Ignorance

Ignorance Plot Summary

Introduction

We've all experienced that familiar scenario: studying for a test by memorizing facts, names, and formulas—desperately trying to cram information into our brains. This approach reflects how most of us think about science and knowledge—as an ever-growing collection of established facts. But what if I told you that this view fundamentally misunderstands how science actually works? What if the most exciting part of science isn't what we know, but what we don't know? In the world of professional scientists, the accumulation of facts is merely a stepping stone. The true engine that drives scientific progress is ignorance—not the kind that stems from willful stupidity or indifference, but the thoughtful recognition of gaps in our understanding. When Stuart Firestein, a neuroscience professor at Columbia University, realized his students were getting the wrong impression about science from fact-heavy textbooks, he created a revolutionary course called "Ignorance." Instead of lecturing about what scientists know, he invited colleagues to discuss what they don't know, what puzzles them, and what questions keep them up at night. This remarkable perspective shift reveals science not as a static collection of facts but as a dynamic adventure into the unknown—a journey driven by questions rather than answers, by curiosity rather than certainty.

Chapter 1: The Value of Not Knowing

Ignorance gets a bad rap in everyday conversation. We typically associate it with a lack of education, awareness, or intelligence. But in science, a particular kind of ignorance—what we might call "knowledgeable ignorance"—serves as the primary fuel for discovery. This isn't mere absence of knowledge, but rather a sophisticated awareness of what we don't know. It's a communal gap in understanding that scientists identify, explore, and refine. When scientists talk among themselves over coffee or beer, they rarely discuss what's already known; instead, they excitedly share the mysteries that still confound them. This productive ignorance stands in stark contrast to data accumulation. While modern society drowns in information—with an estimated 5 exabytes (that's a billion billion bytes) added to the world's store of data in 2002 alone—scientists swim through this sea of facts seeking the boundaries where knowledge ends and ignorance begins. Facts serve primarily as raw materials that help scientists frame better questions. They are starting points, not destinations. As James Clerk Maxwell, one of history's greatest physicists, noted, "Thoroughly conscious ignorance is the prelude to every real advance in science." The history of science reveals that major breakthroughs often emerge not from collecting more data within existing frameworks, but from recognizing fundamental gaps in understanding. Einstein's theories of relativity didn't result from gathering more precise measurements within Newton's model; they came from questioning basic assumptions about space and time. Similarly, Darwin's theory of evolution emerged not just from collecting specimens, but from asking profound questions about patterns he observed that existing explanations couldn't address. Consider how scientists actually work in laboratories. They don't celebrate completing experiments that confirm what they already believed; they get excited about unexpected results that challenge their hypotheses. The physicist Enrico Fermi distinguished between experiments that prove hypotheses (which he called measurements) and those that disprove them (which he called discoveries). This perspective inverts our common understanding: the real victory in science isn't finding evidence that confirms what we think we know—it's uncovering evidence that reveals the limitations of our current understanding. This view transforms how we might approach science ourselves. Rather than feeling intimidated by the mountain of scientific facts we don't know, we can appreciate the excitement of unanswered questions. The frontiers of ignorance invite exploration, creativity, and fresh thinking in ways that memorizing established knowledge never could. Far from being something to hide or overcome, conscious ignorance becomes something to cultivate and refine.

