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Color is a perceptual attribute arising from the optical spectrum reaching an observer and the observer's visual processing and viewing conditions. Cone cells have overlapping spectral sensitivities, so their responses do not provide a separate measurement of every wavelength. Different optical spectra can therefore produce the same color match, an instance of metamerism.

For a transmitting sample, the incident optical spectrum is modified by wavelength-dependent transmittance. Removing part of the blue band can make the remaining light appear yellow, but the absorption maximum alone does not specify the resulting shade. Changing the illuminant or background can change color appearance without changing the sample.

Vision under conditions in which rods and cone cells can both contribute. Describing all dim-light performance as either purely scotopic or purely photopic can miss their interactions.

Distinct spectra can give the same colour response for a specified observer and illumination. In a simplified three-cone cell model, equal values of $\int L(\lambda )R(\lambda )S_i(\lambda )d\lambda$ for $i=1,2,3$ need not imply identical spectral reflectance functions $R$. Altering illumination or observer can break the match.

An OFF retinal bipolar cell is a retinal bipolar cell whose central receptive response shows depolarization for an appropriate decrease in illumination. Its input synapse preserves the sign of a cone photoreceptor's voltage response through excitatory AMPA receptors or kainate receptors, rather than the sign-inverting arrangement of an ON retinal bipolar cell.^[ref]

In a 2000 experiment, simultaneous recordings from connected cone cells and OFF retinal bipolar cells, together with receptor-selective manipulations, distinguished receptor mechanisms in different morphological OFF types. Differences in recovery from receptor desensitization filtered the same cone signal into different temporal components.^[ref]

Thus two OFF types can agree on whether a light decrement excites them while differing in response timing. Response sign, temporal filtering and neuron identity are related measurements, not interchangeable labels. The result from the studied retina should not be taken as an identical receptor assignment for every OFF subtype in every species.

Individual differences show that color vision depends on receptor signals, their neural interpretation and the task used to measure them. They provide natural perturbations of the system, but differences in genes, matching, discrimination, naming and subjective appearance must not be treated as interchangeable.

A useful starting point is the principle of univariance. For a cone cell class $j$, a simplified receptor quantum catch is $Q_j=\int N(\lambda )a_j(\lambda )d\lambda$, where $N$ is the incident photon distribution, including optical filtering, and $a_j$ is effective absorptance. One receptor's absorption count does not uniquely identify wavelength: increasing intensity can compensate for a change in spectral sensitivity. Comparing classes supplies spectral information. With three classes, distinct spectra can satisfy three equal-catch constraints and therefore be metamers. This is information loss: if a spectrum is represented by more than three independent wavelength bins, its three-channel sampling matrix has a nontrivial kernel. Around a strictly positive spectrum, sufficiently small changes along that kernel preserve nonnegative stimulus values and all three catches. Thus the observer, not wavelength alone, defines a color match. With luminance fixed, three independent receptor coordinates leave at most two independent chromatic coordinates.

Inherited differences test that account. Dichromacy removes one independent class of cone input; anomalous trichromacy retains three classes but with altered spectral separation, often weakening particular discriminations. L and M are overlapping long- and middle-wave sensitivities, not detectors that individually label light “red” and “green.” Molecular work by Jeremy Nathans and colleagues identified cone pigment genes, then related inherited red–green variation to changes in the corresponding gene arrays.^[ref][ref] The important inference is a relation between biological variation and available spectral signals, not a claim that all observers with one label have identical vision.

A Rayleigh color match makes that inference measurable. An observer adjusts a mixture of two narrowband lights to match a comparison light, with brightness adjusted separately. The accepted mixture and matching range constrain pigment sensitivities and discriminability. A color discrimination task instead measures whether two stimuli can be distinguished, ideally with luminance cues controlled and a fitted psychometric function. Plate tests and everyday color names are less specific: learned labels or a brightness difference can support a correct response without normal chromatic discrimination. Conversely, reduced discrimination does not imply a world without color.

Individual differences also reveal compensation. Heidi Hofer and colleagues used adaptive optics retinal imaging with selective pigment bleaching and retinal densitometry to classify cone cells in living eyes. In their small sample, men passing normal color-vision tests had L:M cone ratios spanning approximately 1.1:1 to 16.5:1; the paper's corrected abstract supplies the lower value. The ratio was not fixed even within the normal category.^[ref] This rules out a simple requirement for one universal cone proportion. For example, opponent color processing could compare gain-adjusted cone signals rather than raw cell counts. It is compatible with post-receptoral gain adjustment and chromatic adaptation, but normal test scores do not prove identical subjective experiences or one uniquely identified compensatory mechanism.

