Articles

Breaking the Cycle of Oversharing Syndrome Social media has made it easier than ever to overshare, and many oversharers leverage social media to share every detail of their lives. They may post excessively about their relationships, health problems, or political views. While oversharing on social media can be a great way to connect with others, going overboard can have negative consequences. It may lead to a loss of privacy, cyberbullying, and a possible job loss if employers see inappropriate posts. Let’s just face it: Social media has normalized oversharing to the point where people are comfortable with sharing unhealthy amounts of their personal business with total strangers. This may explain why boundaries have become a foreign concept, warranting more education on the subject. It’s easy to fall into this trap for views, likes, and comments of affection, but there comes a time when you have to evaluate whether it’s really worth your privacy and peace of mind. As a reminder, no one is entitled to aspects of your life that you don’t want them to know. Don’t get us wrong: there’s nothing wrong with sharing details that you’re comfortable disclosing publicly with aim to free your conscious or empower others. But if it’s simply a way to compensate for stress, anxiety, unhealed trauma, or other mental health challenges, it might be time to seek treatment from a licensed mental health therapist. If you feel like you’re oversharing, there are steps you can take to stop. Here are some practical tips and tricks to help you stop oversharing: Practice active listening - Take the time to listen to others before jumping in to share your own experiences. Set boundaries for self - Decide what you’re comfortable sharing and what you’re not. Pause before sharing - Take a moment to consider whether what you’re about to share is appropriate for the situation. Find other ways to connect - Try finding common interests with others instead of relying on oversharing to connect. Seek support -Talk to a therapist or counselor if you’re struggling to stop oversharing. Dr. Moye adds that you should also “pay close attention to the social cues of others, as well as the pacing of disclosure within their daily interactions.” Therapy sessions can be a powerful tool for reducing oversharing behavior. A therapist can help you identify the underlying causes of your oversharing and develop strategies for managing it. “ I would also have them practice role-playing in session, to develop the skills of active listening and asking questions,” Dr. Moye says. Therapists can also help you build healthy relationships by teaching you how to communicate effectively and set boundaries.

Feeling Crowded Yet? The US Census Bureau Estimates The World’s Population Has Passed 8 Billion The human species has topped 8 billion, with longer lifespans offsetting fewer births, but world population growth continues a long-term trend of slowing down, the U.S. Census Bureau said Thursday. The bureau estimates the global population exceeded the threshold Sept. 26, a precise date the agency said to take with a grain of salt. The United Nations estimated the number was passed 10 months earlier, having declared November 22, 2022, the “Day of 8 Billion,” the Census Bureau pointed out in a statement. The discrepancy is due to countries counting people differently — or not at all. Many lack systems to record births and deaths. Some of the most populous countries, such as India and Nigeria, haven’t conducted censuses in over a decade, according to the bureau. While world population growth remains brisk, growing from 6 billion to 8 billion since the turn of the millennium, the rate has slowed since doubling between 1960 and 2000. People living to older ages account for much of the recent increase. The global median age, now 32, has been rising in a trend expected to continue toward 39 in 2060. Countries such as Canada have been aging with declining older-age mortality, while countries such as Nigeria have seen dramatic declines in deaths of children under 5. Fertility rates, or the rate of births per woman of childbearing age, are meanwhile declining, falling below replacement level in much of the world and contributing to a more than 50-year trend, on average, of slimmer increases in population growth. The minimum number of such births necessary to replace both the father and mother for neutral world population is 2.1, demographers say. Almost three-quarters of people now live in countries with fertility rates around or below that level. Countries with fertility rates around replacement level include India, Tunisia and Argentina. About 15% of people live in places with fertility rates below replacement level. Countries with low fertility rates include Brazil, Mexico, the U.S. and Sweden, while those with very low fertility rates include China, South Korea and Spain. Israel, Ethiopia and Papua New Guinea rank among countries with higher-than-replacement fertility rates of up to 5. Such countries have almost one-quarter of the world’s population. Only about 4% of the world’s population lives in countries with fertility rates above 5. All are in Africa. Global fertility rates are projected to decline at least through 2060, with no country projected to have a rate higher than 4 by then, according to the bureau.

