Years ago, I was asked to join an online discussion group centered on the book Traditional Oil Painting, Advanced Techniques and Concepts from the Renaissance to the Present by artist and author Virgil Elliott. After joining, I bought the book that inspired the group so my contributions to the forum’s discussions could be framed around the group’s focus. I didn’t get far into the book before I encountered several claims about the nature of visual perception that didn’t align with a modern understanding of the subject. For example, on page 19, the author makes several bold claims about perception’s role in visual art. He writes, “The artist must develop the ability to read all things with total objectivity in order to see the truth. This ability is what distinguishes the true artist from everyone else. The perceptions of others are influenced by irrelevancies. Those of the true artists are not. As artists, we must see things as they really are.” Later, the author further doubles down on these claims on the same page, writing, “Total accuracy and objectivity of perception are achieved only after considerable study,” and “Consequences notwithstanding, the ability to see with total objectivity is essential if one is to create great art.”

Tucking these comments away for the time being, I kept reading, hoping the ideas wouldn’t resurface in any significant way. But lo, it was only a few dozen pages later, in a section of the book titled “The Photographic Image Versus Visual Reality,” that the claims were revisited in the context of the good ol’ “photography-as-a-valid-tool-for-contemporary-painting” argument. This section in the book compared the human eye with a camera in a myriad of ways so as to lead the viewer to conclude that photography is a problematic, distorted source of visual information (as compared with the cultivable objectivity to be found with human perception). It was here that I realized that the author’s argument against photography as a viable tool for observational representationalism hinged on the reality of objective perception. (Keep this in mind.)

With these claims regarding the nature of perception and the usefulness of photography in the context of observational representationalism put forward so unapologetically in the book, it should come as no surprise that these ideas could be found peppered throughout the related online discussion group. Recently, I decided to respond to these ideas in a post of my own. I intended to address these assertions about perception, photography, and observational representationalism, as well as related claims about the idea of “slavish copying.” I wrote:

“I have been reading through a thread here about the contemporary representational painter’s use of photography on this forum, and I must say that I’m shocked to see so many fallacies and misrepresentations allowed to propagate unchecked. I paint, colloquially, “from” life, from photographs, as well as from my imagination. Do you know what the actual case is regarding each scenario? It is that I am painting “from” memory. Each scenario involves the informing and sculpting of behaviors with a vast array of memory resources that are cultivated by visual experience. Yes, the type or mode of reference source can indeed impact behaviors very differently over a wide array of aspects—and those differences can indeed be illuminated thoughtfully—but the differences often put forward today are often fallacy-ridden arguments fueled by irrational dogmatic beliefs.

The argument that the products of photography are somehow a “lesser” form of visual reference source DUE TO the fact that a photograph does not represent “reality” is indeed fallacious as your actual vision system does not provide an “accurate” representation of what we would describe as reality. We’ve known this since the early 1700s. Can an argument be made that even the best possible photograph (surrogate) may provide far less information than the corresponding live percept? Yes—without a doubt. Does either represent actual “reality?” No.

Furthermore, while a photograph and a live percept may produce very different perceptual experiences—“slavish” copying is technically impossible in both scenarios, as visual perception is not, in any way, a strictly linear bottom-up translation of physical measurement.  The idea could not be any more erroneous.”

For those who have followed my writings in the past, the content of the above post is nothing new. In fact, I had visited this topic in 2015 with a paper titled “The “Pitfalls” of reading about Photography “Pitfalls” after reading a similarly titled article, “How to Avoid the Pitfalls of Painting from Photographs” by Courtney Jordan. I again approached the subject of photography again in my writing in the Summer of 2017 with an article titled, “Color, the Pitfalls of Intuition, and the Magic of a Potato.“ The 2015 article directly addressed many of the core arguments in this ongoing “photography-as-a-valid-tool-for-contemporary-painting” debate. The 2017 article is more focused on the nature of perception and the basics of color photography. However, both articles lack a more robust examination of visual perception. That is what I hope to provide here so that some of the arguments swirling around these topics may become easier to navigate.

