Tag Archives: Cognição distribuída

Becoming a centaur (Aeon)

Rounding up wild horses on the edge of the Gobi desert in Mongolia, 1964. Photo by Philip Jones Griffiths/Magnum
The horse is a prey animal, the human a predator. Our shared trust and athleticism is a neurobiological miracle

Janet Jones – 14 January 2022

Horse-and-human teams perform complex manoeuvres in competitions of all sorts. Together, we can gallop up to obstacles standing 8 feet (2.4 metres) high, leave the ground, and fly blind – neither party able to see over the top until after the leap has been initiated. Adopting a flatter trajectory with greater speed, horse and human sail over broad jumps up to 27 feet (more than 8 metres) long. We run as one at speeds of 44 miles per hour (nearly 70 km/h), the fastest velocity any land mammal carrying a rider can achieve. In freestyle dressage events, we dance in place to the rhythm of music, trot sideways across the centre of an arena with huge leg-crossing steps, and canter in pirouettes with the horse’s front feet circling her hindquarters. Galloping again, the best horse-and-human teams can slide 65 feet (nearly 20 metres) to a halt while resting all their combined weight on the horse’s hind legs. Endurance races over extremely rugged terrain test horses and riders in journeys that traverse up to 500 miles (805 km) of high-risk adventure.

Charlotte Dujardin on Valegro, a world-record dressage freestyle at London Olympia, 2014: an example of high-precision brain-to-brain communication between horse and rider. Every step the horse takes is determined in conjunction with many invisible cues from his human rider, using a feedback loop between predator brain and prey brain. Note the horse’s beautiful physical condition and complete willingness to perform these extremely difficult manoeuvres.

No one disputes the athleticism fuelling these triumphs, but few people comprehend the mutual cross-species interaction that is required to accomplish them. The average horse weighs 1,200 pounds (more than 540 kg), makes instantaneous movements, and can become hysterical in a heartbeat. Even the strongest human is unable to force a horse to do anything she doesn’t want to do. Nor do good riders allow the use of force in training our magnificent animals. Instead, we hold ourselves to the higher standard of motivating horses to cooperate freely with us in achieving the goals of elite sports as well as mundane chores. Under these conditions, the horse trained with kindness, expertise and encouragement is a willing, equal participant in the action.

That action is rooted in embodied perception and the brain. In mounted teams, horses, with prey brains, and humans, with predator brains, share largely invisible signals via mutual body language. These signals are received and transmitted through peripheral nerves leading to each party’s spinal cord. Upon arrival in each brain, they are interpreted, and a learned response is generated. It, too, is transmitted through the spinal cord and nerves. This collaborative neural action forms a feedback loop, allowing communication from brain to brain in real time. Such conversations allow horse and human to achieve their immediate goals in athletic performance and everyday life. In a very real sense, each species’ mind is extended beyond its own skin into the mind of another, with physical interaction becoming a kind of neural dance.

Horses in nature display certain behaviours that tempt observers to wonder whether competitive manoeuvres truly require mutual communication with human riders. For example, the feral horse occasionally hops over a stream to reach good food or scrambles up a slope of granite to escape predators. These manoeuvres might be thought the precursors to jumping or rugged trail riding. If so, we might imagine that the performance horse’s extreme athletic feats are innate, with the rider merely a passenger steering from above. If that were the case, little requirement would exist for real-time communication between horse and human brains.

In fact, though, the feral hop is nothing like the trained leap over a competition jump, usually commenced from short distances at high speed. Today’s Grand Prix jump course comprises about 15 obstacles set at sharp angles to each other, each more than 5 feet high and more than 6 feet wide (1.5 x 1.8 metres). The horse-and-human team must complete this course in 80 or 90 seconds, a time allowance that makes for acute turns, diagonal flight paths and high-speed exits. Comparing the wilderness hop with the show jump is like associating a flintstone with a nuclear bomb. Horses and riders undergo many years of daily training to achieve this level of performance, and their brains share neural impulses throughout each experience.

