Color discrimination is not just limited by photoreceptor noise: a comment on Olsson et al.

Color discrimination is not just limited by photoreceptor noise: a comment on Olsson et al. The comprehensive review by Olsson et al. (2018) highlights the versatility and value of Receptor Noise Limited models. Such models are useful in the study of animal vision, because they allow predicting the extent to which biologically salient visual signals and cues are detectable and distinguishable in any species (Clark et al. 2017), based on differences in the number of spectral classes of receptors, their spectral sensitivity functions, and associated noise. One has to be pragmatic about color spaces for nonhuman animals. Even in humans, where psychophysical data are comparatively easily obtained, it has not been possible to obtain a universal color difference formula that predicts color discrimination in all areas of color space, irrespective of stimulus size, intensity, illumination conditions, etc.—therefore, a rough guidance map is better than no map. Thus, using only (estimated) receptor noise to predict color discrimination is a useful starting point. However, one must be cautious in assuming that photoreceptor noise is the only factor constraining color discrimination. Noise occurs in all postreceptor neural processes, but various forms of spatial and temporal integration can counterbalance it. Consider the consequences of spatial summation at the first stage of visual processing. If inputs of multiple receptors are averaged, the signal-to-noise ratio of the receptor channel can be increased at the expense of spatial detail. For example, in honeybees, chromatic contrast sensitivity in behavioral experiments exceeds what we predict from electrophysiological measurements of receptor noise (Dyer et al. 2008); accordingly, the minimal visual angle for color contrast detection (15 degrees) covers 59 ommatidia, equipped with 9 photoreceptors each (Giurfa et al. 1996). It follows that discrepancies between electrophysiological and behavioral estimates of noise from a species (e.g. those reported in Table 1 of Olsson et al. (2018) for rock doves, brown owls, and American kestrels) may actually reflect real differences between the noise of receptors and the noise in more downstream color processing neurons. The differences in achromatic and chromatic discrimination abilities of any given animal, rightly emphasized by the review, also point to the importance of postreceptor processing. Again, in honeybees, achromatic contrast detection is possible at a visual angle of approximately 5 degrees, employing 7 ommatidia, as opposed to the 15 degrees and 59 ommatidia required for chromatic contrast detection (Giurfa et al. 1996). Thus, both achromatic and chromatic signals are summed up, but to a different extent. As a result, the behaviorally estimated noise of the receptor channel is higher for achromatic than chromatic vision (see Table 1 of Olsson et al. 2018). Interestingly, a similar phenomenon appears in human perceptual studies that indicate a much higher sensitivity to achromatic than chromatic blur (Kingdom et al. 2015). The same distinction does not hold true for bumblebees (Dyer et al. 2008). Such differences originate from species-specific postreceptor processing that should not be ignored when dealing with visual perceptual spaces. It is also useful to remember that the RNL model was originally introduced