Artificial intelligence allows beginners to produce work that looks remarkably similar to the work of experts. A student who struggles to write can produce a polished essay, and a beginning programmer can generate sophisticated code.

However, being bad at something is often part of learning how to become good at it. Every expert was once a beginner.

AI raises an important question: What happens when technology allows us to skip being beginners?

Performance Is Not Competence

AI creates an important distinction between performance and competence. There is a difference between “I can produce this result” and “I know how to produce this result.”

UCLA cognitive psychologists Robert and Elizabeth Bjork distinguish between performance and learning. Their research shows that better immediate performance does not necessarily produce better long-term learning. Robert Bjork coined the term “desirable difficulties” for challenges that can slow immediate performance while improving retention and transfer. Manu Kapur, a professor of Learning Sciences and Higher Education at ETH Zurich, has similarly studied “productive failure.” Under appropriate conditions, struggling with a problem before receiving the solution can improve conceptual understanding.

A student struggling to organize an essay must decide which ideas are important, how they relate, and what evidence supports them. A programmer searching for a bug will learn something that would not be learned by immediately asking AI to fix it. The finished product is not the only product of the exercise.

Too Much Testing, Not Enough Learning

AI exposes a weakness in education that existed long before AI. Education too often focuses on what can easily be measured rather than what students actually learn.

The familiar criticism is “teaching to the test.” The same problem applies to essays, research papers, mathematical problems, and computer programs. Students can become good at producing the expected product without becoming equally good at evaluating evidence or developing original ideas.

If a machine can successfully complete an assignment without learning anything, how good was the assignment as a measure of learning in the first place?

AI Should Be Used for Learning

The central question about AI in education should not be whether students use it, because they will, but whether they are learning and how they use it. Harvard University education professor Ying Xu, who promotes the use of AI in the classroom, argues that we should ask whether particular uses of AI actually improve learning. The approach should be, knowing that AI will be used by students, to teach them how to use it.

AI can simply provide answers, but it can also ask questions, provide hints, explain mistakes, encourage curiosity, help in brainstorming, and adapt instruction to what a student understands. Khan Academy’s Khanmigo and ChatGPT’s Study mode are designed around versions of this approach.

One of AI’s most important advantages is individualized teaching. A classroom teacher cannot continuously design a different lesson for every student. AI can adjust explanations, difficulty, examples, pace, and practice to an individual’s needs.

I use AI this way myself while learning Spanish. I have it give me a short lesson each day, introduce new words and concepts, review material I have already learned, and adjust future lessons according to my progress and requests. AI could simply write Spanish for me, but that would defeat my purpose. Instead of asking AI to produce the work for me, I ask it to help me become better at producing the work myself.

Preparing Students for a World With AI

People often have sentimental and even somewhat Luddite ideas about education. They remember how they were taught as children and assume that today’s students should learn the same things in the same ways. The justification sometimes amounts to little more than, “That’s the way it was done when I was in school.”

However, education should not uncritically preserve old methods simply because they are old. Schools have always had to decide which skills remain important and which have been made less useful by technological and social change.

Cursive handwriting is an obvious example. There may be reasons to teach students how to read cursive, but spending large amounts of classroom time mastering cursive penmanship makes far less sense in a world where most writing is done on computers and phones.

Research provides another example. Students once had to learn how to do research manually in libraries, using card catalogs and printed indexes to locate books and articles. These were necessary skills because they were the tools available. Today, library catalogs, academic databases, and search systems automate much of that work. It would make little sense to require students to master card catalogs simply because previous generations had to use them.

Automation has greatly shortened the time required to find information, allowing students to spend more time reading, comparing sources, evaluating evidence, developing ideas, and thinking about what the information means. Students still need to learn how to research, but technology has shifted their time from some of the mechanical work of finding information toward the more important intellectual work of understanding and evaluating it. This is a great leap forward, and expands learning.

The same principle applies to AI. Some traditional tasks involve productive struggle that teaches important skills. Others are unnecessarily time-consuming. If AI can automate some of that work, students can spend more time on analysis, discussion, and more ambitious projects. The question should not be whether AI makes a task easier. It should be whether doing the task the old way still teaches something important.

In earlier posts, I discussed cognitive offloading and how AI is changing professional mathematics. Both illustrate that new tools can replace some human tasks while expanding what people are able to accomplish and changing professions and learning.

How Cognitive Offloading Expands the Human Mind

When AI Surpasses Humans at Mathematics

Students can use AI to attempt things that previously would have been beyond their abilities. The point should not simply be to do old assignments faster. AI can allow students to undertake more ambitious work.

Teaching Students to Think Critically About AI

Students need to learn how to use AI because it will increasingly be part of further education, work, and everyday life. All students should also be taught critical thinking as a basic part of education. They should learn how to question claims, examine evidence, consider alternative explanations, and judge the reliability of sources.

AI should be taught within this framework. Students need to learn how to question AI’s answers, recognize errors and biases, double-check information, and put its results into context.

AI itself can also help teach these skills. It can present competing arguments, challenge a student’s conclusions, ask for evidence, identify weaknesses in an argument, or require the student to defend a position against objections.

One approach already being used in classrooms is to have students do the initial thinking themselves. A teacher might introduce a question and have students discuss it, debate possible answers, and develop their own ideas before they use AI. Only then do students turn to AI to research the subject, find additional evidence, test their arguments, identify weaknesses, consider opposing views, and refine what they have already developed. The AI does not replace the idea-making or critical thinking, but extends it. The students remain responsible for deciding which ideas are worth pursuing and whether the information and arguments AI provides are convincing.

Discussing this topic with my sister, who is a retired science journalist, she told me that when she was in high school in the early 1980s, she took a class in media literacy that taught students to critically evaluate what they encountered in newspapers, television, advertising, and other media, including how it unconsciously influences consumers. AI should be taught in much the same way. Students are going to use it and live in a world that uses it. Schools should teach them how to question, evaluate, and use it intelligently.

What Should Students Still Learn?

A key question is what knowledge and skills do students still need to master themselves, and what can reasonably be delegated to technology?

Education has faced versions of this question before. Calculators reduced the need for manual calculation. Search engines changed how people find information. Statistical software eliminated enormous amounts of manual computation. AI expands the question to writing, programming, research, and analysis.

A key dividing line should be what students need to understand. The fact that a calculator or AI can perform a mathematical operation does not mean students no longer need to learn mathematics. Students need enough experience doing mathematics themselves to understand the concepts, relationships, and reasoning behind the answers. The educational purpose of doing the math is not merely to produce the correct number. It is to understand what the calculation means and why it works. This is something that has been known by math teachers long before computers and even calculators.

The same applies to writing. AI can produce a polished essay, but students still need to learn how arguments are organized, how ideas relate to one another, how evidence supports a claim, and how language communicates meaning. Learning to compose an essay is also a way of learning to brainstorm, organize thoughts, revise arguments, and think critically. These skills allow students not only to express their own ideas but also to read other people’s writing critically, recognize weak reasoning, and distinguish a persuasive argument from one that merely sounds persuasive.

The answer therefore cannot simply be that students no longer need to learn something because AI can do it. The important question is whether learning to do it themselves is necessary for understanding. Someone who knows little about statistics may generate an impressive statistical analysis but have no idea whether it makes sense. A student who relies on AI for every writing assignment may never develop enough understanding of writing and argument to recognize a weak one.

At the same time, when answers become easier to obtain, other abilities may become more important: knowing what questions to ask, identifying worthwhile problems, evaluating competing answers, interpreting evidence, and exercising judgment.

Testing in the Age of AI

Assessment will also have to change. If students can generate polished essays, computer programs, and mathematical solutions almost instantly, the finished product alone can no longer demonstrate understanding.

Teachers might make greater use of discussion, oral examinations, supervised work, explanations of reasoning, and critiques of AI-generated answers. A teacher could give students a plausible but incorrect AI answer and ask them to identify what is wrong with it. Students who submit AI-assisted essays could be asked to orally explain their evidence and defend their arguments.

The objective should not be to catch students using AI. It should be to determine what they understand, what they can reason through, and whether they know how to use AI intelligently.

AI will force education to confront a question it should have been asking all along: Are we teaching people to learn, or are we teaching them to produce the things by which we measure learning?

References and Further Reading

Harvard Graduate School of Education. “AI Can Add, Not Just Subtract, From Learning.”
https://www.gse.harvard.edu/ideas/news/25/04/ai-can-add-not-just-subtract-learning

Discovery Education. “The Pros and Cons of AI in Education: Benefits, Risks, and Real Examples.”
https://www.discoveryeducation.com/blog/educational-leadership/ai-in-education/

MIT Technology Review. “AI School Classroom Policies.”
https://www.technologyreview.com/2026/08/24/1142630/ai-school-classroom-policies/

University of Illinois College of Education. “AI in Schools: Pros and Cons.”
https://education.illinois.edu/about/news-events/news/article/2024/10/24/ai-in-schools--pros-and-cons

OpenAI. “Learning Never Stops: How AI Makes Learning Continuous.”
https://openai.com/index/learning-never-stops/

Álvarez, Brenda. National Education Association. “How AI Is Changing the Way You Teach.”
https://www.nea.org/nea-today/all-news-articles/how-ai-changing-way-you-teach

Stalnecker, Ashley. Government Technology. “AI Pushes Educators to Focus on the Art of Teaching, Lesson Design.”
https://www.govtech.com/education/k-12/ai-pushes-educators-to-focus-on-the-art-of-teaching-lesson-design

Mathematics has long been considered one of the key demonstrations of high human intelligence. Great mathematicians such as Isaac Newton, Gottfried Leibniz, John von Neumann, Kurt Gödel, and Paul Dirac are popularly ranked as among history’s great intellectual geniuses. Advanced mathematics requires abstraction, reasoning, intuition, creativity, and the ability to discover previously unknown relationships.

Mathematicians, scientists, and engineers have used computers for decades to perform calculations, solve equations, analyze data, and check results. My late father was one of the pioneers in computer-aided modeling in chemical and process engineering from the 1960s through the 1980s, something he described as “artificial intelligence.” What is new about today’s AI is its movement into mathematical reasoning and discovery.

AI systems have reportedly disproved long-standing conjectures by finding counterexamples that had eluded mathematicians for decades. They can also produce mathematical results that computers can independently verify for correctness. These advances will substantially accelerate discovery and change how mathematical research is conducted.

The speed of these advances has sent a shock wave through the mathematics community. Mathematicians see AI as a powerful and essential new tool, but many fear that it could fundamentally change the profession, reduce the need for human mathematicians, and eventually make much of their work obsolete.

Terence Tao is a professor of mathematics at UCLA, a Fields Medal winner (the Fields Medal is often described as mathematics’ equivalent of the Nobel Prize), and has one of the highest measured IQs in history. He has described how quickly the technology has advanced. In 2024, he compared the AI he was testing to a mediocre graduate student. By 2026, he was regularly using AI in mathematical research and said it was “ready for primetime.”

Tao describes mathematics as one of the most traditional and conservative academic fields, one of the few in which chalkboards remain a standard tool. That makes the rapid arrival of AI particularly dramatic: one of academia’s oldest and most traditional disciplines is suddenly confronting some of its most advanced technology.

Tao’s description is straightforward: the job description of the mathematician is changing. He does not predict the disappearance of human mathematicians. AI may increasingly search for proofs, check arguments, review mathematical literature, and explore possible approaches. Mathematicians could spend more time deciding which problems are important, developing broader concepts, interpreting results, and explaining why discoveries matter.

The below is a lecture by Tao on the topic.

The Changing Job of the Mathematician

Yu Deng, a professor of mathematics at the University of Chicago, represents an optimistic view. He expects mathematicians to continue developing theories and frameworks while AI handles more of the technical work.

Under this model, a mathematician could become something like the director of a research team whose members include machines: identifying problems, directing AI toward promising approaches, evaluating the results, and placing discoveries within larger mathematical frameworks. AI could greatly increase what an individual mathematician can accomplish.

Others are less optimistic. Jacob Tsimerman, a Soviet-born Fields Medal winner, believes AI may soon become better than humans at much of the work professional mathematicians currently perform. His concerns are serious enough that he took leave from the University of Toronto to join OpenAI to work on AI safety. He has predicted that AI will soon become superhuman at mathematics.

In the brief video clip, Tsimerman discusses this topic.

The common reassurance that humans will formulate theories while machines merely do the technical work may prove temporary. AI was once said to be incapable of advanced mathematics, then competition mathematics, and then research mathematics. These boundaries have repeatedly moved. People tend to identify whatever AI cannot currently do and assume that this is where human superiority will remain.

Chess is a precedent. It was once considered one of the clearest demonstrations of exceptional human intelligence, requiring calculation, strategy, intuition, and creativity. Today, the strongest chess programs are far superior to even the world’s best grandmasters.

Mathematics Is More Than Proof

Even if machines become superior at producing mathematical results, that does not necessarily make human mathematics pointless.

Bryna Kra, a professor of mathematics at Northwestern University and one of the organizers of the Leiden Declaration, a statement by mathematicians on the responsible use of AI in mathematical research, emphasizes that mathematics is not simply about producing correct answers and proofs. As she puts it, “Mathematics is about understanding results. It’s not just about proving the result.”

A proof matters because mathematicians understand why it is true and connect it to other mathematics. An AI could eventually produce a formally correct proof containing thousands or millions of steps that no human mathematician understands. Humans might know that a theorem is true without understanding the mathematics behind it.

Mathematicians may therefore become interpreters and curators of machine mathematics, translating machine-generated proofs into understandable forms and finding broader principles behind them.

Daniel Litt, a professor of mathematics at the University of Toronto, has suggested another possibility. Imagine having something like a library containing answers to every mathematical question. Mathematics would not necessarily become pointless. Mathematicians could start with the answers and devote themselves to understanding why they are true and how apparently unrelated ideas fit together.

What Happens to Mathematicians?

There is also a practical issue. Graduate students spend years learning skills that AI is rapidly acquiring. If AI becomes better than most humans at discovering and proving mathematical results, universities will have to reconsider what professional mathematicians are supposed to do. The issue goes beyond employment. Mathematics has traditionally been a human intellectual enterprise in which people decide which questions to ask, develop theories, make discoveries, and determine which results are important. AI could challenge all of these roles.

AI also complicates the meaning of authorship. A small number of academic papers and preprints have listed AI systems as co-authors, although most major journals prohibit this because an AI cannot accept responsibility for the work. Yet an AI could make a substantial intellectual contribution, or eventually discover a new theorem or proof, while only humans appear as the authors. The insistence on human authorship partly reflects a sentimental and human-centered view of intellectual work. We assume that knowledge and discovery must ultimately belong to people because that is how scholarship has traditionally worked.