These examples originate in elite levels of horse sport, but the same sort of interaction occurs in pastures, arenas and on simple trails all over the world. Any horse-and-human team can develop deep bonds of mutual trust, and learn to communicate using body language, knowledge and empathy.

Like it or not, we are the horse’s evolutionary enemy, yet they behave toward us as if inclined to become a friend

The critical component of the horse in nature, and her ability to learn how to interact so precisely with a human rider, is not her physical athleticism but her brain. The first precise magnetic resonance image of a horse’s brain appeared only in 2019, allowing veterinary neurologists far greater insight into the anatomy underlying equine mental function. As this new information is disseminated to horse trainers and riders for practical application, we see the beginnings of a revolution in brain-based horsemanship. Not only will this revolution drive competition to higher summits of success, and animal welfare to more humane levels of understanding, it will also motivate scientists to research the unique compatibility between prey and predator brains. Nowhere else in nature do we see such intense and intimate collaboration between two such disparate minds.

Three natural features of the equine brain are especially important when it comes to mind-melding with humans. First, the horse’s brain provides astounding touch detection. Receptor cells in the horse’s skin and muscles transduce – or convert – external pressure, temperature and body position to neural impulses that the horse’s brain can understand. They accomplish this with exquisite sensitivity: the average horse can detect less pressure against her skin than even a human fingertip can.

Second, horses in nature use body language as a primary medium of daily communication with each other. An alpha mare has only to flick an ear toward a subordinate to get him to move away from her food. A younger subordinate, untutored in the ear flick, receives stronger body language – two flattened ears and a bite that draws blood. The notion of animals in nature as kind, gentle creatures who never hurt each other is a myth.

Third, by nature, the equine brain is a learning machine. Untrammelled by the social and cognitive baggage that human brains carry, horses learn in a rapid, pure form that allows them to be taught the meanings of various human cues that shape equine behaviour in the moment. Taken together, the horse’s exceptional touch sensitivity, natural reliance on body language, and purity of learning form the tripod of support for brain-to-brain communication that is so critical in extreme performance.

One of the reasons for budding scientific fascination with neural horse-and-human communication is the horse’s status as a prey animal. Their brains and bodies evolved to survive completely different pressures than our human physiologies. For example, horse eyes are set on either side of their head for a panoramic view of the world, and their horizontal pupils allow clear sight along the horizon but fuzzy vision above and below. Their eyes rotate to maintain clarity along the horizon when their heads lie sideways to reach grass in odd locations. Equine brains are also hardwired to stream commands directly from the perception of environmental danger to the motor cortex where instant evasion is carried out. All of these features evolved to allow the horse to survive predators.

Conversely, human brains evolved in part for the purpose of predation – hunting, chasing, planning… yes, even killing – with front-facing eyes, superb depth perception, and a prefrontal cortex for strategy and reason. Like it or not, we are the horse’s evolutionary enemy, yet they behave toward us as if inclined to become a friend.

The fact that horses and humans can communicate neurally without the external mediation of language or equipment is critical to our ability to initiate the cellular dance between brains. Saddles and bridles are used for comfort and safety, but bareback and bridleless competitions prove they aren’t necessary for highly trained brain-to-brain communication. Scientific efforts to communicate with predators such as dogs and apes have often been hobbled by the use of artificial media including human speech, sign language or symbolic lexigram. By contrast, horses allow us to apply a medium of communication that is completely natural to their lives in the wild and in captivity.

The horse’s prey brain is designed to notice and evade predators. How ironic, and how riveting, then, that this prey brain is the only one today that shares neural communication with a predator brain. It offers humanity a rare view into a prey animal’s world, almost as if we were wolves riding elk or coyotes mind-melding with cottontail bunnies.

Highly trained horses and riders send and receive neural signals using subtle body language. For example, a rider can apply invisible pressure with her left inner calf muscle to move the horse laterally to the right. That pressure is felt on the horse’s side, in his skin and muscle, via proprioceptive receptor cells that detect body position and movement. Then the signal is transduced from mechanical pressure to electrochemical impulse, and conducted up peripheral nerves to the horse’s spinal cord. Finally, it reaches the somatosensory cortex, the region of the brain responsible for interpreting sensory information.

