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Maybe monochromatic is not the right word, what I mean is light consisting of a single wavelength (i.e. a spectral color) versus light composed of photons of multiple wavelengths (intermediate color).
For example, suppose we had a single, violet "laser" and a set of "lasers" combining into a purple color.*
Is it possible to combine the latter light sources such that their composite would perfectly match the violet one such that both look the same to a human eye (assuming perfect alignment)?
And would they look the same to all human eyes?
My guess is a no, because people might have different susceptibility to red and blue hues, and their aggregation functions may differ as well.
* Apparently, there are alternative definitions to distinguish the two colors (purple is reddish, violet bluish), but I am referring to the ones found in Wikipedia: Purple being an intermediate color and violet being a pure spectral color.
Interesting article on color perception in the animal kingdom: https://arstechnica.com/science/2020/06/experiments-show-hummingbirds-see-colors-youve-never-dreamed-of/
Artificial Polychromatic Light Affects Growth and Physiology in Chicks
Despite the overwhelming use of artificial light on captive animals, its effect on those animals has rarely been studied experimentally. Housing animals in controlled light conditions is useful for assessing the effects of light. The chicken is one of the best-studied animals in artificial light experiments, and here, we evaluate the effect of polychromatic light with various green and blue components on the growth and physiology in chicks. The results indicate that green-blue dual light has two side-effects on chick body mass, depending on the various green to blue ratios. Green-blue dual light with depleted and medium blue component decreased body mass, whereas enriched blue component promoted body mass in chicks compared with monochromatic green- or blue spectra-treated chicks. Moreover, progressive changes in the green to blue ratios of green-blue dual light could give rise to consistent progressive changes in body mass, as suggested by polychromatic light with higher blue component resulting in higher body mass. Correlation analysis confirmed that food intake was positively correlated with final body mass in chicks (R 2 = 0.7664, P = 0.0001), suggesting that increased food intake contributed to the increased body mass in chicks exposed to higher blue component. We also found that chicks exposed to higher blue component exhibited higher blood glucose levels. Furthermore, the glucose level was positively related to the final body mass (R 2 = 0.6406, P = 0.0001) and food intake (R 2 = 0.784, P = 0.0001). These results demonstrate that spectral composition plays a crucial role in affecting growth and physiology in chicks. Moreover, consistent changes in spectral components might cause the synchronous response of growth and physiology.
Citation: Pan J, Yang Y, Yang B, Yu Y (2014) Artificial Polychromatic Light Affects Growth and Physiology in Chicks. PLoS ONE 9(12): e113595. https://doi.org/10.1371/journal.pone.0113595
Editor: Wilfried A. Kues, Friedrich-Loeffler-Institute, Germany
Received: June 26, 2014 Accepted: October 29, 2014 Published: December 3, 2014
Copyright: © 2014 Pan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper.
Funding: This work was supported by the Chinese Special Fund for Agro-scientific Research in the Public Interest (grant numbers 201303091). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
7 Answers 7
There are plenty of aspects of the physical world that we as humans cannot detect or distinguish between.
- Most of the electromagnetic spectrum is invisible us. Our window of visible light is quite small.
- We cannot distinguish polarized light
- We cannot hear infra- or ultrasonic sound
- We cannot sense electric fields
- We cannot sense magnetic fields
. and we seem to be getting along just fine without that, even though there are plenty of animal species that can perceive these things.
This is simply because the things that we can sense are all that we know. Our senses make up our view of the world, and this is so natural and common to us that we do not even contemplate about these limitations.
If our senses had been different, we would not have been different, because — again — our senses make up our world. And as long as everyone have the same kind of senses, and there is no differentiation, like for instance color blindness while others can still see color, then there is no reason this would make for a major difference in psychology.
Only when we gain or lose part of our senses does it tend to affect us in a major way.
Cavil's rant from Battlestar Galactica about being locked inside a "human" body.
Only when we know what it is like to actually have (or not have) some senses do we have anything to compare with. As long as everything stays the same, we do not know anything expect that which we have always had.
So in short: no, you cannot expect this to have any profound impact. There will be small differences, such as that we will not invent color TV, but in large there will not be much difference at all.