A stronger question is whether more receptor classes create more perceptual dimensions. Heterozygous carriers of certain X-linked pigment variants can, through X-chromosome inactivation, have a retina containing more than three spectrally distinct cone types. Yet four types need not supply four functionally independent signals to behavior. Gabriele Jordan and colleagues combined forced-choice matching, multidimensional scaling and genetic analysis. One of 24 obligate carriers of deuteranomaly showed behavior consistent with tetrachromacy across their tests; most did not.^[ref] This distinction between potential and demonstrated extra dimensionality is crucial. The selected sample cannot establish population prevalence, and additional opsin variants alone cannot establish enhanced discrimination or richer experience.

Finally, optical and experiential differences must be controlled. Lens and macular filtering change the spectrum reaching the cone cells; age, adaptation, illumination and task learning can therefore mimic or modify receptor differences. Repeated measurements should separate within-person variability from stable between-person variation. Matching should be compared with discrimination, naming and appearance judgments rather than assuming that one measures all the others. Color naming can change without changing the receptor information; matching can change without entailing a particular verbal category.

The strongest conclusions arise when genetics, retinal measurements and controlled behavior converge. Variation establishes both the constraints imposed by receptor sampling and the flexibility of later processing; it does not directly disclose another person's color qualia.

A distinction between physical constitution and perceptual response is plausible and scientifically useful. A division into wholly objective primary qualities and wholly unreal secondary qualities is much harder to defend. These are different interpretations of the primary and secondary qualities distinction.

John Locke attributes extension, solidity, shape, number and motion to bodies as primary qualities. Secondary qualities are powers to produce sensations through the constitution of bodies: a body's power to produce a colour experience is distinct from the experience itself. This distinction need not make the power unreal. Nor should the further resemblance thesis, that ideas of primary qualities resemble their causes, be confused with the more modest claim that bodily constitution explains experience.^[ref]

A modern model of colour perception shows what can be retained. For a nonfluorescent surface in fixed viewing geometry, a simplified cone cell response isHere $L$ is the incident spectral irradiance, $R$ the spectral reflectance, and $S_i$ an effective sensitivity for one of three cone cell classes. Neural processing and adaptation also matter, so these responses are not a complete identification of perceived colour. Nevertheless, the model separates the surface, illumination and observer rather than assigning every experienced variation to a changing surface.

It follows that one surface can produce different responses under different illumination. Conversely, distinct spectral reflectance functions can produce the same three responses for a specified illuminant and observer, a case of metamerism. Three weighted integrals do not in general uniquely determine a whole function. Thus identifying experienced colour with one intrinsic surface property loses both the dependence on the observer and the many-to-one character of the response.

The scientific benefits are concrete. A comparison of paints should specify illumination and observer conditions. A measurement intended to characterize a material can instead estimate its spectral reflectance, preserving information that a single colour match discards. In both cases the distinction tells an investigator what to hold fixed and what to vary. It organizes experiments and explains disagreement without requiring that one observer be hallucinating.

However, perceptual variability alone does not distinguish primary qualities from secondary qualities. George Berkeley points out that apparent extension and shape also change with viewpoint and perceiver.^[ref] A circular disc yields different projected shapes at different viewing angles. The stable object is inferred through a model of that variation; it is not simply copied into experience. Primary qualities therefore have no automatic exemption from the interpretative work involved in perception.

Nor is relationality equivalent to subjectivity. In special relativity, measured length depends on an inertial frame, but relations between such measurements are tightly constrained. Conversely, a disposition to affect a specified perceiver under specified conditions can be an objective physical fact. Classifying a property as relational does not settle whether it is real, measurable or explanatory.

The best distinction is consequently functional and revisable: distinguish the experience to be explained from physical properties and relations used to explain it. That retains the scientific benefits of separating constitution, environment and response. It does not establish an immutable list of primary qualities, a simple resemblance relation between experience and object, or a proof of scientific realism.

