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surprising finding was classified as a mere mistake; perhaps a machine malfunctioned or an enzyme had gone stale. “The scientists were trying to explain away what they didn’t understand,” Dunbar says. “It’s as if they didn’t want to believe it.”

The experiment would then be carefully repeated. Sometimes, the weird blip would disappear, in which case the problem was solved. But the weirdness usually remained, an anomaly that wouldn’t go away.

This is when things get interesting. According to Dunbar, even after scientists had generated their “error” multiple times — it was a consistent inconsistency — they might fail to follow it up. “Given the amount of unexpected data in science, it’s just not feasible to pursue everything,” Dunbar says. “People have to pick and choose what’s interesting and what’s not, but they often choose badly.” And so the result was tossed aside, filed in a quickly forgotten notebook. The scientists had discovered a new fact, but they called it a failure.

The reason we’re so resistant to anomalous information — the real reason researchers automatically assume that every unexpected result is a stupid mistake — is rooted in the way the human brain works. Over the past few decades, psychologists have dismantled the myth of objectivity. The fact is, we carefully edit our reality, searching for evidence that confirms what we already believe. Although we pretend we’re empiricists — our views dictated by nothing but the facts — we’re actually blinkered, especially when it comes to information that contradicts our theories. The problem with science, then, isn’t that most experiments fail — it’s that most failures are ignored.

education enabled them to see the error, and for them it was the inaccurate video that triggered the ACC.

But there’s another region of the brain that can be activated as we go about editing reality. It’s called the dorsolateral prefrontal cortex, or DLPFC. It’s located just behind the forehead and is one of the last brain areas to develop in young adults. It plays a crucial role in suppressing so-called unwanted representations, getting rid of those thoughts that don’t square with our preconceptions. For scientists, it’s a problem.

When physics students saw the Aristotelian video with the aberrant balls, their DLPFCs kicked into gear and they quickly deleted the image from their consciousness

when it comes to noticing anomalies, an efficient prefrontal cortex can actually be a serious liability. The DLPFC is constantly censoring the world, erasing facts from our experience. If the ACC is the “Oh shit!” circuit, the DLPFC is the Delete key. When the ACC and DLPFC “turn on together, people aren’t just noticing that something doesn’t look right,” Dunbar says. “They’re also inhibiting that information.”

The lesson is that not all data is created equal in our mind’s eye: When it comes to interpreting our experiments, we see what we want to see and disregard the rest.

There are advantages to thinking on the margin. When we look at a problem from the outside, we’re more likely to notice what doesn’t work. Instead of suppressing the unexpected, shunting it aside with our “Oh shit!” circuit and Delete key, we can take the mistake seriously. A new theory emerges from the ashes of our surprise.

Modern science is populated by expert insiders, schooled in narrow disciplines. Researchers have all studied the same thick textbooks, which make the world of fact seem settled. This led Kuhn, the philosopher of science, to argue that the only scientists capable of acknowledging the anomalies — and thus shifting paradigms and starting revolutions — are “either very young or very new to the field.” In other words, they are classic outsiders, naive and untenured. They aren’t inhibited from noticing the failures that point toward new possibilities.

“They knew more about E. coli than anyone else, but that was what they knew,” he says. Dunbar watched how each of these labs dealt with their protein problem. The E. coli group took a brute-force approach, spending several weeks methodically testing various fixes. “It was extremely inefficient,” Dunbar says. “They eventually solved it, but they wasted a lot of valuable time.”

The diverse lab, in contrast, mulled the problem at a group meeting. None of the scientists were protein experts, so they began a wide-ranging discussion of possible solutions. At first, the conversation seemed rather useless. But then, as the chemists traded ideas with the biologists and the biologists bounced ideas off the med students, potential answers began to emerge.

forced to rely on metaphors and analogies to express themselves. (That’s because, unlike the E. coli group, the second lab lacked a specialized language that everyone could understand.) These abstractions proved essential for problem-solving, as they encouraged the scientists to reconsider their assumptions. Having to explain the problem to someone else forced them to think, if only for a moment, like an intellectual on the margins, filled with self-skepticism.