Riders can sometimes guess that an invisible object exists by detecting subtle equine reactions

This interpretation is dependent on the horse’s knowledge that a particular body signal – for example, inward pressure from a rider’s left calf – is associated with a specific equine behaviour. Horse trainers spend years teaching their mounts these associations. In the present example, the horse has learned that this particular amount of pressure, at this speed and location, under these circumstances, means ‘move sideways to the right’. If the horse is properly trained, his motor cortex causes exactly that movement to occur.

By means of our human motion and position sensors, the rider’s brain now senses that the horse has changed his path rightward. Depending on the manoeuvre our rider plans to complete, she will then execute invisible cues to extend or collect the horse’s stride as he approaches a jump that is now centred in his vision, plant his right hind leg and spin in a tight fast circle, push hard off his hindquarters to chase a cow, or any number of other movements. These cues are combined to form that mutual neural dance, occurring in real time, and dependent on natural body language alone.

The example of a horse moving a few steps rightward off the rider’s left leg is extremely simplistic. When you imagine a horse and rider clearing a puissance wall of 7.5 feet (2.4 metres), think of the countless receptor cells transmitting bodily cues between both brains during approach, flight and exit. That is mutual brain-to-brain communication. Horse and human converse via body language to such an extreme degree that they are able to accomplish amazing acts of understanding and athleticism. Each of their minds has extended into the other’s, sending and receiving signals as if one united brain were controlling both bodies.

Franke Sloothaak on Optiebeurs Golo, a world-record puissance jump at Chaudfontaine in Belgium, 1991. This horse-and-human team displays the gentle encouragement that brain-to-brain communication requires. The horse is in perfect condition and health. The rider offers soft, light hands, and rides in perfect balance with the horse. He carries no whip, never uses his spurs, and employs the gentlest type of bit – whose full acceptance is evidenced by the horse’s foamy mouth and flexible neck. The horse is calm but attentive before and after the leap, showing complete willingness to approach the wall without a whiff of coercion. The first thing the rider does upon landing is pat his equine teammate. He strokes or pats the horse another eight times in the next 30 seconds, a splendid example of true horsemanship.

Analysis of brain-to-brain communication between horses and humans elicits several new ideas worthy of scientific notice. Because our minds interact so well using neural networks, horses and humans might learn to borrow neural signals from the party whose brain offers the highest function. For example, horses have a 340-degree range of view when holding their heads still, compared with a paltry 90-degree range in humans. Therefore, horses can see many objects that are invisible to their riders. Yet riders can sometimes guess that an invisible object exists by detecting subtle equine reactions.

Specifically, neural signals from the horse’s eyes carry the shape of an object to his brain. Those signals are transferred to the rider’s brain by a well-established route: equine receptor cells in the retina lead to equine detector cells in the visual cortex, which elicits an equine motor reaction that is then sensed by the rider’s human body. From there, the horse’s neural signals are transmitted up the rider’s spinal cord to the rider’s brain, and a perceptual communication loop is born. The rider’s brain can now respond neurally to something it is incapable of seeing, by borrowing the horse’s superior range of vision.

These brain-to-brain transfers are mutual, so the learning equine brain should also be able to borrow the rider’s vision, with its superior depth perception and focal acuity. This kind of neural interaction results in a horse-and-human team that can sense far more together than either party can detect alone. In effect, they share effort by assigning labour to the party whose skills are superior at a given task.

There is another type of skillset that requires a particularly nuanced cellular dance: sharing attention and focus. Equine vigilance allowed horses to survive 56 million years of evolution – they had to notice slight movements in tall grasses or risk becoming some predator’s dinner. Consequently, today it’s difficult to slip even a tiny change past a horse, especially a young or inexperienced animal who has not yet been taught to ignore certain sights, sounds and smells.

By contrast, humans are much better at concentration than vigilance. The predator brain does not need to notice and react instantly to every stimulus in the environment. In fact, it would be hampered by prey vigilance. While reading this essay, your brain sorts away the sound of traffic past your window, the touch of clothing against your skin, the sight of the masthead that says ‘Aeon’ at the top of this page. Ignoring these distractions allows you to focus on the content of this essay.