If Human Knowledge Can Disappear So Easily, Why Have So Many Cultural Practices Survived Without Written Records? You can’t speak like they spoke. You can’t farm like they farmed. You can’t dance, or heal wounds, or greet people like they did. In fact, most of the cultural practices your distant ancestors learned have not reached you. They were lost somewhere along the way – failed transmissions that never crossed the generational chains of knowledge that connect us to our past. Those chains, it turns out, can be incredibly brittle. Without physical records, cultural knowledge can easily break down and disappear. Think of the music of Mesopotamia, from around 2000 BCE. The only reason we can still attempt to play it today is because ancient Sumerians inscribed the notation for their songs into stone tablets. Think of the hunting techniques of our Palaeolithic ancestors. The only reason we have an idea about what these techniques involved is because we unearthed their carved weapons and tools. Museums are filled with these enduring messages about past cultural practices, coded into artefacts and ruins, or written onto parchment, papyrus and other kinds of media. These messages can leave us lamenting the uncountable broken chains that separate us from the past: what might we know if more had been recorded? Surely, if our ancestors had just given us written instructions on how to speak, farm, cook, dance, and make music, we could have also learned and transmitted that knowledge. And imagine if they had the recording devices we have today. With a smartphone, they might have recorded the mundane details of their lives, describing their skills in a way that could be easily mastered and shared. The problem, however, is that culture doesn’t always work that way. Not everything can be put into words. Who hasn’t been frustrated when confronted with recipes instructing you to ‘cook until done’, ‘simmer until thickened’ or any other equally ambiguous instruction? And who hasn’t been frustrated when trying to imitate someone demonstrating a skill that requires some previous experience, some tacit knowledge? Not everything can be understood simply by watching someone else do it. Some cultural practices can be learned only by doing. They must be felt. This is what makes cultural chains so brittle. It is why instrument makers in Europe can no longer produce violins like Antonio Stradivarius despite having closely studied the instruments he made, why builders can no longer replicate the stone-fitting techniques of the Inca despite having the necessary tools, and why perfume makers can no longer produce ancient perfumes, despite having the recipes. It is also why I, a French cognitive scientist in my early 30s, am unable to do many of the things that my ancestors once did, including illuminating manuscripts with immaculate handwriting, preparing herbal remedies, hunting with a bow, or making flint tools. Though our collective forgetting is enormous, it is mostly unremarkable to those who study the transmission of culture. What puzzles me, and others who study transmission, is why so much unwritten knowledge has survived. Despite the brittleness of cultural practices, skills proliferate with and without records, chaining generation to generation, and binding us to our ancestors in deep time. So how do these practices persist if the paths of transmission are so brittle? How has anything at all been transmitted without physical records? Answering these questions will help us understand how much of our current culture could be transmitted to the future. Though we are living in a time in which cultural knowledge is being recorded and stored at a higher rate than ever before, there is no guarantee this information will be effectively transmitted. Optimising cultural transmission, I believe, involves more than new technologies, massive digital repositories and artificial intelligences. It involves learning how knowledge is archived in human bodies. Though culture can be brittle, it is often imagined in ways that make it appear solid and enduring. It is portrayed as an expansive sea, an iceberg, a solid ratchet. When imagined as a kind of sea, culture appears everywhere, surrounding us. In the 1960s, the media theorist Marshall McLuhan portrayed culture as a vast and all-encompassing medium. In such a ‘sea’, we can absorb information and practices by osmosis, even unknowingly. In the 1970s, the anthropologist Edward T Hall suggested that culture was more like an iceberg: we can see only a small portion of it, the deeper parts lie hidden. And in the 1990s, the psychologist Michael Tomasello explained the ‘cultural ratchet effect’ in which human learning accumulates over time, like a metal ratchet that moves forward only as we build on knowledge from the past. Imagined in these disparate ways, culture appears as something solid and enduring that moves forward and expands. What is a spacecraft, Stanley Kubrick speculated in 2001: A Space Odyssey (1968), but the distant outcome of the first tools used by our hominin ancestors? A less stable view of culture begins to emerge when we consider some of the problems that bedevil archaeologists and anthropologists. Though they can look at rediscovered Mesopotamian bread moulds or ancient Egyptian dancing wands or Chinese oracle bones, they can’t bake Mesopotamian bread or dance like ancient Egyptians or consult the Chinese oracle. The knowledge possessed by the people who used these items is gone, most likely forever. And this loss isn’t simply because the relevant knowledge wasn’t written down. These and other findings represent forms of culture that likely can’t be recorded. Around the world, teams of researchers have been engaging with these forms of culture by attempting to learn the methods that people once used to make craft objects. Their work shows just how difficult the task of recreating cultural practices can be. The Making and Knowing Project at Columbia University in New York City has attempted to recreate the techniques described in an anonymous 16th-century French manuscript, catalogued as ‘Ms. Fr. 640’. Between 2014 and 2020, the team tackled techniques described in the manuscript, including mouldmaking and metalworking, colour making, optics and mechanics, ephemeral art, printmaking, inscriptions, and impressions. At the Stone Age Institute, an independent research centre in Indiana, a team is trying to understand stone-knapping techniques used to produce hunting technologies such as arrowheads and spear tips. Though practised for millions of years, stone-knapping remains a remarkably difficult skill to learn, requiring extensive training. Recognising how difficult it is to transmit cultural practices, UNESCO has been working to preserve ‘intangible cultural heritage’, which includes many traditions that might become extinct as the last remaining practitioners die. Languages also fall within this category: around 3,000 remain endangered. Some, like Aka-Cari spoken in the Andaman Islands, India, have gone extinct only recently. The last living native speaker, named Licho, died on 4 April 2020. But what about cultural practices that are less connected to reading, writing and speaking? What about forms of knowledge that are more tacit and