Chapter 2: When Knowledge Gets in the Way

Sometimes what we think we know can actually impede scientific progress. Throughout history, established "facts" and theories have occasionally acted as blinders, preventing scientists from seeing alternative explanations or asking better questions. The history of science is littered with concepts that were once accepted as fact but later proved misleading or entirely wrong. Consider the luminiferous ether, a substance that 19th-century physicists believed permeated the universe as a medium for light waves. This concept was so entrenched that when experiments by Albert Michelson failed to detect it, the results were initially considered flawed rather than revolutionary. Only after repeated failures to find evidence for ether did scientists begin questioning its existence, ultimately leading to Einstein's theories of relativity. Ironically, Michelson received a Nobel Prize for an experiment that didn't work as expected—because it revealed that a widely accepted "fact" was actually incorrect. Similar examples abound. Phrenology, the study of skull shapes to determine personality traits and mental abilities, was considered legitimate science for decades. The concept of phlogiston was used to explain combustion and rust before the discovery of oxygen. Even today, outdated ideas persist through repetition rather than evidence. For instance, nearly every biology student learns that different regions of the tongue detect different tastes—sweet at the tip, bitter at the back—complete with "tongue maps" in textbooks. Yet this "fact" stems from a mistranslation of German research from 1901, and experiments easily disprove it. Nevertheless, this misconception has survived for over a century. The danger of established knowledge can be particularly acute in rapidly advancing fields. In neuroscience, researchers spent decades focused on electrical "spikes" as the fundamental language of the brain after their discovery in the early 20th century. While important, this narrow focus potentially blinded scientists to other crucial brain processes. Similarly, the widely cited "fact" that the human brain contains 100 billion neurons and ten times as many glial cells persisted for years without supporting evidence. Recent research reveals both numbers were wrong—there are about 86 billion neurons and roughly an equal number of glial cells. These examples reveal something profound about science: even "settled" knowledge should be periodically reexamined. George Bernard Shaw once proclaimed, "Science is always wrong. It never solves a problem without creating ten more." Rather than viewing this negatively, we might celebrate how science produces ignorance at perhaps a faster rate than it produces knowledge. Each answered question typically spawns multiple new questions, expanding the horizon of what we don't know. This understanding helps explain why genuine scientific progress often requires challenging established views. The most valuable discoveries frequently come not from filling in details within existing frameworks, but from questioning fundamental assumptions. When knowledge becomes dogma, it transforms from a tool for understanding into an obstacle. The truly revolutionary scientific mind isn't just skilled at finding answers within established paradigms—it's skilled at recognizing when those paradigms themselves need revision.

Chapter 3: The Dark Room Without Cats

"It is very difficult to find a black cat in a dark room," warns an old proverb, "especially when there is no cat." This striking metaphor aptly describes much of the scientific process. Scientists often find themselves searching for something that may not exist or may be entirely different from what they imagine. Unlike the popular conception of science as methodically solving predetermined puzzles, real scientific work involves stumbling through darkness, bumping into unidentifiable objects, and looking for barely perceptible phantoms. This process of groping through the dark for black cats represents one of the most exhilarating aspects of science, though it's frequently hidden from public view. Popular science reporting typically highlights successful discoveries while ignoring the messy, uncertain process that produced them. We rarely hear about the false starts, dead ends, and years of fruitless searching that precede breakthroughs. Yet these elements are essential to understanding how science actually works. The black cat analogy has two important variations in scientific practice. In some cases, scientists spend careers searching for something that turns out not to exist—the proverbial cat that isn't there. The luminiferous ether mentioned earlier represents such a non-existent cat. Physicists spent decades trying to detect it before Einstein's work demonstrated it wasn't necessary. More recently, numerous researchers dedicated years to searching for a "gay gene," a specific genetic marker that would explain sexual orientation, before evidence suggested the reality is far more complex involving multiple genetic and environmental factors. In other cases, scientists may avoid searching for the black cat altogether and instead focus on mapping the dark room—measuring its dimensions, temperature, and composition. This seemingly mundane work of careful measurement has yielded some of science's greatest advances. Johannes Kepler spent six years analyzing a tiny 8-minute discrepancy in Mars' orbit, eventually realizing planets move in ellipses rather than perfect circles—a discovery that laid groundwork for Newton's laws of motion and gravity. Similarly, advances in measurement techniques frequently precede theoretical breakthroughs: more precise telescopes, more powerful particle accelerators, more sensitive detectors all enable scientists to explore previously inaccessible realms. What unites both approaches—searching for cats that may not exist and carefully mapping dark rooms—is comfort with uncertainty. Scientists must embrace what poet John Keats called "Negative Capability"—the capacity to remain "in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason." As quantum physicist Erwin Schrödinger noted, "In an honest search for knowledge you quite often have to abide by ignorance for an indefinite period." This tolerance for ambiguity distinguishes scientific thinking from other forms of knowledge pursuit. Perhaps the most challenging aspect of this cat-hunting in dark rooms is that you can never be certain whether continued searching will eventually yield results or whether you're pursuing something that doesn't exist. History shows that persistence through years of apparent failure sometimes leads to revolutionary discoveries, while other equally determined efforts lead nowhere. Science provides no formula for determining in advance which questions will prove fruitful and which will prove futile.