Horses and humans frequently share their respective attentional capacities during a performance. A puissance horse galloping toward an enormous wall cannot waste vigilance by noticing the faces of each person in the audience. Likewise, the rider cannot afford to miss a loose dog that runs into the arena outside her narrow range of vision and focus. Each party helps the other through their primary strengths.

Such sharing becomes automatic with practice. With innumerable neural contacts over time, the human brain learns to heed signals sent by the equine brain that say, in effect: ‘Hey, what’s that over there?’ Likewise, the equine brain learns to sense human neural signals that counter: ‘Let’s focus on this gigantic wall right here.’ Each party sends these messages by body language and receives them by body awareness through two spinal cords, then interprets them inside two brains, millisecond by millisecond.

The rider’s physical cues are transmitted by neural activation from the horse’s surface receptors to the horse’s brain

Finally, it is conceivable that horse and rider can learn to share features of executive function – the human brain’s ability to set goals, plan steps to achieve them, assess alternatives, make decisions and evaluate outcomes. Executive function occurs in the prefrontal cortex, an area that does not exist in the equine brain. Horses are excellent at learning, remembering and communicating – but they do not assess, decide, evaluate or judge as humans do.

Shying is a prominent equine behaviour that might be mediated by human executive function in well-trained mounts. When a horse of average size shies away from an unexpected stimulus, riders are sitting on top of 1,200 pounds of muscle that suddenly leaps sideways off all four feet and lands five yards away. It’s a frightening experience, and often results in falls that lead to injury or even death. The horse’s brain causes this reaction automatically by direct connection between his sensory and motor cortices.

Though this possibility must still be studied by rigorous science, brain-to-brain communication suggests that horses might learn to borrow small glimmers of executive function through neural interaction with the human’s prefrontal cortex. Suppose that a horse shies from an umbrella that suddenly opens. By breathing steadily, relaxing her muscles, and flexing her body in rhythm with the horse’s gait, the rider calms the animal using body language. Her physical cues are transmitted by neural activation from his surface receptors to his brain. He responds with body language in which his muscles relax, his head lowers, and his frightened eyes return to their normal size. The rider feels these changes with her body, which transmits the horse’s neural signals to the rider’s brain.

From this point, it’s only a very short step – but an important one – to the transmission and reception of neural signals between the rider’s prefrontal cortex (which evaluates the unexpected umbrella) and the horse’s brain (which instigates the leap away from that umbrella). In practice, to reduce shying, horse trainers teach their young charges to slow their reactions and seek human guidance.

Brain-to-brain communication between horses and riders is an intricate neural dance. These two species, one prey and one predator, are living temporarily in each other’s brains, sharing neural information back and forth in real time without linguistic or mechanical mediation. It is a partnership like no other. Together, a horse-and-human team experiences a richer perceptual and attentional understanding of the world than either member can achieve alone. And, ironically, this extended interspecies mind operates well not because the two brains are similar to each other, but because they are so different.

Janet Jones applies brain research to training horses and riders. She has a PhD from the University of California, Los Angeles, and for 23 years taught the neuroscience of perception, language, memory, and thought. She trained horses at a large stable early in her career, and later ran a successful horse-training business of her own. Her most recent book, Horse Brain, Human Brain (2020), is currently being translated into seven languages.

Edited by Pam Weintraub

A theory of my own mind (AEON)

Knowing the content of one’s own mind might seem straightforward but in fact it’s much more like mindreading other people

Tokyo, 1996. Photo by Harry Gruyaert/Magnum

Stephen M Fleming is professor of cognitive neuroscience at University College London, where he leads the Metacognition Group. He is author of Know Thyself: The Science of Self-awareness (2021). Edited by Pam Weintraub

23 September 2021

In 1978, David Premack and Guy Woodruff published a paper that would go on to become famous in the world of academic psychology. Its title posed a simple question: does the chimpanzee have a theory of mind?