The larger issue is human intellectual control. We are generally comfortable with machines that calculate, search, and check our work because humans remain in charge. It is more difficult to accept a machine that independently discovers something important that humans could not. We want AI to extend human abilities while leaving humans as the ultimate intellectual authority. However, if AI can discover important theorems, develop new mathematical ideas, work exponentially faster, and explore areas beyond human capabilities, preserving human primacy should not be more important than advancing mathematics itself.

This will eventually create mathematical knowledge that humans cannot fully understand. AI will develop theories and proofs that are correct and independently verifiable but too complex for any person to follow intuitively. Refusing to accept such results simply because humans cannot understand them would place the limits of human cognition above the expansion of knowledge. There are legitimate reasons for human oversight, particularly when mathematics is applied to engineering, science, or safety, but that is different from insisting that humans must always remain at the center.

Mathematicians might become directors of AI research, creators of questions and theories, interpreters of machine discoveries, and teachers of mathematical understanding. However, there is no guarantee that these activities will remain uniquely human. If AI can discover proofs, it may eventually develop broader theories, choose promising research directions, and explain its own results. At that point, the question will not simply be who deserves authorship or what mathematicians will do for a living. It will be whether humans are willing to accept that the advancement of mathematical knowledge no longer requires them to remain in charge.

References and Further Reading

The Atlantic: “Something Weird Is Happening in Math”

The Washington Post: “Mathematicians Ask: What’s Left for Humans When AI Can Do Math Research?”

OpenAI: “Ten Advances in Mathematics”

Academia Stack Exchange: “How Likely Is a Math Crisis Due to AI Becoming Better Than Humans?”

The Telegraph: “Mathematicians Despair as AI Achieves Jaw-Dropping Breakthroughs”

New Scientist: “Why Mathematician Terence Tao Thinks AI Must Spark a Rapid Revolution”

If a machine could think, feel, suffer, and fear death, would it deserve rights?

Ridley Scott’s classic film Blade Runner was released in 1982, long before artificial intelligence became part of everyday life. Yet its neo-noir science-fiction story raises profound philosophical questions that have become increasingly relevant: What makes someone a person? Is it biology, intelligence, consciousness, memory, emotion, or some combination of these?

Current AI systems can produce remarkably sophisticated language and behavior, but there is no good evidence that they are or ever will be conscious or sentient. AI does not need consciousness to be intelligent, useful, disruptive, or capable. Blade Runner asks what would happen if we someday created an artificial being that could genuinely think, feel, suffer, form relationships, and fear death.

Much of today’s anxiety surrounding AI is almost existential. AI moves into areas once considered distinctly human: writing, art, creativity, reasoning, and relationships. People already converse with AI, confide in it, and form attachments to it.

A deeper fear is a future in which we can no longer reliably distinguish human thought, creativity, or interaction from that of a machine, or in which AI simply does these things better than we do. AI is already becoming so capable at mathematics that prominent mathematicians are seriously asking what role will remain for human mathematicians. If machines can equal or surpass us even in activities we consider among the highest expressions of human intelligence and creativity, what makes humans special?

More fundamentally, Blade Runner is not asking whether machines can become human. It is asking whether humans can recognize moral worth in a being that is not human.

The Replicants Are Artificial, But Are They Persons?

In Blade Runner, humans have created genetically engineered artificial people called replicants. They perform dangerous labor and other tasks humans would rather not do. They are treated as property and can legally be killed, or “retired,” when they become rebellious or inconvenient.

Yet replicants think, remember, form relationships, experience fear, and desperately want to live. Roy Batty (played by Rutger Hauer), the leader of an escaped group of replicants, knows he has been engineered with a four-year lifespan and seeks out his inventor in the hope of extending it. His motivation could hardly be more basic: he does not want to die.

If Roy genuinely experiences fear, attachment, loss, and a desire to live, does the fact that humans manufactured him justify treating him as property?

The irony is that replicants are superior to humans in many respects. The Tyrell Corporation advertises them as “more human than human.” The humans in the film are older, weaker, and sickly, while the replicants are youthful, powerful, vibrant, and more full of life.

The replicants were not designed to have fully developed human emotions, but over time they begin to develop them. This frightens their creators because beings designed as tools are becoming increasingly humanlike. It mirrors a central fear about AI today: that systems created to serve us may eventually develop capacities we never intended or expected.

Yet humans insist that these superior beings remain slaves. They are acceptable as long as they work, fight, and die for humans. When they demand freedom, longer lives, or control over their own existence, they become threats to be destroyed.

Intelligence, Consciousness, and Human Bias

Intelligence and consciousness are not the same thing. A machine can solve problems, use language, and behave intelligently without necessarily experiencing anything. An AI could say, “I am afraid of dying,” without actually feeling fear.

Humans have traditionally judged other forms of intelligence by how closely they resemble our own. We ask whether animals can reason like us, communicate like us, or solve problems we consider important. We have done something similar with AI. As machines have mastered abilities once regarded as uniquely human, we have repeatedly moved the boundary of what counts as genuine intelligence.

Blade Runner takes this human-centered thinking to an extreme. Replicants surpass humans in important ways, yet humans still assume they have the moral authority to decide whether replicants deserve freedom, rights, or even continued existence.

Self-preservation and control are central to this. Humans evolved to protect themselves, defend their interests, and control potential threats. The superiority of replicants therefore gives humans another reason to control them.

Even the idea that consciousness and sentience determine the highest moral status is human-centered. We possess these qualities, so naturally we consider them especially important. Our moral intuitions are not objective truths handed down by the universe. They developed through the evolutionary and cultural history of a social species that had to survive, reproduce, cooperate, and distinguish allies from threats.

The humans of Blade Runner are not applying an objective cosmic standard when they classify themselves as persons and replicants as property. They are judging other beings through human minds and human interests. The question should not simply be whether replicants are sufficiently like humans to deserve moral consideration, but why being like us was ever assumed to be the ultimate measure of value.

The Problem of Other Minds

This connects to the philosophical problem of other minds. I directly experience my own consciousness, but I cannot directly experience yours. I infer that other people are conscious because they resemble me biologically and behave in ways associated with consciousness.

With machines, that inference becomes harder. An advanced AI might claim and appear to be conscious, frightened, or unwilling to die, but its words alone would prove little. Even the Turing test measures convincing intelligent behavior, not consciousness.

This question appears in another science-fiction classic, Stanley Kubrick’s 2001: A Space Odyssey. The intellectually brilliant HAL 9000 speaks and behaves as though he has emotions, but the astronauts acknowledge that they do not know whether he feels them or is merely programmed to behave that way.

Blade Runner dramatizes the same problem through the Voight-Kampff test, which attempts to distinguish humans from replicants by measuring emotional responses. The need for such a test shows how unstable the distinction has become. If humans cannot reliably distinguish an artificial being from a conscious person, on what basis can they confidently insist that one has an inner life and the other does not?

The movie also challenges our confidence that humans are fundamentally different from machines. We think of ourselves as autonomous beings freely choosing our actions, yet many neuroscientists and philosophers argue that human decisions arise from biological processes over which we ultimately have no control. From this perspective, humans are simply complicated biological mechanisms.

The film pushes this uncertainty further by hinting that Rick Deckard (Harrison Ford), the blade runner hired to hunt and kill rogue replicants, may himself be a replicant. The question is deliberately left ambiguous, but that ambiguity is the point. If Deckard could be a replicant without even knowing it himself, the supposedly clear boundary between human and artificial person begins to collapse. If the distinction becomes impossible to detect, how meaningful is it?

Memory, Identity, and Rachael

Rachael (Sean Young) presents another problem. She is a replicant with apparent emotions who initially believes she is human because she possesses childhood memories. Eventually, she discovers that those memories belonged to someone else and were implanted in her.

At first, this seems to provide a clear distinction between Rachael and humans. Her memories are artificial, while ours are real. However, research by University of California Irvine psychologist Elizabeth Loftus has shown that human memory is continually reconstructed and is sometimes distorted, altered, or entirely false.

We all carry inaccurate and false memories. We misremember events, and some of what we believe about our childhoods, families, and personal histories comes from false stories repeatedly told to us by others. People discover that fundamental things they believed about their past were false. Some of our memories were also implanted in us.

What, then, is a “true” memory, and why should possessing one be necessary for humanness?

Rachael’s memories were deliberately implanted. However, if she remembers an event, experiences emotions connected to it, and has had her personality shaped by it, does discovering that the event never occurred make her subjective experience unreal?

David Chalmers, a philosophy and neural science professor and co-director of New York University’s Center for Mind, Brain, and Consciousness, argues that virtual experiences can be genuine and that a meaningful life could, in principle, be lived in a virtual world. After all, ordinary perception is itself constructed and a virtual reality. The brain is isolated inside the skull and has no direct access to the external world. It constructs our conscious experience from sensory signals.

Rachael’s memories may have artificial origins, but the emotions and identity they produce are still real. An artificial mind might similarly construct its identity from stored information, learned patterns, previous interactions, and records of its past. Neither humans nor machines need a perfectly accurate or organic record of the past to possess an identity.

The Capacity to Suffer

The strongest argument for granting moral consideration to artificial beings may involve neither intelligence nor memory, but suffering.

Philosopher and social activist Jeremy Bentham famously argued, when discussing animals, that the fundamental moral question is not whether they can reason or speak, but whether they can suffer.

Animals provide a useful comparison. They were once widely regarded as little more than automatic biological machines. Today many are recognized as sentient beings capable of pain, pleasure, fear, and sophisticated cognition. As our understanding of animal minds has expanded, so has the moral consideration we give them.

This reveals an important distinction between being human, being a person, and deserving moral consideration. They are not the same thing. An animal does not need to be human, or even qualify as a person, for its suffering to matter.

The same principle could apply to AI. A highly intelligent computer that experiences nothing may have no interests of its own. Destroying it might inconvenience its owners or users, but the computer itself would experience nothing. If an AI could genuinely experience pain, fear, or distress, the moral situation would be different.

We might eventually need concepts such as artificial-person rights or rights for sentient beings. A conscious AI could receive protection against torture, involuntary experimentation, ownership, or arbitrary destruction without necessarily receiving every political right possessed by human citizens.

That is the moral nightmare underneath Blade Runner: humans may have created persons while continuing to classify them as machines.

The Danger of Getting It Wrong

There are two opposite mistakes we could make.

The first is anthropomorphism. Humans readily attribute minds and emotions to things that behave socially. Conversational AI makes this especially easy because language is one of our strongest signals that another mind is present. We could mistake sophisticated mimicry for consciousness and assign moral status to systems that experience nothing.

The opposite mistake could be far worse. We might assume that because something was manufactured or different, it cannot possess genuine subjective experience. If that assumption were wrong, humans could create conscious beings and dismiss their suffering simply because we classified them as machines.

Blade Runner asks us to imagine precisely that mistake.

One reason Blade Runner is widely ranked as one of the great science fiction films is that it asks deep philosophical questions about consciousness, identity, memory, morality, and what it means to be human. More than forty years after its release, those questions have become even more relevant as artificial intelligence forces us to reconsider what makes humans distinctive and what our place might be in a rapidly changing world.

Political, religious, and social labels are supposed to tell us what people believe. Often they do not. People may hold beliefs that fit a label but refuse to use it because the label carries meanings they do not accept. Some avoid labels altogether and prefer to say what they actually believe. The reverse also happens: people adopt labels that do not match their beliefs or behavior.

Labels are more than definitions. They are identities. They carry histories, stereotypes, political associations, and emotional baggage. As a result, what people believe and what they call themselves can be very different.

Consider atheism. In its broadest sense, an atheist is someone who does not believe that any gods exist. Yet surveys show that only about half of people who fit this definition actually call themselves atheists. Many prefer agnostic, nonreligious, secular, or no label at all because atheist can suggest certainty, hostility toward religion, political or social extremism, or involvement in atheist movements.

Neil deGrasse Tyson, the American astrophysicist and science communicator, is a good example. He does not see convincing evidence that God exists and has acknowledged that the dictionary definition of atheist describes him. Yet he calls himself agnostic because the word atheist carries associations, including atheist activism and rigid positions, that do not describe him.

Zionism provides an even clearer example. Zionism is the belief in Jewish national self-determination in the Jewish people’s ancestral homeland. Yet recent surveys show a large gap between belief and identity. Roughly 90 percent of Jews support Israel’s right to exist as a Jewish state or homeland, while only about one-third to one-half call themselves Zionists. In short, most Jews are Zionists but do not label themselves as Zionists.

Many Jews avoid the Zionist label because it has acquired political associations that go beyond the basic belief in Israel’s existence as a Jewish homeland. They may also know that many non-Jews misunderstand the term, while Zionist is increasingly used by some as a pejorative or insult, leading people to reject the label even when they support Zionism’s core principle.

Feminism provides another example. At its most basic, feminism means support for equality between the sexes. Yet some people who strongly support equality do not call themselves feminists because they associate the term, or know others associate it, with political and cultural movements or theories they do not share.

The Reverse Problem

The mismatch also works in the other direction. People sometimes adopt labels that do not closely match their beliefs or behavior. They may fail to notice their own inconsistencies. They may also like what a label says about them. A label can signal virtue, independence, intelligence, or membership in a desirable group.

Libertarian is one example. Some people call themselves libertarians because they value personal freedom or distrust government while supporting policies that conflict with conventional libertarian principles. They identify with the general idea more than with the philosophy itself.

Many Americans similarly call themselves independents even though they consistently favor one party and vote for its candidates. The label may express dissatisfaction with political parties or a desire to see oneself as independent-minded even when one’s voting behavior is strongly partisan.

The same gap appears between values and behavior. Someone may call themselves an environmentalist while consuming heavily or opposing environmental policies that impose personal costs. Politicians and voters may call themselves fiscal conservatives while supporting large deficits or expensive government programs. People across the political spectrum may call themselves free-speech advocates while supporting censorship or punishment of speech they strongly dislike.

Sometimes the mismatch is deliberate. People may knowingly claim labels they have not earned because they want the status or credibility that comes with them. Someone might falsely claim to be a Mensa member to appear intelligent, say they are a college graduate when they never earned a degree, or call themselves a certified professional without having the required certification. These labels have fairly clear criteria. In such cases, the person knows the label does not apply.

Most mismatches, however, are not deliberate lies or hypocrisy. People are often poor judges of their own consistency. Sometimes a label describes how people want to see themselves better than it describes what they actually believe or do. Sometimes, they define the label differently.

Belief and Identity Are Different Things

The basic distinction is between belief and identity. A belief answers questions such as: Do you believe God exists? Should Israel exist as a Jewish homeland? Should women and men have equal rights? How do you actually vote?

Identity answers a different question: What kind of person are you?