First things first, though, I won’t sugarcoat it: visual perception is not an intuitive subject by any stretch of the imagination. In fact, arguably, one reason the system works so well is that we operate intuitively, believing it does something it does not. Specifically, we intuitively think of visual perception as providing an objective window to the world. Unfortunately, as George Berkeley pointed out several centuries ago, sources underlying visual stimuli are unknowable in any direct sense. This foundational idea deeply influenced later psychological and computational theories of perception, including the concept of visual ambiguity and the inverse problem in modern vision science. The chasm between the physical world and our perceptions of it is significant; we need to acknowledge that what we “see” is a construct of evolved biology, not an accurate, objective measurement of external reality. The mechanics of the visual system should not be confused with devices that can garner reasonably accurate measurements of the physical world (e.g., calipers, light meter, spectrophotometer, etc.) Rather, the visual system responds to stimuli based on experience-cultivated neural networks to produce successful behavior. Simply stated, it is not an external reality that composes the image we “see”–rather, it is the biology of the observer.

So let’s explore our modern picture of this complex, still-mysterious biological system and see whether we can better evaluate the aforementioned author’s claims about objective perception. Visual perception is the ability to interpret the surrounding environment by processing information contained in visible light. It is a fascinating process that takes up about 30% of our brain’s cortical surface area. While we have come to understand much about this remarkable ability, much remains a mystery. Unfortunately, as I’ve stated earlier, very little about our vision system is intuitive. In fact, it is not uncommon to find people who intuitively think of the eyes as tiny cameras that record accurate, clear pictures that are eventually sent on to some inner self for review. What’s more, even though some can push past the little-cameras idea, they still get mired, as we have seen here, in the idea that vision is (at least in some part) veridical (objectively truthful, corresponding to objective measurement or fact). While ongoing research and evidence continue to refute this idea, many still argue that vision is indeed veridical. One argument I recently encountered about this misconception came from a colleague who insisted that vision is likely to become more accurate/veridical when perceptual properties become less complex. Unfortunately, this argument is akin to one that claims the accuracy of one’s “clairvoyance” increases in pick-a-card prediction tasks as the number of cards in play decreases. With this reasoning, if we limit the pile to one card, one would be likely to hit 100% accuracy.

Today, a compelling theoretical approach to vision holds that our visual percepts are generated according to the empirical significance of light stimuli (information derived from past experience), rather than the stimuli’s characteristics. In other words, it holds that our visual system prioritizes successful behavior over objective measurement. To this point, neuroscientist Dale Purves and colleagues write in their 2014 paper, “How biological vision succeeds in the physical world”: “Biological visual systems cannot measure the properties that define the physical world.” They propose that vision instead links the “frequency of occurrence of biologically determined stimuli to useful perceptual and behavioral responses, without recovering the physical properties of the underlying sources.” (Purves et al., 2014, p. 4750)

Support for this idea also comes from evolutionary game theory and computational modeling. Cognitive scientist Donald D. Hoffman, now a professor emeritus at the University of California, Irvine, has investigated these questions through mathematical models and evolutionary simulations. His results support his argument that selection for fitness need not favor veridical perception.

In a 2016 interview with Quanta Magazine, Hoffman states: “The classic argument is that those of our ancestors who saw more accurately had a competitive advantage over those who saw less accurately and thus were more likely to pass on their genes that coded for those more accurate perceptions, so after thousands of generations we can be quite confident that we’re the offspring of those who saw accurately, and so we see accurately. That sounds very plausible. But I think it is utterly false. It misunderstands the fundamental fact about evolution, which is that it’s about fitness functions — mathematical functions that describe how well a given strategy achieves the goals of survival and reproduction. The mathematical physicist Chetan Prakash proved a theorem that I devised that says: According to evolution by natural selection, an organism that sees reality as it is will never be more fit than an organism of equal complexity that sees none of reality but is just tuned to fitness. Never.”

In his 2015 TED Talk, Do we see reality as it is?, Hoffman reports that his laboratory ran hundreds of thousands of evolutionary game simulations. Describing the competing organisms within those simulations, he explains: “Some of the organisms see all of the reality, others see just part of the reality, and some see none of the reality, only fitness. Who wins? Well, I hate to break it to you, but perception of reality goes extinct. In almost every simulation, organisms that see none of reality but are just tuned to fitness drive to extinction all the organisms that perceive reality as it is. So the bottom line is, evolution does not favor veridical, or accurate perceptions. Those perceptions of reality go extinct.”