embodied? Cultural transmission is a term used by researchers to describe the process through which certain forms of knowledge are passed between people. When this knowledge is exchanged, even through passive observation, a ‘transmission event’ has occurred and another link is made in the chain. To understand this process in action, think of something you know how to do but would struggle to explain to someone else. Perhaps it is a specific movement in a sport you play, or a craft technique, or a social skill like knowing the right way to greet another person. Now, try to think about how long this cultural practice has been around. Think about how many transmission events might link its first occurrence to the moment when you first learned how to do it. How many generations have passed since the practice started? How many people had to learn it, and learn it well enough to pass it on to the next person, for the chain to keep extending? In some cases, the chain of knowledge might be incredibly long – so long that thinking about the sequence of transmission events might induce vertigo. This extended sequence can also make the chain appear incredibly delicate. It could have broken at any one of its many transmission events. This is what makes knowledge chains paradoxical for researchers: if they are so brittle, how have so many forms of cultural knowledge survived? Some solutions to this problem have been elegantly synthesised in How Traditions Live and Die (2015) by Olivier Morin, an expert in cultural evolution. Morin argues that surviving cultural practices were never that brittle to begin with because they have one or both of the following features: redundancy and repetition. Both ensure that if a transmission event doesn’t occur (or fails), another transmission is still possible. Redundancy ensures that a person can learn something from multiple people in different contexts. Your aunt, for example, might pass on some of the knitting and tailoring skills your grandmother failed to teach you. Repetition, on the other hand, ensures that even if one transmission fails, other events will help you learn the things you missed. For example, you might not have fully acquired your grandmother’s knitting skills on the first try, but you master them as she demonstrates and teaches them to you over and over. An alternative way of explaining the paradox between brittle transmission chains and the ubiquity of surviving cultural knowledge involves focusing on how knowledge is stored, not just transmitted. Cultural knowledge is held not only in records written on stone tablets, papyrus or other media. It also exists in bodies and nervous systems. At first glance, this may appear to make things more challenging for cultural transmission, since this kind of knowledge typically requires learning how a practice feels, which can’t be conveyed through words alone. This is tacit knowledge, or, as the polymath Michael Polanyi describes it, what we know but cannot say. Neither imitation nor language are much help when it comes to learning this kind of knowledge. For it to be transmitted, you can’t simply watch someone, or read some instructions. Language is perfectly suited to convey all kinds of cultural things that are mainly language to start with, such as stories, but many things need to be experienced firsthand. And what about imitation? Though it can be helpful to learn by watching someone else doing something, the same rule applies: ultimately, you need to do it yourself. Suppose that you’re watching a master glassblower in order to learn how to make a hand-blown cup. What should you pay attention to for you to be able to make a cup using the same technique? Is it how hard the master blows, or how they position their feet, or their hands, or the way they move the molten glass, or something you can’t even see, or all of the above? The gap between seeing someone do something skilfully and performing it yourself is often enormous. To reduce this gap, you would first need to have enough knowledge to know what part of the action to observe exactly. You would already need to know what to pay attention to. Then you would face another, even harder problem: how should you use what you can see (such as the molten glass’s appearance) to infer things you cannot see (such as its temperature, or how hard the glassblower is blowing). Furthermore, the correct action in each situation depends on the context, and this is an important part of transmission, too. In football, for example, a skilful player’s moves will depend on the position and velocity of the ball, of their teammates and of their opponents. You could write 10,000 words about how a goal was scored and still not convey enough information for someone to replicate the kick. So how do we successfully transmit ‘what we know but cannot say’ through our bodies, especially when they are physically limited in so many ways? Think about an embodied or tacit form of cultural knowledge you are familiar with, such as knowing how to make the right facial expression to communicate an emotion, knowing how to ride a horse or make a tennis serve, or knowing how to hold your cup correctly during a Japanese tea ceremony. Now try to break down this practice into bits. In the case of riding horses – a cultural practice that has been around since 3500 BCE – relevant bits might include things such as the location of your hands, the angle between your elbow and your wrist, or the position of your back and the distribution of your weight on the horse’s back. Now, consider how these different bits relate to one another. As Simon DeDeo and I showed in our article ‘The Cultural Transmission of Tacit Knowledge’ (2022), a crucial feature of these relationships is constraint: each separate movement or position is limited by our physical bodies and abilities. Embodied knowledge is strongly constrained. Not all combinations of states for the different bits are possible because there are always physical and anatomical boundaries to what you can and can’t do in any given moment. While riding a horse, for example, if your posture is very straight or you are leaning back slightly, your hands can be only in a limited range of positions; for example, your arms will likely not be long enough to rest high on the horse’s neck. And if your body position changes, and your hands go up, the angle formed by your elbow will shift. Embodied cultural practices always involve physical constraints. In other words, you can start to think of embodied cultural knowledge as a network of interacting bits that influence one another. Not all bits are necessarily influencing one another in all cases. The position of your hands on the reins may not be related to how hard you are gripping. This is important because it suggests that embodied forms of cultural knowledge might not be as difficult to transmit as we assume. They don’t need to be fully explained because our bodies are constrained. Imagine each bit in the network like a switch that can be turned on and off. When one turns ‘on’ (say, your hands are high on the horse’s neck), others will also turn ‘on’ (your back will be angled forward) because they are connected. In other words, you need only to fix a few bits to determine the state of every other bit in