device. However it turns out that there is some mathematical structure that is common to those four types of devices. Any device that possesses that structure is known as an “inference device”. Here I review some (...)
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