They weren’t cogitating, recollecting, differentiating

Tuesday, April 25th, 2017

Radiologists were asked to evaluate X-rays while inside an MRI machine that could track their brain activity:

(There’s a marvellous series of recursions here: to diagnose diagnosis, the imagers had to be imaged.) X-rays were flashed before them. Some contained a single pathological lesion that might be commonly encountered — perhaps a palm-shaped shadow of a pneumonia, or the dull, opaque wall of fluid that had accumulated behind the lining of the lung. Embedded in a second group of diagnostic images were line drawings of animals; within a third group, the outlines of letters of the alphabet. The radiologists were shown the three types of images in random order, and then asked to call out the name of the lesion, the animal, or the letter as quickly as possible while the MRI machine traced the activity of their brains. It took the radiologists an average of 1.33 seconds to come up with a diagnosis. In all three cases, the same areas of the brain lit up: a wide delta of neurons near the left ear, and a moth-shaped band above the posterior base of the skull.

“Our results support the hypothesis that a process similar to naming things in everyday life occurs when a physician promptly recognizes a characteristic and previously known lesion,” the researchers concluded. Identifying a lesion was a process similar to naming the animal. When you recognize a rhinoceros, you’re not considering and eliminating alternative candidates. Nor are you mentally fusing a unicorn, an armadillo, and a small elephant. You recognize a rhinoceros in its totality — as a pattern. The same was true for radiologists. They weren’t cogitating, recollecting, differentiating; they were seeing a commonplace object. For my preceptor, similarly, those wet rales were as recognizable as a familiar jingle.

In 1945, the British philosopher Gilbert Ryle gave an influential lecture about two kinds of knowledge. A child knows that a bicycle has two wheels, that its tires are filled with air, and that you ride the contraption by pushing its pedals forward in circles. Ryle termed this kind of knowledge — the factual, propositional kind — “knowing that.” But to learn to ride a bicycle involves another realm of learning. A child learns how to ride by falling off, by balancing herself on two wheels, by going over potholes. Ryle termed this kind of knowledge — implicit, experiential, skill-based — “knowing how.”

The two kinds of knowledge would seem to be interdependent: you might use factual knowledge to deepen your experiential knowledge, and vice versa. But Ryle warned against the temptation to think that “knowing how” could be reduced to “knowing that” — a playbook of rules couldn’t teach a child to ride a bike. Our rules, he asserted, make sense only because we know how to use them: “Rules, like birds, must live before they can be stuffed.” One afternoon, I watched my seven-year-old daughter negotiate a small hill on her bike. The first time she tried, she stalled at the steepest part of the slope and fell off. The next time, I saw her lean forward, imperceptibly at first, and then more visibly, and adjust her weight back on the seat as the slope decreased. But I hadn’t taught her rules to ride a bike up that hill. When her daughter learns to negotiate the same hill, I imagine, she won’t teach her the rules, either. We pass on a few precepts about the universe but leave the brain to figure out the rest.

Some time after Lignelli-Dipple’s session with the radiology trainees, I spoke to Steffen Haider, the young man who had picked up the early stroke on the CT scan. How had he found that culprit lesion? Was it “knowing that” or “knowing how”? He began by telling me about learned rules. He knew that strokes are often one-sided; that they result in the subtle “graying” of tissue; that the tissue often swells slightly, causing a loss of anatomical borders. “There are spots in the brain where the blood supply is particularly vulnerable,” he said. To identify the lesion, he’d have to search for these signs on one side which were not present on the other.

I reminded him that there were plenty of asymmetries in the image that he had ignored. This CT scan, like most, had other gray squiggles on the left that weren’t on the right — artifacts of movement, or chance, or underlying changes in the woman’s brain that preceded the stroke. How had he narrowed his focus to that one area? He paused as the thought pedalled forward and gathered speed in his mind. “I don’t know — it was partly subconscious,” he said, finally.

“That’s what happens — a clicking together — as you grow and learn as a radiologist,” Lignelli-Dipple told me. The question was whether a machine could “grow and learn” in the same manner.

Spoiler alert: yes.

Comments

  1. Candide III says:

    The radiologist, though, can explain how he came to the conclusion, even if training enabled him to reach it by pattern-matching. A hairball of numbers, which is what a neural network looks like to a human, can pattern-match (if humans have trained it well) but cannot explain, nor teach.

    I knew you came from Afghanistan. From long habit the train of thoughts ran so swiftly through my mind that I arrived at the conclusion without being conscious of intermediate steps. There were such steps, however. The train of reasoning ran, ‘Here is a gentleman of a medical type, but with the air of a military man. Clearly an army doctor, then. He has just come from the tropics, for his face is dark, and that is not the natural tint of his skin, for his wrists are fair. He has undergone hardship and sickness, as his haggard face says clearly. His left arm has been injured. He holds it in a stiff and unnatural manner. Where in the tropics could an English army doctor have seen much hardship and got his arm wounded? Clearly in Afghanistan.’ The whole train of thought did not occupy a second. I then remarked that you came from Afghanistan, and you were astonished.

  2. Mike in Boston says:

    Candide III is correct that vanilla AI suffers from being unable to explain the results obtained. To its credit, DARPA is aware of the problem and working on it.

Leave a Reply