the network. So, if a learner focuses only on mastering those particular traits that matter to a practice, everything else may suddenly click into place more easily. This echoes something else we observe in real life: experts sharing their embodied knowledge need only home in on those few key bits that are essential. For a learner, the interactions between the bits, as determined by the network, will then influence the remaining bits, ideally creating a cultural practice that is close to that of their teachers. This means we don’t need to know everything to learn new embodied knowledge. We need only some of the bits to have a whole, ‘correct’ practice. For teachers, the skill of sharing knowledge involves knowing which bits to focus on. In his description of the pedagogical practices used by capoeira teachers, the neuroanthropologist Greg Downey describes their use of ‘reducing degrees of freedom’. These teachers can create exercises that, Downey explains, ‘place a student’s body into particular starting positions, force them to go only one direction, or otherwise eliminate options for motion’. Such restrictions involve fixing certain bits, at least temporarily, so that other bits will ‘click’ into place, which allows students to feel what it is like to perform a given movement correctly. To help reveal the network of bits to new learners, and to generate a transmission event, teachers commonly use metaphors as short-cuts: ‘follow through’ in tennis; ‘move your whole weight’ in salsa dancing; ‘throw your elbow, not your fist’ in boxing. None of these metaphors make literal sense. ‘Following through’ has no impact on the trajectory of the ball in tennis, since the ball has already left the racquet; ‘moving your whole weight’ happens naturally in salsa with each movement you make; and your fist, not your elbow, is what hits when boxing. However, these instructions are still helpful because they allow learners to fix some parts of their movements. By telling you to ‘throw your elbow’ when throwing a hook, a boxing coach is helping you align your wrist and your elbow, ensuring your body rotates properly and that you are generating a powerful punch. Good teaching often requires metaphors or creative exercises that go beyond the practice itself. Sometimes, teachers may engineer constraints or use metaphors, but artefacts and materials might also exploit the networked relationships between ‘bits’ to transmit cultural practices. These artefacts are usually designed to fulfil a specific function or enable a specific use. Scissors, for instance, are easy to use if you’re right-handed and much more difficult if you try to use them with your left hand. More specialised tools and objects act in the same way. When horse-riding, a dressage saddle, for example, allows for specific positions of the pelvis and legs that are different from those allowed by a jumping saddle – sometimes, changing a tool can shift the network of ‘bits’, facilitating entirely different movement. Materials, like different kinds of wood, earth or stone, also make different actions possible and can help ‘fix’ some part of the network. Think of the early stone knives and arrowheads that our distant ancestors made from flint and obsidian. These minerals were chosen because they could be reliably worked into sharp edges and points. Seeing cultural knowledge as a network of bits that can switch each other on and off means that successful cultural transmission can be achieved even when transmitting only relatively little information. In such cases, transmission exploits how movements are constrained. The unexpected outcome of this is that there can be many ways of doing something, and some learners may even develop unique versions of practices. In the history of sports, this has happened many times, where examples of unusual or unorthodox techniques abound. Take Sadaharu Oh’s distinctive ‘flamingo’ leg kick in baseball, or Donald ‘the Don’ Bradman’s batting technique (and exaggerated follow-through) in cricket. They show that new variants can still be effective, even if they don’t become the dominant style. However, in some cases, unusual techniques become innovations that alter future transmissions. One example, again in the domain of sports, is Dick Fosbury’s backwards flop in high jump. After this new technique helped him win gold at the 1968 Olympic Games in Mexico City, the Fosbury Flop became popular among high-jumpers, who until then prefered techniques that allowed them to land on their feet. Understanding how cultural transmission exploits relationships in a network of ‘bits’ doesn’t only help with the preservation of current knowledge. It can also give us an insight into new cultural practices that might be discovered in the future. In our age of information, more cultural practices than ever are being recorded. As server farms bulge with data and archives swell with books and artefacts, it may seem obvious that our knowledge will be preserved and passed down. But putting our faith in this mountain of data may be a mistake. It is a misunderstanding of the embodied nature of many cultural practices, a misunderstanding of how our ancestors were able to successfully pass practices from generation to generation, despite the inherent brittleness of long cultural chains. Much of our cultural knowledge simply can’t be put into words or recorded. It can, however, be stored in the constrained movements of our bodies. Optimising the transmission of a cultural practice doesn’t always require a larger amount of information. It can be achieved by leveraging how some bits influence others in a network, by learning how some objects and materials exploit those networks, and by understanding how teachers use pedagogical techniques. It is hard to say what forms of culture will exist in another 1,000 or 10,000 years. But if tacit knowledge is still around, then it will likely have been transmitted from body to body, by exploiting our physical constraints. This is how ‘what we know but cannot say’ might someday link our age with the cultures of the deep future.

What has intelligence? Slime moulds, ants, fifth-graders, shrimp, neurons, ChatGPT, fish shoals, border collies, crowds, birds, you and me? All of the above? Some? Or, at the risk of sounding transgressive: maybe none? The question is a perennial one, often dusted off in the face of a previously unknown animal behaviour, or new computing devices that are trained to do human things and then do those things well. We might intuitively feel our way forwards – choosing, for example, to accept border collies and children, deny shrimp and slime moulds, and argue endlessly about different birds – but really it’s impossible to answer this question until we’ve dealt with the underlying issue. What, exactly, is intelligence? Instead of a measurable, quantifiable thing that exists independently out in the world, we suggest that intelligence is a label, pinned by humanity onto a bag stuffed with a jumble of independent traits that helped our ancestors thrive. Though people treat intelligence as a coherent whole, it remains ill-defined because it’s really a shifting array masquerading as one thing. We propose that it’s hard to empirically quantify intelligence because it exists only