There's a little that might be different, particularly when it comes to food. Meat is not safe to eat if it isn't cooked enough, and it's not easy to tell whether it's cooked well enough without color (trust me, I'm colorblind, I know this). Similarly, early civilizations tended to avoid red berries and similar because such a high proportion of them were poisonous. As a result, it's possible that early colorblind civilizations would have stricter self-imposed dietary requirements - no berries at all, and meat might have to be burnt black. Alternatively, they might take a less risk-averse approach - all berries are fine, raw meat is fine. This would result in a pretty high death toll early on, but maybe they'd develop stronger stomachs after a few dozen generations.
I can't imagine that any particular events would be changed, but a number of technologies would be at least superficially different - as it stands, almost everything electronic uses a status light that changes color between a color that means "ready" and another that means "not ready" (and sometimes a third that means "something has gone horribly wrong"). That convention wouldn't work for these people. Since intensity is tricky to modulate, the best approach would be to simply have multiply different lights, clearly labelled.
Traffic lights would have to be set up differently perhaps number of lights instead of color would need to be used.
In art (and aesthetics in general) pattern would be far more important than color. Being a little fanciful here, I could imagine that this might enhance early interest in geometry, and result in a more advanced state of mathematics by the modern era.
Medicine might be slightly inhibited early on - speaking from experience, it is hard to tell the difference between a rash and a bruise when you can't see the colors involved. I can't even tell the difference between a sunburn and a tan. There are a number of other medical conditions that before modern medical technology could only be identified through changes in the body's coloration as far as your colorblind people would be able to tell, these conditions would be completely asymptomatic until the patient died!
The overall sleep architecture did not differ significantly among LED, OLED or dim light conditions (Table 1). To investigate the effects of light exposure on sleep homeostasis, we analyzed the EEG delta power (0.5–4 Hz) during slow-wave sleep. Neither the delta power time course (p = 0.125 Supplementary Fig. 1a) nor the delta power density (p = 0.425 Supplementary Fig. 1b) differed significantly among the conditions. Subjective sleep and sleepiness assessment scores using the Oguri-Shirakawa-Azumi sleep inventory MA version and Karolinska Sleepiness Scale also did not differ significantly among the conditions (Supplementary Table 1, Supplementary Fig. 2).
Mean energy expenditure, respiratory quotient (RQ), and fat oxidation were analyzed separately according to sleep and wake periods (Fig. 1, Supplementary Fig. 3). During sleep, there was a difference in mean energy expenditure (F2,138 = 6.1, p = 0.003) with a significant post hoc comparison between OLED and dim light (p < 0.001 Fig. 1e). Mean RQ during sleep was also different among the light conditions (F1,126 = 3.8, p = 0.029) with significant post hoc comparison between LED and OLED (p = 0.016 Fig. 1f). This was consistent to the decrease in fat oxidation (F1,111 = 6.8, p = 0.003) with post hoc comparison revealing significance between LED and OLED (p = 0.001), and LED and dim light (p = 0.003 Fig. 1g). The effect of light persisted to the next morning after waking (Supplementary Fig. 3). Differences in mean energy expenditure (F1,63 = 7.5, p = 0.002) indicated significant post hoc comparison between dim light and OLED (p = 0.001), and dim light and LED (p = 0.047) (Supplementary Fig. 3a). Mean RQ remained high after waking (F2,78 = 3.7, p = 0.03) with a significant post hoc between LED and dim light (p = 0.024). Fat oxidation was significantly decreased (F2,78 = 5.6, p = 0.005) with post hoc comparison resulting in a significance between LED and dim light (p = 0.003 Supplementary Fig. 3b, c). Carbohydrate (p = 0.163) and protein oxidation (p = 0.307) were unaffected by the light conditions, both during sleep and after waking (Fig. 1h, Supplementary Fig. 3d). A two-way repeated measures ANOVA on the time course of energy metabolism with factors of light condition and time revealed no significant main effect of light condition or of the interaction between condition and time (Fig. 1).
Energy metabolism. Time course (left panel) and mean values during sleep (right panel) are shown for (a) energy expenditure, (b) respiratory quotient, (c) fat oxidation, and (d) carbohydrate oxidation indicated as mean ± SE (n = 10). Significant differences among light conditions in energy metabolism during sleep were assessed by one-way repeated measures ANOVA with Bonferroni’s adjustment, *p < 0.05, **p < 0.01, ***p < 0.001.
A two-way repeated measures ANOVA revealed no significant effect of the light condition on the time course of the core body temperature (p = 0.162), but a significant interaction between condition and time (F30,250 = 1.7, p = 0.016 Fig. 2a). Post hoc analysis showed a significant increase in body temperature in LED compared with dim light before sleep and a significant decrease in OLED compared with dim light during sleep (Fig. 2a). Mean body temperature was significantly lower (F1,120 = 21.1, p < 0.001) with post hoc revealing significant difference between OLED and dim light, and OLED and LED, both during sleep (p = 0.001 Fig. 2e) and after waking (p < 0.001 Supplementary Fig. 4a).
