What science is learning about how we see can help you take more compelling pictures.
In principle: The eye's nighttime ISO has been estimated at about 800. Since day vision is about 600 times less sensitive to light, on a sunny day your eyes have an ISO of close to 1. In practice: The slowest film you can buy is ISO 25, and ISO 50 is low on a digital camera. But with DSLRs now topping ISO 6400, your camera sees in the dark better than you do. So enjoy your camera's nighttime advantage and shoot when the lights are low.
In principle: Eye-movement tracking shows that the eye is drawn to lines, is even more taken with angles, and returns repeatedly to corners. The Mach Effect describes how the eye searches out luminance differences by neurologically exaggerating contrast along edges. In practice: We love lines, especially horizons. So use them to your advantage: Keep lines straight, include points of intersection, and put your subject close to corners.
In principle: We're subliminally influenced by the faces we see. Pupillometrics has demonstrated that when you look at an image of a person, your pupils dilate to the same diameter as the person's in the picture. Tests also have shown that people prefer photos in which the pupils -- human or animal -- are dilated.
In practice: Avoid bright, pupil-contracting lights in the eyes when shooting portraits, or score some belladonna.
In principle: Motion parallax helps us see in 3D because when we stare at a fixed object and then move sideways, nearer objects appear to move in the opposite direction while distant things appear to move in the same direction.
Inpractice: Place someone or something moving to the right in the foreground (perhaps a couple walking on the beach), and in the background above their heads place objects projecting to the left (such as cliffs and headlands).
In principle: The illuminance ratio of sunlight to starlight is 1 billion to 1. Human vision spans the whole range, a spread far better than any camera's.
In practice: Use split neutral-density filters in sunshine and high-dynamic range photography at night to make your pictures look more like your actual experience of the scene.
In principle: Does the eye care whether a photo is black-and-white or color when it comes to where it fixates? No. More important is contrast and whether an object creates a "hotspot" for the eye.
In practice: When shooting b&w, make sure you have enough contrast to keep viewers' attention where you want it. And don't forget those all-important lines and faces.
DEPTH OF FIELD
In principle: With a focal length of about 22mm and a field of view of almost 180 degrees at its extreme, our eyes are capable of f/3.5 at wide open. Then, only the 2 degrees in the center of the retina, an area called the fovea, is sharp.
In practice: This may be why shallow depth of field is so visually appealing. So use it to draw attention straight to your subject. Extreme depth or shallowness can also introduce an element of surprise to your photo.
In principle: Looking at a 120-degree field of view, the eye's resolution is equivalent to about 576 megapixels.
In practice: When we view pictures, our brains can identify and make associations with a range of blurry and indistinct elements, essentially filling in the blanks.
In principle: Researchers studied 225 paintings going back three centuries and found that 75 percent depicted the illumination source above and to the left. Human testing confirmed that, in the absence of clues, the brain of right-handers infers illumination from above left, while southpaws see the light coming from above right.
In practice: If it worked for Vermeer, it'll work for you.
In principle: We're drawn to faces. Our eyes and brain evolved to assess almost instantaneously whether we are seeing a predator, prey, or mate.
In practice: Shoot more portraits! Perhaps the ultimate eye-pleasing photo would include scan lines that link a tiger attacking an ibex to an attractive person looking on. (Good luck with that.)
In principle: Surprise is the strongest known attractor of human attention. The Bayesian Theory of Surprise provides a mathematical framework for quantifying the degree of incompatible data in an image. The scientific definition of surprise? A relationship between objects that changes your beliefs about the world.
In practice: Depict the unexpected, whether you stumble across it
(a good reason always to have a camera with you) or set it up.