relative to our expectations – expectations that are human and, moreover, individual to particular humans. Because of this, much like Monty Python’s Spanish Inquisition, intelligence often turns up in the places we least expect it. Intelligence is not central to the success of most life on Earth. Consider the grasses: they’ve flourished across incredibly diverse global environments, without planning or debating a single step. Planarian worms regrow any part of their body and are functionally immortal, a trick we can manage only in science fiction. And a microscopic virus effectively shut down global human movement in 2020, without having any notion of what humans even are. As archaeologists, however, when we track the success of our species over millennia, the temptation is to tie it all to some single, objective trait, a bright guiding star. That is where the concept of intelligence comes in. Our evolutionary success seems to map directly onto our smarts, through the invention of increasingly elaborate tools by our increasingly clever great-great-great-etc-grandparents. In this pervasive – albeit stylised and narrow – version of the human story, stone hand-axes and symbolic beads led inevitably to agriculture, writing and mechanised landscapes, setting the stage for more recent triumphs, including winning wars and Nobel Prizes, accumulating wealth, and reaching the Moon (first, preferably). Despite its nebulous nature, intelligence is important to us, and so we seek it in others – in romantic partners, pets, leaders, friends and coworkers. We sometimes imbue ornery or helpful objects of daily use with intelligence, for example, when we are helped by a new smartphone app or foiled by a Machiavellian padlock. It’s a trait that we wonder about and endlessly debate the existence of in nonhuman animals (hereafter, animals), from wild elephants and dolphins to caged monkeys and cats. Massive effort is currently directed towards trying to understand intelligence, and build vastly more of it, under the umbrella of artificial intelligence (AI) programs. It is even a fundamental part of what we hope to find in alien life, made explicit in the long-running search for extraterrestrial intelligence (SETI). But despite facilitating the global reach of our species, intelligence remains notoriously slippery to define. When pressed, scholars often point to more tractable mental skills such as abstraction, problem-solving, efficiency, learning, planning, social cognition and adaptability – even numeracy or the ability to recognise oneself in a mirror – although they quibble over which ones most demonstrate intelligent behaviour. This plurality is precisely what we should anticipate: intelligence is not and never has been a single entity. Instead, it is a hominin-shaped heuristic, a way for us to easily perceive valued characteristics in other people. Like beauty, it lies in the eye of the beholder. And just as we cannot expect to automate the personal, shifting lens through which each of us sees beauty, a search for anything like artificial general intelligence (AGI) misses the point: nothing in intelligence makes sense except in the light of humanity, and our own evolved perceptions. African grey parrots have the smarts of a human child, but much smaller brains than might be expected The natural world overflows with animals that see, hear, smell and feel in very different ways than we do, along with living in conditions that would crush, freeze, dissolve or cook us alive. There are also a multitude of smaller and single-celled organisms thriving in ways that don’t easily fit into our scale of reality, not to mention the kingdoms of plants and fungi out there. Every species alive today can be considered our equal in the success game, by the simple virtue of continued existence. Physically speaking, humans are a middling mammal with an odd hair pattern, a badly evolved back, and a mouth that no longer fits all our adult teeth. All of which is why we really like brains. Absolute brain size, relative brain size, brain organisation, and neuronal density have all been used to predict where intelligence will emerge. Among living animals, Homo sapiens has the highest encephalisation quotient, meaning that our brains are much bigger than expected for our body size. This plays to our vanity, but some of the smartest creatures out there have brains quite unlike ours – cuttlefish, for example, rely on neurons in their arms for complex problem-solving. African grey parrots have the smarts of a human child, but much smaller brains than might be expected. Shrews, on the other hand, have some of the highest neuronal densities among mammals but, ironically, they aren’t terribly shrewd. Tiny-brained digger wasps use tools, and monarch butterflies perform continent-spanning annual migrations. Large brains are important for human intelligence, but life finds other ways to succeed. Adding to the mire, intelligent behaviour in people is not always the result of conscious choice or rational strategy, but can arise from autonomic processes. The cognitive bubbling up of hunches, intuitions and gut feelings can often be credited to ‘lower-order’ systems such as the sympathetic nervous system or the amygdala, or manifest as subliminal or subconscious conditioned responses to environmental cues. In some contexts, the brain itself has been suggested as a poor candidate for the locus of intelligence. Supporters of swarm or collective intelligence tell us that the problem of problem-solving can be shared among a host of similar entities, as in a shoal of fish or a surge of grasshoppers. Ants build boats, bridges and metropolises with populations in the millions, and yet their individual cerebral horsepower doesn’t amount to much. The boundaries of an interacting group – the nest, the shoal, the rational mind, the nation-state – all can be argued as the scale at which true intelligence arises. Paradoxically, we value intelligence as a marker of individual success, yet it exists both as a collective of our own neurons, and an aggregate of collective behaviour. To paraphrase Inigo Montoya, we keep using this word, but perhaps it does not mean what we think it means. If we are going to continue talking about intelligence, we need to at least make sure we’re talking about the same thing. Our starting point is (we hope) uncontroversial: intelligence is a label that humans use to help dissect the world. The label’s existence does not automatically mean that there is a single, true thing to which it corresponds; just as, a few centuries ago, having the word phlogiston did not guarantee the existence of a special substance contained in burnable things. That may seem obvious, but it emphasises that ultimately it is people who choose and name what matters. To answer the question of what intelligence is, we first need to recognise that it’s us – people – asking that question. Unlike most other organisms, we don’t usually solve our problems with parts of our bodies. We needn’t have the warmest down, sharpest teeth, most toxic skin secretions, or larynxes best optimised for echolocation. Instead, we think about stuff, and then we modify our environments to our advantage; we create tools, employ strategies, construct complex habitats, and