Across three guest posts, Dr. Brockett focuses on the ways in which the neuroscience of visual perception impacts our appreciation of photography. These pieces help uncover how the brain sees an image, what it detects, and ultimately how to utilize the neuroscience of perception to take better photos. In the first piece, he discussed the differences between eyes and cameras. Post 2 now examines the neuroscience of vision, and how visually sensitive regions contain a near-endless supply of visual information that helps us make sense of what we’re looking at

Attention and Why the Brain is Biased in Visual Processing

Consider this scenario: imagine you are trying to describe to a friend an amazing sunrise you saw on a recent beach trip. Rather than continue describing it, you whip out your phone and decide to just show them a picture. You begin scrolling through your camera roll, but at some point realize that you have gone well past the photo you meant to show. You immediately scroll back and then, with some difficulty, finally click on the one or two pictures that you think could be the ones you were thinking of.

If this scenario sounds familiar, rest assured, you’re not alone. This happens to me all the time; I take a photo that I think is good, I want to show it to someone or post it on Instagram, I open my camera roll and before long I find myself looking at pictures from last Christmas.

So, why do we all struggle to find our amazing sunrise photos? 

It could be that our photos were just never that good in the first place. In fact, research looking at the things we find good, or rewarding, has shown that we tend to discount how good something is over time (Brockett et al., 2018). This explanation is possible, but remember that we liked this experience so much that it inspired us to both take a picture of it and to share that experience with someone else, so bad photography aside, what else could it be?

Fortunately, the degree of disappointment and uncertainty we feel upon finally finding the shot(s) we were looking for provides us with another clue. It turns out that our brain doesn’t process all the information it receives from our eyes equally. Our brains are biased to preferentially sift through this mountain of visual information looking for certain features that reliably offer us the most information about our visual scene. If we begin to consider which features of a photograph, or scene, the brain preferentially pays attention to, we might be able to gain a better understanding of why this discrepancy occurs, and how photographers, armed with a basic knowledge of neuroscience, can start creating better images. 

The Neuroscience Of Vision

As information from our retina streams into our brain, it ultimately makes its way to the visual cortex, an area of the brain located at the back of our heads. One of the most important things our visual cortex (and ultimately our entire brain) must do to make sense of a visual scene is to figure out who or what the subject is. Light and color information on its own may be pretty, but from the visual cortex’s perspective is fairly uninformative. Research studying what features of a visual stimulus most strongly activates the visual centers of our brain has shown that areas in the visual scene where there is a change in light or color, also known as contrast, appear to offer the visual system some of the best clues about what it is we are looking at.

Contrast is essentially anything that allows us to separate a subject from its background, and everything from the rods in our retina to the retinal ganglion cells that receive information from the photoreceptors care most about which areas in the visual scene are light and which are dark. This is because the parts of the visual scene in which there are contrast (i.e., a difference in the light and dark area or difference in red and blue) form natural edges that give objects shape and meaning. We then can use information about the edges in the visual scene to tell us things like whether the looming shadow coming towards us is a small insect or a large pterodactyl. 

In terms of photography, this is likely the same reason that images that emphasize black shadows and bright highlights are deemed more interesting than uniformly lit subjects (Gershoni & Kobayashi, 2006; Lu et al., 2015; Tinio et al., 2011). From the brain’s perspective, the subject and its relation to other parts of the image is clear and readily understood. It is also for this reason that high dynamic range (HDR) images, such as the ones you get in HDR mode on your phone, are often described as “fake” looking because these images distort the natural contrast in a scene, something our brains are expertly aware of. The importance of contrast can even be seen very early on in human development as newborn babies rely heavily on contrast to identify what is a face and what is not (Farroni et al., 2005).

Beyond lighting, other forms of contrast, such as color contrast, are also shown to be highly captivating and contribute directly to our perceived enjoyment of an image (Gershoni & Kobayashi, 2006; Lu et al., 2015; Tinio et al., 2011). Contrast also directly biases what in an image we pay attention to. As our eyes scan the visual scene, the fovea bounces, or saccades, looking for points of contrast. This sampling ensures that we capture high-resolution snapshots of the important parts of the visual scene, but also biases what our brains choose to fixate on, such as particularly high-contrast features, like the Washington Monument interrupting an otherwise flat landscape. This preference for a unique feature in a scene, be it a 555-foot obelisk or a brightly colored sunset, helps the brain understand what the subject of a scene is more efficiently, but may also affect how we experience the scene and what we remember from it.

