The path to colour discrimination is S-shaped: behaviour determines the interpretation of colour models
- 332 Downloads
Most of our current understanding on colour discrimination by animal observers is built on models. These typically set strict limits on the capacity of an animal to discriminate between colour stimuli imposed by physiological characteristics of the visual system and different assumptions about the underlying mechanisms of colour processing by the brain. Such physiologically driven models were not designed to accommodate sigmoidal-type discrimination functions as those observed in recent behavioural experiments. Unfortunately, many of the fundamental assumptions on which commonly used colour models are based have been tested against empirical data for very few species and many colour vision studies solely rely on physiological measurements of these species for predicting colour discrimination processes. Here, we test the assumption of a universal principle of colour discrimination only mediated by physiological parameters using behavioural data from four closely related hymenopteran species, considering two frequently used models. Results indicate that there is not a unique function describing colour discrimination by closely related bee species, and that this process is independent of specific model assumptions; in fact, different models produce comparable results for specific test species if calibrated against behavioural data.
KeywordsSigmoidal Vision Bee Receptor noise Hexagon
The authors acknowledge Professor Leo Fleishman for discussions and comments on a previous version of the manuscript, and Dr. Lalina Muir for careful editing. We also thank the two anonymous reviewers and editors for valuable feedback on our study.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Bobeth H (1979) Dressurversuche zum Farbensehen der Bienen: Die Sättigung von Spektralfarben. PhD thesis, Albert-Ludwigs-Universität zu Freigburg i.BrGoogle Scholar
- Bukovac Z, Dorin A, Dyer AG (2013) A-bees see: a simulation to assess social bee visual attention during complex search tasks. In: Lio’ P, Miglino O, Nicosia G, Nolfi G, Pavone M (eds) Advances in artificial life ECAL 2013. Proceedings of the twelfth European conference on the synthesis and simulation of living systems, Taormina, September 2013. MIT Press, Cambridge, Complex adaptive systems, pp 276–283Google Scholar
- Chittka L (1992) The colour hexagon: a chromaticity diagram based on photoreceptor excitations as a generalized representation of colour opponency. J Comp Physiol A 170:533–543Google Scholar
- Daumer K (1956) Reizmetrische Untersuchung des Farbensehens der Bienen. Z Vergleich Physiol 38:413–478Google Scholar
- von Frisch K (1914) Der Farbensinn und Formensinn der Biene. Zool Jahrb Abt allg Zool Physiol Tiere 37:1–187Google Scholar
- von Helmholtz H (1970) Physiological optics. In: MacAdam D (ed) Sources of color science. MIT Press, Cambridge, pp 84–100Google Scholar
- van der Kooi CJ, Elzenga JTM, Staal M, Stavenga DG (2016) How to colour a flower: on the optical principles of flower coloration. Proc R Soc B 283:1–9Google Scholar
- Kulikowski J, Walsh V (1991) On the limits of colour detection and discrimination. In: Kulikowski J, Walsh V, Murray I (eds) Limits of vision. MacMillan Press, London, pp 202–220Google Scholar
- Lythgoe JN (1979) The ecology of vision. Oxford University Press, New YorkGoogle Scholar
- MacAdam DL (1985) Color measurement theme and variations, 2nd edn. Springer, BerlinGoogle Scholar
- Martínez-Harms J, Vorobyev M, Schorn J, Shmida A, Keasar T, Homberg U, Schmeling F, Menzel R (2012) Evidence of red sensitive photoreceptors in Pygopleurus israelitus (Glaphyridae: Coleoptera) and its implications for beetle pollination in the southeast Mediterranean. J Comp Physiol A 198:451–463CrossRefGoogle Scholar
- Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2016) nlme: linear and nonlinear mixed effects models. R package version 3.1-125Google Scholar
- R Core Team (2016) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/
- Shrödinger E (1926) Thresholds of color differences. In: MacAdam DL (ed) Sources of color science. MIT Press, Cambridge, pp 183–193Google Scholar
- Tabanick BG, Fidell L (2007) Using multivariate statistics, 5th edn. Pearson Allyn and Bacon, BostonGoogle Scholar
- Wyszecki G, Stiles W (1982) Color science concepts and methods, quantitative data and formulae, 2nd edn. Wiley, New YorkGoogle Scholar