move symbols around. This is how humans work. Intelligence does not refer to a single measurable trait or quality, but rather it indexes behaviours and capacities that have arisen at different times throughout our species’ evolutionary history. Rather than a package that popped into being fully formed at a single point in time, this patchwork of selective advantages accrued over many millennia. No surprise, then, that the traits recognisable to us as intelligence co-occur almost exclusively in modern humans. Intelligence evolved and is evolving. Some 7 million years ago, our last common ancestors with chimpanzees were already capable of cultural behaviours and tool use, and probably had advanced understandings of physical cause and effect. Around 3.4 million years ago, our clever 105 great-grandmother (someone like Lucy the australopith) made, then used, sharp stone tools to strategically scavenge meat. Access to meat gave her descendants the extra energy they needed to fuel their costly brain tissues, with which they then formulated ever more complicated tools and strategies. Intelligence is a way we identify co-occurring traits that, in our species, are likely to mean ‘success’ Since that time, our lineage has been doubling down on intelligence as an investment strategy. Our Homo erectus ancestors, from 1.8 million years ago, endowed us with the ability to hunt, maybe to cook, and make elaborate tools like hand-axes, watercraft and baby slings. The increasing need to transfer knowledge and strategically coordinate with one another gave a selective advantage to those who were good communicators. Some kind of speech probably burbled up between 2 million and 500,000 years ago, between Homo erectus and our last common ancestor with Neanderthals and Denisovans. The ability to encode information externally – in symbolic media such as beads, tally sticks, tattoos or cave paintings – also heralds back to one of our Middle Pleistocene ancestors. What is clear is that our own species took this ball and ran with it, inventing writing, concrete, iPhones, chambers of commerce, and quantum computers all in the past 10,000 years. Looking back, it makes sense that human intelligence is hard to pin down. Intelligence is not a single empirical, positivist quality that exists in nature – it’s a way we identify co-occurring traits that, in our species, are likely to mean ‘success’. Intelligence is real, because it’s real to us, but it’s not one thing. As an analogy, think of a rainbow. Rainbows exist, sure, but only to someone watching water droplets with the sun at less than a 42-degree angle at their back. A rainbow is a unified concept that indexes a known thing, and yet a rainbow is inherently a matter of perspective. Assuming these preconditions for rainbow-viewing are met, what we’re really talking about is an aggregate of culturally derived (is turquoise blue or green?), discrete yet overlapping (red or orangey red or orange?), arbitrary yet real (blue isn’t yellow, but grades into it) divisions in the spectrum of visible light. Moreover, a rainbow makes sense to us as a concept only because we have an evolved sensory apparatus that can perceive it, as primates who typically have three eye cone-cell types. Intelligence has much the same properties: think of it as the ever-shifting rainbow our ancestors used to get things done. It indexes an aggregate of evolutionarily adaptive components, with discrete yet overlapping (numeracy, tool use, symbolic thinking) and arbitrarily divided yet real (chess grandmaster intelligence vs diplomat intelligence vs rocket scientist intelligence vs customer service intelligence) capacities that kept our ancestors alive. So why do we keep insisting that all these things go together as a unified whole? What is the point of discerning intelligence in one another, and why does it matter so much to us that we’ve spent billions trying to find and create it in machines? Throughout our history, assessing the capacities of other humans – the default actors in our evolutionary social world – has been a matter of life and death. The adaptive advantage in not merely having but in recognising intelligence has paid great evolutionary dividends. It is a priceless cipher, alerting us to skills in communication, coordination, technology, strategy, planning, pattern recognition, and using the environment to our advantage. We can view human life as a set of reliably occurring problems, literally everyday problems, that revolve around survival, comfort, and finding meaning in maintaining our existence. Most of those problems are common enough across our friends, family and neighbours that any solutions they come up with will work for us too, alongside any social benefits that come from just fitting in with the group. Likewise, their mistakes – especially the fatal ones – offer valuable lessons for our own actions that should not or could not be learned independently. Picking up on those cues is a key part of surviving in the human world. The capacity to enact, recognise and transmit novel, adaptive, ‘intelligent’ behaviours kept our ancestors alive, but not through feats of strength or physical prowess. Consider again one of our australopith ancestors, who – using just their body – didn’t stand a chance against the business end of a leopard. Every one of Lucy’s neighbours who tried to go it alone against a big cat risked a quick death, but those who paid attention to the ways that their fellow hominins survived leopard encounters were forewarned and literally forearmed. Leopards are a solvable problem, and those who solved it were displaying a quality that our species has decided to call intelligence. It doesn’t matter if the solution was self-camouflage, fashioning a pointy stick, coordinating an australopith horde, constructing a covered pit, yelling instructions to your friend in a tree, gaining the high ground, or offering the cat a mouse (each requiring different levels of environmental, technological or social coordination and planning); its reliance on the judgment of observers means intelligence is outcome-based. Obviously, group members who manifest this trait are those we should seek to align ourselves with – emulate, befriend, marry, have on our team, listen to, promote as leaders – or to watch out for. In a human evolutionary context, assessing intelligence served as a gloss for ways of doing that gave our ancestors a unique competitive edge. While the outputs of intelligence may have changed over time, they continue to grab our attention because together they advertise fitness, and have been maintained across generations through adaptive social selection to ensure humans’ survival. Finding intelligence triggers a mental alarm bell, then, in the same way that seeing beauty does. Its survival value means that we are predisposed to look for it. The human brain has been described as a prediction machine, one that builds a statistical model of the world from all the stuff that flows through our senses, and then tracks how well that model matches new information as it comes in. Having an accurate model makes processing reality more efficient – a benefit for expensive tissue like your brain – since you need to pay attention only to those rare bits of information