The Science of Getting Stuck (And Why That Might Be the Point)

This article explores why conventional medical solutions sometimes fail to resolve persistent health problems and what that resistance might be telling us about the nature of healing itself.

There's a particular feeling that happens when you've tried everything reasonable and nothing works. Not the initial frustration of a setback - that's sharp, almost energizing. This is different. It's the slow realization that you might be asking the wrong question entirely.

Common Cognitive Traps in Clinical Problem-Solving - Recognition & Solutions

Cognitive Trap	How It Manifests	Warning Signs	How to Break Free
Diagnostic Momentum	Initial working diagnosis persists despite contradictory evidence; all new symptoms interpreted through original framework	Patient has seen multiple specialists with no improvement; diagnosis label influences every subsequent evaluation	Perform a "zero-based" reassessment; ask "What would I think if seeing this patient fresh?"
Functional Fixedness	Viewing symptoms only through conventional diagnostic categories; missing atypical presentations	Repeatedly using same treatment approach despite lack of response; dismissing patient reports that don't fit expected pattern	Deliberately reframe: "If this weren't [initial diagnosis], what else causes these exact symptoms?"
Method-Limited Thinking	Problem approach constrained by training specialty; asking only questions your tools can answer	Persistent failure with standard protocols; feeling like "everything's been tried"	Consult outside your specialty; ask "How would a [different specialty] approach this?"
Framework Protection	Explaining away contradictory evidence rather than revising working theory	Increasingly complex explanations for treatment failures; patient labeled as "difficult" or "non-compliant"	List evidence that contradicts current theory; force yourself to generate alternative explanations
Single-Lens Analysis	Attempting to explain complex multi-system problem with single mechanism	Treating depression as purely neurochemical; viewing pain as only structural	Map problem across multiple domains simultaneously (biological, psychological, social, systemic)

I see this play out in different ways. Sometimes it's a patient who's been through five specialists, twelve medications, and three different diagnostic theories about their symptoms. Sometimes it's watching trainees hit that point where textbook knowledge stops being useful and they have to learn to think differently. And sometimes - maybe most instructively - it's in how science itself gets unstuck when conventional approaches fail.

What's interesting isn't just that people get stuck. It's what happens next. Because there's a pattern to how breakthroughs actually occur, and it's nothing like we imagine.

The Architecture of Being Wrong

Here's something I've noticed: we're exceptionally bad at recognizing when our frameworks are the problem, not our execution. We assume we're doing it wrong, not thinking about it wrong.

This shows up everywhere. In medicine, we call it diagnostic momentum - when a working diagnosis gets established early and then everything gets interpreted through that lens. A patient gets labeled with anxiety, so chest tightness becomes hyperventilation, not cardiac ischemia. Or someone gets tagged as drug-seeking, so real pain gets dismissed as manipulation.

The dangerous part isn't the initial framing. It's that once established, these frameworks become remarkably resistant to revision. You can present contradictory evidence and watch it get explained away rather than integrated. The framework protects itself.

Cognitive scientists have a term for this: functional fixedness. It's the tendency to see objects or problems only in terms of their conventional use. A hammer is for hammering, so you don't think to use it as a paperweight. A cough is respiratory, so you don't consider heart failure. The problem is fixed not in space but in thought.

What breaks functional fixedness isn't usually more information. It's reframing. Seeing the same thing from an angle that makes different features salient.

When Methods Become Prisons

There's a specific kind of trap in scientific research - and honestly, in any technical field - where the methods you're trained in become the boundaries of what questions you can ask.

Molecular biologists think in terms of genes and proteins. Physiologists think in terms of systems and regulatory loops. Epidemiologists think in terms of populations and risk factors. These aren't just different areas of expertise. They're different ways of parsing reality.

The problem emerges when complex problems require multiple frameworks simultaneously. Depression isn't just a neurotransmitter imbalance. It's also altered circuit connectivity, disrupted circadian rhythms, chronic inflammatory states, learned helplessness, social isolation, and meaning deficit. If you only have the serotonin hypothesis, you're going to miss most of what's actually happening.