Chapter 4: How Questions Drive Science Forward

If science were merely about accumulating facts, it would be a fairly straightforward enterprise. But real science is driven by questions—not just any questions, but carefully crafted ones that can open new pathways of inquiry. The quality of these questions, rather than the quantity of data collected, often determines scientific progress. As Rene Descartes observed back in 1637, years of education had left him with little more than "the increasing discovery of my own ignorance." What makes a good scientific question? Scientists think deeply about this issue, though their approaches vary widely. Columbia University mathematician Maria Chudnovsky suggests that interesting questions are those that connect to other questions—those that, when explored, open doors to broader realms of inquiry rather than leading to isolated dead ends. However, other scientists find value in studying apparently isolated phenomena, believing that seemingly disconnected questions sometimes unexpectedly connect to mainstream inquiry in transformative ways. One approach to question-forming comes from Nobel laureate immunologist Peter Medawar, who called it "The Art of the Soluble." Medawar suggested focusing on questions that appear answerable with available tools and techniques. This pragmatic approach is captured in the story of a scientist searching for lost keys under a streetlamp—not because that's where he dropped them, but because "the light is so much better here." While this might seem misguided, looking where discovery is possible often proves more productive than pursuing questions beyond current technical capabilities. Yet an opposite strategy has also proven valuable: pursuing the seemingly impossible question, the black cat that might not exist. Revolutionary advances frequently come from scientists willing to investigate problems others consider intractable. When Einstein published his theory of general relativity in 1915, few believed it could be experimentally verified. Four years later, observations during a solar eclipse confirmed his predictions, transforming our understanding of gravity and space-time. The size and scope of questions also varies among scientists. Some prefer addressing grand challenges—consciousness, the origin of life, the nature of dark matter—while others focus on narrowly defined problems within established fields. Both approaches drive science forward. Ambitious questions inspire broad exploration, while targeted inquiries generate concrete advances. As astronomer Carl Sagan demonstrated, the same scientist might publish highly technical papers on the chemical composition of Venus' atmosphere while also contemplating profound questions about humanity's place in the cosmos. One particularly effective strategy involves using smaller questions to address larger ones through "model systems." Neuroscientists studying the human brain's 86 billion interconnected neurons might examine the 302-neuron nervous system of a nematode worm as a more manageable system. Similarly, Darwin didn't just observe finches in the Galapagos; he studied pigeon breeding and cultivated plants in his greenhouse as models for understanding evolution's broader principles. This approach recognizes that studying the simplest examples of complex phenomena often reveals fundamental principles applicable to more complex systems. What unites these diverse approaches is a commitment to framing questions that generate more questions. The most valuable scientific inquiries don't lead to definitive endpoints but rather to expanded horizons of exploration. When scientists praise a colleague's work as "opening up a whole new field," they're acknowledging this question-generating quality. Each answered question that spawns ten new ones represents not failure but success—the continuing engine of scientific progress.

Chapter 5: The Art of Cultivating Uncertainty

In most fields, uncertainty is something to minimize or eliminate. In science, however, uncertainty is carefully cultivated and refined. Not all uncertainties are equally valuable; scientists develop sophisticated approaches to distinguish productive uncertainties from unproductive ones. This cultivation of high-quality ignorance is perhaps the most underappreciated skill in scientific practice. Scientists express this idea in various ways. Some speak of "precisely locating our ignorance"—identifying exactly what we don't know and why it matters. Others describe the process as "finding the right dark room" to search for black cats. Either way, scientists invest considerable effort in evaluating, refining, and prioritizing uncertainties. When reviewing grant proposals or planning research programs, the central question isn't just "What will we learn?" but rather "Will this generate valuable new questions?" This perspective challenges the popular emphasis on hypothesis-driven research. While the scientific method as taught in schools centers on formulating and testing hypotheses, many scientists view hypotheses with surprising skepticism. Neurophysiologist Alan Hodgkin would reportedly show interest in a student's data only when it contradicted expectations—when results didn't match the hypothesis. Expected results merely confirmed what was already suspected; unexpected results opened doors to new understanding. Similarly, Nobel laureate physicist Enrico Fermi distinguished between measurements (experiments that confirm hypotheses) and discoveries (experiments that contradict them). This distinction highlights how science advances more through surprises than confirmations. As Fermi noted, "If an experiment agrees with theory, it is a measurement; if it disagrees, it's a discovery." Some scientists worry that hypotheses can become intellectual prisons, creating psychological commitments to particular outcomes that bias observation and interpretation. When a scientist becomes emotionally invested in a hypothesis being correct, they may unconsciously favor evidence supporting it while dismissing contradictory findings. This bias can be particularly insidious because it operates below the level of conscious awareness—even the most honest researchers can fall prey to it. An alternative approach is what some call "curiosity-driven research"—exploration guided by questions rather than predicted answers. Critics sometimes dismiss this as "just fishing expeditions," but many significant breakthroughs began with scientists simply wondering "what would happen if..." Louis Pasteur's famous observation that "chance favors the prepared mind" acknowledges that discoveries often appear serendipitous but actually emerge from minds primed to recognize significance in unexpected observations. Cultivating productive uncertainty also involves revisiting seemingly settled questions. One neuroscientist advises students to examine work at least ten years old rather than chasing the latest findings. With time and technological advances, questions that couldn't be addressed earlier become answerable. Sometimes the most valuable ignorance lies hidden within what we think we already know. Perhaps most importantly, scientific uncertainty isn't experienced as paralyzing doubt but as intellectual adventure. The physicist Richard Feynman captured this spirit when he wrote: "I can live with doubt and uncertainty and not knowing. I think it's much more interesting to live not knowing than to have answers which might be wrong." This comfort with the unknown—even celebration of it—characterizes the scientific mindset at its best.