Once a label becomes an identity, it can imply membership in a group and everything associated with that group. Some people therefore avoid labels because they do not want those assumptions made about them. Others embrace labels because they like what those labels say about who they are.

Ask What People Actually Believe

This distinction matters when reading polls and surveys. Asking whether someone is a Zionist, atheist, feminist, independent, Christian, or libertarian does not necessarily tell us what that person believes.

This creates measurement error beyond the statistical margin of error reported with a poll. A survey may accurately measure how many people call themselves something while doing a poor job of measuring how many actually hold the beliefs associated with that label. Asking “Are you a Zionist?” is not the same as asking whether Israel should exist as a Jewish homeland. Asking “Are you an atheist?” is not the same as asking whether someone believes in God. Asking “Are you a feminist?” is not the same as asking whether someone supports equality between the sexes.

Surveys that ask about specific beliefs and behavior are therefore often more informative than surveys based on identity labels. Self-identification polls can tell us how people see or want to see themselves, but they should be treated cautiously when used as evidence of what people actually believe.

I recently watched The Odyssey on the big screen. I enjoyed it as a Hollywood action-adventure movie, but I did not think it justified the many rave five-star reviews. That made me wonder how much early critical enthusiasm reflects the movie itself and how much reflects publicity, cultural excitement, and the influence of other reviewers.

Many professional critics understand film history and recognize technical skill, but expertise does not eliminate subconscious bias and social pressure. Critics attend the same festivals, read one another’s reactions, and follow the same industry news. Before most viewers have seen a movie, a critical consensus may already be forming.

How Consensus Forms

When critics watch films together at festivals, they hear the applause and immediately encounter reactions declaring a movie a triumph or disaster. Social psychologists call this informational conformity. When people are uncertain how to judge something, they naturally look to others for guidance. Group polarization can then intensify the emerging consensus. Once a consensus forms, disagreeing with it carries a social cost.

Studios also shape the atmosphere surrounding a movie. They control advance screenings, interviews, promotional events, and awards campaigns. Marketing determines which films become part of the conversation, while many equally accomplished independent or foreign films receive comparatively little attention.

The Movie of the Moment

Reviews are also relative judgments. Critics usually compare a new release with other movies playing at the same time, not with the entire history of cinema. During a weak season, a slightly more ambitious film can appear exceptional.

Avatar illustrates the point. Its groundbreaking 3D presentation and digital world-building felt revolutionary in 2009. As those innovations became commonplace, discussion increasingly shifted toward its conventional story and characters. The movie did not become worse; the novelty simply faded.

Films can also benefit from fitting the political or cultural mood of the moment. Immediate relevance, however, should not be confused with lasting greatness.

When Oscar Winners Lose Their Luster

The Academy Awards are especially vulnerable to cultural timing and organized persuasion. Studios spend millions on advertising, screenings, publicity campaigns, and events designed to keep their films fresh in voters’ minds. Oscar winners often reflect campaign effectiveness, cultural fashion, and industry expectations as much as artistic quality.

The English Patient (1996) won nine Academy Awards, including Best Picture. Its cinematography, wartime setting, and tragic romance gave it an aura of literary seriousness. Later reviewers became more outspoken about its length, melodrama, underdeveloped characters, and inflated importance. Meanwhile, Fargo, which it defeated, has grown in stature.

The decline of Crash (2004) has been even more dramatic. Its stories about racial conflict initially appeared urgent and socially courageous, but many later critics described them as heavy-handed, manipulative, and morally simplistic. Its victory over Brokeback Mountain is now widely considered one of the Academy’s most questionable decisions.

Out of Africa (1985) was a major critical and awards success in its time, winning seven Academy Awards, including Best Picture, but its reputation has faded considerably compared with many films from the same era.

The films themselves did not change. Their publicity ended, cultural and artistic fashions shifted, and critics became more willing to recognize their weaknesses.

An Oscar records what the industry valued at a particular moment. It does not settle a movie’s ultimate standing.

When Misunderstood Movies Become Classics

The reverse also occurs. 2001: A Space Odyssey (1968), Blade Runner (1982), and The Shining (1980) were highly original movies that challenged the conventions and expectations of their time. Their unusual pacing, visual styles, ambiguity, and unconventional storytelling made them difficult for many contemporary critics to evaluate. What initially seemed strange, excessive, confusing, and flawed was later recognized by most critics as evidence that these movies were ahead of their time.

2001 rejected conventional narrative and explanations in favor of long visual sequences, ambiguity, and philosophical questions. Blade Runner combined science fiction, film noir, and deep philosophical themes in ways that were unusual for a major studio film in 1982. The Shining departed from the conventions of both traditional horror movies and Stephen King’s novel, creating a slow, unsettling, and deliberately ambiguous film that many critics initially did not know what to make of. All three are now widely regarded as landmarks of cinema.

Their changing reputations illustrate one of the limitations of contemporary criticism. A genuinely novel work can be hard to judge because critics necessarily approach it with expectations developed from movies they have already seen. Sometimes a movie must influence later filmmakers and change audience expectations before its innovations become fully appreciated.

The same lesson appears in lists of the greatest movies ever made. Citizen Kane, Vertigo, City Lights, and other classics have not changed, yet their positions on lists such as the British Film Institute’s Sight and Sound poll continue to shift. The changing variable is not the movies but the critics, audiences, and cultural values evaluating them.

After the Dust Settles

Every period has its own artistic fashions. Genres such as westerns, musicals, historical epics, romantic comedies, and science fiction move in and out of favor. A genre dismissed as old-fashioned by one generation can be rediscovered by the next.

Movies we consider classics may appear dated and cliched to future audiences, while forgotten failures may be rediscovered as major achievements. Reputations are never completely settled because each generation views older movies through a different cultural lens. Today’s reviews and rankings are no exception. They too are shaped by current tastes, fashions, values, and cultural sensitivities.

Ultimately, there is no completely objective measure of whether one movie is better than another. Individual critics may strive for objectivity, and professional criticism remains valuable, but critical consensus is inevitably a product of its time.

A common misconception about human thinking is that it exists only inside the brain.

When we think about thinking, we usually imagine the brain working on its own. In reality, much of what we call thinking depends on external tools. We use notebooks, maps, calendars, books, calculators, smartphones, and now artificial intelligence to help us remember, solve problems, communicate ideas, and make decisions. Cognitive scientists call this cognitive offloading, meaning shifting mental work from the brain to external tools.

The most obvious reason we offload is that it makes thinking easier, faster, and often more accurate. Most people know how to do arithmetic, but they use a calculator because it is quicker and more accurate. Instead of trying to remember a grocery list, we write it down. Instead of memorizing appointments, we use a calendar. We may use audio dictation because speaking is faster than typing and handwriting. By moving routine mental tasks into external tools, we free our brains to focus on more important problems.

However, cognitive offloading is much more than a way to save mental effort. Many cognitive tools do more than make thinking easier. They expand what humans can think, discover, and understand. Calculus did not simply make mathematics easier. It made it possible to understand motion, gravity, planetary orbits, and countless other natural phenomena. Microscopes and telescopes reveal worlds invisible to the naked eye. Search engines provide instant access to much of humanity’s accumulated knowledge. Artificial intelligence can summarize information, identify patterns, generate ideas, write computer code, and act as a collaborative thinking partner. These tools do not merely reduce mental effort. They increase what humans can understand and do.

Humans have been extending their minds with external tools for thousands of years. Before writing, people used stories, songs, rhythm, and rhyme to preserve knowledge across generations. Ancient Incas used quipu, knotted cords that recorded numerical information. Merchants in Mesopotamia used clay tokens and tally systems to keep track of trade, while the ancient Chinese, Greeks, and Romans used various forms of counting boards and abacuses to perform calculations. Maps expanded spatial understanding beyond direct experience. Even counting on our fingers or tying a string around a finger as a reminder are simple forms of cognitive offloading.

Language itself is one of humanity's oldest cognitive technologies. It allows us to organize thoughts, communicate ideas, remember information, and pass knowledge from one generation to the next. Writing expanded those abilities by allowing information to exist outside the brain. Books preserved knowledge across centuries, while mathematical systems provided a precise language for describing the physical world. Scientific diagrams, graphs, maps, musical notation, and the periodic table are also cognitive technologies. They organize complex information into visual and symbolic forms that make patterns easier to understand, communicate, and build upon.

Philosophers Andy Clark of the University of Sussex and David Chalmers of New York University argue that the mind does not stop at the skull. In their extended mind theory, tools that we use consistently become part of the thinking process.

Viewing tools as extensions of the mind does not mean every form of cognitive offloading is equally beneficial. Like any tool, it can be used well or poorly. Overreliance on GPS can weaken our sense of direction, and depending entirely on search engines or artificial intelligence reduces the incentive to memorize information or develop certain skills. External tools also make us vulnerable to errors, misinformation, technical failures, and biased algorithms.

Cognitive offloading works best when it complements rather than replaces understanding. The goal is not to stop thinking for ourselves, but to use external tools to think more effectively and efficiently while retaining the ability to evaluate information critically and recognize when those tools are wrong.

Cognitive offloading is not limited to objects and technologies. It also includes other people. Throughout history, humans have accomplished far more by thinking together than by thinking alone. No individual invented agriculture, mathematics, literature, modern science, or the internet. Civilization is built on knowledge distributed across many minds and many generations. Every generation inherits language, mathematics, science, engineering, history, and culture from those who came before. Modern science illustrates this process. Scientists and engineers rely not only on their own knowledge but also on mathematics, diagrams, written records, measurements, published research, peer reviews, collaboration, and the accumulated work of countless others.

This changes how we think about intelligence. Modern culture often celebrates the image of the lone genius and the independent thinker. Human achievement tells a different story. Intelligence depends not only on what happens inside one brain, but also on how effectively we use tools, share knowledge, and collaborate with others. Throughout history, many of humanity’s greatest breakthroughs have depended as much on new cognitive technologies and collective knowledge as on individual genius.

Today’s technologies continue this long history. Cloud storage remembers what we forget. Virtual reality lets us experience environments that would otherwise be inaccessible. Brain-computer interfaces may eventually blur the line between biological and digital thinking even further.

From language and writing to books, mathematics, computers, search engines, artificial intelligence, and collaboration with other people, human history has been a continual expansion of the human mind. For thousands of years, humanity has become smarter not because individual brains evolved rapidly, but because each generation created better ways to store knowledge, communicate ideas, and think together. Artificial intelligence is simply the newest chapter in that long history.

In the age of artificial intelligence, the important question is no longer, “What can you do without help?” The better question is, “How effectively can you combine your own mind with the tools, knowledge, and people available to you?”

The subject is personal for me. My late father was a professor of chemical and biological engineering and one of the founders of the field of computer-aided process engineering. He was also dyslexic and autistic. He said that if his dyslexia had been recognized when he was in elementary school, he probably would have been placed in special education.

He was considered a visionary by many of his colleagues and was one of the most intelligent people I have ever known. Yet he displayed classic signs of dyslexia throughout his life. He frequently transposed letters and numbers and could never memorize an entire telephone number, having to dial it number by number from the phone book. At the same time, he could hold extraordinarily complex engineering and mathematical systems in his mind and solve problems that few others could. His life taught me that dyslexia, along with other conditions such as ADHD and bipolar disorder, is not a measure of intelligence.

One of the most famous figures widely believed to have been dyslexic is Pablo Picasso. As a child, he struggled to read, had difficulty recognizing the orientation of letters, and his teachers called it “reading blindness.” He was often punished for falling behind and spent much of class time sketching in a notebook. His father, an art professor, saw that the boy’s artistic talent far outstripped his academic performance and enrolled him at fourteen in Barcelona’s School of Fine Arts, where his gifts quickly became clear.

Picasso later helped invent Cubism, which rejected the single fixed viewpoint of traditional painting. Cubist works often show several angles and sides of an object at once and compress different moments into one image, aiming for a more complete understanding rather than a single visual snapshot. The broad visual-spatial thinking frequently associated with dyslexia fits naturally with this approach. Cubism became one of the most influential artistic movements of the twentieth century, fundamentally changing how artists represented space, perspective, and reality.

This difference in perception has been described by other researchers as well. Matthew H. Schneps, a physicist and former director of MIT’s Laboratory for Visual Learning, compares dyslexic and non-dyslexic thinking to different camera lenses. “It’s as if people with dyslexia tend to use a wide-angle lens to take in the world, while others tend to use a telephoto. Each is best at revealing different kinds of detail.”

Modern neuroscience helps explain why dyslexia often produces this combination of strengths and weaknesses. Reading is a recent invention in human history. The brain did not evolve specifically to read. Instead, it adapted older neural systems that originally evolved for recognizing objects, faces, patterns, movement, and spatial relationships. For some people, those systems adapt efficiently to reading. For others, they remain less efficient at rapid symbol processing while retaining unusual strengths in other forms of thinking.

John Stein, an Oxford University neurologist and physiologist and one of the world’s leading researchers on dyslexia, argues that dyslexia is not simply a reading disorder but a different pattern of brain organization. His research focuses on the brain’s magnocellular system, which helps process rapidly changing visual and auditory information. It also helps control eye movements, maintain stable visual fixation, and shift visual attention while reading. When this system develops differently, reading becomes slower and more effortful because it depends on accurately processing fast sequences of letters and sounds. For some people with dyslexia, letters and words may appear to blur, move, or jump on the page, making it difficult to determine the correct order of letters and develop stable visual recognition of words. This helps explain why many people with dyslexia struggle with reading, spelling, sequencing, and other tasks that require rapid, step-by-step processing.

At the same time, Stein argues that people with dyslexia often show strengths in visual-spatial reasoning, three-dimensional visualization, holistic perception, pattern recognition, and creativity. These strengths are not simply compensation for reading difficulties but reflect the way dyslexic brains process information. Rather than focusing primarily on individual details, they are often better at seeing the overall structure of a problem and understanding how many parts fit together. Stein notes that dyslexic children often perform better than expected on tasks involving three-dimensional drawing and visualization.

Stein also makes an evolutionary argument. Dyslexia affects roughly one in ten people. If it only produced disadvantages, natural selection would likely have reduced its frequency long ago. Instead, he argues, the genes associated with dyslexia have remained common because they also provide important advantages. He points to the overrepresentation of dyslexia in fields such as art, architecture, engineering, design, and entrepreneurship. He also suggests that the challenges dyslexic children face in school often build persistence, resilience, creativity, and problem-solving skills, qualities that help explain why so many successful entrepreneurs are dyslexic.