for determining color thresholds and not for calculating perceptual differences between easily distinguishable colors. Perceptual differences may or may not scale linearly with differences in opponent receptor responses, and we caution against making such an assumption before this issue has been convincingly settled. In any case, current evidence indicates nonlinearity of visual spaces. For example, a set of behavioral experiments, using several species of bees, showed that the success of discrimination from a gray background scales nonlinearly with color difference (Dyer et al. 2008; Dyer and Neumeyer 2005; Garcia et al. 2017; Spaethe et al. 2014). Finally, it is important that color vision has many cognitive elements (Skorupski and Chittka 2011). In humans for example, even language constrains color discrimination (Winawer et al. 2007). It is thus impossible to predict receptor noise from behavioral data; instead, noise must be measured with appropriate electrophysiological procedures (e.g. Skorupski and Chittka 2010). Color spaces, including those based on receptor noise, are useful in the same way as a Metro map is: they provide a rough guidance as to what is where, and how far A is from B, but one should be cautious in making overly precise predictions based on them. REFERENCES Clark RC, Santer RD, Brebner JS. 2017. A generalized equation for the calculation of receptor noise limited colour distances in n-chromatic visual systems. R Soc Open Sci . 4: 170712. Google Scholar CrossRef Search ADS PubMed  Dyer AG, Neumeyer C. 2005. Simultaneous and successive colour discrimination in the honeybee (Apis mellifera). J Comp Physiol A . 191: 547– 557. Google Scholar CrossRef Search ADS   Dyer AG, Spaethe J, Prack S. 2008. Comparative psychophysics of bumblebee and honeybee colour discrimination and object detection. J Comp Physiol A . 194: 617– 627. Google Scholar CrossRef Search ADS   Garcia JE, Spaethe J, Dyer AG. 2017. The path to colour discrimination is S-shaped: behaviour determines the interpretation of colour models. J Comp Physiol A . doi: 10.1007/s00359-017-1208-2. Giurfa M, Vorobyev M, Kevan P, Menzel R. 1996. Detection of coloured stimuli by honeybees: minimum visual angles and receptor specific contrasts. J Comp Physiol A . 178: 699– 709. Google Scholar CrossRef Search ADS   Kingdom FA, Bell J, Haddad C, Bartsch A. 2015. Perceptual scales for chromatic and luminance blur in noise textures. J Vis . 15: 6. Google Scholar CrossRef Search ADS   Olsson P, Lind O, Kelber A. 2018. Chromatic and achromatic vision: parameter choice and limitations for reliable model predictions. Behav Ecol . 29: 273– 282. Skorupski P, Chittka L. 2010. Differences in photoreceptor processing speed for chromatic and achromatic vision in the bumblebee, Bombus terrestris. J Neurosci . 30: 3896– 3903. Google Scholar CrossRef Search ADS PubMed  Skorupski P, Chittka L. 2011. Is colour cognitive? Opt Laser Technol . 43: 251– 260. Google Scholar CrossRef Search ADS   Spaethe J, Streinzer M, Eckert J, May S, Dyer AG. 2014. Behavioural evidence of colour vision in free flying stingless bees. J Comp Physiol A . 200: 485– 496. Google Scholar CrossRef Search ADS   Winawer J, Witthoft N, Frank MC, Wu L, Wade AR, Boroditsky L. 2007. Russian blues reveal effects of language on color discrimination. Proc Natl Acad Sci USA . 104: 7780– 7785. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behavioral Ecology Oxford University Press