Chapter 6: Finding the Right Problems to Solve

Not all scientific problems are created equal. Some questions, when answered, transform our understanding of the world; others lead to incremental advances or dead ends. The ability to identify the most promising problems to pursue represents a crucial scientific skill—one that combines intuition, experience, and calculated risk assessment. How do scientists decide which dark corners of ignorance to explore? Scientists describe various strategies for problem selection, often using revealing metaphors. Some view problem-finding as akin to prospecting—searching for intellectual veins of gold that others have overlooked. Others describe it as navigating a landscape of difficulty and significance, seeking problems that are challenging enough to be interesting but not so difficult as to be intractable. As Nobel Prize-winning physicist Richard Feynman advised young scientists: "Study hard what interests you the most in the most undisciplined, irreverent and original manner possible." One crucial consideration involves technical feasibility. Is the question answerable with current tools and approaches? When the Human Genome Project was proposed in the 1980s, many scientists considered it premature and prohibitively expensive. As sequencing technology rapidly improved, what initially seemed impossible became achievable ahead of schedule and under budget. Conversely, questions that appear answerable sometimes prove stubbornly resistant to investigation despite technological advances. The potential for broader impact also guides problem selection. Scientists often seek questions that, when answered, will illuminate multiple related problems. When Barbara McClintock studied unusual patterns of corn kernel coloration in the 1940s and 1950s, few appreciated how her work would eventually transform understanding of genetic regulation across all organisms. Her discovery of "jumping genes" (transposable elements) earned her a Nobel Prize decades after her initial work, once its broader significance became apparent. Timing plays a crucial role as well. Some questions become ripe for investigation only after prerequisite knowledge accumulates or necessary techniques develop. The structure of DNA might have remained mysterious for decades longer had X-ray crystallography not advanced sufficiently by the early 1950s. Scientists must judge whether a problem's time has come or whether fundamental obstacles remain. As molecular biologist Sydney Brenner observed, "Progress in science depends on new techniques, new discoveries, and new ideas, probably in that order." Risk tolerance varies among scientists and institutions. Established researchers with secure positions can afford to pursue high-risk, potentially transformative questions that might yield nothing for years. Early-career scientists often balance riskier projects with safer ones more likely to produce publishable results. Government agencies and foundations similarly maintain portfolios ranging from conservative to speculative investments in scientific problems. Ultimately, problem selection involves balancing competing considerations: significance versus tractability, novelty versus foundation, risk versus reward. The choices scientists make reflect not just rational calculation but personal values, aesthetic judgments, and intuitive responses to the unknown. Richard Feynman captured this personal dimension when he wrote: "Physics is like sex: sure, it may give some practical results, but that's not why we do it." The most successful scientists develop what Nobel laureate François Jacob called "night science"—the intuitive, exploratory thinking that complements the logical, methodical "day science" of formal experimentation. This night science involves playing with ideas, making unexpected connections, and following hunches that might reveal which problems contain hidden potential. Finding the right problems to solve requires not just analytical skill but creative imagination—the ability to see possibilities where others see only darkness.