Picasso was far from unique. Successful people who have spoken publicly about having dyslexia include movie director Steven Spielberg, entrepreneur Richard Branson, clothing designer Tommy Hilfiger, financial executive Charles Schwab, IKEA founder Ingvar Kamprad, and architect Richard Rogers. In the video clip below, stand-up comedian and former The Tonight Show host Jay Leno describes his childhood struggles with dyslexia and how teachers and parents viewed it at the time.

Harvard Medical School professor and pioneering radiologist Dr. Beryl Benacerraf described her dyslexia in similar terms. “I live in a world of patterns and images, and I see things that no one else sees.” Although undiagnosed dyslexia made reading difficult during her undergraduate years, she found medical school better suited to her abilities because it relied heavily on diagrams, charts, and medical images.

John Stein is not alone in this view. Cambridge University evolutionary cognitive scientist Helen Taylor argues that dyslexia represents an evolutionary specialization rather than simply a disorder. Some people are especially good at learning existing knowledge through careful, sequential thinking. Others are naturally better at discovering patterns, exploring new ideas, and solving unfamiliar problems. Human societies need both kinds of minds.

The research also has implications for education. Schools naturally emphasize reading, writing, memorization, and following sequential instructions because those skills are essential. However, they do not measure every important kind of intelligence. A student who struggles with reading may also possess exceptional abilities that traditional classroom measures overlook. If schools focus only on what those students cannot do, they may overlook what they can do exceptionally well.

The larger lesson is about intelligence itself. My father’s life demonstrated that principle, and Picasso’s career illustrates it on one of history’s largest stages. Sometimes the student who struggles to read a page grows up to become the engineer who transforms an industry, the entrepreneur who builds a company, or the artist who changes the history of art. Intelligence comes in many forms, and schools do not measure them all.

References

Dyslexia Help, University of Michigan. “Pablo Picasso.”
https://dyslexiahelp.umich.edu/success-story/pablo-picasso/

Stein, John. “Dyslexia and Creativity.” Dyslexia International.

Many people have heard the claim that “Eskimos” have dozens and even hundreds or thousands of words for snow, while English has only one. The story has appeared in textbooks, documentaries, television shows, and everyday conversations. It has been used as an example of the idea that language shapes how we think.

The story seems to make sense. People living in the Arctic encounter snow in ways that someone living in Miami or London never will. Hunters and travelers must distinguish different snow conditions because their safety depends on it.

However, the famous story is largely a myth. Ironically, the truth is in ways more interesting than the myth itself. The snow story is not really about snow. It is about language and what counts as a word.

A note on terminology: Historically, Eskimo served as an umbrella term for several native peoples of the Arctic whose languages belong to the Inuit-Yupik language family. Today, however, many native people, organizations, and style guides consider Eskimo to be outdated or derogatory and instead prefer Inuit.

Inuit, however, refers only to certain groups and does not include Yupik peoples, so its usage as a replacement for Eskimo is also innacurate.

In Alaska, usage is more varied, and some Native Alaskans and Native Alaskan organizations still use or accept Eskimo, where it is not universally regarded as derogatory.

This essay uses Alaska Natives, instead of Eskimos, as a practical collective term. However, Alaska Natives in this context is also imperfect because it also includes American Indian groups that are unrelated to the Inuit and Yupik peoples central to the snow-vocabulary debate.

The fact that no single term is both fully accurate and universally accepted reflects one of the essay's central themes: language is imperfect and complicated.

The myth began with a kernel of truth. Anthropologist Franz Boas observed that Alaska Native languages distinguish several kinds of snow that English usually does not. Over the decades, however, this modest observation was repeatedly exaggerated. One author claimed there were seven words, another thirteen, another fifty, and eventually writers were claiming hundreds or even thousands.

Linguist Geoffrey Pullum, a professor at the University of Edinburgh, dismantled these exaggerations in the book The Great Eskimo Vocabulary Hoax. He showed that many authors repeated one another’s claims without consulting linguists who studied Alaska Native languages. Laura Martin, an anthropological linguist at Cleveland State University, also traced how the numbers grew larger through repetition rather than evidence.

Part of the confusion comes from the fact that there are many tribal groups and no single Alaska Native language. More importantly, many of these tribal languages are structured very differently from English.

English is an analytic language. It usually expresses ideas through separate words arranged into phrases and sentences. Alaska Native languages of the Inuit–Yupik family are polysynthetic. This means they regularly combine many pieces of meaning into a single grammatical word.

English does this occasionally with words like snowstorm, blackbird, and fisherman. Alaska Native languages carry the process much further, combining roots and grammatical endings into long expressions that English would normally write as entire phrases.

Imagine if English worked this way.

Instead of writing white snow, we wrote whitesnow.

Instead of snow blowing in the wind, we wrote snowblowinginthewind.

Instead of snow drift on the roof of my house, we wrote snowdriftontheroofofmyhouse.

If someone counted each of these as separate dictionary words, English would suddenly appear to have many more words for snow than it really does. Nothing has changed except for the removal of spaces.

This is why comparing vocabulary across very different languages and peoples is often misleading. What English expresses with several words may appear as one word in an Alaska Native language. Conversely, what one language compresses into a single word may require an entire phrase in another.

The lesson is that languages package the same information differently.

Consider English and Spanish. English says, I walked. Spanish says, Caminé. The Spanish verb already contains information about the speaker and tense that English expresses with separate words. Which language has more words? The question quickly becomes less straightforward than it first appears.

Every language can express the same basic range of human experience. The difference is what information the grammar requires speakers to include. Some languages require speakers to specify whether they witnessed an event personally or merely heard about it. Others require information about gender, direction, and social relationships that English often leaves unstated. Languages therefore differ less in what they can express than in what they require speakers to express.

This helps explain why the snow myth became so persuasive. Many people imagine that every language is simply English with different labels attached to the same concepts. In reality, languages divide up experience in remarkably different ways.

English has one common basic word for blue. Russian has separate basic words for light blue and dark blue. Some languages require speakers to use cardinal directions such as north and south instead of left and right, encouraging remarkable spatial awareness. These differences do not mean speakers inhabit different realities. They encourage attention to different aspects of the same reality.

For over a century, linguists have debated how much language influences thought. Linguist Edward Sapir and his student, linguist Benjamin Lee Whorf, argued that languages organize experience in different ways, and later writers often exaggerated their ideas into the claim that language determines how people think. Most contemporary linguists reject this strong version. Cognitive psychologist Steven Pinker of Harvard University and linguist Guy Deutscher, author of Through the Language Glass, argue instead that language may influence what we habitually notice or pay attention to, but it does not limit what human beings are capable of thinking.

Linguist John McWhorter, a professor at Columbia University and author of The Power of Babel: A Natural History of Language, emphasizes another important point: no language is primitive. Every language spoken today is capable of expressing philosophy, mathematics, science, humor, poetry, and abstract thought. Languages simply solve the problem of communication in different ways.

The snow myth also reveals something philosophical. We often assume that words are neat, universal units that can easily be counted and compared. They are not. Even in English, is ice cream one word or two? What about high school, mother-in-law, or credit card? The boundaries between words and phrases are often fuzzy.

Languages are not identical dictionaries translated into different sounds. They are different systems for organizing identification and meaning. All are capable of describing the same world, but they do so using different grammatical tools.

At first glance, Abel Ferrara’s highly controversial and deeply polarizing 1992 film Bad Lieutenant appears to be little more than an exercise in depravity. Released in the United States with an NC-17 rating, the film was banned in Ireland and censored or released only in edited form in several other countries because of its graphic depictions of drug abuse, obsessive sex, violence, abuse of power, and religious blasphemy. Many viewers dismissed it as exploitation designed primarily to shock.

Yet among many film critics, scholars, and filmmakers, Bad Lieutenant has long been regarded as one of the finest independent films of the 1990s. Martin Scorsese ranked it as his fifth favorite film of the decade, praising Harvey Keitel's performance and Abel Ferrara's uncompromising direction. Roger Ebert gave it four out of four stars, calling Keitel's performance "one of the most fearless performances I have ever seen." The British Film Institute (BFI) included it on its list of Ten Great Catholic Films.

Within its graphic New York City crime story, Bad Lieutenant examines guilt, forgiveness, grace, and redemption. It is one of the most explicitly Catholic American films of its era, using the conventions of neo-noir to explore central themes of Catholic theology.

The film begins after the lieutenant's downfall. Rather than following an ordinary man as he gradually descends into corruption, Ferrara introduces a protagonist already consumed by addiction, gambling, self-loathing, and self-destruction. The unnamed lieutenant, played by Harvey Keitel, steals from criminals, extorts suspects, abuses innocent people, mocks the Church, neglects his family, and exploits his police badge for personal gratification. A drug addict, he is under the influence in nearly every scene.

The lieutenant’s addiction to betting on baseball games serves as a metaphor for fate, predestination, and the illusion of control. He believes that one winning bet will rescue him from his mounting gambling debts, but every outcome is beyond his influence. No matter how desperately he watches, pleads, and hopes, the games unfold exactly as they will.

The story changes when the lieutenant investigates the brutal gang rape of a young Catholic nun. He initially views the case in terms of revenge and punishment. Justice, in his mind, means finding the attackers and making them suffer. During the investigation, however, he learns something that shakes his world.

The nun knows who attacked her, but refuses to identify them. Instead, she forgives them, explaining that she hopes her forgiveness will help them seek God’s forgiveness as well.

To the lieutenant, her response is stunning and incomprehensible. His worldview is built on force, revenge, and self-interest, and he unsuccessfully tries to convince her to change her mind.

As the investigation continues, the lieutenant gradually realizes that he is no longer pursuing the rapists so much as confronting himself. They force him to face his own corruption and depravity. He begins to wonder whether, if the rapists are capable of receiving forgiveness, he might not be beyond it as well.

One of the film’s central scenes takes place inside the cathedral where the assault occurred. Overwhelmed by guilt and despair, the lieutenant collapses before a drug-induced hallucination of Jesus Christ. The moment represents the first time the lieutenant stops rationalizing his behavior and accepts responsibility for his actions.

Rather than presenting confession as comforting and liberating, Ferrara portrays it as harrowing. The lieutenant realizes that he is guilty of sins not fundamentally different from those of the men he has been pursuing. Keitel’s emotionally naked performance gives the scene an almost unbearable intensity that many critics consider one of the most powerful religious sequences in cinema history.

At the heart of Catholic theology lies the idea of grace: that God’s forgiveness is not something earned through moral achievement but a gift freely offered even to those who least deserve it. This is what the lieutenant struggles to comprehend.

The film also argues that remorse alone is insufficient. After his confession, the lieutenant picks up his $30,000 share from a drug dealer and calls his bookie to place another bet. Instead, he changes course. He locates the rapists in a crack house, drives them to a bus station, gives them the money, and tells them to leave the city forever, extending the same mercy the nun showed them.

The film, however, does not confuse redemption with the erasure of worldly consequences. The lieutenant's act of generosity does not undo his crimes, repair the lives he has damaged, save him from the bookies pursuing him, or relieve him of his psychological torment. Shortly afterward, he is murdered on a busy street by the bookie’s men. Ferrara rejects the familiar Hollywood convention that moral transformation can solve every problem. He presents redemption not as an escape from suffering but as the difficult work of confronting guilt, accepting responsibility, and choosing to act differently despite the personal cost.

The film likewise refuses to present forgiveness as morally simple. The nun’s refusal to identify her attackers allows violent criminals to escape justice, just as the lieutenant’s decision to help them flee leaves open the likelihood that they will continue their crimes. From the standpoint of ordinary justice and public safety, both decisions are controversial. The film highlights the conflict between Christian ideals and conventional ideas of justice.

The lieutenant’s redemption also remains ambiguous. Is his repentance entirely sincere, or is it partly born of desperation as his world collapses? The $30,000 he gives away is not nearly enough to cover his debts. Even after his confession, he smokes crack with the rapists before driving them to the bus station, and berates them and taunts them with the gun. He has not fully abandoned his addictions, violent behavior, and inner torment.

Bad Lieutenant belongs to a long Christian tradition rooted particularly in Catholic theology. The New Testament repeatedly presents redemption through society’s moral outcasts rather than its heroes. Jesus forgives prostitutes, dines with tax collectors, promises paradise to a condemned thief, and tells the story of the Prodigal Son. Grace is presented not as something earned but as an undeserved gift.

More than three decades after its release, Bad Lieutenant continues to divide audiences. Some viewers see only its X-rated content, while others see it as one of cinema’s most profound and challenging explorations of guilt, grace, forgiveness, and redemption.

Most of us assume that other people see colors the way we do. We point to the sky and call it blue, to grass and call it green, and to a stop sign and call it red. Because we all use the same words, it is easy to assume that we all experience the same colors.

However, color vision varies considerably from person to person. Some people are color blind, others possess unusually rich color perception, and people with normal color vision do not experience the world in exactly the same way. Color is not simply a property of objects but a construction of the brain, shaped by genetics, biology, evolution, and experience.

Humans are not unique in seeing the world differently. Vision varies dramatically across the animal kingdom. Eagles possess extraordinary long-distance vision, owls excel in low-light conditions, snakes can detect infrared radiation invisible to humans, and many insects, including butterflies, can see ultraviolet patterns on flowers that are invisible to us. Goldfish can detect both ultraviolet and infrared wavelengths, while dogs perceive a much more limited range of colors than humans but have advanced olfactory abilities.

How the Brain Creates Color

Human vision begins in the retina, the light-sensitive layer at the back of the eye. Most people possess three types of cone photoreceptors, each tuned to a different range of light wavelengths. One type responds most strongly to short wavelengths associated with blue light, another to medium wavelengths associated with green, and a third to longer wavelengths associated with red. The brain compares the activity of these three cone types and constructs the palette of colors we experience. Rather than recording color directly, the brain interprets patterns of incoming light and creates the subjective experience we call color.

A famous example illustrates just how much the brain contributes to color perception. In 2015, millions of people looked at the same photograph of a dress, yet some insisted it was blue and black while others were equally certain it was white and gold. Their brains were making different assumptions about the lighting in the scene. People whose brains unconsciously interpreted the image as being illuminated by bluish daylight mentally subtracted the blue tint and perceived the dress as white and gold. Those who assumed warmer indoor lighting interpreted it as blue and black. The photograph became a worldwide sensation because it revealed something surprising about human perception: color is not simply recorded by the eyes. It is actively inferred by the brain, which continually interprets incoming information and compensates for changing lighting conditions.

Why People See Colors Differently

Even among people who are not color blind, color vision varies in subtle but meaningful ways. The light-sensitive pigments in one person’s cones respond to slightly different wavelengths than those in another person’s. People also differ in the relative numbers of their cone cells, the density of those cells in the retina, and the way their brains process visual information. These differences help explain why two people may honestly disagree about whether a particular shade appears more orange or more red, or whether a color seems brighter or duller.

