Identifying the structure in cuttlefish visual signals

Author:

Crook Anne C.1,Baddeley Roland2,Osorio Daniel2

Affiliation:

1. Department of Zoology and Animal Ecology, University College, Lee Maltings, Cork, Ireland ()

2. Laboratory of Experimental Psychology and School of Biological Sciences, University of Sussex, Falmer, Brighton BN1 9QG, UK

Abstract

The common cuttlefish ( Sepia officinalis ) communicates and camouflages itself by changing its skin colour and texture. Hanlon and Messenger (1988 Phil. Trans. R. Soc. Lond . B 320, 437–487) classified these visual displays, recognizing 13 distinct body patterns. Although this conclusion is based on extensive observations, a quantitative method for analysing complex patterning has obvious advantages. We formally define a body pattern in terms of the probabilities that various skin features are expressed, and use Bayesian statistical methods to estimate the number of distinct body patterns and their visual characteristics. For the dataset of cuttlefish coloration patterns recorded in our laboratory, this statistical method identifies 12–14 different patterns, a number consistent with the 13 found by Hanlon and Messenger. If used for signalling these would give a channel capacity of 3.4 bits per pattern. Bayesian generative models might be useful for objectively describing the structure in other complex biological signalling systems.

Publisher

The Royal Society

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology

Reference25 articles.

1. Baddeley R. J. Crook A. C. & Osorio D. 2003 Relating cuttlefish signalling to its environment: insights from nonlinear regression. (In preparation.)

2. Bishop C. M. 1995 Neural networks for pattern recognition. Oxford: Clarendon Press.

3. Cheeseman P. & Stutz J. 1996 Bayesian classification (AutoClass): theory and results. In Advances in knowledge discovery and data mining (ed. U. M. Fayyad) pp. 153-180. Boston MA: AAAI/MIT Press.

4. Ghahramani Z. & Hinton G. E. 1996 Parameter estimation for linear dynamical systems University of Toronto technical report CRG-TR-96-2.

5. A Bayesian classification of the IRAS LRS atlas;Goebel J.;Astron. Astrophys.,1989

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