Thermoregulatory measures. Time course (left panel) and mean values during sleep (right panel) are illustrated. (a) Core body temperature, (b) distal temperature, (c) proximal temperature, and (d) distal proximal gradient expressed as mean ± SE. Time course analysis from two-way repeated measures ANOVA, post-hoc pairwise comparison with Bonferroni’s adjustment *p < 0.05, ***p < 0.001 between OLED and dim light, #p < 0.05 between LED and dim light. Mean temperature during sleep by light condition from one-way repeated measures ANOVA, post-hoc pairwise comparison with Bonferroni’s adjustment *p < 0.05, **p < 0.01, ***p < 0.001. (n = 10 core body temperature n = 8 skin temperature).
Skin temperature was assessed on the basis of proximal and distal temperatures from a total of eight locations. A two-way repeated measures ANOVA on the time course of the proximal temperature revealed no significant effect of the light condition (p = 0.327), but a significant interaction between condition and time (F30,201 = 1.8, p = 0.009). Post hoc analysis indicated a significant decrease in OLED compared with dim light after waking at 10:00 (Fig. 2c). Furthermore, proximal temperature, assessed at the forehead demonstrated a significant increase in OLED compared with LED during and after sleep, but no significant differences were observed in other locations (Supplementary Fig. 5). No significant main effects of light condition were detected for distal temperature or the distal proximal gradient (DPG) nor was there any interaction between condition and time.
Mean temperature during sleep was then assessed using a one-way repeated measures ANOVA on distal and proximal temperatures and on the DPG (Fig. 2). During sleep, the proximal temperature was different among the conditions (F2,96 = 8.6, p = 0.0004) with significant post hoc comparison between LED and OLED (p = 0.004), and LED and dim light (p = 0.002) (Fig. 2c). A greater widening of the DPG (F2,96 = 7.2, p = 0.001) was observed in both dim light (p = 0.02) and OLED (p < 0.001) compared with LED after post hoc analysis (Fig. 2h). The distal temperature did not differ significantly among the light conditions (Fig. 2f).
Relationship between body temperature and energy expenditure during the sleep and wake periods are shown in Fig. 3. In all lighting conditions, core body temperature and energy expenditure showed a higher value during wake period compared to that of sleep period (Fig. 3).
Correlation between core body temperature and energy expenditure. Correlation plotted as 5-min mean during sleep (circle) and wake (triangle) for dim (black), LED (blue), and OLED (orange) conditions.
Total urinary excretion of 6-sulfatoxymelatonin (aMT6s) did not differ between the light conditions (p = 0.923 Fig. 4a). Urinary melatonin metabolites and energy metabolism were then further analyzed stratified by light conditions. Urinary aMT6s positively correlated with energy expenditure under OLED (Supplementary Fig. 6). Urinary melatonin metabolites showed no correlation with RQ in each of the light conditions dim light (p = 0.52), OLED (p = 0.42), and LED (p = 0.41 Supplementary Fig. 6). A significant positive correlation between urinary aMT6s and fat oxidation was observed under OLED (r 2 = 0.46, p = 0.032). There was no correlation under dim light (r 2 = 0.36, p = 0.068) or LED (p = 0.96 Fig. 4b).
Urinary 6-sulfatoxymelatonin (aMT6s). (a) Concentration of total aMT6s normalized to urinary creatinine concentration. (b) Correlation between urinary aMT6s and fat oxidation of dim (black), LED (blue), and OLED (orange) light.
Light processing has been studied extensively in the context of circadian biology which emphasizes nonvisual (or non-image-forming) effects of environmental light (irradiance). These nonvisual effects include the synchronization of the circadian system, suppression of melatonin, regulation of sleep, as well as improvements of alertness and cognition –. We have shown that nonvisual responses related to alertness and cognition are associated with changes in regional brain activity detected by positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) –. A number of recent studies, using a wide variety of methodologies, revealed that acute or longer term human nonvisual responses are most sensitive to monochromatic light of wavelengths between ∼460 and 480 nm –, –. This sensitivity is much shorter than the overall maximum sensitivity of the photopic system (∼555 nm), and does not coincide with the maximum sensitivity of any of the individual classical photoreceptors (rods: ∼505 nm S-cones: ∼430 nm M-cones: ∼530 nm L-cones: 560 nm) , .