This is why other people are so helpful: They shock us out of our cognitive box

Rogers encourages academic researchers to consider the entrepreneurial option and to be honest about their motivations. "You need to want to do this because it is different from an academic environment, from a purely basic research environment," he says.

At the same time, Rogers says, academic researchers should be ready to recognize their own limitations and partner with colleagues who can bring other crucial expertise to the enterprise, adding, "People should do what they know and find other people to do the things they don't know."

most of these collaborations are not hierarchical, they can create an environment that fosters mentoring of early-career researchers and accelerates their maturation as scientists. As a result, early-career scientists develop a reputation in the community more rapidly, which helps them make a mark in the field.

In fact, mentoring can greatly speed career independence.

• DO make a compelling case for why the question is important and must be addressed, and place this early in the proposal; after one page, the reviewer should be excited about the proposed research.
• DO describe in detail who will provide the requisite expertise needed to accomplish the proposed research; establishing a collaboration is one of the easiest ways to ensure that the proper expertise is represented on an application.
• DO write the proposal in such a way that any reviewer can understand it. Applicants should remember that proposals are evaluated by multiple reviewers with varying scientific expertise and backgrounds.
  
• DO follow each and every rule of the funding guidelines.
• DO make the proposal relevant to the program's core objectives.
• DO NOT present a continuation of doctoral work without a clear distinction of how this will advance the science to the next stage.
• DO NOT propose too much; it is easy for a young investigator to become overly ambitious—and to be criticized as a result.

It's more important to do 10 high-risk experiments, nine of which fail completely. But the one that's succeeded is of earthshaking importance," Ramachandran says.

But how many young scientists can afford to fail completely nine times out of 10?

"It's more important to do 10 high-risk experiments, nine of which fail completely. But the one that's succeeded is of earthshaking importance," Ramachandran says.

But how many young scientists can afford to fail completely nine times out of 10?

Learn to juggle

Being a professor feels like having about three full-time jobs.

junior group leader positions that include institutional support, lab space, and funding sufficient to start a small, independent research group. Altogether, "this adds up to quite a significant number" of opportunities, Armbruster says.

"Of course, it can go wrong, and if it goes wrong, you're the only person that will be blamed

ERC-type grants and junior PI positions at the top, soft money positions at the bottom, and fellowships and standard employment contracts in between.

Such increased competition means that many postdocs will have their hopes dashed early, Armbruster says. "But on the other hand, ... it is quite good that you're being forced to consider your options relatively early" so that you can get started sooner on a different career trajectory. "There is nothing sadder than the prevalent waste of talent in Europe, which is all these people who are allowed to go on with their academic careers until they are 40 only to find out that they'll never make it."

For students with a clearly defined idea of what they want to study, perhaps the best resource will be a professor at an undergraduate institution who currently serves as an adviser or mentor. A student who has worked with a professor in a lab during undergraduate studies and wants more of the same may ask for a recommended professor or program of related research at another institution.

However, not all students have a clearly defined path when they go to graduate school, so one strategy for a successful match is to pick a larger school, with many different types of programs and qualified faculty

10 questions to ask




…a current student





1. What do you see as the strengths of the program?
2. What do you see as the weaknesses of the program?
3. How accessible are the program faculty?
4. What are the research facilities like—does there seem to be adequate funding, supplies, and equipment?
5. If you had it to do over, would you select this graduate school again? Why? Why not?
6. What are the surroundings of the school campus like? Are there leisure activities, good housing, and other amenities?
7. What is campus life like—do you have an opportunity to become involved and/or take on leadership roles?
8. Do you feel that there is an adequate level of financial and academic support to complete your studies?
9. How competitive and difficult are the academic standards here?
10. Is there anyone else I might talk to who could help provide an accurate picture of the program?
11. What are the major strengths of this program?
12. What are potential weaknesses of the program?
13. How would you describe the faculty?
14. Are the classes team-taught or taught by one individual?
15. How would you describe the academic environment here compared with other schools?
16. What attributes does a graduate student need to be successful at this school/in this program?
17. Do you see any common characteristics in students who have quit their doctoral degree programs here—if so, what were they?
18. What research facilities are available and how adequate is intramural and extramural funding for the labs?
19. What have recent graduates of the same program done with their degrees?
20. What unique features are offered by this school compared to other similar schools?