The path to colour discrimination is S-shaped: behaviour determines the interpretation of colour models

Garcia, Jair E.; Spaethe, Johannes; Dyer, Adrian G.

doi:10.1007/s00359-017-1208-2

Cited by 46 publications

(93 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although we have focused on this popular approach here, particularly due to its utility for non-model organisms, a breadth of available modelling tools are capable of offering similar, and in some cases superior, insight (Kemp et al ., 2015; Price & Fialko, 2017; Renoult et al ., 2017). The hexagon model of Chittka (1992), for example, has been extensively tested and validated in honeybees, and may outperform the receptor-noise model when suitably parameterised (Garcia et al ., 2017). It too offers a psychophysiologically-informed measure of perceptual distance, as well as discrimination thresholds, and so may be readily applied within our suggested framework.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing colours using visual models

Maia

White

2017

Preprint

View full text Add to dashboard Cite

Lay Summary 13An outstanding challenge for the study of colour traits is how best to use "colour 14 spaces" to represent their visual perception, particularly when asking questions 15 of colour-difference (e.g. the (dis)similarity of males and females, mimics and 16 models, or sister species, to a given viewer). We use simulations to show that 17 existing methods fail to statistically and biologically estimate the separation of 18 groups in colour space, and we suggest a flexible, robust, alternative that avoids 19 those pitfalls. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/175992 doi: bioRxiv preprint first posted online Aug. 18, 2017; Abstract 21Colour in nature presents a striking dimension of variation, though understanding 22 its function and evolution largely depends on our ability to capture the perspec- (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Crucially, we can test and refine these models using psychophysical data (e.g. Dyer & Neumeyer, 2005; Garcia et al ., 2017;…”

Section: Introductionmentioning

confidence: 99%

Comparing colours using visual models

Maia

White

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Distances in chromaticity diagrams are assumed to represent chromaticity similarities between two colors. The assumption is that the longer the distance, the more dissimilar the two perceived colors are (note, however, that this relationship is not necessarily linear; see for instance Garcia et al., ). Chromaticity distances between a pair of reflectance spectra ( a and b ) are found by calculating the Euclidian distance between their color loci in the color space:

normalΔ S = \sqrt{{(X_{1_{normala}} - X_{1_{normalb}})}^{2} + {()(X_{2_{a}} - X_{2_{b}})}^{2} + \dots + {()(X_{n_{a}} - X_{n_{b}})}^{2} .}

…”

Section: A Generic Methods For N‐dimensional Modelsmentioning

confidence: 99%

“…1 JND when the background is red, but much higher when the background is green (Lind, ). Furthermore, the relationship between ΔS values and probability of discriminability varies between species and it is not necessarily linear, in particular for ΔS values that greatly surpass threshold values (Garcia, Spaethe, & Dyer, ). In addition, correct model parametrization is vital for RNL models, which are very sensitive to correct photoreceptor noise values (Lind & Kelber, ; Olsson et al., ) and the relative abundance of photoreceptors in the retina (Bitton et al., ).…”

Section: Guidelines and Limitationsmentioning

confidence: 99%

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Gawryszewski

2018

Ecology and Evolution

View full text Add to dashboard Cite

The development of color vision models has allowed the appraisal of color vision independent of the human experience. These models are now widely used in ecology and evolution studies. However, in common scenarios of color measurement, color vision models may generate spurious results. Here I present a guide to color vision modeling (Chittka (1992, Journal of Comparative Physiology A, 170, 545) color hexagon, Endler & Mielke (2005, Journal Of The Linnean Society, 86, 405) model, and the linear and log‐linear receptor noise limited models (Vorobyev & Osorio 1998, Proceedings of the Royal Society B, 265, 351; Vorobyev et al. 1998, Journal of Comparative Physiology A, 183, 621)) using a series of simulations, present a unified framework that extends and generalize current models, and provide an R package to facilitate the use of color vision models. When the specific requirements of each model are met, between‐model results are qualitatively and quantitatively similar. However, under many common scenarios of color measurements, models may generate spurious values. For instance, models that log‐transform data and use relative photoreceptor outputs are prone to generate spurious outputs when the stimulus photon catch is smaller than the background photon catch; and models may generate unrealistic predictions when the background is chromatic (e.g. leaf reflectance) and the stimulus is an achromatic low reflectance spectrum. Nonetheless, despite differences, all three models are founded on a similar set of assumptions. Based on that, I provide a new formulation that accommodates and extends models to any number of photoreceptor types, offers flexibility to build user‐defined models, and allows users to easily adjust chromaticity diagram sizes to account for changes when using different number of photoreceptors.