A fifth retinal photopigment, melanopsin, was recently discovered  and shown to be expressed in retinal ganglion cells (RGC) that are intrinsically light sensitive , with a maximum sensitivity between 420 to 480 nm –. Melanopsin-expressing RGC are implicated in nonvisual responses to light , . They project to numerous brain structures in rodents , , including hypothalamic nuclei such as the suprachiasmatic nucleus (SCN) and the ventrolateral preoptic area (VLPO), as well as many non-hypothalamic structures including the olivary pretectal nucleus (OPN), and amygdala. Melanopsin-expressing RGC also project to areas typically involved in vision such as the lateral geniculate nucleus (LGN) and the superior colliculus. In addition, melanopsin-expressing RGC project to the LGN and OPN in Macaques . These neuroanatomical pathways provide a mechanism by which irradiance changes could affect many brain functions, i.e. circadian entrainment, pupillary constriction, arousal, attention, and emotion regulation, as well as vision –, , , , , . However, classical visual photoreceptors are necessary to induce complete nonvisual responses to light . In addition, RGC which do not express melanopsin, and presumably are not intrinsically photosensitive, project to the SCN, intergeniculate nuclei (IGL) of the thalamus, and VLPO, suggesting that signal arising from the classical retinal photoreceptors reaches these structures , . The relative contribution of the different retinal photoreceptors has not been fully assessed.
Rod and cone responses to light are typically time-locked to the exposure, i.e. responses start and cease within a few ms after light is turned on and off, respectively. In addition, quick attenuation of rod and cone signals occurs in the presence of a constant light stimulus . Intrinsic light responses of the melanopsin-expressing RGC are much more sluggish and do not show attenuation: they are only detected seconds after light onset, and firing is maintained for minutes after the end of the light exposure. This feature suggests that these cells are able to account for the long integration time of the nonvisual system , . However, melanopsin-expressing RGC receive extrinsic inputs from rods and the three classes of cones, which enable melanopsin-expressing RGC to instantaneously respond to light exposure, and suggest an important role for rods and cones in the nonvisual response to light early in the exposure . Accordingly, assessment of the relative efficacy of different wavelengths indicates that M-cones contribute importantly to the initiation of the response in rodents, but later the melanopsin-expressing RGC are the dominant contributor . Similarly, at lower irradiance classical photoreceptors are sufficient to drive pupillary constriction in rodents while, at higher irradiance, melanopsin-expressing RGC are required to induce a full response . In addition, the wavelength sensitivity of rat SCN neuronal responses to light flashes suggests a contribution of rods and all cones to brief light exposures .
A role for S-cones in nonvisual responses in humans was inferred from data showing a greater increase in subjective alertness under violet light exposure (420–440 nm) . However, most human studies investigating the mechanisms of nonvisual responses to light employed monochromatic exposures stimulating most melanopsin-expressing RGC or M- and L-cones, but not S- cones , , , . S-cone contribution to nonvisual responses to light using violet light preferentially triggering these photoreceptors remains to be firmly established. In addition, nonvisual responses to different wavelengths in humans have only been characterized after relatively long duration exposures (at least tens of minutes), i.e. presumably after substantial attenuation of rod and cone signals. Thus, the relative contributions of blue, violet and green lights, and by inference of melanopsin-expressing RGC, S- and M-cones, in the establishment of nonvisual responses to light have not been assessed in humans.
Besides the known projections of melanopsin-expressing and non-melanopsin-expressing RGC to brain structures involved in nonvisual functions, most of the brain mechanisms and pathways mediating nonvisual responses to light exposure are unknown. In rodents, the SCN and thalamic IGL receive light irradiance information almost directly and appear therefore to be strongly implicated in eliciting nonvisual responses to light , . The SCN and IGL project to many brain structures involved in arousal regulation ,  and a functional indirect connection between the SCN to the brainstem locus coeruleus (LC) has been established . This SCN -brainstem projection may be the pathway by which light modulates alertness. However, beyond these candidate subcortical and brainstem structures, the brain mechanisms involved in generating physiological or behavior nonvisual responses to light have not been characterized in animals.