Post-receptoral processing compares cone signals, organizes those comparisons across space and time, and uses them to represent surfaces and objects. Three receptor classes do not imply three neurons labelled with three color names. A complete answer must distinguish the information available at the receptors from its neural transformation and its perceptual interpretation.

The principle of univariance supplies the starting constraint. For one cone class, an absorption-dependent response does not identify wavelength independently of intensity. Comparing differently sensitive classes makes chromatic information available. Relative to an adapted background, cone contrasts can be written $c_j=(Q_j-Q_{j0})/Q_{j0}$, where $Q_{j0}>0$ is the background catch. This representation separates a change relative to the prevailing input from the raw number of absorbed photons. It is a useful coordinate system, not a claim that every neuron implements this exact formula.

Retinal circuitry immediately transforms the signals. Light hyperpolarizes cone cells and changes their transmitter release. ON and OFF bipolar pathways respond differently to changes in cone output, while horizontal and amacrine circuitry contribute spatial and temporal interactions. Retinal ganglion cells transmit spike trains rather than simply forwarding a cone's voltage. Their center–surround organization compares regions, so chromatic and spatial structure are already intertwined. Near the central retina, the midget pathway can oppose an L- or M-dominated center to a surround containing other cone input. The same circuitry can carry information useful for fine spatial structure and for chromatic contrast; these are not necessarily separate wires.

A distinct S-cone route illustrates the anatomical specificity. Dennis M. Dacey and colleagues combined physiological characterization with identification of ganglion-cell morphology in primate retina, finding that the S-ON opponent response belonged to a small bistratified ganglion-cell type rather than simply another midget cell.^[ref] This supports parallel circuits with different cone combinations. Calling the pathway “blue-on” is shorthand for its stimulus response, not proof that the cell itself produces a unique blue experience.

At a simplified computational level, an achromatic-like combination and two chromatic combinations can be represented asThis invertible three-channel opponent transform reorganizes three coordinates rather than creating additional independent spectral information. Their matrix has determinant $-2$, so the transform is invertible: $c_L=(y_0+y_1)/2$, $c_M=(y_0-y_1)/2$ and $c_S=y_2+y_0/2$. Actual weights depend on cell, location, adaptation and stimulus conditions. This algebra is an illustrative opponent transform, not a fitted retinal wiring diagram or a complete perceptual color space.

The lateral geniculate nucleus preserves several parallel retinal pathways while also participating in cortical interactions. A. M. Derrington and colleagues studied macaque geniculate cells with gratings whose luminance and chromatic modulation could be varied independently on a calibrated color television display. Finding directions that changed cone stimulation without driving a cell constrained its linear cone weights.^[ref] For a nonzero linear weight vector, the zero-response contrasts obey $r={\mathbf{w}}^{\mathsf{T}}\mathbf{c}=0$, defining a chromatic null plane. A null response therefore tests a relation between inputs, rather than merely assigning the cell a preferred color name. The resulting opponent directions need not coincide with human unique hues.

Cortical processing adds more complex spatial comparisons. Bevil R. Conway mapped cone inputs to color-responsive neurons in alert macaque V1 using cone-isolating spots and eye-position-corrected reverse correlation. Some cells combined chromatic opponency with opposite tuning in adjacent spatial subregions.^[ref] Such double-opponent cells can respond strongly to a chromatic boundary while responding less to a spatially uniform change. This offers a mechanism useful for separating surface differences from shared illumination changes. It does not establish that one V1 population alone solves color constancy under arbitrary scenes.

Further processing must relate color to form, illumination, material and the current task. The same cone contrasts can be interpreted differently in a shadow, highlight or translucent object. Surface color judgments, discrimination thresholds and color naming therefore constrain different computations. Feedback and attention can influence which information is selected, while learning can alter how a signal is interpreted or normalized. A lesion or imaging response can constrain contributions to those tasks, but an area responsive to color is not automatically an isolated “color center.”

The strongest picture is consequently a hierarchy of interacting computations: cone-specific signals, opponent and spatial filtering, combinations tuned to richer image structure, and object-level judgments. Trichromatic sampling limits spectral information; later processing determines how that information supports boundaries, stable surface appearance and behavior.

Photoreceptors determine which optical differences are available for vision, with what noise and spatial–temporal precision. They strongly constrain light, color and material perception without uniquely determining the eventual percept. Later processing can exploit context and action, but cannot recover arbitrary information already lost in receptor sampling.