But the vision system’s counterintuitiveness isn’t limited to the big picture (pardon the pun). Even at the earliest steps of the visual process, we encounter one surprising feature after another. For example, did you know that when light stimulates the eye’s light-sensitive cells, they actually release less of a chemical messenger, not more? (These cells continuously release this chemical in darkness, and light turns that faucet down.) Or consider the retina’s arrangement: much of the neural circuitry that processes signals from these cells lies in front of them, so incoming light must pass through those layers first. And while many of you might know that incoming light patterns are projected upside down and left–right reversed onto the retina, did you know that retinal blood vessels also lie in the light’s path? These vessels can cast shadows, yet we normally don’t notice them. So, with that in mind, let’s take a closer look at the machinery and processes that support our perception.

Let’s begin with light. As I mentioned earlier, visual perception is the ability to interpret the surrounding environment by processing information contained in visible light. This term, visible light, describes the portion of the electromagnetic spectrum visible to the human eye. While it depends on the observer’s sensitivity and the amount of light reaching the retina, a typical human eye responds to wavelengths ranging from about 390 to 700 nanometers.

Visible light rays enter the eye through a dome-shaped transparent structure called the cornea, then pass through a small aperture called the pupil. The cornea, along with a lens behind the iris and pupil, focuses light onto the retina (the layer of tissue lining the back of the eye). This tissue is the eye’s neural component and contains specialized light-sensitive cells that I mentioned earlier, called photoreceptors. These specialized photoreceptors convert environmental energy (light) into electrical signals our brains can use. This process is called phototransduction.

Now, after reading that last paragraph, some might quickly say that, so far, the eye really does sound like a type of camera. I mean, the basic idea behind photography is to record a projected light pattern with an electronic sensor or light-sensitive plate that can be used to generate an image. And in some ways it’s a fair comparison. A digital camera sensor contains an array of tiny light-sensitive sites called photosites. During an exposure, these sites absorb photons and accumulate an electrical charge. Within the sensor’s normal operating range, more light produces more charge. Of course, the amount also depends on the exposure time and the sensor’s sensitivity to different wavelengths. The camera converts these charges into numbers that can be processed to create an image. To record information for a color image, many cameras place a patterned array of filters—called Bayer filters or masks—over the sensor, which measures an integrated light response over a particular spectral sensitivity range. Color information can then be subsequently reconstructed from the mosaic of filtered measurements.

So is this how the eye or vision works? Do the responses of individual photoreceptors simply combine to produce a clear, objective recording of the environment?

Hardly. As we just saw, there are indeed some basic similarities between the eye and a camera in terms of optics and photosensitive materials—but these similarities do not make photographic recording and visual perception equivalent by any stretch. Let’s take a look at some of the differences.

First, our photoreceptor landscape differs substantially from the regular array of photosites found in a typical digital image sensor. The retina, spanning about 32 mm (ora to ora), has a highly uneven photoreceptor distribution. Two main types, known as rods and cones, differ significantly in number, morphology, function, and synaptic connections. Rods are far more numerous than cones (about 120 million rods to 8 million cones*), are far more light-sensitive, provide very low spatial resolution, and hold only one photopigment. Conversely, cones are far fewer, less light-sensitive, provide very high spatial resolution, and come in three types, each carrying a photopigment differentially sensitive to specific wavelengths of light (thus facilitating what we understand as color vision). Cone density is much lower in the peripheral retina and rises sharply toward the foveal center, a region about 1.5 mm in diameter. Rods are abundant outside the foveal center but decline toward it and are absent from the approximately 0.35 mm central region called the foveola. To better appreciate the size of our high-acuity window within the visual field resulting from this photoreceptor distribution, just look at your thumbnail at arm’s length. (*This is a very common estimated ratio; however, a study by Curcio et al. (1990) reported averages of approximately 92 million rods and 4.6 million cones in its sampled human retinas.)

In regard to receptor distribution, there is another intuitive misconception worth mentioning here. It is related to acuity: things get increasingly blurry as we move outward from the fovea to the periphery. The truth is, this lower acuity isn’t simply blurred vision; it’s spatial imprecision. Neuroscientist Margaret Livingstone illustrates this point in her book Vision and Art: The Biology of Seeing. Here is a recreation of that demonstration:

Second, we need to look at what is happening with the output of our photoreceptors. It is very important to understand here that the retina does not simply record a pattern of light for later viewing. Responses from its specialized photoreceptor cells feed into highly dynamic, complex processing through multiple cell layers and circuits with a variety of receptive-field properties. The resulting signals from this retinal activity are then ushered off to our next stop on the visual pathway–the thalamus. But before we head over to this well-known “relay station” in the brain, I would like to present another issue at the level of the retina worth consideration when comparing a camera with the eye–the quality of that initial light pattern projection.

