As we walk about a site and talk to people, we can note our observations. At this stage, we try to store the information we gain in some accurate way, carry a notebook, or a camera and tape-recorder, and make small sketches. The notes we end up with can later be used to devise design strategies.
We do not just see and hear, smell and taste, but we sense heat and cold, pressure, stress from efforts of hill-climbing or prickly plants, and find compatible or incompatible sites in the landscape. We note good views, outlooks, soil colours and textures. In face, we use (consciously) all our many senses and become aware of our bodies and responses.
Beyond this, we can sit for a time and notice patterns and processes: how some trees prefer to grow in rocks, some in valleys, others in grasslands or clumps. We see how water flows on the site, where fires have left scars, winds have bent branches or deformed the shape of trees, how the sun and shadows move, and where we find signs of animals resting, moving, or feeding. The site is full of information on every natural subject, and we must learn to read it well.
In biology, when researchers want to observe animals in their natural habitat, it is paramount that they find a way to do so without disturbing those animals. Otherwise, the behavior they see is unlikely to be natural, because most animals (including humans) change their behavior when they are being observed.
Perceptual: "They couldn't figure out what to do next", "they couldn't find the feature", "they didn't know they could click that button..." etc.
Domain-specific: "We need a way to jump back here because in their workflow this happens..."
In general, usability testing only catches type 1 perceptual problems. Because in those tests you take people out of the real world and assign them tasks. Usability testing doesn't catch domain-specific problems because they only come up in real life use.
I once read a good definition of aptitude. Aptitude is how long it takes you to learn something. The idea is that everybody can learn anything, but if it takes you 200 years, you essentially have no aptitude for it. Useful aptitudes are in the <10 years range.
Your first short story takes 10 days to write. The next one 5 days, the next one 2.5 days, the next one 1.25 days. Then 0.625 days, at which point you’re probably hitting raw typing speed limits. In practice, improvement curves have more of a staircase quality to them. Rather than fix the obvious next bottleneck of typing speed (who cares if it took you 3 hours instead of 6 to write a story; the marginal value of more speed is low at that point), you might level up and decide to (say) write stories with better developed characters. Or illustrations. So you’re back at 10 days, but on a new level.
This kind of improvement replaces quantitative improvement (optimization) with qualitative leveling up, or dimensionality increase. Each time you hit diminishing returns, you open up a new front. You’re never on the slow endzone of a learning curve. You self-disrupt before you get stuck.
The interesting thing is, this is not purely a function not of raw prowess or innate talent, but of imagination and taste.