For cone class $j$, a simplified expected quantum catch over one sampling interval iswhere $N$ is the incident photon-number spectrum at the receptor, including ocular filtering, and $a_j$ effective absorption probability. An energy spectrum requires conversion by photon energy $hc/\lambda$ before this counting interpretation. The integral is many-to-one: equal catches do not imply equal spectra. The principle of univariance means that an individual receptor does not independently report photon wavelength and number.

Multiple receptor classes provide comparisons, but ordinary cone-mediated trichromacy still samples a spectral distribution with only three class responses at a specified location and time. In a discretized model, $\mathbf{Q}=A\mathbf{n}$ with three rows and more than three spectral bins. The matrix has a nonzero kernel. Around a strictly positive spectrum, a sufficiently small perturbation in that kernel preserves nonnegative intensities while leaving all three catches unchanged. This derives the possibility of metamers; it does not mean all real-world spectral differences are unobservable under every condition. Altering illumination, observer or spatial–temporal context can break a particular match.

Sensitivity also has a photon budget. In an ideal Poisson model, mean catch $N$ has variance $N$, so relative counting uncertainty is $1/\sqrt{N}$. Gathering more photons reduces this uncertainty but requires a larger effective collecting area or more integration time. Temporal averaging can obscure rapid change, while spatial pooling can sacrifice fine detail. Receptor and downstream noise add limitations beyond shot noise. Rods support low-light detection with different sensitivity and pooling from cone cells; if only one receptor class provides usable signals, wavelength–intensity ambiguity cannot be resolved by comparisons within that class. Mesopic vision, where rods and cone cells can both contribute, is not captured by declaring all dim-light vision achromatic.

The receptor array is also uneven. Christine A. Curcio and colleagues mapped rods and cone cells in whole-mounted human retinas using microscopic counts. Cone density was highest centrally and fell strongly with eccentricity, while the rod distribution differed and the central foveal region was rod-free.^[ref] Thus acuity and sensitivity should not be treated as uniform over the visual field. Optical blur and downstream sampling constrain performance alongside receptor spacing. The S-cone mosaic is sparser than the combined L/M mosaic, so equal spatial detail cannot simply be assumed for all chromatic signals.

A regular one-dimensional model of photoreceptor sampling makes one limitation explicit. Sampling a sinusoid at spacing $d$ gives identical samples for frequencies $f$ and $f+k/d$, because the added phase at sample $n$ is $2\pi kn$. Restricting a real signal to frequencies below $1/(2d)$ avoids this basic aliasing ambiguity. Real retinal mosaics are two-dimensional, irregular and optically filtered, so this is a benchmark rather than a literal universal retinal cutoff. Fine material texture can consequently disappear or alias even when coarser color and shape remain visible.

Individual variation changes the sampling system itself. Heidi Hofer and colleagues' adaptive-optics measurements showed large L:M cone-ratio variation among observers passing normal color tests.^[ref] Gabriele Jordan and colleagues found behavioral evidence consistent with an extra chromatic dimension in one of a selected set of carriers with potential extra pigment diversity, rather than in every carrier.^[ref] These findings separate receptor availability from downstream use: receptor counts and pigment variants constrain possibilities without directly specifying appearance or functional dimensionality.

Material perception adds an inverse problem. For a matte surface under a single simplified illuminant, the reflected photon spectrum is proportional to $E(\lambda )R(\lambda )$, so catches mix illumination and surface reflectance. The same catches can therefore arise from different surfaces, even before considering three-channel spectral compression. Specular reflection, subsurface scattering, translucency and fluorescence introduce further dependencies on direction, geometry or wavelength conversion. Three cone values cannot independently label every physical material property.

Nevertheless, humans can exploit cues across space, movement and time. Highlights change with viewpoint; texture and shading constrain shape; surrounding surfaces provide illumination evidence. Such information can reduce ambiguity under useful assumptions, while metamerism and sampling limits remain. Receptor properties define an information bottleneck and a set of trade-offs. Successful material recognition depends on combining what survives that bottleneck with contextual constraints, not on receptors directly measuring “wood,” “metal” or intrinsic surface color.

L, M and S describe the relative spectral sensitivities of cone classes. Calling them red, green and blue confuses receptor sampling with the color appearance constructed from comparisons and context. The shorthand also wrongly suggests three narrow, nonoverlapping detectors or the primaries of a display.

