Abstract
This article establishes grounds on which attributions of information and encoding in animal signals are warranted. As common interest increases between evolutionary agents, the theoretical approach best suited to describing their interaction shifts from evolutionary game theory to communication theory, which warrants informational language. The take-home positive message is that in cooperative settings, signals can appropriately be described as transmitting encoded information, regardless of the cognitive powers of signalers. The canonical example is the honeybee waggle dance, which is discussed extensively in the second and third sections. The take-home negative message is that signals are not always a consequence of coadaptation. The communication theory approach is just one end of a continuum explored more thoroughly by evolutionary game theory. The fourth and fifth sections explore this wider framework, as well as overturning some widely held misconceptions about information theory.
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Notes
Stegmann’s edited volume (Stegmann 2013a) characterizes the debate as between information-based and influence-based definitions. However, as pointed out by several entries in that volume, information and influence do not form a strict dichotomy. Here I am primarily concerned with establishing grounds for the attribution of information, so I make little mention of influence-based definitions.
It is less clear how useful the dance is across different contexts, and the purpose for which it originally evolved. Several lines of evidence count in favor of nest site selection as the significant factor (Beekman et al. 2008; I’Anson Price and Grüter 2015). For simplicity I regretfully ignore this possibility.
Haldane and Wiener knew each other personally. For some interesting remarks on their relationship, see Dronamraju’s recent biography of Haldane (Dronamraju 2017, pp. 259–260).
The appropriate measure for the Gaussian distribution is presented in Shannon and Weaver (1949, p. 89). Haldane and Spurway cite Wiener (1948, p. 62) who provides a general formulation of information in a continuous distribution and does not appear to discuss the Gaussian case explicitly. For a derivation (and explanation) of the equation used by Haldane and Spurway (1954, p. 255) see Wilson (1962, Appendix).
In fact the von Mises distribution would have been more appropriate (Schürch and Ratnieks 2015). The Gaussian is an acceptable approximation.
The original example mentioned ants. Here and below I substitute bees without loss of generality. I retain the alphabetical labeling of types for ease of comparison with Pfeifer’s article. Space precludes discussion of model ABCD.
A typo in the published version of Pfeifer (2006, p. 325) erroneously cites this value as 1.96 bits/signal.
This definition has pedigree from at least two sources. First, Millikan’s definition of mapping rules between signal and world sustains the requirement of codesign and takes as a canonical example the articulation of the waggle dance (Millikan 1984, p. 107). Second, Skyrms’s account of the evolutionary emergence of conventional meaning outlines correspondences, resulting from coadaptation, between signals and the behaviors they cause (Skyrms 2010, \(\S \S\)3–5).
The closest I can find to the traditional irrelevance claim is on p. 395: “Measurements of Shannon information do not necessarily reveal anything about semantic information, although they often do.”
There is at least one prominent school of dissent to this orthodoxy, in the shape of the sender-receiver paradigm headed by Brian Skyrms (2010). Though Skyrms accepts that the total quantity of information in any given signal is silent on its content, he proposes a definition of information content that makes use of informational measurements (Skyrms 2010, \(\S\)3). Importantly, it does not cut between signals and cues. Like ORR’s definition of Shannon information, it is defined in terms of probabilities, not function. In what follows I leave the Skyrms account to one side. Though it is useful and innovative, there is a much more direct way to demonstrate the relationship between information in MCT and meaning in biological signals.
“Subpersonal” is applied to brain states that are not assumed to play a role in conscious thought. I use it here as a modifier to “content” that makes no assumptions further than codesign of signalers and receivers. It is therefore applicable to subcognitive and noncognitive systems in the manner of Shea (2007) and Millikan (2013).
Measured in bits per symbol, not per second.
Oliver Lean has made the same point (Lean 2016, pp. 239–240).
Signals with this dual character were labeled “neutral” by Lewis (1969), “pushmi-pullyu” by Millikan (1995) and “primitive” by Harms (2004). The latter is the preferred term. Incidentally, it might be thought that the instruction/information distinction is just another way of describing the influence/information distinction. Unfortunately, the latter is far more ambiguous.
The distinction between code-as-correspondence and code-as-redundancy mirrors a well-known duality in communication theory between data compression (“source coding”) and data transmission (“channel coding”). Speculatively, the selective pressures on animal communication may be skewed far more towards solving the latter problem. Hailman’s book (Hailman 2008) and Wiley’s essays (Wiley 1983, 2013) are examples.
Though see Bergstrom and Lachmann (2003) for conditions under which the slower-evolving organism enjoys the benefit, a phenomenon they call the Red King effect.
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Acknowledgements
Thanks are due to Jessica Pfeifer, Justin Bruner, Ron Planer, and two anonymous referees for comments on earlier drafts, and to Siva Kalyan for assistance with diagrams. Thanks also to audiences at the 2017 Sydney-ANU philosophy of biology workshop and the University of Sydney Social Insects lab, especially Madeleine Beekman and Isobel Ronai. 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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Mann, S.F. Attribution of Information in Animal Interaction. Biol Theory 13, 164–179 (2018). https://doi.org/10.1007/s13752-018-0299-5
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DOI: https://doi.org/10.1007/s13752-018-0299-5