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Consequences of a Functional Account of Information

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Abstract

This paper aims to establish several interconnected points. First, a particular interpretation of the mathematical definition of information, known as the causal interpretation, is supported largely by misunderstandings of the engineering context from which it was taken. A better interpretation, which makes the definition and quantification of information relative to the function of its user, is outlined. The first half of the paper is given over to introducing communication theory and its competing interpretations. The second half explores three consequences of the main thesis. First, a popular claim that the quantification of information in a signal is irrelevant for the meaning of that signal is exposed as fallacious. Second, a popular distinction between causal and semantic information is shown to be misleading, and I argue it should be replaced with a related distinction between natural and intentional signs. Finally, I argue that recent empirical work from microbiology and cognitive science drawing on resources of mathematical communication theory is best interpreted by the functional account. Overall, a functional approach is shown to be both theoretically and empirically well-supported.

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Notes

  1. For a history of approaches to information based on purposive behaviour, see Adams (2003, §3). More recent work includes Bergstrom and Rosvall (2011), which offers a user-relative definition of genetic information, and Rathkopf’s account of neural information, which defends a function-relative definition against the worry that such an approach might threaten scientific objectivity (Rathkopf 2017). See also Dennett (2017, §6) and Fresco et al. (2018). Space precludes a comparison between these works and the account discussed here, but I suspect they are all broadly compatible.

  2. For more on informational readings of classical conditioning, see Rescorla (1988) and Gallistel (2003).

  3. The ‘success semantics’ account of mental content has a similar structure (Whyte 1990).

  4. See also Mann (2018, 10–12).

  5. The irrelevance claim has been challenged by others, but it seems tough to overturn: “By the time of our Third London Symposium on Information Theory in 1955, it had become something of an accepted saying that ‘information theory has nothing to do with meaning’. The time seemed ripe to question this hardening dogma...” (MacKay 1969, 79).

  6. In the terminology of traditional philosophy of language, signs can have both kinds of ‘direction of fit’ at once (Millikan 1995).

  7. It is not immediately clear how to interpret the formalism, but here is a suggestion: while 1 bit of information allows the receiver to infer one out of two equiprobable states of the world, perhaps 1 bit of instruction allows the receiver to choose one out of two equifavourable acts.

  8. Shannon (1959, 326-7) points out that source and target sets need not be identical, nor even ‘symbols’ in the familiar sense. Lean (2016, 239-40) makes a similar point, forging a path for the adoption of informational models in other domains.

  9. A recent trend seeks to distinguish two concepts I treat as equivalent. The distinction, advocated by several authors including Price (2008, §5), Hutto and Myin (2013, 67), Rescorla (2013) (who cites Burge (2010) as inspiration) and Lean (2014), runs as follows. Simple signalling systems carry information in the guise of reliable correlation (“functional isomorphism”, “Shannon information”) – tokens that correspond to worldly states in a manner sufficient for successful behaviour. But correlational information is to be distinguished from the much richer notion of content, which is characterised by truth conditions. There is far more to say about this distinction and its motivations than can be addressed here. For a defence of the use of semantic concepts in simple signalling systems, see Millikan (2013b). For some remarks on the term “Shannon information” see below, page 11.

  10. Due to space constraints I neglect several possible positions. For example, it is possible to accept the causal interpretation of information and deny that anything further is needed for semantic information; see Skyrms (2010, §3). I also ignore Grice’s term “non-natural meaning” to prevent confusion.

  11. I omit a third category, manipulations, which are influential behaviours performed by senders to the detriment of receivers. These may display the same ‘ritualised’ qualities described for signals. The theoretical status of manipulations is still in dispute, with some arguing they should be included in the definition of signals (Owren et al. 2010). I resist that categorisation because I see coadaptation (or more broadly, codesign) as central to the mathematical and conceptual tools we use to analyse the varieties of information. On the other hand, Owren et al. (2010) see information as a deeply problematic concept that should be left out of animal communication studies altogether. See Mann (2018) for a fuller discussion.

  12. It is this difference that Millikan (2013a) leverages to analyse the correctness conditions of intentional signs in terms of their function. Natural signs, by definition, have no correctness conditions; they are neither true nor false.

  13. In particular by Rathkopf (2017), who advocates a relativist approach to neural information. Rathkopf also criticises the overly permissive notion of Shannon information as being out of step with the mathematical definition of information at work in engineering.

References

  • Adams, F. 2003. The informational turn in philosophy. Minds and Machines 13(4): 471–501.

    Google Scholar 

  • Artiga, M. 2016. Teleosemantic modeling of cognitive representations. Biology & Philosophy 31(4): 483–505.