Chapter 7: From Ignorance to Innovation

How does acknowledging ignorance lead to practical innovations that improve lives? This question addresses a common misconception about scientific progress—that practical applications emerge directly from targeted problem-solving rather than from exploring fundamental questions. The reality of how science transforms into technology tells a different story, one where embracing ignorance often provides the most direct path to world-changing innovations. Consider the development of magnetic resonance imaging (MRI), now an essential medical diagnostic tool. MRI didn't result from medical researchers specifically trying to invent better imaging technology. Instead, it emerged from physicists studying the fundamental properties of atomic nuclei in magnetic fields—work that initially seemed to have no practical application whatsoever. When physicist Paul Dirac developed equations describing electrons in 1928, he inadvertently predicted the existence of positrons (anti-electrons), particles nobody had observed. Four years later, physicist Carl Anderson confirmed their existence. Decades later, this fundamental discovery became crucial for positron emission tomography (PET) scanning, another vital medical imaging technique. This pattern—fundamental research leading to unexpected applications—appears throughout scientific history. When Michael Faraday demonstrated electromagnetic induction in the 1830s and was asked about its practical use, he reportedly answered, "Of what use is a newborn baby?" Yet his theoretical work became the foundation for electrical generators, motors, and ultimately the entire electrical grid. Similarly, research on quantum mechanics, initially pursued to understand atomic behavior, eventually enabled technologies from lasers to transistors to computers. The journey from ignorance to innovation typically follows an unpredictable path with multiple contributors. When James Watson and Francis Crick determined DNA's structure in 1953, they weren't trying to create biotechnology or genetic medicine. They simply wanted to understand the molecule's fundamental architecture. The applications that followed—from insulin production to genetic testing to personalized medicine—emerged gradually through contributions from thousands of scientists, most of whom weren't specifically pursuing these applications. This reality challenges the common distinction between "basic" and "applied" research. Rather than separate activities, they represent points on a continuous spectrum of inquiry. The most transformative innovations typically emerge not from directed application-focused efforts but from the pursuit of fundamental understanding. As physicist James Clerk Maxwell noted, "In strict science, all we can do is to learn mathematical and dynamical laws...and draw out the consequences." The time scale of this process varies tremendously. Sometimes applications emerge quickly—the transistor moved from laboratory demonstration to commercial use in less than a decade. In other cases, fundamental discoveries may wait decades or even centuries before finding practical applications. When mathematician George Boole developed Boolean algebra in the 1840s, he couldn't have imagined it would eventually become the mathematical foundation for digital computing a century later. This unpredictable journey from ignorance to innovation carries important implications for science policy and funding. Attempts to direct scientific research exclusively toward predetermined practical goals often prove less productive than supporting exploration of fundamental questions. The internet, widely considered one of the most transformative innovations of recent decades, emerged not from a targeted commercial development program but from basic research funded by government agencies interested in reliable communication networks. As physicist Abraham Pais observed, "The usual result of fundamental research is to find connections between facts that had been thought unrelated." By acknowledging what we don't know and pursuing understanding rather than immediate application, scientists create the knowledge foundation from which innovations naturally emerge. The most direct path to solving practical problems often begins with embracing, rather than avoiding, our fundamental ignorance.

Summary

Throughout this exploration of scientific ignorance, we've encountered a profound paradox: what drives science forward isn't what scientists know, but what they don't know. This shift in perspective transforms how we understand scientific progress. Rather than seeing science as a steadily growing mountain of facts, we recognize it as an expanding frontier of questions—each answered question spawning multiple new ones, each discovery revealing previously unimagined territories of ignorance. The most successful scientists aren't those who accumulate the most knowledge, but those who cultivate the most productive uncertainties. This perspective offers valuable insights beyond the laboratory. For educators, it suggests teaching science not as settled conclusions but as evolving questions—lighting the fire of curiosity rather than filling buckets with facts. For citizens navigating a world increasingly shaped by scientific developments, understanding the centrality of ignorance provides a more accessible entry point to scientific thinking than attempting to master mountains of technical information. And for anyone facing uncertainty in their own life, the scientific embrace of the unknown offers a powerful model: not fearing what we don't know, but using it as a compass to guide exploration. As we navigate our complex world, perhaps we would do well to adopt the scientist's paradoxical wisdom—that acknowledging the limits of our understanding isn't an admission of failure but the first essential step toward deeper insight.

About Author

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Stuart Firestein

Stuart Firestein is an American neuroscientiest and biologist.After earning his Ph.D. in neurobiology, Firestein was a researcher at Yale Medical School, then joined Columbia University in 1993. At the Columbia University Department of Biological Sciences, Firestein is now studying the sense of smell.Dedicated to promoting the accessibility of science to a public audience, Dr. Firestein seeks to reach broader audiences through nonscientific writing, public appearances, and his support of science in the arts.

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