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Relatedness influences signal reliability in evolving robots
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- Sara Mitri
- Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de Lausanne, EPFL, Station 11, 1015 Lausanne, Switzerland
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- Dario Floreano
- Laboratory of Intelligent Systems, Ecole Polytechnique Fédérale de Lausanne, EPFL, Station 11, 1015 Lausanne, Switzerland
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- Laurent Keller
- Department of Ecology and Evolution, University of Lausanne, Biophore, 1015 Lausanne, Switzerland
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Description
<jats:p>Communication is an indispensable component of animal societies, yet many open questions remain regarding the factors affecting the evolution and reliability of signalling systems. A potentially important factor is the level of genetic relatedness between signallers and receivers. To quantitatively explore the role of relatedness in the evolution of reliable signals, we conducted artificial evolution over 500 generations in a system of foraging robots that can emit and perceive light signals. By devising a quantitative measure of signal reliability, and comparing independently evolving populations differing in within-group relatedness, we show a strong positive correlation between relatedness and reliability. Unrelated robots produced unreliable signals, whereas highly related robots produced signals that reliably indicated the location of the food source and thereby increased performance. Comparisons across populations also revealed that the frequency for signal production—which is often used as a proxy of signal reliability in empirical studies on animal communication—is a poor predictor of signal reliability and, accordingly, is not consistently correlated with group performance. This has important implications for our understanding of signal evolution and the empirical tools that are used to investigate communication.</jats:p>
Journal
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- Proceedings of the Royal Society B: Biological Sciences
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Proceedings of the Royal Society B: Biological Sciences 278 (1704), 378-383, 2010-09
The Royal Society
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Keywords
Details 詳細情報について
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- CRID
- 1364233269009741056
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- ISSN
- 14712954
- 09628452
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- Data Source
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- Crossref