    Google Scholar 

  • Bar-Hillel, Y., and R. Carnap. 1953. Semantic information. The British Journal for the Philosophy of Science 4(14): 147–157.

    Google Scholar 

  • Bergstrom, C.T., and M. Rosvall. 2011. The transmission sense of information. Biology & Philosophy 26(2): 159–176.

    Google Scholar 

  • Burge, T. 2010. Origins of objectivity. Oxford: Oxford University Press.

    Google Scholar 

  • Dall, S.R.X., L.-A. Giraldeau, O. Olsson, J.M. McNamara, and D.W. Stephens. 2005. Information and its use by animals in evolutionary ecology. Trends in Ecology & Evolution 20(4): 187–193.

    Google Scholar 

  • Dennett, D.C. 1983. Intentional systems in cognitive ethology: The “Panglossian paradigm” defended. Behavioral and Brain Sciences 6(3): 343–355.

    Google Scholar 

  • Dennett, D. C. 2017. From bacteria to bach and back: The evolution of minds. Penguin UK.

  • Donaldson-Matasci, M.C., C.T. Bergstrom, and M. Lachmann. 2010. The fitness value of information. Oikos 119(2): 219–230.

    Google Scholar 

  • Dretske, F. 1981. Knowledge and the flow of information. Cambridge: MIT Press.

    Google Scholar 

  • Dretske, F. 1983. Précis of knowledge and the flow of information. Behavioral and Brain Sciences 6(1): 55–90.

    Google Scholar 

  • Dretske, F. 1988. Explaining behavior: Reasons in a world of causes. Cambridge: MIT Press.

    Google Scholar 

  • Frank, S.A. 2012. Natural selection. V. How to read the fundamental equations of evolutionary change in terms of information theory. Journal of Evolutionary Biology 25(12): 2377–2396.

    Google Scholar 

  • Fresco, N., E. Jablonka, and S. Ginsburg. 2018. Functional information: A graded taxonomy of difference makers. Review of Philosophy and Psychology. (this issue).

  • Gallistel, C.R. 2003. Conditioning from an information processing perspective. Behavioural Processes 62(1–3): 89–101.

    Google Scholar 

  • Godfrey-Smith, P., and K. Sterelny. 2016. Biological information. In The Stanford encyclopedia of philosophy, ed. Zalta E.N. Summer 2016 edition.

  • Grice, H.P. 1957. Meaning. The Philosophical Review 66(3): 377–388.

    Google Scholar 

  • Hájek, A. 2007. The reference class problem is your problem too. Synthese 156(3): 563–585.

    Google Scholar 

  • Harms, W.F. 2004. Primitive content, translation, and the emergence of meaning in animal communication. In Evolution of communication systems: A comparative approach, eds. Oller D.K. and Griebel U., 31–48. Cambridge, MIT Press.

  • Hartley, R.V.L. 1928. Transmission of information. Bell System Technical Journal 7(3): 535–563.

    Google Scholar 

  • Huttegger, S.M. 2007. Evolutionary explanations of indicatives and imperatives. Erkenntnis 66(3): 409–436.

    Google Scholar 

  • Hutto, D.D., and E. Myin. 2013. Radicalizing enactivism: Basic minds without content. Cambridge: MIT Press.

    Google Scholar 

  • Iglesias, P.A. 2016. The use of rate distortion theory to evaluate biological signaling pathways. IEEE Transactions on Molecular, Biological and Multi-Scale Communications 2(1): 31–39.

    Google Scholar 

  • Kelly, J.L. 1956. A new interpretation of information rate. Bell System Technical Journal 35(4): 917–926.

    Google Scholar 

  • Kraemer, D.M. 2015. Natural probabilistic information. Synthese 192 (9): 2901–2919.

    Google Scholar 

  • Krebs, J.R., and R. Dawkins. 1984. Animal signals: Mind-reading and manipulation. In Behavioural ecology: An evolutionary approach, 2nd ed., eds. Krebs J.R. and Davies N.B., 380–402. Oxford, Blackwell Scientific.

  • Lean, O.M. 2014. Getting the most out of Shannon information. Biology & Philosophy 29(3): 395–413.

    Google Scholar 

  • Lean, O.M. 2016. Biological information. Bristol: PhD thesis, University of Bristol.

    Google Scholar 

  • Lewis, D. 1969. Convention: A philosophical study. Oxford: Blackwell.

    Google Scholar 

  • Lombardi, O., F. Holik, and L. Vanni. 2015. What is Shannon information? Synthese 193(7): 1983–2012.