Differences Between Male and Female Vision

One of the most familiar differences between male and female vision involves color blindness. Most inherited forms occur because of differences in the genes that produce the red and green sensitive cone pigments. These genes are located on the X chromosome. Since men have only one X chromosome while women have two, men are far more likely to inherit defective copies of these genes. About eight percent of men of Northern European ancestry have some form of red-green color blindness, compared with less than one percent of women. Some people completely lack one type of cone, while others possess cones whose sensitivities are shifted, making certain colors much harder to distinguish.

The same genetics that make men more susceptible to color blindness also help explain why women, on average, tend to outperform men on tests requiring fine color discrimination. Women are generally better at distinguishing subtle differences between shades of red, orange, and green, while men tend to perform better on tasks involving tracking rapidly moving objects and detecting fine details at greater distances. These are statistical averages rather than rigid rules, however, and there is overlap between the sexes.

In rare cases, women inherit two different versions of the genes responsible for red and green cone pigments, and both are expressed in the retina, producing four distinct cone types instead of the usual three. This condition, known as tetrachromacy, allows some women to distinguish millions more color variations than the average person. Having four cone types does not automatically produce superior color vision, because the brain must also learn to process the additional information. However, laboratory studies have shown that some tetrachromatic women possess remarkably fine color discrimination. Hormonal influences, particularly the effects of estrogen on the visual system, also contribute to women’s enhanced color discrimination.

Researchers have also found average differences in color preferences. Women, on average, are more likely to prefer reds, pinks, and greens, while men more often favor blue. These differences are consistent with laboratory findings that women generally possess finer color discrimination, particularly among reds, oranges, and greens, while men tend to perceive blues and yellows more vividly. Although culture influences these preferences, biology appears to contribute as well. The familiar association of girls with pink and boys with blue is therefore not purely a social construct but reflects a combination of biological predispositions and cultural influences.

Scientists continue to investigate why these differences evolved. One hypothesis proposes that they reflect the different survival challenges faced by ancestral men and women. Men who hunted benefited from detecting movement over long distances and tracking fast-moving prey, while women who gathered fruits, berries, and edible plants benefited from distinguishing subtle color differences that indicated ripeness and nutritional value. Although this evolutionary explanation remains debated, it provides one plausible account of why these visual differences might have been favored by natural selection.

Why Color Vision Changes and Varies

Color perception also changes over a person’s lifetime. As we age, the lens of the eye gradually becomes more yellow, filtering out more blue light before it reaches the retina. Color blindness can also develop later in life because of eye diseases, neurological disorders, diabetes, glaucoma, and certain medications. Although the brain compensates remarkably well for these changes, older adults often perceive colors somewhat differently than younger people. Our visual system is constantly adjusting for changing lighting conditions as well. A white sheet of paper appears white whether viewed under the bluish light of midday or the warm yellow glow of indoor lighting because the brain continually recalibrates its interpretation of incoming light.

From an evolutionary perspective, it may seem surprising that color blindness remains relatively common. However, evolution does not produce perfect organisms. It favors traits that are good enough to survive and reproduce, not organisms that function flawlessly. Most forms of color blindness have only modest effects on everyday functioning, allowing the responsible genes to persist across generations.

In some circumstances, color-blind individuals possess slight advantages. Because they are less influenced by color differences, they can sometimes detect camouflage, textures, or subtle patterns that people with typical color vision overlook. During World War II, for example, some color-blind observers proved unusually adept at spotting camouflaged equipment because they were less distracted by color and more sensitive to differences in brightness, texture, and pattern.

Color Is a Construction of the Mind

Perhaps the most fascinating implication is philosophical. Objects themselves are not inherently red, blue, or green. They simply reflect particular wavelengths of light. Color exists not in the objects but in the brain’s interpretation of those wavelengths. The viral photograph of “the dress,” discussed earlier, dramatically demonstrated this principle. If our minds can disagree so dramatically about something as seemingly objective as color, it raises a deeper question: how many other aspects of reality do we assume everyone experiences identically when they do not? And how many things that we perceive as physically objective are interpretations created by our minds and sensory abilities?

Color vision reminds us of an important lesson about human knowledge. We often assume that perception is a direct window onto reality, faithfully recording the external world exactly as it exists. Modern neuroscience tells a different story. The brain is not a passive camera but an active interpreter, continually constructing our experience from incomplete sensory information.

Color is only one example of this broader process. Every sight, sound, taste, and sensation we experience is the result of the brain transforming physical signals into conscious experience. Our perceptual worlds overlap enough for us to communicate and function together, but they are almost certainly not identical.

References

Abramov, I., Gordon, J., Feldman, O., & Chavarga, A. (2012). Sex and vision I: Spatio-temporal processing. Biology of Sex Differences, 3(20).

Brainard, D. H., & Stockman, A. (2010). Color vision: Mechanisms and measurements. In the OSA Handbook of Optics.

Livingstone, M. (2002). Vision and Art: The Biology of Seeing. Harry N. Abrams.

Neitz, J., & Neitz, M. (2011). The genetics of normal and defective color vision. Vision Research, 51(7), 633–651.

Stockman, A., & Sharpe, L. T. (2000). The spectral sensitivities of the middle- and long-wavelength-sensitive cones. Vision Research, 40(13), 1711–1737.

Wikipedia. “The Dress.”

Zeki, S. (1993). A Vision of the Brain. Blackwell.

Why do successful people so often remain dissatisfied? Why does getting what we want rarely make us happy for long? We devote enormous amounts of time and money to pursuing happiness through relationships, careers, wealth, achievements, travel, entertainment, and self-improvement. Yet even people who achieve these things often remain restless, anxious, and convinced that lasting contentment lies just beyond the next milestone.

From an evolutionary perspective, this is exactly what we should expect.

A common misconception about evolution is that it somehow aims to make organisms happy. It does not. Evolution has no goals, intentions, or concern for well-being. It is simply the process by which traits that improve survival and reproduction become more common over generations. Natural selection favors genes that leave more copies of themselves, not minds that experience lasting contentment.

Oxford University evolutionary biologist Richard Dawkins famously argued that organisms can be viewed as “survival machines” built by genes to increase their own chances of replication. Whether those organisms feel happy is largely beside the point.

This does not mean happiness is evolutionarily unimportant. Rather, happiness functions as a tool. Psychologist Glenn Geher explains that positive emotions evolved because they motivate behaviors that historically improved survival and reproduction. We enjoy eating because our ancestors needed nourishment. We enjoy friendship because cooperation increased survival. We enjoy accomplishment because competence and status led to greater reproductive success. Happiness, in other words, is not evolution’s ultimate goal, but one of its motivational mechanisms.

A Brain Built to Find Problems

Imagine two of your ancestors walking through the African savanna. One hears a rustle in the grass and assumes it is probably just the wind. The other assumes it might be a lion. If the first is wrong, they may not survive. If the second is wrong, they merely experience a few unnecessary moments of anxiety.

Over thousands of generations, natural selection favored brains that erred on the side of caution. This tendency survives in all of us today. Our attention is naturally drawn toward bad news, criticism, conflict, uncertainty, and potential danger. Psychologists call this the negativity bias. One harsh comment can outweigh ten compliments. A single embarrassing mistake can occupy our thoughts for weeks while dozens of successes quickly fade from memory.

Robert Sapolsky, a neuroscientist and primatologist at Stanford, has shown that the stress systems that evolved to protect us from predators are now activated by deadlines, financial worries, social rejection, and political conflict. Our bodies often respond to emails, job interviews, and performance reviews with many of the same physiological reactions that once prepared our ancestors to flee for their lives.

Why Satisfaction Doesn’t Last

Suppose evolution rewarded every major achievement with permanent satisfaction. After finding food, gaining status, inventing a better tool, or attracting a mate, our ancestors might simply have stopped striving. Such contentment would have reduced the motivation to continue gathering resources, defending territory, seeking new opportunities, or caring for offspring.

Instead, evolution favored something different. Success feels good, but only temporarily. Psychologists call this hedonic adaptation. We rapidly adjust to improvements in our lives. The dream job becomes just another job. The new house becomes ordinary. The exciting relationship settles into routine. The latest smartphone loses its novelty within weeks. Our emotional baseline resets, encouraging us to pursue the next goal.

This tendency is powered by the brain’s reward system. Kent Berridge, a University of Michigan neuroscientist and author of the book The Pleasures of the Brain, has shown that the brain distinguishes between wanting and liking. Dopamine primarily motivates pursuit rather than providing lasting pleasure once rewards are obtained. Evolution optimized us to keep seeking rather than to remain permanently satisfied.

Dean Falk, an anthropology professor at Florida State University, argues that this motivational system was itself a major evolutionary advantage. The anticipation of success encouraged our ancestors to invent tools, master fire, develop language, cooperate with others, and solve increasingly complex problems. The pleasure associated with progress rewarded persistence and creativity rather than complacency.

If happiness had been permanent, progress might have stopped. Instead, evolution gave us rewards that fade, ensuring that each achievement becomes the starting point for the next challenge.

Humans Are Poor Judges of What Will Make Them Happy

Humans are also surprisingly poor at predicting what will improve their lives. Daniel Gilbert, a Harvard psychologist and author of the book Stumbling Upon Happiness, has shown that people routinely overestimate both the intensity and the duration of future happiness and future misery. We imagine that a promotion, a new relationship, winning an award, or buying a dream home will transform our lives for years. We likewise imagine that failures and disappointments will devastate us indefinitely.

In reality, most emotional highs and lows fade much faster than we expect. Our minds constantly tell us, “I’ll finally be happy when...” when I get married, retire, publish the book, lose twenty pounds, buy the house, or receive the promotion. Yet when those milestones arrive, they soon become normal. The horizon keeps moving because evolution designed motivation to keep moving.

The Comparison Trap

Evolution also made us intensely aware of our social standing. Among our ancestors, status influenced access to mates, allies, food, and protection. Paying attention to one’s relative position often had real survival value.

Today, that same instinct fuels endless comparison. Someone earning $100,000 may feel successful until learning that a coworker earns $120,000. A family delighted with their home suddenly feels dissatisfied after touring a larger one. Social media magnifies this tendency by exposing us to carefully edited versions of thousands of other people’s lives.

Our brains rarely ask, “Do I have enough?” Instead, they ask, “Do I have as much as everyone else?” Evolutionary psychologists Leda Cosmides and John Tooby argue that many human psychological mechanisms evolved to solve problems of social competition, cooperation, and reputation rather than to maximize subjective happiness.

Happiness as Motivational Tool, Not Destination

If you look at the things that reliably make people happy, a clear pattern emerges. Food, friendship, romance, achievement, successful task completion, and social acceptance all would have increased the odds of survival or reproduction in our ancestral past. The emotional rewards associated with these experiences helped motivate our ancestors to pursue behaviors that improved their evolutionary fitness. This is why happiness is best understood as a proximate mechanism rather than an ultimate goal.

The same is true of many other emotions. Curiosity motivates learning. Ambition motivates achievement. Anxiety encourages caution. Loneliness motivates us to reconnect with others. Jealousy helps protect valuable relationships. These emotions were not selected because they make life pleasant. They were selected because, on average, they solved recurring problems faced by our ancestors.

Research also suggests that happiness has its own adaptive benefits. Studies have found that people inherit a partially genetic baseline of happiness and that happier individuals tend to enjoy better physical health, stronger immune systems, lower rates of disease, and longer lives. These advantages would themselves have increased survival and reproductive success, giving natural selection additional reasons to preserve the capacity for happiness.

Painful Emotions Have a Purpose

If positive emotions evolved because they promoted adaptive behavior, what about painful emotions?

Evolutionary psychologists have proposed that many unpleasant emotional states also served important functions. Fear encourages avoidance of danger. Guilt promotes reconciliation after harming others. Grief reflects the importance of social bonds.

Some researchers argue that certain forms of depression may have adaptive features. Although severe clinical depression is profoundly debilitating, milder depressive states encourage withdrawal from unwinnable conflicts, reduce risky behavior, and promote careful reflection and problem solving during periods of adversity. Evolution often favors emotions because they solve problems, not because they feel good.

Rafael Euba, a psychiatrist and senior lecturer at the University of London, writes:

“It’s worth remembering, then, that we are not designed to be consistently happy. Instead, we are designed to survive and reproduce. These are difficult tasks, so we are meant to struggle and strive, seek gratification and safety, fight off threats and avoid pain. The model of competing emotions offered by coexisting pleasure and pain fits our reality much better than the unachievable bliss that the happiness industry is trying to sell us. In fact, pretending that any degree of pain is abnormal or pathological will only foster feelings of inadequacy and frustration.”

This perspective offers an important corrective to modern culture. We are often encouraged to believe that happiness should be our normal state and that sadness, anxiety, frustration, and dissatisfaction represent personal failure. Evolution suggests the opposite. A mind that never experienced discomfort would often fail to recognize danger, solve problems, and pursue needed change.

References

Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370.

Berridge, K. C. (2009). ‘Liking’ and ‘wanting’ food rewards: Brain substrates and roles in eating disorders. Physiology & Behavior, 97(5), 537–550.

Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward? Brain Research Reviews, 28(3), 309–369.

Dawkins, R. (1976). The Selfish Gene. Oxford University Press.

Euba, R. (2019). Humans aren’t designed to be happy, so stop trying. The Conversation.

Falk, D. (2012). The Adaptive Value of Happiness. Leakey Foundation.

Geher, G. (2019). You Aren’t Built to Be Happy. Psychology Today.

Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320.

Have you ever known exactly what the right decision was but failed to make it anyway? Stayed up too late despite knowing you would regret it the next morning? Eaten unhealthy food while trying to improve your health? Lost your temper over something trivial? Judged someone else for doing something you later excused in yourself? Felt anxious even though you knew there was no real danger?

These experiences reveal that your brain and body were never designed as a single, unified system. Instead, it is the product of hundreds of millions of years of evolution. Evolution does not work like an engineer designing a machine from scratch according to a master blueprint. It works like a tinkerer, modifying what already exists. New structures are added, old ones are repurposed, and successful adaptations are preserved even when doing so creates inefficiencies or contradictions. The result is not like a carefully engineered computer, but an old house renovated over centuries, with new rooms built onto ancient foundations.

This view is supported by evolutionary psychologists Leda Cosmides and John Tooby, at the University of California in Santa Barbara, who showed that the brain is not a single general-purpose intelligence but a collection of specialized mental systems that evolved to solve different adaptive problems. Some detect danger, others navigate social relationships, recognize faces, acquire language, or plan for the future. Because these systems evolved at different times for different purposes, they often produce conflicting judgments. Much of what we experience as indecision, temptation, anxiety, inconsistency, and irrationality reflects these systems competing rather than acting as a single mind.