In humans, using PET and fMRI, we have identified neural correlates of the alerting effects of a bright white light exposure (>7000lux), delivered at night or during the day in brain areas such as the intraparietal sulcus (IPS), hippocampus, thalamic pulvinar, insula, and hypothalamus , . More recently we demonstrated that brain activity related to a working memory task is maintained (or even increased) by blue (470 nm) monochromatic light exposure, whereas it decreases under green (550 nm) monochromatic light exposure . These effects were detected in areas implicated in working memory such as the thalamus, insula, IPS, supramarginal gyrus, and middle frontal gyrus (MFG). However, these studies were carried out using relatively prolonged light exposures (17 to 21 min). The brain areas first affected by light exposure, and by inference involved in establishing nonvisual responses to light, are therefore largely unknown in humans.
In the present study we used fMRI to specifically assess early effects of light over the entire brain while participants were performing an auditory working memory task. We used alternating violet (430 nm), blue (473 nm), or green (527 nm) monochromatic light exposures of equal photon density to investigate the processing of stimuli preferentially triggering S-cones, melanopsin-expressing RGC, or M-cones, respectively. Light exposures lasted 50 s, a very short duration from a human circadian biology perspective. We hypothesized that such short duration exposures would induce sustained modulation of the brain responses related to the blocks of the task performed, and that these modulations were wavelength-dependent. This would allow insight in the relative contributions of the different retinal photoreceptors early on in the establishment of nonvisual responses to light. On such a short time scale it is difficult to establish whether the detected brain activity modulations constitute nonvisual or visual responses. This, however, is not essential for our aim, which was to identify brain mechanisms involved in establishing responses to light exposures which eventually will lead to nonvisual responses such as changes in cognition and alertness. We also hypothesized that such short exposures would not induce wavelength-specific responses in a large number of brain areas but would mainly affect a few areas involved in the establishment of the responses, presumably subcortical and brainstem areas. The results support our hypotheses and suggest a prominent role of melanopsin-expressing RGC in the establishment of brain responses to light.
What are Rods
Rods are rod-shaped, light-sensitive cells on most of the peripheral parts of the retina in the vertebrate eye. About 120 million of rods are found in the retina and they are very sensitive to the light. The vision gained by rods is called scotopic vision. Since rods are sensitive to the scattered light, they provide the vision at night. However, rods are not sensitive to colors. Thereby, they provide a monochromatic vision. Rods mainly occur in the peripheral regions of the retina and the fovea centralis, which is the center region of the retina is free with rods. The structure of a rod cell is shown in figure 1.
Figure 1: Rod Cell
Rhodopsin is the type of visual pigments present in rods. All the membrane stacks of the rod cells contain rhodopsin. Therefore, only one type of rods can be identified in the retina. The light response of the rod cells sharply peaks at the blue color.
Results and Discussion
The color discrimination model indicated that the vast majority of sexually monochromatic human species sampled in this study were actually sexually dichromatic to the avian visual system, regardless of the thresholds used to define dichromatism (Fig. 1f ). At a ΔS = 1.0 jnd threshold, 129 of 139 (92.8%) species sampled possessed at least one dichromatic avian feather patch. Even when the threshold for discrimination was doubled (ΔS = 2.0 jnd), 84 of 139 (60.4%) species were dichromatic avian (Fig. 1f ). Passerine birds represent the largest avian radiation [>5,000 species (20)], and previous workers have estimated that 69% of these species are sexually monochromatic human (30) (Fig. 1a ). Whereas some have pointed out that this estimate may be slightly high (13), my results indicate that it is a vast overestimate when avian visual capabilities are considered. Even under conservative thresholds for discrimination (1.5–2.0 jnd), I reestimated that only 18.4–27.3% of all passerines are sexually monochromatic avian (Fig. 1 d and e ). Historical dependence on human color perception has led to overestimates of numbers of species that are functionally monochromatic (i.e., monochromatic avian ) my results revealed widespread conflict between human and avian perception of plumage for a large proportion of these species (Fig. 1 b–e ).
Percent of passerine species classified as sexually monochromatic (black) or sexually dichromatic (white). (a) Estimated proportions of all passerines (by 30) based on human visual capabilities. (b–e) Reestimated proportions of all passerines from an avian visual perspective, based on the results of this study. (f) Percent of 139 human perceived sexually monochromatic species sampled defined as sexually monochromatic or sexually dichromatic under the Vorobyev–Osorio model (19), assuming different discrimination thresholds of ΔS (in jnd). Each reestimation (b–e) is derived from the corresponding (directly below) percentage of dichromatic species shown in f, based on the indicated discrimination threshold value. The plumage classifications of the majority of all passerine species are in conflict from an avian visual perspective compared with a human visual perspective (compare a with b–e), unless threshold discrimination was set very conservatively (≥2.0 jnd).