show abstract

“…We used the average reflectance from 20 species of Eucalyptus (Average Green Leaf) as background reflectance ( I B (λ)) for our calculations. We used a open sky, daylight ambient illumination equivalent to CIE 6,500 K [72], that represent typical daylight conditions for foraging insects [73]. The transduction of photoreceptor captures ( P ) into receptor excitations ( E ) is given by …”

Section: Methodsmentioning

confidence: 99%

Australian native flower colours: does nectar reward drive bee pollinator flower preferences?

Shrestha

Garcia

Burd

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

24Colour is an important signal that flowering plants use to attract insect pollinators 25 like bees. Previous research in Germany has shown that nectar volume is higher for flower 26 colours that are innately preferred by European bees, suggesting an important link between 27 colour signals, bee preferences and floral rewards. In Australia, flower colour signals have 28 evolved in parallel to the Northern hemisphere to enable easy discrimination and detection 29 by the phylogenetically ancient trichromatic visual system of bees, and native Australian 30 bees also possess similar innate colour preferences to European bees. We measured 59 31 spectral signatures from flowers present at two preserved native habitats in South Eastern 32 Australia and tested whether there were any significant differences in the frequency of 33 flowers presenting higher nectar rewards depending upon the colour category of the flower 34 signals, as perceived by bees. We also tested if there was a significant correlation between 35 chromatic contrast and the frequency of flowers presenting higher nectar rewards. For the 36 entire sample, and for subsets excluding species in the Asteraceae and Orchidaceae, we 37 found no significant difference among colour categories in the frequency of high nectar 38 reward. This suggests that whilst such relationships between flower colour signals and 39 nectar volume rewards have been observed at a field site in Germany, the effect is likely to 40 be specific at a community level rather than a broad general principle that has resulted in 41 the common signalling of bee flower colours around the world. 42 43 44 45 46 Shrestha et al., p. 3 47 Introduction 48 49 Many floral traits play a role in the reproduction of animal-pollinated angiosperms 50 [1-5]. Colour is one of the most important signals used by flowering plants to 51 communicate to their pollinators [6-10]. Flowers typically present nutritional rewards like 52 nectar to entice floral visitors [6,[11][12][13][14][15], and nectar is a reward that can promote learning 53 and neural changes in a bees visual system [16]. What relationship between floral colour 54 signals and nectar as a reward that promotes motivation in insect pollinators, should evolve 55 in plants? Whilst nectar has been well studied in flowering plants [15,[17][18][19][20], the question 56 of a potential relationship has been rarely considered because many animals have very 57 different colour vision to humans [21]. Thus, colour is not an unambiguous trait, and to test 58 colour as a factor in a biologically meaningful way it is necessary to map how relevant 59 pollinators like bees perceive and use colour information. 60Both male and female fitness of plants should often benefit from pollinator visits, 61 especially given widespread pollen-limitation of seed set [22]. Such fitness benefits for 62 plants will be especially strong with flower visitors like bees that tend to be 'flower 63 constant,' that is, to use colour signals to repeatedly visit flowers of the same plant speci...

show abstract

The path to colour discrimination is S-shaped: behaviour determines the interpretation of colour models

Cited by 46 publications

References 68 publications

Comparing colours using visual models

Comparing colours using visual models

Color vision models: Some simulations, a general n‐dimensional model, and the colourvision R package

Australian native flower colours: does nectar reward drive bee pollinator flower preferences?

Contact Info

Product

Resources

About