    Google Scholar 

  • MacKay, D.M. 1969. Information, mechanism and meaning. Cambridge: M.I.T. Press.

    Google Scholar 

  • Mann, S. F. 2018. Attribution of information in animal interaction. Biological Theory. https://doi.org/10.1007/s13752-018-0299-5.

  • Millikan, R.G. 1995. Pushmi-Pullyu representations. Philosophical Perspectives 9: 185–200.

    Google Scholar 

  • Millikan, R.G. 2001. What has natural information to do with intentional representation? Royal Institute of Philosophy Supplements 49: 105–125.

    Google Scholar 

  • Millikan, R.G. 2013a. Natural information, intentional signs and animal communication. In Animal communication theory, ed. Stegmann U.E., 133–146. New York, Cambridge University Press.

  • Millikan, R.G. 2013b. Reply to Rescorla. In Millikan and her critics, eds. Ryder D., Kingsbury J., and Williford K., 103–106. New York, Wiley.

  • Millikan, R.G. 2017. Beyond concepts: Unicepts, language, and natural information. Oxford: OUP.

    Google Scholar 

  • Noel, A., Y. Fang, N. Yang, D. Makrakis, and A.W. Eckford. 2017. Using Game Theory for Real-Time Behavioral Dynamics in Microscopic Populations with Noisy Signaling.

  • Owren, M.J., D. Rendall, and M.J. Ryan. 2010. Redefining animal signaling: Influence versus information in communication. Biology & Philosophy 25 (5): 755–780.

    Google Scholar 

  • Pfeifer, J. 2006. The use of information theory in biology: Lessons from social insects. Biological Theory 1(3): 317–330.

    Google Scholar 

  • Piccinini, G., and A. Scarantino. 2011. Information processing, computation, and cognition. Journal of Biological Physics 37(1): 1–38.

    Google Scholar 

  • Price, H. 2008. Two readings of representationalism.

  • Rathkopf, C. 2017. Neural information and the problem of objectivity. Biology & Philosophy 32(3): 321–336.

    Google Scholar 

  • Rescorla, M. 2013. Millikan on honeybee navigation and communication. In Millikan and her critics, eds. Ryder D., Kingsbury J., and Williford K., 87–102. Wiley.

  • Rescorla, R.A. 1988. Pavlovian conditioning: It’s not what you think it is. The American Psychologist 43(3): 151–160.

    Google Scholar 

  • Rouder, J.N., R.D. Morey, N. Cowan, and M. Pealtz. 2004. Learning in a unidimensional absolute identification task. Psychonomic Bulletin & Review 11 (5): 938–944.

    Google Scholar 

  • Sarkar, S. 2013. Information in animal communication: When and why does it matter? In Animal communication theory, ed. Stegmann U.E., 189–205. New York, Cambridge University Press.

  • Scarantino, A. 2015. Information as a probabilistic difference maker. Australasian Journal of Philosophy 93(3): 419–443.

    Google Scholar 

  • Shannon, C.E. 1948. A mathematical theory of communication (Part 1). Bell System Technical Journal 27(3): 379–423.

    Google Scholar 

  • Shannon, C.E. 1959. Coding theorems for a discrete source with a fidelity criterion. In Collected Papers, Wiley-IEEE Press, pp 325–350.

  • Shannon, C.E., and W. Weaver. 1949. The mathematical theory of communication. Urbana: University of Illinois Press.

    Google Scholar 

  • Shea, N., P. Godfrey-Smith, and R. Cao. 2017. Content in simple signalling systems. The British Journal for the Philosophy of Science, axw036. https://doi.org/10.1093/bjps/axw036.

  • Sims, C.R. 2016. Rate–distortion theory and human perception. Cognition 152: 181–198.

    Google Scholar 

  • Skyrms, B. 2010. Signals: Evolution, learning, and information. Oxford: Oxford University Press.

    Google Scholar 

  • Stegmann, U.E. (Ed.) 2013. Animal communication theory: Information and influence. Cambridge University Press: New York.

  • Whyte, J.T. 1990. Success semantics. Analysis 50(3): 149–157.

    Google Scholar 

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Acknowledgements

Thanks to Ron Planer, two anonymous referees, and the editors for comments. Thanks also to Manolo Martínez for pointing me in the direction of rate-distortion theory. This research is supported by an Australian Government Research Training Program (RTP) Scholarship and Australian Research Council Laureate Fellowship Grant FL130100141.

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    Stephen Francis Mann

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Mann, S.F. Consequences of a Functional Account of Information. Rev.Phil.Psych. 11, 669–687 (2020). https://doi.org/10.1007/s13164-018-0413-4

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