Do you remember the last time you took a picture with your camera when debris or a smudge was on the lens? Did it ruin the shot you were trying to take? How about the last time you had to deal with a piece of tape blocking light from entering a section of the lens? Or the last time you pulled up a pile of plant roots to suspend in front of your camera before taking that nice portrait shot?

Do those last two questions seem a bit ridiculous? Well, those seemingly ridiculous factors raise real issues that our visual system has to deal with very early in perception. As I mentioned when first describing the visual system’s counterintuitiveness, much of the retinal circuitry that processes photoreceptor output lies in front of the photoreceptors themselves. Across much of the retina, incoming light must pass through these inner retinal layers before reaching the light-sensitive portions of the rods and cones. (The central fovea is a notable exception, where the overlying inner layers are displaced.) These tissues are relatively transparent, but another feature of this arrangement becomes apparent where the retinal output leaves the eye.

After retinal circuitry processes signals, retinal ganglion cell axons carry them toward the brain. Axons are long, slender projections of neurons that typically conduct electrical impulses away from the cell body, or soma. These axons converge to form the optic nerve as they leave the eye. The region where they exit is called the optic disc. Because it contains no photoreceptors, it creates a corresponding blind spot in the visual field. Its dimensions vary among individuals, but a published estimate is approximately 1.86 × 1.75 mm. For comparison, the rod-free central region of highest visual acuity is approximately 0.35 mm across—meaning the optic disc is roughly five times wider.

If you like, you can even “experience” your blind spot with a simple exercise:

Another consideration worth mentioning here concerns something you might have already noticed in the fundus photograph above—a rich web of blood vessels. As you might suspect, these little buggers also impact incoming light heading toward the photoreceptors. Their shadows normally stay relatively fixed on the retina, and through sensory adaptation, we become less sensitive to this unchanging pattern and usually don’t notice it—much like you stop noticing the feel of your clothes after a while. However, by moving the incoming light, we can shift those shadows across the photoreceptors and make the network visible. To do this, you’ll need an index card, a pencil or something to poke a small hole in the card, and a plain, evenly illuminated surface.

So, if we consider that initial light pattern suggestion that we often think is a clear window on the world, we need to consider that this “image” on the retina is inverted, left-right reversed, passing through a forest of machinery that ultimately results in a relatively large blind spot, facing occlusion by a significant web of blood vessels, and all of this falling onto an unevenly distributed array of varied photoreceptors. So what might that look like?

Now many might be quick to intuitively think that this just can’t be true. The image I “see” is so rich and detailed; how could all of this “stuff” be in the way? Again, because visual perception does not involve a clear window on the world. Take a moment to consider age-related macular degeneration (AMD). This unfortunate condition, which has no cure although treatments are available, is a leading cause of vision loss among older adults. An estimated 19.8 million Americans aged 40 and older had AMD in 2019. It damages the central portion of the retina, known as the macula.

It just so happens that damage in this region does not necessarily appear as an obvious gap in our vision. In one study, 56% of AMD patients with binocular central scotomas—areas of reduced or absent vision affecting the combined visual field of both eyes—were unaware of those defects. This doesn’t mean their vision was unimpaired; it means the missing information wasn’t experienced as an obvious “hole” in what they saw. Our brain takes the sensory data it has and responds through processes shaped by evolution and experience. I can’t stress enough how much more this involves than a camera taking snapshots: what we perceive is not simply a recording of incoming light.

Oh, and I almost forgot! There really isn’t any “color,” in the sense of experienced hue, in this initial projection, as color is not a property of the environment. Contrary to what some may believe, we do not directly detect color as an intrinsic property of incoming light. That may also sound counterintuitive, but it is also quite true. Color is the visual experience that arises from our biology interacting with the spectral composition of the light (electromagnetic radiation) that is emitted, transmitted, or reflected by the environment. Our different cone classes respond differently to the spectral composition of light, and comparisons among these responses contribute to what we eventually experience as “color vision.” In other words, light falling on the retina has a spectral composition, but it does not carry the experienced “redness” of the apple. That redness arises through the interaction of the light with our visual system. To emphasize this distinction, I have removed the hues from the illustration below. (Note, this is a visual convention: it does not mean that the retinal projection is literally black and white, or that the spectral differences contributing to color vision are absent.)

