Modelling biases and biasing models: The role of 'hidden preferences' in the artificial co-evolution of symmetrical signals

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Recently, within the biology literature, there has been considerable interest in exploring the evolutionary function of animal displays through computer simulations of evolutionary processes (Arak Enquist, 1993, 1995a; Enquist & Arak, 1993, 1994; Johnstone, 1994; Hurd, Wachtmeister, Enquist, 1995; Krakauer Johnstone, 1995). Whilst we applaud biologists' adoption of the simulation techniques pioneered within the artificial sciences (see, for example, Meyer Wilson, 1991; Meyer, Roitblat, Wilson, 1993; Cliff, Husbands, Meyer, Wilson, 1994, for collections of such research), and feel that bi-directional cross-fertilisation between natural and artificial sciences has a bright future, we suggest that the application of such techniques to evolutionary modelling may prove to be problematic. Some debate has accompanied the work (Cook, 1995; Johnstone, 1995; Arak Enquist, 1995b; Stamp Dawkins Guildford, 1995) but attention to the methodology employed within this embryonic research paradigm has been cursory. Here we provide a critique of this methodology, concentrating on Enquist and Arak's (1994) exploration of the evolutionary function of complex symmetrical displays. We investigate their hypothesis that complex signal form, rather than being the product of evolutionary pressure for information exchange, is the product of `hidden preferences' inherent in sensory systems (i.e. sensory biases). Through extending their work and relaxing their assumptions we reveal that the `hidden preference' for symmetry proferred by Enquist and Arak (1994) is in reality a preference for homogeneity. We show that the flaws present in Enquist and Arak's (1994) study are immanent in any such evolutionary simulation model, and must be challenged if research within this paradigm is to prove worthwhile.
Original languageUndefined/Unknown
PublisherUniversity of Sussex
Publication statusPublished - 1996

Bibliographical note

Cognitive Science Research Paper, CSRP 414

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