The human–avian perceptual conflict of plumage for many birds calls into question current interpretations of evolutionary patterns of sexual dichromatism. Mistaken designation of a large proportion of dichromatic avian species as monochromatic focused research on evolution of sexual dichromatism to a state detectable with human visual capabilities, consequently interpreting gains of sexual dichromatism as the origins of sexual dichromatism. Furthermore, although dichromatic forms have often been assumed to be derived from monochromatic forms by means of sexual selection for ornamentation (2), recent research has found sexual dichromatism commonly to be the ancestral condition, with selection acting to reduce ornamentation in one sex (reviewed in ref. 5). The prevalence of dichromatic avian species found in this study, including several basal passerine lineages (31), suggests that comparative methods would reconstruct a dichromatic avian ancestor for all passerines, although detailed analyses remain to be conducted. In general, my results support a revised picture: A few monochromatic forms evolved from dichromatic forms, although an extreme bias in the evolution of dichromatism from monochromatic avian ancestors cannot be ruled out (32). Ultimately, further targeted taxon sampling combining analyses of plumage coloration relevant to avian visual capabilities with comparative methods is needed to determine the directionality of plumage change within passerine lineages.
The Vorobyev–Osorio model defines color discrimination based on integration across the entire range of visual wavelengths, giving no indication as to the relative contributions of specific wavelengths causing perceptual differences. UV plumage colors have been shown recently to be taxonomically widespread in birds (18, 33, 34), contributing to sexual dichromatism that humans cannot see (11–16). However, logistic regression modeling showed that only 23 of 552 (4.2%) feather patches sampled (representing 22 species) had strong correlations between only Q1 (i.e., the UV quantum catch) and sex (see Table 1). In contrast, 198 of 552 (35.9%) feather patches had strong correlations between at least one of Q2–Q4 and sex, indicating that wavelengths within the human visual range (400–700 nm) were strong predictors of sex, even though a given pair of homologous feather patches appeared as identical to the human eye. Hence, sexual dichromatism hidden from human perception commonly spans wavelengths within human visual capabilities (Q2–Q4), and not just in the UV. My data suggest that future studies need to consider the potential for intersexual color differences perceptible to the avian visual system even on species lacking plumage with likely UV-reflecting colors (e.g., blue or violet see refs. 15, 16, and 18).
For 119 of 139 (85.6%) species sampled, at least one quantum catch for one feather patch was strongly correlated with sex (Fig. 2). For 33 of 552 (6.0%) feather patches (found on 28 species) there was no overlap in quantum catch values between males and females for at least one Q i, and thus that portion of coloration predicted sex perfectly (see Table 1). My results are based on relatively small sample sizes for each feather patch comparison (n = 5 males and 5 females), and larger sample sizes would provide useful information regarding within-sex variance in coloration. If between-sex variances in coloration for many of the feather patches were exceeded by within-sex variances, then the biologically functional relevance (i.e., reliability of plumage color to indicate sex) of intersexual color differences would be in question. Overall, the logistic regression showed small within-sex variances in relation to between-sex variance for many feather patches. Because the goal of this study was to sample widely across taxa, sample sizes were exchanged for taxonomic breadth nonetheless, my results support the average colors used in the color discrimination model as biologically functional information (29), and a functional correlation between human-invisible coloration and sex for many of the monochromatic human species sampled in this study appears clear.
Example of one quantum catch (Q i) that was strongly correlated with sex under a logistic regression model. The example shown here, from the head coloration of Prinia atrogularis, used a logit link function to back transform the predicted probability of an individual being male given a value for Q3 (i.e., quantum catch for the middle-wave sensitive cone Eq. 1 ), based on the regression coefficient estimate (Table 1) from logistic regression modeling (see Materials and Methods). The five male Q3 (black circles) and five female Q3 (white circles) values used to estimate the regression coefficient are plotted to confirm that increasing values of Q3 strongly correlate with an increasing probability of an individual being male in this example.