First, each class has a broad sensitivity curve. A long-wavelength-sensitive cone responds to many wavelengths, and its sensitivity maximum is not a unique red stimulus. L- and M-cone sensitivities overlap extensively, while S cone cells are relatively more sensitive at shorter wavelengths. Light at one wavelength can therefore excite more than one class. Andrew Stockman and colleagues derived L- and M-cone sensitivity estimates using color-matching data and measurements from observers of known pigment genotype. Their analysis emphasizes broad overlap and the need for isolation procedures to measure one class without contamination from others.^[ref] Exact curves depend on pigment variant and optical filtering, so the letters denote relative classes rather than three invariant color labels.

Second, the principle of univariance prevents one cone from identifying hue. For a monochromatic stimulus, let mean absorption be $Q=Ia(\lambda )$. Whenever $a({\lambda }_1)$ and $a({\lambda }_2)$ are positive, intensities satisfying $I_2=I_{1}a({\lambda }_1)/a({\lambda }_2)$ give the same $Q$. The single receptor cannot distinguish those two inputs on absorption count alone. Calling that receptor a red detector would assign a color judgment to a signal that cannot even uniquely distinguish the two wavelengths. It remains true that spectral sensitivity constrains the comparison; univariance does not mean all receptor types are identical.

Third, perceptual color requires combinations. Comparing L and M signals and comparing S with L+M supplies chromatic information; summing appropriately weighted signals contributes to luminance-related processing. An L-cone increment can be part of several stimulus changes depending on what happens to M and S and on the adapted background. Neither an L response nor a raw L:M ratio by itself specifies a context-independent color word. The retinal and cortical opponent circuitry demonstrated by Dennis M. Dacey and Bevil R. Conway provides mechanisms for comparisons, rather than a relabelling of three cone outputs as three sensations.^[ref][ref]

Fourth, “blue” or “red” need not identify a single wavelength. Spectrally different lights can be metamers, and a purple appearance can arise from a mixture rather than a monochromatic wavelength. Conversely, the same physical spectrum can look different against another surround or after adaptation. Cone labels cannot replace the distinction between emitted or reflected spectrum, receptor catch, opponent representation and reported hue.

Fifth, the display analogy is backwards. An RGB screen emits mixtures from three selected primary spectra. The cone cells receive and integrate the resulting spectrum; their response curves are not identical to those primary spectra. If primary intensities are $p_k$ and cone sensitivities to each primary form matrix $B$, then $\mathbf{Q}=B\mathbf{p}$. Off-diagonal terms are generally nonzero because each primary can excite several cone classes. If $B$ is nonsingular, an intended catch change can be computed through $B^{-1}$ within the display's feasible gamut. It does not follow that every requested catch vector is realizable with nonnegative bounded primary intensities. Calibration and observer differences therefore matter in cone-isolating experiments.

Finally, color terms themselves vary across languages and tasks, whereas receptor class is a biological classification. Receptor genes, color matches, discrimination and linguistic categorization are related but distinct measurements. The labels L, M and S prevent an unwarranted assumption that one anatomical cell class directly instantiates one learned word or unique subjective hue.

The research challenge is to retain causal control while measuring appearance under the coupled spectral, spatial and temporal variations of actual scenes. Simply replacing a flat patch with a photograph does not guarantee that either the stimulus or the task is ecologically informative.

A display's RGB values do not specify the spectrum reaching a participant's cone cells. Primary spectra, viewing geometry, calibration, luminance range and the observer's lens and pigment sensitivities matter. A photograph can clip highlights, alter white balance and compress dynamic range; a display cannot necessarily reproduce the original spectrum, brightness or angular light field. A researcher studying spectral constancy should measure radiance and illuminants or specify the limits of a display simulation, rather than treating an uncalibrated image as physical ground truth.

Real materials also vary in more than diffuse reflectance. Shape, roughness, specularity, translucency and surrounding illumination jointly determine an image. Changing one rendering parameter can unintentionally change highlight area, contrast and apparent shape. Conversely, holding an image statistic constant can produce an unnatural stimulus. Experiments need independent manipulation of candidate causes and a clear statement of which physical scene properties are represented by the apparatus.