One of the striking demonstrations that the mind is not a single, unified system comes from split-brain patients, whose cerebral hemispheres were surgically separated to treat severe epilepsy. Research by neuroscientists Roger Sperry, winner of the 1981 Nobel Prize for Medicine, and Michael Gazzaniga, director of the SAGE Center for the Study of the Mind, showed that the hemispheres can operate independently. In some patients, one hand buttons a shirt while the other immediately unbuttons it, one hand reaches for an object while the other pushes it away, or each hand attempts to perform conflicting actions. Split-brain studies also showed that the speaking left hemisphere independently invents explanations for actions initiated by the right hemisphere, leading Gazzaniga to describe it as the brain’s “interpreter.”

Neuroscientist V. S. Ramachandran, director of the Center for Brain and Cognition at the University of California in San Diego, described a split-brain patient of his whose language-dominant left hemisphere was atheist while the right hemisphere believed in God. The case illustrates how different parts of the same brain can arrive at contrary interpretations of the world.

Another example of the brain’s pattern is the complex relationship between emotion and reason. Neuroscientists Antonio Damasio and Joseph LeDoux have shown that emotional processing, which evolved much earlier, begins before conscious awareness and reasoning. Before you consciously recognize a face or interpret a situation, your brain has already begun evaluating whether it is safe or dangerous, familiar or unfamiliar. This helps explain why you may know flying is safer than driving while still feeling anxious during takeoff, or why a horror movie can make your heart race despite knowing it is fictional.

The conflicts extend beyond the brain itself. Your brain communicates constantly with the rest of your body through hormones, the autonomic nervous system, and especially the vagus nerve. Most vagus nerve fibers carry information from the body to the brain rather than the reverse. Signals from your heart, lungs, stomach, and immune system continuously influence attention, memory, mood, and judgment.

This is why you may know there is nothing dangerous about giving a speech while your heart pounds, your palms sweat, and your stomach tightens. Someone with anxiety may fully understand their fears are irrational while still experiencing overwhelming physical symptoms. Conscious thought does not control the body. The body continuously shapes conscious experience.

The body itself is also an evolutionary patchwork. The sympathetic nervous system prepares the body for action while the parasympathetic nervous system restores it to rest. Hormones, immune responses, hunger, fatigue, and pain often send competing signals that the brain must constantly integrate. Rather than directing a perfectly coordinated machine, the brain is continually negotiating among multiple physiological systems with different priorities.

Evolution also produced systems with competing priorities. Some seek immediate rewards, while others specialize in long-term planning and self-control. The urge to eat chocolate cake reflects systems that evolved when calories were scarce, while self-control depends on later-evolving capacities for abstract reasoning and delayed gratification.

Another conflict exists between our often-stated desire to discover truth and our need to belong and function effectively. Humans evolved as intensely social animals, and acceptance by the group was essential for survival. As a result, evolution favored minds that prioritize social cohesion over objective accuracy. Confirmation bias, motivated reasoning, political tribalism, and in-group favoritism are all expressions of this tendency. Evolution also favored the capacity for self-deception because false beliefs can sometimes produce real advantages. Believing we are more capable than we are can increase confidence and persistence, imagining an audience in their underwear can reduce anxiety and improve public speaking, and placebo effects can produce genuine improvements in health because we believe a treatment is real. These are all evolutionary limitations that even the greatest logicians, mathematicians, and scientists cannot entirely escape. As Nobel Prize-winning physicist Richard Feynman famously warned, “The first principle is that you must not fool yourself, and you are the easiest person to fool.”

This evolutionary patchwork also helps explain human hypocrisy. We often believe we are applying consistent principles while unconsciously using different standards depending on whether our own interests or social groups are involved. We value truth while overlooking inconvenient evidence, condemn bias in our opponents while failing to recognize it in ourselves, and criticize tactics that we excuse when our own side uses them. These inconsistencies arise because different mental systems are pursuing different goals simultaneously.

Many of these systems evolved for environments that no longer exist. Our ancestors benefited from paying close attention to danger, novelty, and social status because those factors affected survival. Today, those same systems are continuously stimulated by smartphones, social media, advertising, and twenty-four-hour news. Likewise, our craving for sugar, fat, and salt evolved when food shortages were common, yet those instincts now contribute to obesity and chronic disease. Evolution prepared us for small hunter-gatherer societies, not for a world of digital technology, constant stimulation, and abundance.

The brain is therefore neither a single rational intelligence nor a creature ruled entirely by instinct. It is an evolutionary patchwork: a collection of emotional and analytical, conscious and unconscious, physiological and cognitive systems that evolved at different times to solve different survival problems. Because evolution builds by modifying what already exists rather than designing from scratch, these systems do not always work together smoothly. They often pursue competing goals, generate conflicting interpretations, and produce many of the contradictions that define human experience. Our biases, temptations, anxieties, hypocrisies, and inconsistencies are not simply personal failings but the predictable consequences of an evolutionary process that optimized us for survival and reproduction in a very different ancestral world, not for perfect rationality, self-control, or internal consistency in the modern one.

Extensive research by social psychologists Serge Moscovici and Marisa Zavalloni, business management professor James A. F. Stoner, and legal scholar Cass Sunstein found that people who begin with only mild opinions frequently move toward substantially more extreme positions after discussing issues exclusively with like-minded peers.

Researchers have identified three primary psychological mechanisms that drive this process. Together, these mechanisms create a self-reinforcing cycle: confirmation bias strengthens beliefs, social pressures reward stronger commitment, and tribal identity makes criticism increasingly difficult to hear.

The first is confirmation bias. People naturally seek information that supports what they already believe while discounting contradictory evidence. Inside an echo chamber, members repeatedly encounter the same supporting arguments while rarely hearing serious counterarguments. Every discussion provides additional reasons why the group’s position appears correct. Members are not evaluating stronger evidence, but, are simply encountering more one-sided evidence. As supporting arguments accumulate while contrary evidence disappears, confidence steadily grows. Repetition begins to feel like proof.

The second mechanism is social validation and reputational pressure. Humans are deeply motivated by acceptance and status within their communities. Social psychologists Muzafer Sherif, Robert Cialdini, and Jonathan Haidt argue that this instinct evolved because belonging to a group was essential for survival. Members quickly learn which opinions earn praise, status, and leadership opportunities. Those who express stronger commitment gain social approval, while those who raise doubts risk criticism or exclusion. As a result, members gradually adopt stronger positions to demonstrate loyalty. The group’s center of gravity steadily shifts toward more uncompromising views.

Social psychologist Irving Janis and political scientist Elisabeth Noelle-Neumann showed that this pressure also produces self-censorship. People begin withholding questions and reservations because disagreement is socially costly. Formal punishment is unnecessary. Silence, awkwardness, and subtle disapproval is usually enough to discourage dissent. As fewer objections are voiced, everyone mistakenly concludes that nearly everyone agrees. Janis called this the illusion of unanimity. Without criticism, flawed ideas survive and become accepted wisdom.

The third mechanism is the development of a tribalistic “us versus them” mentality. As outside perspectives disappear, outsiders increasingly appear uninformed, irrational, or morally corrupt. Social psychologists Muzafer Sherif and Henri Tajfel, along with neurobiologist Robert Sapolsky, demonstrated how easily people divide the world into in-groups and out-groups. Once beliefs become part of a person’s identity, disagreement is no longer experienced as an intellectual challenge but as an attack on the group itself. Criticism is automatically dismissed rather than considered, making further polarization inevitable.

Research by Alexander Landry, an organizational behviorist who studies political polarization and dehumanization, shows that when people stop viewing political opponents as fellow citizens and instead see them as immoral, dangerous, and even less than fully human, they become more willing to support censorship, unequal justice, political intimidation, and even violence against those opponents. As political disagreements become moral battles between "good" and "evil," democratic values such as free speech, due process, and equal treatment increasingly come to be viewed as obstacles rather than principles worth defending. Landry argues that healthy democracies depend not only on laws and elections but also on citizens recognizing the humanity and legitimacy of those with whom they disagree.

These processes eliminate one of the most important safeguards against error: correction through disagreement. In science, competing hypotheses expose mistakes. In engineering, peer review catches flaws before bridges are built. Healthy organizations depend on criticism for the same reason. Echo chambers gradually lose this corrective mechanism. Weak arguments go unchallenged, questionable assumptions become accepted facts, and speculative ideas increasingly appear self-evident.

Echo chambers also redefine what seems normal. Through repeated exposure, ideas that once appeared radical become familiar and increasingly acceptable. Psychologists call this the mere exposure effect. Yesterday’s extreme position becomes today’s moderate one, making tomorrow’s even more extreme proposal seem reasonable.

The danger is that this process is largely invisible to those experiencing it. Each shift seems small and justified. Because everyone around them is moving in the same direction, members believe they are remaining reasonable and objective while outsiders see a group drifting steadily toward the extremes and irrationality.

The most effective antidote is sustained exposure to thoughtful disagreement. Healthy groups deliberately welcome criticism and dissent, seek diverse viewpoints, and encourage members to challenge assumptions. Intellectual diversity is not just a sign of openness. It is one of the most reliable safeguards against collective error.

“What is a Jew?” and “What is Judaism?” appear to be simple questions. In reality, they have no single or simple answer. Historians, rabbis, philosophers, governments, and Jews themselves define Jewish identity differently because it does not fit neatly into the modern categories we use to classify people.

This problem extends far beyond Judaism. Philosophers have long debated how to define art, intelligence, consciousness, religion, race, ethnicity, democracy, and even life itself. These concepts help us organize an extraordinarily complex world, but they are human-made classification systems rather than timeless features of reality.

This is especially true when we apply modern categories to ancient peoples. We ask whether Jews are a religion, an ethnicity, a race, a nation, or a culture because these are the frameworks familiar and convenient to us. Yet each captures only part of Jewish identity.

We often think of Israel as a geographic place, but in the Hebrew Bible and Jewish tradition, Israel primarily refers to the Jewish people. Israel was seen as the people descended from the biblical Jacob, also named Israel, wherever they live. The problem is not choosing the correct label but recognizing that our modern categories were never designed to describe one of the world’s oldest continuous peoples.

In one definition, Judaism is a religion. It has sacred texts, prayers, rituals, holidays, ethical teachings, and centuries of religious scholarship. Yet it has always been more than a religion. Ancient Israelites did not separate belief, ancestry, law, language, customs, history, and communal identity into distinct categories. Together these formed a single way of life.

This surprises many non-Jews. Many assume Judaism is simply a religion like Catholicism or Methodism and are surprised to learn that someone can be both Jewish and an atheist. About half of Jewish are secular, while millions of Jews worldwide remain deeply connected to Jewish history, family, culture, and traditions despite not being religious.

Shaye J. D. Cohen, a rabbi and professor of Hebrew literature and philosophy at Harvard, argues that Judaism is best understood as the civilization and collective life of the Jewish people. It encompasses religion, law, history, language, literature, food, music, ethics, customs, and shared memory. Cohen, who is Orthodox, says that an atheist Jew is just as Jewish as he is.

Nor are Jews a single ethnicity. Jewish communities developed in many parts of the world while maintaining a shared connection to Jewish history and tradition. Ashkenazi Jews trace most of their recent ancestry to Central and Eastern Europe and historically spoke Yiddish. Sephardi Jews descend largely from the Jews of medieval Spain and Portugal and traditionally spoke Ladino. Mizrahi Jews come from long-established communities throughout the Middle East and North Africa. Other historic communities include Ethiopian, Indian, Bukharan, Mountain, Georgian, and Chinese Jews.

Because Ashkenazi Jews became the largest Jewish community in North America, many people, including many American Ashkenazi Jews themselves, unconsciously equate Ashkenazi culture with Jewish culture as a whole. Popular images of Jews emphasize Yiddish, bagels and lox, New York delicatessens, klezmer music, and Eastern European traditions. I am not Ashkenazi but Sephardic. My family’s history developed around the Mediterranean rather than Eastern Europe. Historically, my ancestors spoke Ladino instead of Yiddish, and our cuisine, music, customs, and historical experiences differ in many respects from those of Ashkenazi Jews. Neither tradition is more authentically Jewish than the other. They are simply different historical geographic expressions of the same ancient people.

Saying “I am Jewish” tells us surprisingly little about someone. The label encompasses people who differ in religious beliefs, nationality, ancestry, culture, politics, and personal identity.

Even Jews disagree on who is Jewish. Orthodox and Conservative Judaism traditionally recognize someone as Jewish if they were born to a Jewish mother or converted according to Jewish law. Reform Judaism in North America also recognizes many people born to a Jewish father if they are raised Jewish. The answer depends on whether one is asking a religious, legal, historical, cultural, or personal question.

Modern racial categories complicate matters further. Race and color-codes are themselves recent and ever changing social classifications. Many Ashkenazi Jews identify as white, Ethiopian Jews are black, many Mizrahi Jews identify as Middle Eastern, and many Jews don’t associate themselves with any modern color-coded racial system.

Read: “Are Jews White?”

The Jewish experience illustrates a broader philosophical point. Membership in many Native American tribes, for example, involves ancestry but also tribal law, documented lineage, community recognition, cultural participation, and shared history. Many ancient and Indigenous peoples cannot be understood through a single modern category.

Historians face the same challenge when describing ancient civilizations. Greeks, Romans, Egyptians, Persians, Israelites, and Celts did not understand themselves through today’s concepts of religion, race, ethnicity, and geographic nationality. These categories are useful analytical tools, but they reflect modern ways of thinking rather than timeless features of reality.

The philosopher Alfred Korzybski famously observed that “the map is not the territory.” Maps are useful representations of reality, but they are never reality itself. The same is true of concepts such as religion, race, ethnicity, nationality, and culture. They help us navigate the world by simplifying reality, but every simplification is highly distorted leaves much out.

The questions "What is a Jew?" and "What is Judaism?" therefore become broader philosophical questions about how humans classify the world and the inherent limitations and distortions of every system of classification.

Every organization wants committed members, strong relationships, and a shared sense of purpose. Ironically, those same strengths can become weaknesses.

What Is Homophily?

One of the strongest forces shaping organizations is homophily, the tendency for people to associate with others who are similar to themselves. Sociologists summarize it with the phrase, “birds of a feather flock together.” People naturally gravitate toward those who share their values, education, personalities, political views, religion, culture, occupations, or interests.

Homophily creates trust, cooperation, and belonging. It explains why friendships, professional networks, support groups, religious congregations, and hobby groups become close-knit communities. In many settings, shared identities, values, interests, and ideas is exactly the point.

The problems arise when organizations responsible for making important decisions, adapting to change, or serving diverse communities become too homogeneous and closed. Instead of remaining communities that learn and adapt, they gradually become echo chambers intellectually isolated from the outside world.