While we are on this color issue, exploring the counterintuitiveness of our vision system, and examining the comparison between a camera and the eye, I’d like to take a moment to share an interesting argument that I encountered quite a while ago from a professional artist who was absolutely convinced that color is indeed part of the environment. (I explore this idea further in the above-mentioned 2017 article.) No matter what evidence I could present to show this was NOT the case, he wouldn’t budge. When I asked him to present evidence to justify his position, he said color MUST be a physical property of the environment “because a camera can record it.”

Now I am sure many of you reading this have already realized the glaring fatal flaw in this argument, but for many who have stuck with me here and are less familiar with the fundamentals of color vision and color photography, this logic can seem to have some teeth. However, like most intuitive arguments for a flat-earth or faked moon landing, such arguments quickly deteriorate as scientific literacy and critical thinking increase. Just to be clear, though—color photography, electronic sensors, and light-sensitive chemicals respond to specific parts of the electromagnetic spectrum at the time of exposure. The recorded information is then used to create a percept surrogate by combining different amounts of light with different spectral distributions (“additive color,” used for video displays, digital projectors, and some historical photographic processes), or by using dyes or pigments to remove various proportions of the particular wavelengths in white light (“subtractive color,” used for prints on paper and transparencies on film). The color you perceive in a surrogate, like a traditional photograph or digital image, comes from the wavelengths of light the surrogate emits, reflects, or transmits, not because the camera “captured” color like some fairy in the garden.

What might make wrapping our head around this even easier is if we take a moment to clearly define two basic terms concerning the manner with which we interact with the environment: sensation and perception. These terms are often used synonymously–but they indeed describe two different aspects of what we normally understand as “sensory experiences.” Sensation describes a low-level process in which specific receptor cells respond to specific stimuli. At the level of sensation, our sensory organs engage in transduction (or, in our case, phototransduction), converting environmental energy into a form our nervous system can use.

Perception, on the other hand, for our purposes here, can be defined simply as assigning “meaning” to a sensation.

So what is actually happening when we visually encounter something that we might understand or describe as “blue”?

Putting aside scenarios where the object is an actual source of light, or a structure producing color through effects such as interference or scattering, a common explanation is that the surface preferentially reflects relatively short wavelengths while absorbing more of other visible wavelengths. Now the typical human observer has a specific type of photoreceptor cell in the retina—the S-cone—that is “tuned” to relatively short wavelengths. When light is absorbed by these receptors, it initiates a complex cascade of electrochemical events that contributes to further processing along the route we are currently exploring—the “visual pathway.” We could define this low-level cascade initiation as a sensation.

The events initiated in the retina involve comparisons among the responses of the different cone classes, rather than the activity of S-cones alone. Signals arising from retinal processing reach other regions, including parts of the thalamus, the striate and extrastriate cortex of the occipital lobe, and regions of the temporal lobe—areas we will be heading to soon as our walkthrough continues. It is through the aggregate activity of these regions and their interactions that we find a perceptual response—in our case here—“blue.”

So as you might already be starting to suspect, in this example, the object in the environment does not possess the experienced quality of “blue”; the wavelengths reflected off its surface are not themselves blue, nor do the photosensitive cells referenced here “sense” blue. Blue is not a quality directly sensed by an individual photoreceptor—rather, our experience of blue is a perceptual outcome of neural processing.

Hopefully that makes sense, clears up a few more counterintuitive points, and lets us continue our glimpse of a few key points on the visual pathway.

From the retina, we will jump to what is often referred to as the main relay station of the brain—the thalamus—or, more specifically, a region called the lateral geniculate nucleus, or LGN. Yet the LGN is much more than a mere gateway to the visual cortex. It contains multiple layers of sophisticated neural circuitry. What makes this area even more fascinating is that retinal inputs account for only about 5–10% of the synaptic inputs to LGN relay cells. The remainder arise from other sources, including substantial feedback from the visual cortex, other subcortical structures, and local inhibitory neurons. Now, this doesn’t mean that retinal inputs are unimportant. Quite the contrary—they provide powerful driving signals, despite their relatively small numbers. But all this additional circuitry allows for considerable modulation of those signals, including top-down influences from the visual cortex. In other words, the visual cortex helps regulate how visual information is processed and relayed back to the visual cortex. Hopefully, by now, many of you are starting to better appreciate how unlike a camera this system really is.