Color discrimination is not just limited by photoreceptor noise: a comment on Olsson et al.

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Abstract

The comprehensive review by Olsson et al. (2018) highlights the versatility and value of Receptor Noise Limited models. Such models are useful in the study of animal vision, because they allow predicting the extent to which biologically salient visual signals and cues are detectable and distinguishable in any species (Clark et al. 2017), based on differences in the number of spectral classes of receptors, their spectral sensitivity functions, and associated noise. One has to be pragmatic about color spaces for nonhuman animals. Even in humans, where psychophysical data are comparatively easily obtained, it has not been possible to obtain a universal color difference formula that predicts color discrimination in all areas of color space, irrespective of stimulus size, intensity, illumination conditions, etc.—therefore, a rough guidance map is better than no map. Thus, using only (estimated) receptor noise to predict color discrimination is a useful starting point. However, one must be cautious in assuming that photoreceptor noise is the only factor constraining color discrimination. Noise occurs in all postreceptor neural processes, but various forms of spatial and temporal integration can counterbalance it. Consider the consequences of spatial summation at the first stage of visual processing. If inputs of multiple receptors are averaged, the signal-to-noise ratio of the receptor channel can be increased at the expense of spatial detail. For example, in honeybees, chromatic contrast sensitivity in behavioral experiments exceeds what we predict from electrophysiological measurements of receptor noise (Dyer et al. 2008); accordingly, the minimal visual angle for color contrast detection (15 degrees) covers 59 ommatidia, equipped with 9 photoreceptors each (Giurfa et al. 1996). It follows that discrepancies between electrophysiological and behavioral estimates of noise from a species (e.g. those reported in Table 1 of Olsson et al. (2018) for rock doves, brown owls, and American kestrels) may actually reflect real differences between the noise of receptors and the noise in more downstream color processing neurons. The differences in achromatic and chromatic discrimination abilities of any given animal, rightly emphasized by the review, also point to the importance of postreceptor processing. Again, in honeybees, achromatic contrast detection is possible at a visual angle of approximately 5 degrees, employing 7 ommatidia, as opposed to the 15 degrees and 59 ommatidia required for chromatic contrast detection (Giurfa et al. 1996). Thus, both achromatic and chromatic signals are summed up, but to a different extent. As a result, the behaviorally estimated noise of the receptor channel is higher for achromatic than chromatic vision (see Table 1 of Olsson et al. 2018). Interestingly, a similar phenomenon appears in human perceptual studies that indicate a much higher sensitivity to achromatic than chromatic blur (Kingdom et al. 2015). The same distinction does not hold true for bumblebees (Dyer et al. 2008). Such differences originate from species-specific postreceptor processing that should not be ignored when dealing with visual perceptual spaces. It is also useful to remember that the RNL model was originally introduced for determining color thresholds and not for calculating perceptual differences between easily distinguishable colors. Perceptual differences may or may not scale linearly with differences in opponent receptor responses, and we caution against making such an assumption before this issue has been convincingly settled. In any case, current evidence indicates nonlinearity of visual spaces. For example, a set of behavioral experiments, using several species of bees, showed that the success of discrimination from a gray background scales nonlinearly with color difference (Dyer et al. 2008; Dyer and Neumeyer 2005; Garcia et al. 2017; Spaethe et al. 2014). Finally, it is important that color vision has many cognitive elements (Skorupski and Chittka 2011). In humans for example, even language constrains color discrimination (Winawer et al. 2007). It is thus impossible to predict receptor noise from behavioral data; instead, noise must be measured with appropriate electrophysiological procedures (e.g. Skorupski and Chittka 2010). Color spaces, including those based on receptor noise, are useful in the same way as a Metro map is: they provide a rough guidance as to what is where, and how far A is from B, but one should be cautious in making overly precise predictions based on them. REFERENCES Clark RC, Santer RD, Brebner JS. 2017. A generalized equation for the calculation of receptor noise limited colour distances in n-chromatic visual systems. R Soc Open Sci . 4: 170712. Google Scholar CrossRef Search ADS PubMed  Dyer AG, Neumeyer C. 2005. Simultaneous and successive colour discrimination in the honeybee (Apis mellifera). J Comp Physiol A . 191: 547– 557. Google Scholar CrossRef Search ADS   Dyer AG, Spaethe J, Prack S. 2008. Comparative psychophysics of bumblebee and honeybee colour discrimination and object detection. J Comp Physiol A . 194: 617– 627. Google Scholar CrossRef Search ADS   Garcia JE, Spaethe J, Dyer AG. 2017. The path to colour discrimination is S-shaped: behaviour determines the interpretation of colour models. J Comp Physiol A . doi: 10.1007/s00359-017-1208-2. Giurfa M, Vorobyev M, Kevan P, Menzel R. 1996. Detection of coloured stimuli by honeybees: minimum visual angles and receptor specific contrasts. J Comp Physiol A . 178: 699– 709. Google Scholar CrossRef Search ADS   Kingdom FA, Bell J, Haddad C, Bartsch A. 2015. Perceptual scales for chromatic and luminance blur in noise textures. J Vis . 15: 6. Google Scholar CrossRef Search ADS   Olsson P, Lind O, Kelber A. 2018. Chromatic and achromatic vision: parameter choice and limitations for reliable model predictions. Behav Ecol . 29: 273– 282. Skorupski P, Chittka L. 2010. Differences in photoreceptor processing speed for chromatic and achromatic vision in the bumblebee, Bombus terrestris. J Neurosci . 30: 3896– 3903. Google Scholar CrossRef Search ADS PubMed  Skorupski P, Chittka L. 2011. Is colour cognitive? Opt Laser Technol . 43: 251– 260. Google Scholar CrossRef Search ADS   Spaethe J, Streinzer M, Eckert J, May S, Dyer AG. 2014. Behavioural evidence of colour vision in free flying stingless bees. J Comp Physiol A . 200: 485– 496. Google Scholar CrossRef Search ADS   Winawer J, Witthoft N, Frank MC, Wu L, Wade AR, Boroditsky L. 2007. Russian blues reveal effects of language on color discrimination. Proc Natl Acad Sci USA . 104: 7780– 7785. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2017. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Behavioral EcologyOxford University Press

Published: Mar 1, 2018

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