Contrast is not the only feature that our brains prefer. The brain also sifts through visual information based on predictions it makes about what it expects to see in the scene as a whole. We know for example that people rate symmetrical faces and scenes as more beautiful than less symmetrical faces and scenes, suggesting that the brain has an expectation for what people and scenes should look like (Jones et al., 2007; Valentine et al., 2004).

Photographers can take advantage of this, not just by ensuring their images have symmetry in them, but also by attempting to violate our brain’s expectation about how the visual world works. Consider this image of the Lincoln Memorial, Washington Monument and Capitol taken from the Netherlands Carillon in Arlington, Virginia. Part of what makes this image so unique is that we are not often used to seeing these three structures so close together. Anyone that has stood on the steps of the Lincoln Memorial or viewed a map of the National Mall knows that there is quite a bit of space (roughly 2.5 miles) between the Lincoln and the Capitol. The visual intrigue in the scene is caused by taking advantage of a phenomenon known as compression or what occurs when the background of the scene appears closer/larger than it really is. 

Perceptually, the compression phenomenon is an example of what happens when our brain’s prediction about what the world should look like is at odds with what the photo we are viewing looks like. In essence, this discrepancy becomes itself a point of contrast that merits further investigation by the eyes and the visual centers of our brain (Bosco et al., 2015). Errors between what is expected and what is observed, also known as prediction errors, tend to be more memorable (Rouhani et al., n.d.).

Why the Brain Prefers Contrast, Symmetry and Visual Intrigue in Photography

We have seen that the brain takes a very active role in trying to understand the information sent to it from the eyes. Rather than passively viewing everything that comes in, the visual system instead is optimized to pay attention to contrast to figure out what the subject is. Moreover, the visual system also actively makes predictions about what it expects to see, and photographers can use this to their advantage when creating an image. Despite how good the brain is at making sense of the visual world, why then do we struggle to recognize the sunrise image in our camera roll?

This preference for contrast, symmetry, and visual intrigue has a cost. While these features help us readily identify a subject in a scene, they also distort our memory of that scene or experience. Back to the sunrise example from earlier, it is possible the disconnect between the way we remember the scene and our impression of the photograph in front of us is due to this biasing. Perhaps while we were experiencing the sunrise, our eyes focused too much on the amazing high contrast sky, and now the sky is over-emphasized in our memory of the scene, causing us to pay less attention to an unsavory element in the foreground that we are now seeing in the photo.

Similarly, it is possible that because we spent so much time sampling more of the sky, our memory of the colors is slightly more vivid than the more real-life image our camera produced, causing us to question whether the image we see on our phone is really from that sunrise. In fact, researchers have intentionally altered the contrast in images to cause people to rate an image as either better or worse (Gershoni & Kobayashi, 2006; Lu et al., 2015; Tinio et al., 2011). Interestingly, a different study suggests that just the act of taking a photo may reduce our memory of a scene entirely (Henkel, 2014). 

The interplay between a photograph and an experience is complicated, but by understanding how and what the visual areas of the brain pay attention to when viewing a scene, photographers can hopefully make more interesting and memorable images. Now that we know how our eyes work and what our visual systems pay the most attention to, in the final post we will look at where this information goes and how it might influence the types of decisions photographers make when taking a photo.

