Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-14T23:10:22.406Z Has data issue: false hasContentIssue false
  • English
  • Français

Representations Are Rate-Distortion Sweet Spots

Published online by Cambridge University Press:  01 January 2022

Manolo Martínez
Get access Rights & Permissions [Opens in a new window]

Abstract

Theorists working on information and representation are often not centrally concerned with “Shannon” information, as it is often put, but with some other, sometimes called “semantic,” kind of information. This perception is wrong. Shannon’s theory of information is the only one we need. I sketch a (Shannon) informational account of representation, for a certain important family of cases. This account, which represents a significant departure from the Dretskean philosophical mainstream, will show how a number of popular proposals about the purportedly noninformational ingredients in representation actually belong in the same coherent, purely information-theoretic picture.

Type
Cognitive Sciences
Information
Philosophy of Science , Volume 86 , Issue 5 , December 2019 , pp. 1214 - 1226
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

This work is funded by the Spanish Ministry of Science, Innovation and Universities, through grants PGC2018-101425-B-I00 and RYC-2016-20642.

References

Arimoto, S. 1972. “An Algorithm for Computing the Capacity of Arbitrary Discrete Memoryless Channels.” IEEE Transactions on Information Theory 18 (1): 14–20..CrossRefGoogle Scholar
Artiga, M. 2016. “Liberal Representationalism: A Deflationist Defense.” Dialectica 70 (3): 407–30..CrossRefGoogle Scholar
Artiga, M.. Forthcoming. “Beyond Black Spots and Nutritious Things: A Solution to the Indeterminacy Problem.” Mind and Language.Google Scholar
Bergstrom, C. T., and Rosvall, M.. 2011. “The Transmission Sense of Information.” Biology and Philosophy 26 (2): 159–76..Google Scholar
Blahut, R. 1972. “Computation of Channel Capacity and Rate-Distortion Functions.” IEEE Transactions on Information Theory 18 (4): 460–73..CrossRefGoogle Scholar
Boyd, R. 1989. “What Realism Implies and What It Does Not.” Dialectica 43 (1–2): 5–29.CrossRefGoogle Scholar
Burge, T. 2010. Origins of Objectivity. Oxford: Oxford University Press.CrossRefGoogle Scholar
Cover, T. M., and Thomas, J. A.. 2006. Elements of Information Theory. New York: Wiley.Google Scholar
Dretske, F. 1981. Knowledge and the Flow of Information. Cambridge, MA: MIT Press.Google Scholar
Lewis, D. 1983. “New Work for a Theory of Universals.” Australasian Journal of Philosophy 61 (4): 343–77..CrossRefGoogle Scholar
Lewis, D.. 1984. “Putnam’s Paradox.” Australasian Journal of Philosophy 62 (3): 221–36..CrossRefGoogle Scholar
Martínez, M. 2013. “Teleosemantics and Indeterminacy.” Dialectica 67 (4): 427–53..CrossRefGoogle Scholar
Millikan, R. G. 1984. Language, Thought and Other Biological Categories. Cambridge, MA: MIT Press.Google Scholar
Neander, K. 2017. A Mark of the Mental: In Defense of Informational Teleosemantics. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Rathkopf, C. 2017. “Neural Information and the Problem of Objectivity.” Biology and Philosophy 32 (3): 321–36..CrossRefGoogle Scholar
Ryder, D. 2004. “SINBAD Neurosemantics: A Theory of Mental Representation.” Mind and Language 19 (2): 211–40..CrossRefGoogle Scholar
Ryder, D.. 2006. “On Thinking of Kinds.” In Teleosemantics, ed. Macdonald, G. and Papineau, D., 1–22. Oxford: Clarendon.Google Scholar
Scarantino, A. 2015. “Information as a Probabilistic Difference Maker.” Australasian Journal of Philosophy 93 (3): 419–43..CrossRefGoogle Scholar
Schulte, P. 2015. “Perceptual Representations: A Teleosemantic Answer to the Breadth-of-Application Problem.” Biology and Philosophy 30 (1): 119–36..CrossRefGoogle Scholar
Seyfarth, R. M., Cheney, D. L., and Marler, P.. 1980a. “Monkey Responses to Three Different Alarm Calls: Evidence of Predator Classification and Semantic Communication.” Science 210 (4471): 801–3..CrossRefGoogle Scholar
Seyfarth, R. M., Cheney, D. L., and Marler, P.. 1980b. “Vervet Monkey Alarm Calls: Semantic Communication in a Free-Ranging Primate.” Animal Behaviour 28 (4): 1070–94..CrossRefGoogle Scholar
Sider, T. 2013. Writing the Book of the World. Oxford: Clarendon.Google Scholar
Skyrms, B. 2010. Signals: Evolution, Learning and Information. New York: Oxford University Press.CrossRefGoogle Scholar
Sterelny, K. 2003. Thought in a Hostile World: The Evolution of Human Cognition. Malden, MA: Wiley.Google Scholar