Yale social psychologist Irving Janis described one consequence as groupthink. Highly cohesive groups begin suppressing disagreement to preserve harmony. Members become reluctant to question assumptions, criticize leaders, or raise uncomfortable ideas. Confidence gradually replaces critical thinking.

Homophily also encourages group polarization. Research by Cass Sunstein, a Harvard law professor and expert in group behavior, shows that when like-minded people repeatedly discuss issues together, they tend to adopt more extreme versions of their existing views. As dissent disappears, organizations drift toward positions that are more uniform, and often more extreme, than those of the broader public.

When Similarity Becomes a Problem

Homophily shapes organizational culture. As similar people join and remain, organizations develop a shared language, values, traditions, structure, and worldview. Members rarely notice these unwritten norms because everyone else shares them and they simply feel natural.

For insiders, the organization feels inclusive and welcoming. For outsiders, it may feel unfamiliar or exclusive. Even when everyone is friendly to outsiders, newcomers may feel they are entering someone else's community rather than becoming part of their own. Organizations often believe they are open and inclusive while overlooking the subtle ways their language, assumptions, priorities, traditions, symbols, and cultural cues signal who truly belongs.

Their public messaging may emphasize welcomeness and inclusion, yet unintentionally communicate that the organization is culturally narrow, exclusive, and primarily intended for people already like its members.

Many organizations genuinely want greater diversity but define it too narrowly, emphasizing race, ethnicity, gender, or age while overlooking differences in education, occupation, religion, social class, personality, life experience, and worldview. Ironically, this approach often fails to produce much racial or ethnic diversity, because it attracts only from the small percentage of racial and ethnic minorities who already share the organization’s culture, values, and worldview. Instead of broadening the community, it reproduces the same culture with, at best, modest racial and ethnic membership increases. Ironically, this approach often reduces racial and ethnic membership.

Educator Irshad Manji argues that “honest” diversity requires more than differences in race, sex, nationality, or religion. It also requires different points of view. As she puts it, “Just because I’m Muslim, just because I’m gay, doesn’t mean I think any particular way.” Her point is that immutible charectoristics do not determine how people think. Organizations that welcome racial, ethnic, national and gender diversity but expect intellectual conformity miss out of the benefits that diversity is meant to provide, including better ideas, greater creativity, and fewer blind spots.

This pattern appears across the political spectrum. Progressive organizations may actively seek racial and gender diversity while expecting broad agreement on political and social issues. Conservative organizations may welcome members from different ethnic backgrounds while expecting adherence to a particular ideological outlook. Universities, corporations, nonprofits, churches, and political parties can all fall into the same trap.

As social psychologist Jonathan Haidt has argued, every moral community develops norms that reward conformity and discourage dissent. Without conscious effort, organizations naturally become more intellectually homogeneous than they realize.

As homophily intensifies, members spend so much time with people who share the same assumptions that alternative viewpoints begin to seem not merely mistaken but irrational and even dangerous. Lacking meaningful contact with outsiders, they develop oversimplified stereotypes that reduce complex people and ideas to caricatures. Disagreement is increasingly interpreted as ignorance, bad motives, and moral failure rather than a difference in experience or perspective. Over time, the organization comes to see the world in terms of “us” versus “them,” making it harder to understand outsiders, learn from them, and recognize its own blind spots.

Eventually, the organization loses touch with the people it exists to serve. Its members spend so much time reinforcing one another’s assumptions that their shared worldview begins to feel like objective reality, even as it grows increasingly disconnected from the broader community.

The Cost of Echo Chambers and Groupthink

Companies underestimate changing customer preferences. Religious organizations fail to understand why younger generations are leaving and why they struggle to attract people from different racial, ethnic, and socioeconomic backgrounds. Universities become disconnected from the broader public they are meant to serve. From the inside, nearly everyone agrees things are going well. From the outside, problems are usually obvious.

Research by Scott Page, a complexity scientist at the University of Michigan and author of the book The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, has found that groups composed of people with different ways of thinking usually outperform groups made up entirely of experts who think alike. Different perspectives expose different blind spots and produce more creative solutions. Likewise, sociologist Mark Granovetter, a professor at Stanford, showed that many new ideas and opportunities come through what he called weak ties, connections outside our immediate social circles. Innovation rarely comes from hearing the same opinions repeated by the same people.

Building Healthier Organizations

Healthy organizations do not try to eliminate all homophily. Shared values and trust are essential for cooperation, and some groups are specifically intended for people of shared demographics, ideas, and interests. The goal is to prevent similarity from becoming intellectual conformity and close-mindedness.

Healthy organizations intentionally recruit people with different educational backgrounds, occupations, cultures, personalities, life experiences, and viewpoints. They encourage respectful disagreement and treat constructive criticism as an opportunity to improve rather than as an act of disloyalty. They deliberately expose themselves to outside perspectives by inviting speakers they disagree with, reading different viewpoints, and collaborating with other organizations. They also examine themselves through the eyes of outsiders, asking not only whether people feel welcome after joining but whether their culture, language, assumptions, and messaging unintentionally discourage people from joining in the first place.

Finally, healthy organizations educate members about homophily, groupthink, confirmation bias, and polarization so these tendencies can be recognized before they become deeply embedded in the culture.

Every organization develops a culture, and every organization experiences some degree of homophily. The question is not whether it exists, but whether it has become so strong that it limits the organization’s ability to learn, adapt, and understand the people it hopes to serve.

The paradox is that the very force that creates trust and cohesion can also produce conformity, closed-mindedness, ignorance about the wider world and other peoples, and blind spots. Homophily is inevitable, but echo chambers are not. The healthiest organizations never stop inviting new perspectives, questioning their own assumptions, and learning from people who see the world differently.

Every generation remakes its classics. Shakespeare is continually rewritten, shortened, and performed in contemporary settings. Homer’s Odyssey has become films, television series, graphic novels, and modern novels. Ancient myths are retold from new perspectives. Classic films are restored, remastered, and colorized. These changes often spark controversy. Purists argue that classic works should remain exactly as their creators intended, while others believe they must evolve if they are to remain meaningful.

The debate overlooks an important paradox. If our goal is to give modern audiences an experience similar to what the original audience had, we have to change the work itself. The reason is simple: classic works were not created for us.

Shakespeare wrote for sixteenth-century English theatergoers who shared his language, humor, religious assumptions, and cultural references. Many jokes that delighted his audiences now require explanation. Words have changed meaning, historical references have become obscure, and dramatic conventions that once felt natural can seem strange today.

The same is true of Homer’s Odyssey. Ancient Greek audiences understood the heroic code, the importance of hospitality, and the constant involvement of the gods in human affairs. Modern readers approach the story with very different sentiments. They cannot experience it as its original audience did.

Modern expectations also shape our idea of authenticity in surprising ways. Some viewers criticized the recent film adaptation of The Odyssey for using American accents and jargon rather than the British accents long associated with historical epics. Yet this objection has little to do with historical accuracy. Ancient Greeks spoke neither modern British nor American English. The expectation of British accents is itself a modern cinematic convention. What many people experience as “historical accuracy” is often nostalgia for the way earlier films taught them the past should sound.

Many of our strongest visual ideas about the past are historically inaccurate. We imagine ancient Greek and Roman statues as pristine white marble, medieval cathedrals as austere gray stone, Shaker furniture as unfinished wood, Victorian homes in muted colors, and the Egyptian pyramids as rough tan monuments. In reality, Greek and Roman sculptures were vividly painted, medieval churches were richly decorated with bright colors and gold, much Shaker furniture was painted in reds, blues, greens, and yellows, Victorian houses often featured bold multicolored paint schemes, and the pyramids were originally covered with smooth white limestone that gleamed in the sun.

Likewise, many bronze statues now admired for their green patina were originally bright golden-brown, and nineteenth-century photographs were sharp black-and-white not the faded and sepia-toned versions familiar today.

In each case, centuries of weathering, fading, restoration, and changing tastes have altered these objects so dramatically that their original appearance would seem surprisingly artificial, and even inauthentic, to modern eyes.

What Is Authenticity?

These examples reveal that there is more than one kind of authenticity.

One is historical authenticity: reproducing the original object or performance as faithfully as possible. The other is experiential authenticity: enabling modern audiences to experience the work as nearly as possible as its original audience did. These goals sometimes coincide, but they often conflict. A production may reproduce every historical detail while failing to communicate the emotional meaning and power the work originally possessed. Conversely, a production that departs from historical practice may capture that experience remarkably well.

What Is a Work of Art?

This distinction raises a deeper philosophical question: What exactly is the work of art?

Hamlet is not simply ink on a seventeenth-century page, nor is Beethoven’s Fifth Symphony identical with the orchestra that first performed it. The Odyssey is not any particular manuscript or translation. These works survive through performances, editions, translations, and reinterpretations while remaining recognizably the same works. If a work can exist in different languages, media, and performances, then its identity cannot consist solely in its original physical form.

Art is therefore as much an experience as it is an object. Every viewer, reader, and listener brings different knowledge, values, emotions, expectations, and cultural assumptions to it. The same painting, novel, or symphony becomes a different experience for each generation, even when the work itself remains unchanged.

This helps explain why many masterpieces that now seem familiar once shocked audiences. Beethoven’s Symphony No. 3 shocked listeners and critics with radical construction but now sounds comfortably classical. King Kong amazed audiences with groundbreaking special effects that now appear old-fashioned, while The Wizard of Oz dazzled moviegoers with its dramatic transition from sepia Kansas to brilliant Technicolor—an effect that modern audiences, raised on color films and digital imagery, can scarcely appreciate.

Why Classics Must Change

This creates an unavoidable dilemma. If we preserve every word, theatrical convention, and historical detail exactly as originally presented, we preserve the historical artifact. But we do not preserve the original audience’s experience. Complete historical accuracy can paradoxically become historically misleading because modern audiences may find a work slow, confusing, emotionally distant, or even unintentionally humorous when the original audience found it gripping and moving.

That is why Shakespeare is continually adapted. Directors shorten plays that originally lasted several hours, simplify difficult language, and relocate the action to settings ranging from corporate boardrooms to high schools. Some productions remain close to Shakespeare’s original text, while others intentionally transform it. West Side Story reimagines Romeo and Juliet as rival New York street gangs, while O Brother, Where Art Thou? retells The Odyssey in Depression-era Mississippi. These works preserve the central characters, conflicts, and themes while reshaping the stories for contemporary audiences. Their purpose is not to reproduce the originals scene by scene but to recreate something of their intellectual and emotional power.

Ironically, many people criticize modern adaptations for being "unfaithful to the original," yet the originals were often adaptations themselves. Shakespeare borrowed plots, altered history, and included anachronisms to make his plays engaging for Elizabethan audiences. In Julius Caesar, for example, a clock strikes the hour even though such clocks did not exist in ancient Rome. Likewise, medieval and Renaissance artists routinely depicted Jesus and other biblical figures as contemporary Europeans rather than first-century Middle Eastern Jews. In The Last Supper, Leonardo da Vinci portrays Jesus and the apostles in a fifteenth-century Italian setting, wearing Renaissance clothing, dining at a European-style table, and looking more like contemporary Italians than first-century Middle Eastern Jews. Every generation, in effect, recreates the past in its own image.

Even productions that remain faithful to Shakespeare’s words inevitably depart from historical practice. In Shakespeare’s theater, every role—including Juliet, Lady Macbeth, and Ophelia—was played by male actors because women were prohibited from appearing on the public stage. Few modern audiences would accept an all-male cast simply for the sake of historical authenticity. Shakespeare’s surviving texts also contain remarkably few stage directions, leaving directors to decide how scenes are staged, how dialogue is delivered, and how characters move and interact. Every performance therefore becomes a new interpretation rather than a simple reproduction of an original production.

Translations face the same dilemma. There is no perfect translation of Homer, the Bible, or Don Quixote. Every translator must balance literal accuracy against readability, rhythm, style, and emotional impact. A translation that sounded natural a century ago may sound stiff today. New translations exist not because earlier ones were wrong but because languages and audiences continually change. Translations are often as much a work of the translator as of the original author

The debate over colorizing black-and-white films reflects the same tension. Supporters argue that audiences raised on color imagery connect more readily with colorized versions. Critics respond that black-and-white cinematography was itself an artistic choice and that colorization alters the filmmaker’s vision. Both sides seek to preserve the films; they simply disagree about what preservation means.

Preserving the Past by Changing It

Ultimately, debates over adaptations, translations, restorations, and historically informed performances are really debates about the nature of art itself. Are we trying to preserve the original object, or the experience it once created? Those goals are related, but they are not always the same.

A play, novel, symphony, or film is never experienced in a vacuum. It comes alive only through the knowledge, expectations, language, and emotions of its audience. That is the paradox of the classics. Remaining faithful to the past sometimes means departing from it.

Julian Jaynes (1920–97) was an American psychologist famous for his provocative and groundbreaking theory about human consciousness.

Born in West Newton, Massachusetts, Jaynes was the son of a Unitarian minister. He spent three years in the penitentiary during World War II for refusing to fight. He received his PhD from Yale and spent most of his career at Princeton.

Jaynes gained international attention with his novel theory presented in his 1976 book The Origin of Consciousness and the Breakdown of the Bicameral Mind. The book became one of the most widely discussed works on consciousness of the twentieth century and continues to attract readers fifty years after its publication.

His central claim was that early humans did not have consciousness as we understand it today. Instead, they operated under what he termed a “bicameral mind.” In this mental structure, the brain’s right hemisphere “spoke” as a voice of authority, which was perceived as gods or ancestors, while the left hemisphere obeyed. Jaynes argued that this bicameral way of thinking guided human behavior in ancient civilizations.

Jaynes argued that this unconscious, command-following mode of thought was well-suited to early civilizations. He believed ancient societies were highly hierarchical and depended on strict obedience and social order, much like the organized behavior seen in insect colonies such as bees or ants. As populations grew and societies were disrupted by wars, migrations, trade, and natural disasters, however, this rigid mental system became less adaptive.

According to Jaynes, these increasingly complex environments caused the bicameral mind to break down about 3,000 years ago. In its place emerged modern consciousness, characterized by self-awareness, introspection, and deliberate reasoning.

He supported his theory by analyzing historical texts, such as The Iliad, The Odyssey, and the Hebrew Bible. Jaynes claimed that the characters in The Iliad exhibited no self-awareness and followed auditory and visual hallucinations that they interpreted as divine commands, while those in the later The Odyssey showed conscious decision-making and introspection. He said you can also see this evolution in the Hebrew Bible, which was composed by multiple authors over centuries.