I think this is partly why interdisciplinary work is so difficult. It's not just coordinating schedules or learning jargon. It's genuinely hard to hold multiple frameworks in mind at once. Each one wants to be the primary lens. They compete for conceptual space.

What's interesting about Charlie Swanton's story - the young researcher from Part 1 who couldn't get his protein binding studies to work - is what happened when he hit that wall. He didn't just try harder with the same methods. He found someone working in a completely different domain (structural biology versus molecular genetics) and asked: how would you think about this?

The Accidental Advantages of Desperation

There's something about running out of conventional options that makes you receptive to unconventional ones. Not because you've suddenly become more creative, but because the filters have dropped. You're no longer optimizing within constraints - you're questioning whether the constraints make sense.

Swanton's breakthrough came from using crystal structures as templates for predicting binding sites. This was obvious in retrospect - structural biology exists precisely to reveal molecular architecture - but it wasn't obvious when you were trained in genetics and cell cycle regulation. Those were different worlds.

What made the collaboration work wasn't just technical complementarity. It was that Swanton had reached the point where he couldn't afford to care about disciplinary boundaries. His project was failing. He needed answers from wherever they came.

I wonder sometimes if this is the real function of hitting bottom. Not character building or resilience training, but filter removal. When you're comfortable, you can afford to stay within your expertise. When you're desperate, you become genuinely curious about other people's tools.

The neuroscientist John O'Keefe, who discovered place cells - neurons in the hippocampus that create cognitive maps of space - has talked about this. His lab was physiological. They recorded from individual neurons in rats while the rats navigated mazes. But the breakthrough came from talking to cognitive psychologists about spatial memory theories. Two different frameworks colliding.

The Map Is Not the Territory (But Sometimes It's Close Enough)

Here's where it gets practically useful: sometimes you don't need perfect models. You need adequate ones.

Swanton realized he could use cyclin A's crystal structure - a protein similar to but not identical to his target - as a template. This was an approximation. A best guess based on evolutionary conservation and structural similarity. In strict terms, it was scientifically impure.

But it worked. By mapping surface-exposed amino acids from the template onto his target protein and making educated predictions about binding sites, he could design focused experiments. Instead of screening everything blindly, he could test specific hypotheses.

This is a broader principle in science - and medicine - that we don't always acknowledge openly: good-enough models that let you make progress beat perfect models you can't build yet.

When I'm trying to figure out what's causing someone's chronic fatigue, I'm not going to sequence their whole genome and analyze their proteome and measure every cytokine. I'm going to use pattern recognition from thousands of similar presentations, combined with basic labs and targeted questions, to narrow possibilities. It's template-matching, essentially. Imperfect but functional.

The danger, obviously, is when good-enough solidifies into dogma. When the approximation becomes treated as truth. But that's a different failure mode than being paralyzed by lack of perfect information.

The Collaboration Problem

What strikes me about most breakthrough stories - when you dig into them - is how collaborative they actually are, even when they're credited to individuals. Someone has a problem they can't solve. Someone else has a technique or framework that might help. The breakthrough happens in translation.

But collaboration is weird. It requires admitting limitation, which academic culture doesn't exactly reward. It requires explaining your problem to someone who doesn't share your background assumptions, which is harder than it sounds. And it requires being willing to have your elegant theory messily revised by someone else's data.

I've watched this play out in clinical contexts. The really excellent doctors I've worked with - the ones who consistently catch things others miss - tend to be the ones who actually talk to other specialists. Not just sending referrals, but having real conversations about cases. "Here's what I'm seeing. Here's what doesn't fit. What am I missing?"

It's vulnerable. You're exposing the edges of your understanding. But that's where the useful information lives - at the boundaries where your framework stops making sense.

