Understanding Consciousness as Data Compression
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In this article we explore the idea that consciousness is a language-complete phenomenon, that is, one which is as difficult to formalise as the foundations of language itself. We posit that the reason consciousness resists scientific description is because the language of science is too weak; its power to render phenomena objective is exhausted by the sophistication of the brain’s architecture. However, this does not mean that there is nothing to say about consciousness. We propose that the phenomenon can be expressed in terms of data compression, a well-defined concept from theoretical computer science which acknowledges and formalises the limits of objective representation. Data compression focuses on the intersection between the uncomputable and the finite. It has a number of fundamental theoretical applications, giving rise, for example, to a universal definition of intelligence (Hutter, 2004), a universal theory of prior probability, as well as a universal theory of inductive inference (Solomonoff, 1964). Here we explore the merits of considering consciousness in such terms, showing how the data compression approach can provide new perspectives on intelligent behaviour, the combination problem, and the hard problem of subjective experience. In particular, we use the tools of algorithmic information theory to prove that integrated experience cannot be achieved by a computable process.
Abstract
In this article we explore the idea that consciousness is a language-complete phenomenon, that is, one which is as difficult to formalise as the foundations of language itself. We posit that the reason consciousness resists scientific description is because the language of science is too weak; its power to render phenomena objective is exhausted by the sophistication of the brain’s architecture. However, this does not mean that there is nothing to say about consciousness. We propose that the phenomenon can be expressed in terms of data compression, a well-defined concept from theoretical computer science which acknowledges and formalises the limits of objective representation. Data compression focuses on the intersection between the uncomputable and the finite. It has a number of fundamental theoretical applications, giving rise, for example, to a universal definition of intelligence (Hutter, 2004), a universal theory of prior probability, as well as a universal theory of inductive inference (Solomonoff, 1964). Here we explore the merits of considering consciousness in such terms, showing how the data compression approach can provide new perspectives on intelligent behaviour, the combination problem, and the hard problem of subjective experience. In particular, we use the tools of algorithmic information theory to prove that integrated experience cannot be achieved by a computable process.
참고문헌 (69)
- Aru, J., "Distilling the neural correlates of consciousness", Neuroscience & Biobehavioral Reviews, 36(2), pp.737~746 (2012)
- Baars, B. J., "The Blackwell companion to consciousness", pp.236~246 (2007)
- Bennett, C. H., "The Universal Turing Machine A Half-Century Survey", pp.207~235 (1995)
- Bringsjord, S., "Cognition is not computation : The argument from irreversibility", Synthese, 113(2), pp.285~320 (1997)
- Calude, C. S., "Information and randomness: an algorithmic perspective" (2013), Springer Science & Business Media
- Casali, A. G., "A theoretically based index of consciousness independent of sensory processing and behavior", Science Translational Medicine, 5(198), pp.198ra105~ (2013)
- Chaitin, G., "The limits of reason", Scientific American, 294(3), pp.74~81 (2006)
- Chalmers, D. J., "Facing up to the problem of consciousness", Journal of Consciousness Studies, 2(3), pp.200~219 (1995)
- Chater, N., "From universal laws of cognition to specific cognitive models", Cognitive Science, 32(1), pp.36~67 (2008)
- Chater, N., "Simplicity: a unifying principle in cognitive science?", Trends in Cognitive Sciences, 7(1), pp.19~22 (2003)
- Dennett, D. C., "Consciousness explained" (1991), Little, Brown
- Dessalles, J.-L., "Coincidences and the encounter problem: A formal account", arXiv preprint arXiv:1106.3932, ~ (2011)
- Dunbar, R. I., "Evolution in the social brain", Science, 317(5843), pp.1344~1347 (2007)
- Fodor, J. A., "The mind doesn’t work that way: The scope and limits of computational psychology" (2001), MIT press
- French, R. M., "Moving beyond the turing test", Communications of the ACM, 55(12), pp.74~77 (2012)
- Friston, K., "A duet for one", Consciousness and Cognition, 36, pp.390~405 (2015)
- Gauvrit, N., "Representation and reality : Humans, animals and machines" (2015), Springer
- Gräff, J., "Epigenetic priming of memory updating during reconsolidation to attenuate remote fear memories", Cell, 156(1), pp.261~276 (2014)
- Halligan, P., "Consciousness isn’t all about you, you know", New Scientist, August 15, ~ (2015)
- Hameroff, S., "Quantum computation in brain microtubules? The Penrose-Hameroff ‘Orch OR’ model of consciousness", Philosophical Transactions-Royal Society of London Series A Mathematical Physical and Engineering Sciences, pp.1869~1895 (1998)
- Hesslow, G., "Conscious thought as simulation of behaviour and perception", Trends in Cognitive Sciences, 6(6), pp.242~247 (2002)
- Humphrey, N., "Seeing red: A study in consciousness" (2006), Harvard University Press