Herein, the visual model calculated distance in avian perceptual color space (ΔS) between each conspecific male and female feather patch. Thus, ΔS calculations represent a means to quantify sexual dichromatism (Fig. 3), explicitly considering birds' color discriminatory abilities. Assuming a threshold for discrimination of 1.0 jnd, the magnitude of dichromatism for a feather patch can be calculated as ΔS - 1. ΔS values for feather patches sampled in this study ranged from 0.06 jnd (head coloration of Pseudochelidon eurystomina) to 12.71 jnd (crown coloration of Phlegopsis nigromaculatus) 324 of 552 (58.7%) feather patches sampled would be perceptible as sexually dichromatic to birds (ΔS > 1.0 jnd Fig. 3). Although previous research quantifying dichromatism established dichromatism values across species (e.g., refs. 3, 4, and 35), lower bounds for this continuum were set by the limits of human color discrimination. As a result, most studies of sexual selection for elaborate color ornamentation have focused on only a subset of the continuum that is functionally relevant to birds (e.g., many examples in ref. 2). Recent research concluded that avian plumage signals can exploit intertaxon perceptual differences (36), and inclusion of conspecific signals hidden from human investigators but shown in this study to be available to birds could further facilitate understanding of functions of plumage coloration. For example, since the discovery of sexual differences in the blue-UV crown coloration of blue tits (11–12), color in this feather patch has been shown to be important in social interactions (37), mate choice (38), parental care (39), and offspring sex ratios (40).
Proportions of 552 feather patches sampled from 139 human-perceived sexually monochromatic species with different magnitudes of sexual dichromatism from an avian visual perspective. Dichromatism scores are equivalent to ΔS (in jnd) calculated from Eq. 2 . Assuming a threshold for color discrimination of 1.0 jnd, a large proportion of feather patches sampled (all ≥1.00 jnd) lie along a continuum of avian sexual dichromatism that is not detectable with human visual capabilities.
In conclusion, the Vorobyev–Osorio color discrimination model (19) offers an approach to quantifying sexual dichromatism in relation to avian visual capabilities. My results indicate that sexually monochromatic avian passerine bird species are much less common than previously thought. These results have far-reaching implications for behavioral and ecological studies of birds, because plumage signals hidden from human perception might be a pervasive feature of avian coloration and not merely restricted to UV wavelengths. Furthermore, the results of this study refocus questions of plumage evolution toward an explanation of the rarity of monochromatic species, rather than dichromatic species, which could provide novel insights into the role of different selective pressures driving avian plumage evolution.
Some have claimed to have glimpsed galaxies three million light years away
The trillion stars in the Andromeda Galaxy, on account of their extreme distance, add up to just a fuzzily luminous patch in the sky. That said, the Andromeda Galaxy is colossal. In terms of its apparent size, even quintillions of miles away, the galaxy is six times the width of the full Moon. But so few of its photons reach our eyes that this celestial behemoth is rendered faint.
How clearly can we see?
Nevertheless, why is it that we can't pick out individual stars in the Andromeda Galaxy? The limits of our visual resolution, or acuity, come into play here. Visual acuity is the ability to discern a detail such as a point or line as separate from another without them blurring together.
You might therefore think of acuity's limits as the number of "pixels" we can discern.
Several factors set the boundaries for visual acuity, such as the spacing between the cones and rods packed onto the retina. The optics of the eyeball itself, which as we mentioned before prevent every available photon from alighting upon a photoreceptor cell, are important as well.
Eye charts test our ability to see the black and white differences that form a letter (Credit: Thinkstock)
Theoretically, studies have shown, the best we can do is about 120 pixels per degree of arc, a unit of angular measurement. That works out to about a fingernail held at arm's length with 60 horizontal and 60 vertical lines on it, alternating in black and white, creating a checkerboard pattern. "That's about the finest pattern you could ever see," says Landy.
Vision tests, like the popular Snellen eye chart at your optician's with the progressively smaller letters on it, operate on the same principle. The chart gauges at what point someone can no longer separate out a white gap in a black letter, distinguishing a capital F from a capital P, for instance. These acuity limits help explain why we cannot discern and focus on a single, dim, biological cell that's mere micrometres across.
But let's not sell ourselves short. A million colours single photons galactic realms quintillions of miles distant – not bad for the blobs of jelly in our eye sockets, wired to a 1.4 kilogram sponge in our skulls.
In a classical wavelength discrimination experiment, the observer views a bipartite field, one half filled with light of a standard wavelength and the other with light of a comparison wavelength. The wavelength of the comparison field is changed in small steps and the observer adjusts the radiance of the comparison field following each change in an attempt to make the two fields perceptually identical. Wavelength discrimination threshold is reached when the observer reports that the two fields always appear different, regardless of the radiance of the comparison . This discrimination threshold in humans is a “w” shaped function of the wavelength of the light: it has a central peak at around wavelength nanometers (nm), minima at and nm, and rises up sharply for nm and for very short wavelengths similar results hold for the macaque monkey and presumably other old world primates.