Pattern Recognition and Its Discontents

There's a tension in how expertise develops. Early on, you learn rules. Explicit, memorizable, step-by-step procedures. If you see symptom A, think condition B, order test C. This is necessary. You need scaffolding.

But eventually, if you stick with it, something shifts. The rules fade into background and what emerges is pattern recognition. You walk into a room and something feels wrong before you can articulate why. A lab value is technically normal but feels off in context. A story doesn't quite hang together.

The neuroscience of this is fascinating. Pattern recognition happens in older, faster brain circuits - systems evolved for detecting threats and opportunities quickly. It bypasses conscious reasoning. This is why experienced clinicians can often make accurate diagnoses within seconds of meeting a patient, then spend the next ten minutes figuring out why they think that.

But here's the problem: pattern recognition can also lock you into seeing what you expect to see. It's efficient precisely because it's shortcuts. And shortcuts work great until they don't.

The breakthrough moments - whether in research or clinical medicine or any complex domain - often come from someone who doesn't quite fit the pattern recognizing something that the pattern-matchers have smoothed over.

Swanton was young enough that he hadn't fully developed pattern-matcher's blindness. He could still see structural biology as potentially relevant because he wasn't yet locked into "this is how cell cycle research is done."

What Actually Breaks the Logjam

I've been thinking about what actually creates the conditions for breakthrough. Not the breakthrough itself - that's often luck and timing - but the conditions that make it possible.

Three things seem necessary:

First, genuine confusion. Not just "this is hard" but "I don't understand what's happening." That gap between expectation and reality is information. Most of the time we paper over it. We force the data into existing frameworks. But if you can sit with genuine confusion long enough, it reveals framework problems.

Second, access to different perspectives. This doesn't mean just asking more people. It means finding people who think differently - different training, different assumptions, different tools. Someone who can say "oh, you're thinking about it as X, but what if it's actually Y?"

Third - and this is the uncomfortable one - enough desperation or freedom that you're willing to look foolish. Most breakthrough ideas sound dumb at first. If you need to maintain credibility at every step, you're never going to pursue them.

Academia is terrible at this last part. The pressure to publish, to maintain funding, to not waste time on dead ends - it all pushes toward incremental advances within established frameworks. The safest paper to write is one that confirms existing models with slightly better data.

Clinical medicine has some of the same problems. Differential diagnosis lists get narrower, not broader, as you gain experience. You learn what's common, and common things being common, you get reinforced for thinking that way. Until you miss something rare because you stopped considering it.

The Return Trip

Here's what happened after Swanton's mid-term review meeting - the one where his supervisors told him to change projects or leave. He took the structural biology approach, made predictions about where p21 bound to cyclin D, designed targeted experiments, and within months had results worth publishing.

The paper that came out of this work in 1995 has been cited over 700 times. Not because it solved cancer - it didn't - but because it provided a template for understanding CDK inhibitor binding that others could build on. Sometimes the breakthrough isn't the final answer. It's a useful way of thinking about the question.

What I find most interesting is what Swanton says about this period in retrospect: that hitting the wall forced him to learn structural biology, and that combination - cell cycle regulation plus structural thinking - became foundational to how he approaches cancer research now. The thing that nearly ended his PhD created the framework for his career.

I'm not trying to romanticize failure. Failed experiments are mostly just failed experiments. But there's something about the specific flavor of "nothing is working and I don't know why" that creates space for different thinking.

Maybe the real insight isn't about method or framework. Maybe it's about maintaining enough cognitive flexibility that you can actually see when your current approach has stopped being useful. Which is harder than it sounds. Our frameworks protect themselves. They make alternative perspectives literally harder to see.

I don't have a clean ending for this. I'm still watching people get stuck - in research, in clinical thinking, in their own health journeys - and trying to understand what helps them get unstuck. Sometimes it's new information. Sometimes it's different framing. Sometimes it's just time and the accumulated weight of contradictions until something has to give.

What do you think? When have you been stuck in a way where the problem wasn't information but framework?