that are out of alignment. The result is that most of the external world can be ignored much of the time, as you move through an environment largely populated by background caricatures of trees, clouds, buildings and even people. However, our minds also have a set of alerts that can drag you out of cruise control. Predators trigger those alerts, as does a sudden loud noise, an unexpected fall, a delicious smell from a nearby bakery or hotdog stand, or a particularly attractive person walking by. What these events have in common is not their intrinsic nature, but what they elicit in us: surprise. Since we’re quick to assign intelligence to surprising solutions, we are also prone to false positives Surprisal, as it is sometimes formally called, is what happens when the expected world and the reported one don’t match up. This technical version of surprise aligns neatly with everyday experiences that lead to laughter, shock, fear and so on, depending on whether the surprise is welcome or not. Crucially here, surprise is subjective and fluid over time. It is relational, existing only in comparison to our expectations. Surprise’s job is to alert us that there is something about the world that requires our attention, something we didn’t anticipate – favourable or not. Intelligence, then, invokes a particular flavour of surprise, when we see someone achieve an outcome that goes beyond our own model – built from our personal experiences to that point – of how the world can, or will, be solved. Since we’re quick to assign intelligence to surprising solutions, we are also prone to false positives. Unexpected actors activate our cognitive tripwire. We might be surprised, for example, and see unexpected intelligence in decision-making slime moulds that navigate to solve a maze, or in an octopus named Otto solving the problem of nearby bright lights by shooting water jets to short out his aquarium’s electrical network. And it is hard not to be surprised when we learn of California ground squirrels that chew up discarded rattlesnake skin and rub it on their body, disguising their scent from the predator. But when the same squirrel freezes in oncoming traffic, we certainly don’t index that as intelligent behaviour. What we may fail to realise is that squirrels are hardwired to avoid detection by not triggering their natural predators’ sensitivity to movement. Behaving inflexibly – enacting a rigid, time-tested behavioural pattern as a response to certain stimuli – will, just like the ‘intelligent’ snakeskin-rubbing behaviour, usually ensure the animal’s survival. In each case, the core of intelligence lies not in what the slime mould, octopus or squirrel is doing, nor in the adaptive context for a particular behaviour, but comes from within us. We hallucinate intelligence. Animals are particularly well suited to ring our evolved alarm bells. Many of them move and interact with the world in ways that we can broadly understand, living at a speed and size that we can comfortably watch, and, like us, facing a daily search for food, shelter and mates. And the more like us an animal is – if it has two eyes and a jaw and four limbs and lives on land – the more readily we can map their solutions to their own problems onto our own expectations. But even things that don’t resemble us regularly catch us out. When an animal surprises us by achieving a goal, solving a problem, or enacting a successful strategy that we did not expect, we are primed to register the mismatch between the demonstrated behaviour and our expectations as intelligence. This happens more than we might think, for example, when we mistakenly think that something is too simple or small to perform a complex sequence of actions. In this way, bees or bacteria can appear more intelligent the more we get to know them. However, we have inbuilt limits to how long we can remain surprised. Continued enquiry may ultimately set a new baseline of expectations, to the extent that we lose our surprise and dial back how much of their behaviour we label as intelligence, until eventually we come to see it as explicable evolutionary programming. We recalibrate our expectations, just in time to stop short of ascribing ‘true’ intelligence to nonhuman entities. For example, we tell ourselves that humans do something clever or tactical because our brains have simulated that this course of action will produce favourable outcomes, but when we learn that ants do the same thing by enacting preprogrammed responses to pheromones, surely that doesn’t count. This cycle emphasises again that the watcher plays the central role, rather than any innate characteristic of – or favourable outcome for – the watched. Just as your ability to feel surprise is fluid, dependent on your age, your cultural background, and what you know and expect from a situation, so too is your assignment of intelligence relational and changeable. Take the example of salticid spiders, like those of the genus Portia, which can plan a complicated route from where they stand to potential prey, and then follow that path even if they can no longer see the prey during their journey. If, like us, you expected spiders not to be capable of essentially creating and using a mental map, that is a surprising discovery. But it doesn’t change what those spiders had been doing this whole time, under our very noses – it tells us about what we predicted they could do. Moreover, when we describe other animals or things as having intelligence, we may inadvertently impute them with other human-like qualities. If a sea otter can use tools, we might unconsciously assume that it is like us in other ways; maybe it has counting skills, thinks abstractly, plans ahead, or knows its reflection in a mirror. If it’s intelligent, how could it not? But that is an unwarranted leap, emerging from the way we have built self-centred definitions of intelligence. In humans, skilful tool use is a highly accurate indicator of a certain level of development in theory of mind (the ability to attribute mental states to others), delayed gratification and impulse control, procedural strategy, and numeracy, because those traits co-develop as human children grow up. A person speaking to you using a complex, recursive language very likely can also plan what they will have for dinner and execute on that plan, not because language is the sign of intelligence, but because language is a sign of a human, and humans are also good at planning, compared with other known life forms. Like life and time, intelligence is a helpful shorthand for a complex idea that helps us structure our lives, as people. It is primarily a synonym for humanness, and judging other animals by this metric does a disservice to their own unique sea otterness, worminess, or sharkfulness. In our view, intelligence has inadvertently become a ‘human success’-shaped cookie cutter we squish onto other species. Switching from baking to sports metaphors, we could say that everyone else – animals, amoebas, AIs and aliens – has to play the game on a field that we have laid out, according to rules that we have established and proven ourselves extremely competent at following. We prize novelty and efficiency, so we are surprised when an animal, swarm or program does things more quickly