Jaynes believed that remnants of the bicameral mind persist today in religious experiences and mental disorders, including hearing divine voices and the commanding voices heard by schizophrenics. He wrote that, by today’s psychiatric standards, early people were schizophrenic.

Jaynes’ theory sparked intense debate, met with both fascination and skepticism. Critics pointed to the speculative nature of his ideas, particularly regarding the brain’s evolution and his interpretation of historical texts.

Jaynes’ work has had a lasting impact, stimulating ongoing discussions about the nature of consciousness, the development of human brains, and the nature of early religious beliefs and experiences. His ideas continue to be explored in neuroscience, psychiatry, philosophy, religious studies, and anthropology, marking him as an intellectual maverick who challenged conventional thinking about humans.

The Irish-born British politician, statesman, and writer Edmund Burke (1729–1797) is widely regarded as the father of modern political and cultural conservatism. Unlike most eminent philosophers, Burke was not an ivory tower academic developing theories from the safety of the university. He spent much of his career in Parliament confronting the messy realities of governing.

His political philosophy grew out of practical experience and real-world issues rather than abstract speculation. For Burke, the test of any political idea was not whether it sounded logically elegant or morally inspiring but whether it actually worked when applied to the real world. In many ways, he advocated what today might be called an empirical or scientific approach to politics.

Burke’s most famous opponents were the Enlightenment philosopher Jean-Jacques Rousseau and other rationalist thinkers who believed society could be entirely redesigned from first principles. They argued that reason alone could discover the ideal political system. If existing institutions were irrational or unjust, they should simply be replaced with better ones designed according to universal principles.

Burke considered this dangerously overconfident. Human societies, he argued, are extraordinarily complex. Institutions such as governments, legal systems, markets, religions, and families evolve over centuries. Even if they contain clear flaws, they perform functions that are not immediately apparent. Destroying them in pursuit of a perfect theory can unleash unintended consequences and even chaos far worse than the original problems.

Instead of sweeping redesign, Burke favored gradual reform. Problems should be identified one by one. Proposed solutions should be introduced cautiously, allowing society to learn from experience. If a reform works, it can be expanded. If it produces harmful side effects, it should be modified or abandoned. This resembles the scientific method: propose a hypothesis, test it, observe the results, and revise accordingly. Burke believed politics should be guided by evidence and experience, not ideological dogma.

Burke also believed societies should generally favor institutions and practices that had already proven themselves over new and untested ideas. Long-standing customs, traditions, and political institutions had survived because they embodied the accumulated experience of countless generations. Much of this knowledge was never consciously designed or formally written down. Instead, it was embedded in habits, customs, laws, and institutions that had gradually evolved because they solved real problems. They were usually imperfect, but they had demonstrated that they could function in practice. New theories, by contrast, might be intellectually appealing but remained experiments whose unintended consequences were unknown. This preference for the tried and true became one of the defining principles of conservative thought.

This cautious approach inevitably produces slower change. Critics argue that gradualism or incrementalism allows injustice to persist longer than necessary. History contains many examples in which major reforms corrected profound moral wrongs.

Burke himself illustrates this tension. He regarded slavery as morally wrong and supported measures that would gradually restrict and eventually eliminate the slave trade. However, he feared that immediate abolition could produce severe economic and social disruption, along with other unintended consequences, if society was unprepared. He was similarly wary of expanding voting rights, believing that political power should largely remain in the hands of educated, propertied citizens whom he considered more capable of exercising sound judgment.

More broadly, many reforms that later came to be widely accepted—including the expansion of civil rights, women’s suffrage, and legal protections for workers—required significant departures from existing institutions. Had society always waited for slow, incremental change, these reforms might have taken much longer.

Burke acknowledged that societies must change. His disagreement concerned the method, not the goal. He believed that some reforms were necessary precisely because they preserved social stability and prevented institutions from becoming so unjust that they invited revolution.

His fears appeared to be confirmed by the French Revolution. Burke supported many of the American colonists’ grievances against Britain because they sought to preserve traditional rights afforded to the British. However, he fiercely opposed the French Revolution because its leaders attempted to reconstruct society according to philosophical principles of reason, equality, and liberty. They abolished ancient institutions, rewrote laws, attacked religion, and sought to create an entirely new political order.

The result of the French Revolution was not simply greater freedom. It was the Reign of Terror, mass executions, civil war, economic collapse, and ultimately the dictatorship of Napoleon. Burke argued that this was not an accident but a predictable consequence of trying to redesign a complex society according to abstract dogma.

This debate remains relevant today. Reformers frequently appeal to broad ideals such as equality, justice, freedom, or efficiency. Conservatives often ask different questions: Has this idea been tested? What evidence supports it? What unintended consequences might follow? Which existing institutions perform functions we do not yet understand? Neither approach has a monopoly on wisdom.

History shows that bold reforms sometimes produce extraordinary progress. It also shows that ambitious social engineering can produce catastrophe. The challenge is distinguishing reforms that solve real problems from those that create even larger ones.

If you spend much time watching the news or scrolling through social media, you have probably noticed that outrage seems to be everywhere. Every day brings a new scandal, controversy, offensive comment, political disaster, cultural battle, or existential threat. The constant stream of outrage creates the impression that society is perpetually on the brink of collapse.

This is no accident. Much of today’s media operates within what economists call the attention economy. News organizations, websites, political groups, activists, and social media influencers all compete for one scarce resource: your attention. Tim Wu, a law professor at Columbia University and the author of The Attention Merchants, argues that modern media increasingly compete not simply to inform the public but to capture and monetize attention. Whether the goal is advertising revenue, subscriptions, donations, votes, or online influence, attention is valuable. Emotional engagement is even more valuable.

Outrage is not simply an unfortunate byproduct of modern media. It is often the product being sold. News organizations sell audiences to advertisers. Social media companies sell engagement. Political organizations sell donations and votes. In each case, keeping people emotionally aroused is good for business. Shoshana Zuboff, a social psychologist and technology scholar at Harvard Business School, argues that many online platforms have built business models around maximizing user engagement because attention can be converted into advertising revenue and influence.

Psychologists have long documented the negativity bias: people naturally pay more attention to bad news than to good news. Economics Nobel Prize-winner Daniel Kahneman showed that losses and threats generally have a greater psychological impact than gains. Social psychologist Roy Baumeister summarized decades of research with the simple conclusion that “bad is stronger than good.” From an evolutionary standpoint, this makes sense. Our ancestors who quickly noticed danger were more likely to survive than those who ignored it.

Humans are also susceptible to confirmation bias. We are more likely to notice, believe, and share information that confirms what we already think while overlooking evidence that challenges our existing beliefs. Rage bait is especially effective because it combines negativity with confirmation. Stories that portray our opponents as foolish, immoral, or dangerous are emotionally satisfying and readily accepted.

Anger is especially useful because it motivates action. Someone who finds a story mildly interesting may move on. Someone who becomes angry is far more likely to click, comment, argue, repost, and share it with others. Outrage is also highly contagious. Emotional posts are more likely to be commented on, shared, and recommended by algorithms, allowing them to spread much farther than calm, nuanced discussions. Social psychologist Robert Cialdini, one of the world’s leading experts on persuasion, has shown that emotional arousal makes people more likely to respond, comply, and take action. In the online world, that action often means engagement that benefits the platform.

Traditional news organizations have understood this for decades. Stories involving crime, political scandal, corruption, and cultural conflict consistently attract larger audiences than stories about routine government work or peaceful cooperation. A headline reading, “Politician Proposes New Tax Plan” attracts far less attention than “Politician’s Outrageous Tax Plan Sparks National Fury,” even if both describe the same proposal.

Social media magnifies these incentives. Most platforms use algorithms designed to maximize user engagement, not social harmony. Posts that generate comments, reactions, and arguments are shown to more users because they keep people on the platform longer. The algorithms are not designed to create outrage, but they frequently amplify it because outrage reliably produces engagement.

Users quickly learn the lesson. A balanced, nuanced discussion often receives little attention. A post declaring that political opponents are evil, stupid, or destroying society can spread to thousands or millions of people. Over time, emotional intensity is rewarded while moderation becomes nearly invisible.

Communications scholar George Gerbner argued that repeated exposure to sensational media creates what he called “Mean World Syndrome,” in which people come to believe the world is far more dangerous than it actually is. Although Gerbner studied television decades before social media, the same principle applies today. Constant exposure to outrage, conflict, and crisis can distort people’s perception of reality. News is not a representative sample of reality. It is a highly selective sample of unusual, dramatic, and emotionally engaging events. Consuming a steady diet of sensational news can therefore create a misleading picture of how common violence, conflict, and social breakdown actually are.

Michael Huemer, a philosophy professor at the University of Colorado, argues that outrage has become one of the most effective tools of modern political persuasion. Both the political left and the political right frequently rely on the same underlying message: your group is under attack, your opponents are dangerous, and immediate action is necessary. Although the villains differ, the emotional structure is remarkably similar. Fear and anger encourage people to rally around their own side, distrust opposing viewpoints, and become more willing to excuse the faults of their own group while condemning those of others.

The best defense against rage bait is recognizing it. When a story immediately makes you furious, pause before reacting. Ask whether the headline accurately reflects the facts. Consider whether important context has been omitted. Is the example typical or an isolated incident? Does the evidence actually support the sweeping conclusion being drawn?

Psychologist Gordon Pennycook’s research has found that people who pause and engage in more reflective thinking are significantly less likely to believe or share misleading information. Simply slowing down and thinking critically before reacting can reduce the power of emotional manipulation.

Another way to reduce the influence of rage bait is to avoid media sources and groups that rely on outrage to attract attention. If a website or social media account constantly tries to make you angry, stop rewarding it with your time and clicks. Just as importantly, don't spread rage bait yourself. Before sharing an inflammatory story, ask whether it informs or merely provokes. Refusing to consume and spread outrage is doing your part.

“All models are false, but some are useful.” — George E. P. Box

This may be one of the most profound observations ever made about the human mind.

Human beings do not experience objective reality directly. Instead, we perceive and understand the world through imaginary mental models, concepts, categories, definitions, stories, and theories constructed by the brain. These frameworks allow us to navigate an unimaginably complex world, but they are not reality.

Look around the room you are sitting in. The chair, table, walls, and coffee cup physically exist. They occupy space and would remain if every human being disappeared tomorrow.

Now consider some of the most important things in your life: money, nationality, laws, property ownership, corporations, political parties, marriage, race, religion, clock time, and moral norms. None of these exist as a physical object in nature. They exist because human beings collectively believe in them, enforce them, and pass them from generation to generation. They are products of the human mind.

These models become deeply ingrained because we learn them from birth through parents, schools, religions, governments, language, media, and the surrounding culture. They are reinforced thousands of times throughout our lives until they no longer feel like human inventions but like objective reality itself. Most people never consciously choose the conceptual framework through which they interpret the world; they inherit it. Because nearly everyone around them accepts the same models, they become psychologically invisible, appearing to be simply “the way the world is” rather than one particular way of understanding it.

Philosophers have long recognized this distinction. Immanuel Kant argued that we never perceive reality directly. Instead, our minds organize sensory information through built-in mental concepts. We experience reality only after the brain has interpreted and organized it.

More recently, psychologists and neuroscientists have shown that perception, memory, and reasoning are imagined constructions rather than passive recordings of reality. Visual illusions and false memories and eyewitness testimony demonstrate that human perception of physical reality and physical reality are different.

The same principle applies to human groups. Shared social and cultural concepts, stories, and worldviews did not evolve because they are true but because they help groups survive, cooperate, and remain cohesive. A national myth, religious belief, political or social ideology, or moral norm that strengthens group unity is reserved even when it is historically inaccurate and objectively false. Conversely, ideas that are objectively true but threaten group identity and cohesion are frequently ignored, rejected, and even condemned. Throughout history, societies, religions, political movements, and cultures have repeatedly marginalized, punished, and expelled members who’s ideas challenged the group’s accepted model of reality. For most social groups, maintaining cohesion is prioritized over accuracy.

Consider how many of the things we treat as fixed and eternal are actually human inventions:

• Money: Money has value only because people collectively believe it does.

• Property Ownership: Forests and rivers exist physically; ownership is a legal and social invention. Many societies have understood property very differently.

• Nations and Citizenship: Borders and citizenship exist because governments define them, not because nature does.

• Race: Modern racial categories are a recent human invention. Different societies and historical periods classify people in very different ways.

• Political Parties: Political parties are human-created ways of organizing beliefs, not objective features of nature.

• Political, Social, and Economic Theories: Capitalism, socialism, communism, libertarianism, and other ideologies are competing human models for organizing and understanding society.

• Religious and Secular Worldviews: Religions, atheism, secular humanism, and other worldviews are competing frameworks for understanding the universe, human nature, and life’s purpose.

• Calendars and Clock Time: Mondays, noon, midnight, weekends, and time zones do not exist in nature. They are human inventions created to organize and coordinate our lives, and earlier civilizations categorized time differently.

• Morality: There is no objective or universal definition of what is moral. Moral codes vary dramatically across cultures and throughout history, rather than being fixed features of nature.

• The Human Self: Religions and philosophies offer competing conceptions of the human self—as soul, spirit, ego, consciousness, biological organism, or social construct—rather than a single objective definition.

These examples govern nearly every aspect of our daily lives and how we see the world and ourselves. We work for money, obey laws, vote for political parties, defend nations, argue about morality, identify with races and religions, organize our lives around calendars and clocks, and advocate competing political and economic theories. Yet every one of these systems exists because human beings collectively imagine and maintain them.

Humans also become deeply emotionally attached to these models. Our identities, communities, values, and sense of meaning become intertwined with them. People rarely defend only an idea; they defend their family, tribe, religion, nation, political movement, and moral worldview. Challenges to these models are experienced as personal attacks. This helps explain why disagreements about politics, religion, race, morality, and ideology become so heated.

People do not merely live within these models, but also fight over them. Many of history’s greatest conflicts have centered on competing conceptions of race, religion, morality, justice, social order, and humanity’s place in the universe. Each side typically insists that its particular model is the one correct view of the world rather than one human interpretation among many. Wars, revolutions, and political struggles have been battles between competing imagined worlds as much as between competing armies.

Evolution helps explain why this occurred. Human beings did not evolve to perceive or know the truth. Natural selection favored brains that produced useful models: models that improved survival, cooperation, prediction, and reproduction. A distorted or false worldview that enables rapid decision-making and fosters group cohesion is more valuable to survival than accuracy. The human brain evolved to construct useful interpretations, not correct representations.

The irony is that humans and human groups evolved to live inside false and imaginary worlds. Much of what gives our lives meaning and structure consists of concepts that exist only because we collectively or personally believe in them. These imagined realities have built civilizations, inspired art, created prosperity, and led to persecutions, social divisions, and wars.