This work aims to see if human monochromatic light discrimination thresholds can be understood as optimal decoding of the sensory input using the information available in the cones, regardless of the specific neural mechanisms involved. In particular, we derive and evaluate a photon noise limited ideal observer that performs wavelength discrimination based on the numbers of photons absorbed in the three classes of cone. It is well known that human performance does not approach that of a photon noise limited ideal observer, [ 4], [ 5], [ 6], and thus our primary aim here is to determine how well the shape of the human wavelength discrimination function is explained by the ideal observer, regardless of its overall amplitude. If the shape were perfectly explained, then it would imply that the neural mechanisms following the cones are equally efficient for different wavelengths.
Wavelength discrimination of monochromatic lights is one of the visual tasks most suited to ideal observer analysis for the following reasons. Input sampling by the photoreceptors is among the best quantitatively understood process along the visual processing pathway. In particular, the wavelength sensitivities of cones are known, and the stochastic nature of the cone absorption levels can be described by Poisson distributions of absorption levels. The discrimination task is simple because it involves purely chromatic discrimination, so the spatial and temporal aspects of the inputs can be ignored or absorbed by the scale for the total input intensity. Therefore, total cone absorptions by the excited cones can lead to sufficient statistics for analysing the consequent decoding and its uncertainty of the input stimulus.
There have been many previous studies using ideal observer analysis to understand human visual performance, [ 3], [ 4], [ 5], [ 8], [ 6]. Geisler in particular used such an analysis to understand many human discrimination tasks based on cone responses. Among these tasks analyzed is our task of monochromatic light discrimination. His work and the current work are both based on the maximum likelihood method which can be used to optimally estimate or discriminate sensory inputs from their evoked neural responses. These two methods are approximately equivalent in the principle of maximum likelihood discrimination of two stimuli. However, this previous work did not identify an important issue that is essential for fully understanding the behavioral data. This issue is that of a confound in perception of multiple sensory features – in particular, human observers can easily confuse an input color change with an input intensity change when monochromatic lights are the inputs for example a long wavelength input may appear darker when the input wavelength is increased while input intensity is held fixed. This confusion reduces human ability in hue discrimination when observers do not have the full knowledge of input intensities. To fully account for the behavioral data, this confound should be formulated explicitly in the ideal observer analysis.
The current work presents an augmented formulation of the ideal observer analysis to address sensory discrimination under a perceptual confound, and applies it to wavelength discrimination behavior. The sensory input includes both sensory feature dimensions: one is the input wavelength dimension whose discrimination is of interest, and the other is the input intensity dimension which interferes or interacts with wavelength discrimination through the perceptual confound and the experimental methods used. Our mathematical formulation of this problem of sensory discrimination under perceptual confound is general. While it is applied specifically to the wavelength discrimination problem in this paper, it can also be applied elsewhere. It will enable us to identify experimental methods which can provide more reliable measurments of the discrimination performance. From our formulation, we derive how the threshold is related to the cones' wavelength sensitivities and the input light intensity, illustrate how sensitively the predictions depend on the relative densities of the three types of cones in the retina, and analyze why the discrimination threshold varies with the input wavelength in the ways observed. We show that our theoretical predictions from the augmented ideal observer analysis to accommodate the perceptual confound can give a better account of the behavioral data. Furthermore, we show how different sizes of stimuli used by different experiments may explain their different patterns of results. A preliminary report about this work has been presented elsewhere.
Rods Do Not See Red!
The light response of the rods peaks sharply in the blue they respond very little to red light. This leads to some interesting phenomena:
Red rose at twilight: In bright light, the color-sensitive cones are predominant and we see a brilliant red rose with somewhat more subdued green leaves. But at twilight, the less-sensitive cones begin to shut down for the night, and most of the vision comes from the rods. The rods pick up the green from the leaves much more strongly than the red from the petals, so the green leaves become brighter than the red petals!
The ship captain has red instrument lights. Since the rods do not respond to red, the captain can gain full dark-adapted vision with the rods with which to watch for icebergs and other obstacles outside. It would be undesirable to examine anything with white light even for a moment, because the attainment of optimum night-vision may take up to a half-hour. Red lights do not spoil it.
These phenomena arise from the nature of the rod-dominated dark-adapted vision, called scotopic vision.