From here, we follow a large fan of axons radiating outward, aptly called the optic radiations, to the occipital lobe at the back of the brain. These projections lead primarily to a region called the primary visual cortex (also known as V1 or the striate cortex), located largely along the calcarine sulcus. Beyond this region are numerous interconnected visual processing areas collectively known as the extrastriate cortex. Now, before you shudder at the prospect of a lengthy tour through all these regions—don’t worry. I’m going to skip ahead to two broad processing streams that will help us better understand how this elaborate system works.

So where do things go from here?

From V1, visual processing continues through interconnected regions broadly organized into two streams: a ventral stream extending toward the temporal lobe and a dorsal stream extending toward the parietal lobe.

The ventral stream is often called the “what” pathway because it plays a key role in recognizing objects and identifying what we’re looking at. Within this stream, neurons are sensitive to features such as color, shape, and texture, as well as more complex patterns associated with faces, bodies, and entire objects. Different regions contribute to different aspects of recognition and work with memory and other systems to help us identify and interpret what we see.

The dorsal stream, often called the “where” or “how” pathway, plays an important role in processing motion and spatial relationships and in guiding our actions. Here we find neurons sensitive to features such as location, direction, speed, size, and orientation—information useful when reaching for an object or navigating our surroundings.

Now, it’s important to understand that these streams are not entirely separate systems. They continuously interact and exchange information. Although the ventral stream plays a prominent role in color perception and object recognition, the dorsal stream can also use color information. The distinction between these streams is therefore one of functional specialization within a highly interconnected system.

So, like with our blind spot, blood vessels, and the spatial imprecision of our periphery, can we see some demonstration of how these two streams process things differently?

You betcha.

One of my favorite examples involves introducing a stimulus that sort of pushes different visual-processing systems out of balance. If we could make an object or image readily detectable by one system but much less effective at stimulating another, we might experience something quite interesting. And indeed we can—with color.

Remember that although the dorsal, or “where/how,” stream is not entirely colorblind, some of the visual mechanisms that contribute to motion and spatial processing are much more sensitive to differences in luminance than to differences in color alone. As demonstrated in classic research by Margaret Livingstone and David Hubel, this distinction can produce some rather surprising perceptual effects. We can explore this by presenting a stimulus containing two or more colors that are approximately equiluminant—that is, colors with roughly the same luminance despite their different hues.

Here is one such example:

Do you notice an odd visual shimmer, jitter, or vibration when trying to read the letters here? This is because color-sensitive mechanisms in your visual system can readily distinguish the letters from their background, but other mechanisms that process spatial relationships rely much more heavily on luminance differences. When those differences are minimized, these mechanisms have a heck of a time precisely defining the boundaries of those letters, contributing to their apparent instability.

One of the most famous examples of this phenomenon in art is Claude Monet’s Impression, Sunrise. As neuroscientist Margaret Livingstone has pointed out, the orange sun has relatively little luminance contrast with the surrounding sky, despite standing out vividly in color. This may help explain why some viewers describe the sun as appearing to shimmer, pulse, or vibrate.

Neurobiologist Margaret Livingstone explains the peculiarity of Monet’s equiluminant sun in her book Vision and Art: The Biology of Seeing. She writes:

“The sun in this painting seems both hot and cold, light and dark. It appears so brilliant that it seems to pulsate. But the sun is actually no lighter than the background clouds, as we can see in the grayscale version. It is precisely equiluminant with—that is, it has the same lightness as—the gray of the background clouds. This lack of luminance contrast may explain the sun’s eerie quality: to the more primitive subdivision of the visual system (which is concerned with movement and position) the painting appears as it does in the grayscale version; the sun almost invisible. But the primate-specific part of the visual system sees it clearly. The inconsistency in perception of the sun in the different parts of the visual system gives it this weird quality. The fact that the sun is invisible to the part of the visual system that carries information about position and movement means that its position and motionlessness are poorly defined, so it may seem to vibrate or pulsate. Monet’s sun really is both light and dark, hot and cold.” (Livingstone, 2002, p. 40)

And while you can find countless visual demonstrations in textbooks, classrooms, and websites (often labeled as illusions) demonstrating that what we see does not accord with an objective reality–I couldn’t pass up an opportunity to share one of my favorite, canonical examples for experiencing it–the simultaneous brightness/lightness contrast demonstration.

So at this point—with this limited glimpse at the mountain of interdisciplinary evidence available to us—I hope that you can better evaluate claims that visual perception is an objective, unmediated representation of the environment. If such a claim serves as the foundation for further arguments, those arguments must also be reconsidered.