Photo by Adam Brockett

References for The Neuroscience and Psychology of Attention and it’s Impact in Photography

Bosco, A., Lappe, M., & Fattori, P. (2015). Adaptation of Saccades and Perceived Size after Trans-Saccadic Changes of Object Size. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 35(43), 14448–14456. https://doi.org/10.1523/JNEUROSCI.0129-15.2015

Brockett, A. T., Pribut, H. J., Vazquez, D., & Roesch, M. R. (2018). The impact of drugs of abuse on executive function: Characterizing long-term changes in neural correlates following chronic drug exposure and withdrawal in rats. Learning & Memory (Cold Spring Harbor, N.Y.), 25(9), 461–473. https://doi.org/10.1101/lm.047001.117

Brockett, A. T., & Roesch, M. R. (2020). The ever-changing OFC landscape: What neural signals in OFC can tell us about inhibitory control. Behavioral Neuroscience. https://doi.org/10.1037/bne0000412

Brockett, A. T., Vázquez, D., & Roesch, M. R. (2021). Prediction errors and valence: From single units to multidimensional encoding in the amygdala. Behavioral Brain Research, 113176. https://doi.org/10.1016/j.bbr.2021.113176

Canon USA. (2015). Canon—Obsession Experiment.

Farroni, T., Johnson, M. H., Menon, E., Zulian, L., Faraguna, D., & Csibra, G. (2005). Newborns’ preference for face-relevant stimuli: Effects of contrast polarity. Proceedings of the National Academy of Sciences of the United States of America, 102(47), 17245–17250. https://doi.org/10.1073/pnas.0502205102

Gershoni, S., & Kobayashi, H. (2006). How We Look at Photographs as Indicated by Contrast Discrimination Performance Versus Contrast Preference. Journal of Imaging Science and Technology, 50(4), 320–326. https://doi.org/10.2352/J. ImagingSci.Technol.(2006)50:4(320)

Henkel, L. A. (2014). Point-and-shoot memories: The influence of taking photos on memory for a museum tour. Psychological Science, 25(2), 396–402. https://doi.org/10.1177/0956797613504438

Ikeda, T., Matsuyoshi, D., Sawamoto, N., Fukuyama, H., & Osaka, N. (2015). Color harmony represented by activity in the medial orbitofrontal cortex and amygdala. Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00382

Ishizu, T., & Zeki, S. (2011). Toward a brain-based theory of beauty. PloS One, 6(7), e21852. https://doi.org/10.1371/journal.pone.0021852

Jain, A., Bansal, R., Kumar, A., & Singh, K. (2015). A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first-year students. International Journal of Applied and Basic Medical Research, 5(2), 124–127. https://doi.org/10.4103/2229-516X.157168

Jones, B. C., DeBruine, L. M., & Little, A. C. (2007). The role of symmetry in attraction to average faces. Perception & Psychophysics, 69(8), 1273–1277. https://doi.org/10.3758/bf03192944

Lu, Z., Guo, B., Boguslavsky, A., Cappiello, M., Zhang, W., & Meng, M. (2015). Distinct effects of contrast and color on subjective rating of fearfulness. Frontiers in Psychology, 6, 1521. https://doi.org/10.3389/fpsyg.2015.01521

Maslund, Richard. (2020). We know it when we see it: What the neurobiology of vision tells us about how we think (1st ed.). Basic Books.

Rouhani, N., Norman, K. A., Niv, Y., & Bornstein, A. M. (n.d.). Reward prediction errors create event boundaries in memory. 38.

Sawh, Kishore. (2015). Canon’s “Obsession Experiment” | See How The Average Person vs Pro Views Image Details. https://www.slrlounge.com/canons-obsession-experiment-see-average-person-vs-pro-views-image-details/

Snowden, R., Snowden, R. J., Thompson, P., & Troscianko, T. (2012). Basic Vision: An Introduction to Visual Perception. OUP Oxford.

Tinio, P. P. L., Leder, H., & Strasser, M. (2011). Image quality and the aesthetic judgment of photographs: Contrast, sharpness, and grain teased apart and put together. Psychology of Aesthetics, Creativity, and the Arts, 5(2), 165–176. https://doi.org/10.1037/a0019542

Valentine, T., Darling, S., & Donnelly, M. (2004). Why are average faces attractive? The effect of view and averageness on the attractiveness of female faces. Psychonomic Bulletin & Review, 11(3), 482–487. https://doi.org/10.3758/bf03196599

Zeki, S. (1998). Art and the Brain. Daedalus, 127(2), 71–103.