than we expected, or takes unexpected shortcuts to solving a problem (as the AI AlphaGo did in game two, move 37 of its matches against the world Go champion Lee Sedol in 2016). Humanity’s relationship to AI is characterised by similar cycles of underestimation and surprise, followed by exploration, understanding and explanation, and a subsequent downgrading of our belief that intelligence is currently at play. Current large language models (LLMs) such as ChatGPT converse in sentences that are almost indistinguishable from those of another person, and their rapid search ability, multiple layers of tweakable parameters, and training on massive bodies of human knowledge allow them to succeed at standard intelligence tests. However, the brittleness and uncertain mechanisms of these programs have led to doubt about whether this is ‘true’ artificial intelligence, which instead might be found only when machines can deal with abstract concepts, generalising from small numbers of examples to predict missing pieces – or the next piece in a series of puzzles, something we humans happen to be good at. Once again, human minds are the shibboleth in the shadows: if a computer exhibits one trait of human intelligence, but not the others, it slips in our estimation of true smarts. What might success look like to a tardigrade, or a pigeon, or a horseshoe crab? This is sometimes called the ‘AI effect’, explained by the computer scientist Larry Tesler as our tendency to believe that ‘Intelligence is whatever machines haven’t done yet.’ Now that it is possible for machines to beat human chess grandmasters, the game is no longer widely seen as a marker of ‘true’ intelligence. In areas of medicine where AI diagnoses are more reliable than those of doctors, diagnosing those diseases will similarly be considered unintelligent, mere rote computing. What changes is not the theoretical ability of a machine to match or exceed a human, but our understanding of what a given system is capable of. Once we can reliably predict its success, it is no longer surprising, and the machine’s intelligence is relegated as merely mechanistic. The goalposts move of their own accord. Mobile intelligence goalposts are not unique to animals and AI, and we expect they have been around as long as there have been humans. Many of our recent and distant ancestors lived in tectonically active regions, prone to volcanoes and earthquakes. These notoriously unpredictable and occasionally catastrophic events are ripe for being seen as the handiwork of intelligent – if mercurial – gods or spirits. However, with greater knowledge has come greater understanding, and predictions and explanations of eruptions and earthquakes are increasingly (albeit not yet perfectly) accurate. A child might still be surprised by the sudden noise of a thunderstorm and attribute some form of punitive or malevolent intelligence to it. An educated adult knows better, and instead attributes human-like intelligence to a new chatbot – but only for now. These are normal reactions to an unpredictable world. We do best when we know whom to appease, and with whom our allegiances lie. The things we call intelligence have transformed us from small, slow, physically weak apes to the solar system’s most lethal apex predators. However, when we ask whether other animals are intelligent, we’re not usually asking what capacities or kinds of bodies were advantageous in their evolutionary past. We’re really asking whether they do things the way we do. Sometimes, the Venn diagram of animals’ success strategies overlaps with ours (hello dolphins!). But in seeking intelligence, we’re really seeking ourselves – seeking success strategies that match those found in our own evolutionary story. If a machine trained on human speech passably reproduces human speech; if a squirrel enacts a stereotyped behaviour as a response to a stimulus; if a bear, or a daffodil for that matter, won’t learn to press a lever that allows it to open a box to get a treat – so what? A focus on behaviours that resemble ours often plasters over much more interesting questions. What might success look like to a tardigrade, or a pigeon, or a horseshoe crab? Would a peacock mantis shrimp, able to see an almost unfathomable array of colours (as well as polarised light) and strike with incredible force while generating ultrasonic cavitation bubbles, be moved by our ability to beat them at checkers? Where does all this leave intelligence as a marker of human success? Actually, it’s nicely intact. We will continue to correlate intelligence with adaptation, sophistication, learning, planning, strategy, abstraction and so on that we see in the people around us. We’ve evolved to do it, so we’ll keep doing it. If somebody’s capacity for those things elicits your attention, and then surprises you, intelligence is there. After all, intelligence is a unifying concept, but what it unifies is the human experience: it is the little drawing on our badge of success as a species. Viewed this way, intelligence is unshackled from any one parochial definition. Parents will follow the changing intelligence of their growing children, animal lovers will be delighted by what they see as the intelligence of their pets, and AI researchers will authoritatively state that playing chess just isn’t intelligent behaviour (any more). Rather than seeking to quantifiably compare these things, we can instead realise that they don’t – and don’t need to – align at a deeper level. Eventually, instead of talking about how machines, animal collectives, or individual birds and bugs exhibit intelligence, we should be better prepared to investigate how they evolved or iterated those actions in their own evolutionary spaces, unshackled from human-shaped standards. For those seeking a middle ground, we might be tempted to say that each species has its own intelligence, but that claim carries too much baggage at this point. A planet full of problem-solving life exists apart from humans, and none of it is obligated to fit neatly into our subjective, self-serving mindset. We need to avoid the real risk that we will miss animal or machine (or plant, fungal, bacterial, or even extraterrestrial) ways of succeeding just because they are fundamentally alien to our conceptual toolkit. Like gazing through a stained-glass window at a vibrantly coloured, snow-covered landscape, intelligence isn’t just what we’re looking for, it’s what we are looking through. Humans value intelligence, and that is not about to change. What may change is our capacity to appreciate other kinds of life on their own terms, divorced from anthropocentric box-checking. What we hope our suggestion does is prevent any one limited metric from skewing or obscuring the diverse kinds of success that exist in our world, including those we have yet to discover. We won’t just see more clearly, we’ll see more than we did before. If intelligence is no longer a default metric for species’ worthiness, how might our value judgments shift? Would we be more inclined toward wonder, and might this wonder impel us to conserve the other wondrous creatures with whom we share this planet, and the environments in which they evolved their own flavours of success? We think that would be the smart thing to do.