Before closing, I would like to briefly address two additional issues that are connected to the topics discussed here thus far. One is the idea that learning observational representationalism is, in fact, “learning to see.” You will remember that our aforementioned artist and author, Virgil Elliott, continues to promote this idea. Unfortunately, this is indeed not the case. While your perceptions can indeed be molded by experience and assumption, the practice of observational representationalism will not fundamentally change the nature of your vision system. What the practice CAN do is cultivate the relationships between specific visuomotor responses and specific types of visual information that are conducive to representational efforts.

The second is an intuitive argument that was put forward in the online thread I started that I did not address adequately at the time. I felt the answer needed to be grounded in the more robust explanations and insights I hope I provided here. The argument was that a photograph (a percept surrogate) should be considered a “less objective” reference source because a surrogate is one step removed from the actual subject (the live percept). And while this may initially sound like a solid argument, it contains a fatal intuitive error. You see, the idea of objectivity in this context is describing the nature of the perceiving entity–not the subject of the perception. This is almost like arguing that the closer you are to a live piano being played, the more objectively you will perceive the sound. In actuality, you will respond to any perceptible, appropriate pressure waves in the manner that you and your species have cultivated. No perceived sound originating from a piano, whether you are 10 feet away from a Steinway or listening to a CD of your favorite ivory-key hits, will be more or less “accurately” objective. I hope that is clear enough now.

Again, as I stated in the 2015 article I mentioned, using photography in the pursuit of representational painting or drawing definitely comes with problems. However, the points that are often put forward to argue against the use of photography in representational painting communicate more of a general misunderstanding of visual perception than anything else. Now I agree that there are some truly GREAT reasons not to use photography in specific painting and drawing scenarios. However, you must understand the artist’s goal or intention before you can effectively determine the advantage of any reference source. Yes, some photographic processes may have specific limitations—but those limitations may not outweigh the advantages in all cases, across all contexts.

As we reach the end, I sincerely hope you’ve found this paper informative and insightful (I know some sections were a bit dense!). My goal was to provide a more comprehensive resource than I could offer in the flurry of social media comments that originally inspired this writing. If you notice any factual errors, please don’t hesitate to let me know. I also welcome corrections, clarifications, or refutations from anyone and invite them to submit them here in the comments below.

References and Further Reading

1. The Nature of Visual Perception, Empirical Vision, and Objectivity

Berkeley, G. (1709). An essay towards a new theory of vision. Aaron Rhames.

Elliott, V. (2007). Traditional oil painting: Advanced techniques and concepts from the Renaissance to the present. Watson-Guptill.

Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.

Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic Bulletin & Review, 22, 1480–1506.
https://doi.org/10.3758/s13423-015-0890-8

Purves, D., Wojtach, W. T., & Lotto, R. B. (2011). Understanding vision in wholly empirical terms. Proceedings of the National Academy of Sciences, 108(Supplement 3), 15588–15595.
https://doi.org/10.1073/pnas.1012178108

Purves, D., Monson, B. B., Sundararajan, J., & Wojtach, W. T. (2014). How biological vision succeeds in the physical world. Proceedings of the National Academy of Sciences, 111(13), 4750–4755.
https://doi.org/10.1073/pnas.1311309111

Additional sources discussed in the article:

Gefter, A. (2016, April 21). The evolutionary argument against reality. Quanta Magazine.
https://www.quantamagazine.org/the-evolutionary-argument-against-reality-20160421/

Hoffman, D. D. (2015). Do we see reality as it is? [TED Talk]. TED Conferences.
https://www.ted.com/talks/donald_hoffman_do_we_see_reality_as_it_is

2. Retinal Anatomy, Photoreceptors, Peripheral Vision, and Blind Spots

Curcio, C. A., Sloan, K. R., Kalina, R. E., & Hendrickson, A. E. (1990). Human photoreceptor topography. The Journal of Comparative Neurology, 292(4), 497–523.
https://doi.org/10.1002/cne.902920402

Roorda, A., & Williams, D. R. (1999). The arrangement of the three cone classes in the living human eye. Nature, 397, 520–522.
https://doi.org/10.1038/17383

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4. The LGN, Visual Cortex, and Dorsal and Ventral Processing Streams

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5. Artistic Expertise, Perceptual Learning, and Observational Representation

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