- Hutter, M., "On universal prediction and Bayesian confirmation", Theoretical Computer Science, 384(1), pp.33~48 (2007)
- "Human knowledge compression contest" (2009)[URL]
- Jackson, F., "What Mary didn’t know", The Journal of Philosophy, pp.291~295 (1986)
- James, W., "The consciousness of self", The Principles of Psychology, 8 (1890)
- Kirchherr, W., "The miraculous universal distribution", The Mathematical Intelligencer, 19(4), pp.7~15 (1997)
- Koch, C., "The quest for consciousness" (2004), New York
- Koubeissi, M. Z., "Electrical stimulation of a small brain area reversibly disrupts consciousness", Epilepsy &Behavior, 37, pp.32~35 (2014)
- Legg, S., "A collection of definitions of intelligence", Frontiers in Artificial Intelligence and Applications, 157, pp.17~ (2007)
- Levine, J., "Materialism and qualia : The explanatory gap", Pacific Philosophical Auarterly, 64(4), pp.354~361 (1983)
- Li, M., "An introduction to Kolmogorov complexity and its applications" (2013), Springer Science & Business Media
- Maguire, P., "Consciousness is data compression", Proceedings of the thirty-second conference of the cognitive science society, pp.748~753 (2010)
- Maguire, P., "Is consciousness computable? Quantifying integrated information using algorithmic information theory", Proceedings of the thirty-sixth annual conference of the cognitive science society (2014)
- "Proceedings of the thirty-fifth annual conference of the cognitive science society", 2013, 960~965
- "Data compression programs" (2009)[URL]
- McGinn, C., "The problem of consciousness: Essays toward a resolution" (1991), Blackwell Publishers
- Metzinger, T., "Being no one: The self-model theory of subjectivity" (2004), MIT Press
- Ramirez, S., "Creating a false memory in the hippocampus", Science, 341(6144), pp.387~391 (2013)
- Rathmanner, S., "A philosophical treatise of universal induction", Entropy, 13(6), pp.1076~1136 (2011)
- Rissanen, J., "Modeling by shortest data description", Automatica, 14(5), pp.465~471 (1978)
- Rolls, E. T., "The neuronal encoding of information in the brain", Progress in neurobiology, 95(3), pp.448~490 (2011)
- Schacter, D. L., "Memory distortion : an adaptive perspective", Trends in cognitive sciences, 15(10), pp.467~474 (2011)
- Schmidhuber, J., "An on-line algorithm for dynamic reinforcement learning and planning in reactive environments", 1990 IJCNN international joint conference on neural networks, pp.253~258 (1990)
- Schmidhuber, J., "A possibility for implementing curiosity and boredom in modelbuilding neural controllers", From animals to animats: Proceedings of the first international conference on simulation of adaptive behavior (sab90) (1991)
- Schmidhuber, J., "Learning complex, extended sequences using the principle of history compression", Neural Computation, 4(2), pp.234~242 (1992)
- Schmidhuber, J., "Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts", Connection Science, 18(2), pp.173~187 (2006)
- Schmidhuber, J., "Anticipatory Behavior in Adaptive Learning Systems", pp.48~76 (2009), Springer
- "My first deep learning system of 1991 + deep learning timeline 1960-2013" (2013)[URL]
- "I am Jürgen Schmidhuber" (2014)[URL]
- Schmidhuber, J., "On learning to think: Algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models", arXiv preprint arXiv:1511.09249, ~ (2015)
- Shannon, C. E., "Prediction and entropy of printed English", Bell System Technical Journal, 30(1), pp.50~64 (1951)
- Solomonoff, R. J., "A formal theory of inductive inference. Part I", Information and Control, 7(1), pp.1~22 (1964)
- Soon, C. S., "Unconscious determinants of free decisions in the human brain", Nature Neuroscience, 11(5), pp.543~545 (2008)
- Stanley, G. B., "Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus", The Journal of Neuroscience, 19(18), pp.8036~8042 (1999)
- Strawson, G., "Soul dust", The Observer, January 8, ~ (2011)
- Tal, E., "How accurate is the standard second?", Philosophy of Science, 78(5), pp.1082~1096 (2011)
- Tal, E., "Old and new problems in philosophy of measurement", Philosophy Compass, 8(12), pp.1159~1173 (2013)
- Tal, E., "Making time: A study in the epistemology of measurement", The British Journal for the Philosophy of Science, pp.axu037~ (2014)
- Tononi, G., "An information integration theory of consciousness", BMC Neuroscience, 5(1), pp.42~ (2004)
- Tononi, G., "Consciousness as integrated information : a provisional manifesto", The Biological Bulletin, 215(3), pp.216~242 (2008)
- Tononi, G., "Integrated information theory of consciousness: an updated account", Arch Ital Biol, 150(2-3), pp.56~90 (2012)
- Tononi, G., "Integrated information theory", Scholarpedia, 10(1), pp.4164~ (2015)
- Tononi, G., "Complexity and coherency : integrating information in the brain", Trends in Cognitive Sciences, 2(12), pp.474~484 (1998)
- Tononi, G., "A measure for brain complexity : relating functional segregation and integration in the nervous system", Proceedings of the National Academy of Sciences, 91(11), pp.5033~5037 (1994)
- Turing, A. M., "Computing machinery and intelligence", Mind, pp.433~460 (1950)
- Vitányi, P., "Minimum description length induction, bayesianism, and kolmogorov complexity. Information Theory", IEEE Transactions on, 46(2), pp.446~464 (2000)
- Wolff, J. G., "Computing, cognition and information compression", AI Communications, 6(2), pp.107~127 (1993)
- Zenil, H., "Approximations of algorithmic and structural complexity validate cognitive-behavioural experimental results", arXiv preprint arXiv:1509.06338, ~ (2015)