James M. Kraft and David H. Brainard demonstrated a productive compromise with a chamber of actual surfaces. They independently controlled illumination and surface reflectances and used a projection colorimeter to let observers adjust a test patch to appear achromatic. Manipulations could hold candidate image statistics approximately fixed while changing the illumination. The results rejected explanations based solely on local adaptation, the image mean or the most intense region.^[ref] The achievement was not realism alone: it was enough realism combined with manipulations that made competing mechanisms disagree. These results do not show that local adaptation never contributes.

For material appearance, Roland W. Fleming and colleagues used rendered objects illuminated by natural illumination distributions and systematically altered those distributions. Reflectance matching was more reliable under realistic illumination statistics than under some altered statistics.^[ref] This makes illumination an experimental variable, not merely a nuisance background. It also warns that a model tested only under a small set of studio lights can exploit cues unavailable or misleading elsewhere.

The dependent variable needs equal care. Selecting the same paint, matching apparent color, adjusting an achromatic point, detecting an illuminant change and naming a material do not measure the same outcome. A person may correctly infer that two patches are the same material while still noticing an appearance difference. Instructions, response format, adaptation time and available context can alter the task. Eye and head movements change retinal exposure, so fixation constraints should be chosen deliberately and their cost to natural viewing acknowledged.

A strong program therefore combines calibrated simplified stimuli, controlled real scenes, validated rendering and tests on previously unseen objects and illumination. It should sample more than one observer and record relevant visual variation, report both accuracy and uncertainty, and test model generalization across scenes rather than only across random frames of the same scene. Proposed interventions can vary geometry while preserving spectral input, or alter illumination while suppressing a particular cue. Their outcomes must be measured, not assumed. The goal is interpretable generalization: experimental control sufficient to distinguish mechanisms and scene diversity sufficient to test whether those mechanisms remain useful.

Scotopic vision is vision at low illumination dominated by rod cells. It provides high sensitivity to weak light but little of the color discrimination associated with cone cells. Consequently equal radiant powers at different wavelengths need not appear equally bright, and brightness comparisons change with illumination level.

A simple observation compares colored patches as illumination is reduced after adaptation: their relative brightness can change even though the illuminating optical spectrum is held approximately fixed. Controlled measurements must allow adaptation and avoid stray illumination. A photopic luminous efficiency function is appropriate to a different viewing regime and cannot be used indiscriminately for scotopic vision.

Selig Hecht was a researcher in the physiology of vision who measured how sensitivity changes during light adaptation and dark adaptation. In a 1937 study, Selig Hecht and colleagues varied prior illumination and then measured recovery of the human eye's ability to detect faint test lights.^[ref]

The recovery curve depended on the preceding exposure. Under sufficiently bright preadaptation, an early phase associated with cone cells was followed by a slower phase associated with rod cells; other exposure conditions did not reveal both phases in the same way.^[ref]

This makes prior conditions part of the experiment. Two observers tested after the same time in darkness need not be in comparable states if their preceding illumination differed. A curve measured over time reveals changes that a single final sensitivity measurement would hide. The historical illumination unit called photons in this paper should not be interpreted as a count of individually detected photons.

S. H. DeVries investigated how parallel pathways in the retina extract different temporal components from a shared cone cell signal. In a 2000 study, simultaneous recordings from synaptically connected cone cells and OFF retinal bipolar cells linked distinct bipolar-cell types to different AMPA receptors or kainate receptors.^[ref]

The glutamate signal released by one cone cell can therefore enter several postsynaptic channels with different response dynamics. The study combined paired measurements with receptor-selective pharmacology in ground-squirrel retina, making it possible to compare the input and output at an identified connection.^[ref]

The motivating question is how the same changing light signal can support both a response to a rapid transition and a response to a more sustained change. Different receptor kinetics supply one experimentally testable part of that explanation. A common transmitter does not require identical temporal filtering in every receiving neuron; neither does a result in these cell types establish a universal receptor assignment across every species and retina.

Color vision using four functionally independent spectral channels. Four pigment variants or four potential cone cell types do not alone demonstrate four-dimensional behavioral discrimination. Tests must establish information use beyond the matches available to a specified trichromat.^[ref]

Conversion of absorbed light into a photoreceptor response. In vertebrate rods and cone cells, activation of visual pigment changes a signaling cascade and light-sensitive membrane current, typically hyperpolarizing the cell and changing transmitter release. A receptor voltage, ganglion-cell spikes and a reported color are distinct measurement levels.