In coining the term ‘theory of mind’, Premack and Woodruff were referring to the ability to keep track of what someone else thinks, feels or knows, even if this is not immediately obvious from their behaviour. We use theory of mind when checking whether our colleagues have noticed us zoning out on a Zoom call – did they just see that? A defining feature of theory of mind is that it entails second-order representations, which might or might not be true. I might think that someone else thinks that I was not paying attention but, actually, they might not be thinking that at all. And the success or failure of theory of mind often turns on an ability to appropriately represent another person’s outlook on a situation. For instance, I can text my wife and say: ‘I’m on my way,’ and she will know that by this I mean that I’m on my way to collect our son from nursery, not on my way home, to the zoo, or to Mars. Sometimes this can be difficult to do, as captured by a New Yorker cartoon caption of a couple at loggerheads: ‘Of course I care about how you imagined I thought you perceived I wanted you to feel.’

Premack and Woodruff’s article sparked a deluge of innovative research into the origins of theory of mind. We now know that a fluency in reading minds is not something humans are born with, nor is it something guaranteed to emerge in development. In one classic experiment, children were told stories such as the following:

Maxi has put his chocolate in the cupboard. While Maxi is away, his mother moves the chocolate from the cupboard to the drawer. When Maxi comes back, where will he look for the chocolate?

Until the age of four, children often fail this test, saying that Maxi will look for the chocolate where it actually is (the drawer), rather than where he thinks it is (in the cupboard). They are using their knowledge of the reality to answer the question, rather than what they know about where Maxi had put the chocolate before he left. Autistic children also tend to give the wrong answer, suggesting problems with tracking the mental states of others. This test is known as a ‘false belief’ test – passing it requires one to realise that Maxi has a different (and false) belief about the world.

Many researchers now believe that the answer to Premack and Woodruff’s question is, in part, ‘no’ – suggesting that fully fledged theory of mind might be unique to humans. If chimpanzees are given an ape equivalent of the Maxi test, they don’t use the fact that another chimpanzee has a false belief about the location of the food to sneak in and grab it. Chimpanzees can track knowledge states – for instance, being aware of what others see or do not see, and knowing that, when someone is blindfolded, they won’t be able to catch them stealing food. There is also evidence that they track the difference between true and false beliefs in the pattern of their eye movements, similar to findings in human infants. Dogs also have similarly sophisticated perspective-taking abilities, preferring to choose toys that are in their owner’s line of sight when asked to fetch. But so far, at least, only adult humans have been found to act on an understanding that other minds can hold different beliefs about the world to their own.

Research on theory of mind has rapidly become a cornerstone of modern psychology. But there is an underappreciated aspect of Premack and Woodruff’s paper that is only now causing ripples in the pond of psychological science. Theory of mind as it was originally defined identified a capacity to impute mental states not only to others but also to ourselves. The implication is that thinking about others is just one manifestation of a rich – and perhaps much broader – capacity to build what philosophers call metarepresentations, or representations of representations. When I wonder whether you know that it’s raining, and that our plans need to change, I am metarepresenting the state of your knowledge about the weather.

Intriguingly, metarepresentations are – at least in theory – symmetric with respect to self and other: I can think about your mind, and I can think about my own mind too. The field of metacognition research, which is what my lab at University College London works on, is interested in the latter – people’s judgments about their own cognitive processes. The beguiling question, then – and one we don’t yet have an answer to – is whether these two types of ‘meta’ are related. A potential symmetry between self-knowledge and other-knowledge – and the idea that humans, in some sense, have learned to turn theory of mind on themselves – remains largely an elegant hypothesis. But an answer to this question has profound consequences. If self-awareness is ‘just’ theory of mind directed at ourselves, perhaps it is less special than we like to believe. And if we learn about ourselves in the same way as we learn about others, perhaps we can also learn to know ourselves better.

A common view is that self-knowledge is special, and immune to error, because it is gained through introspection – literally, ‘looking within’. While we might be mistaken about things we perceive in the outside world (such as thinking a bird is a plane), it seems odd to say that we are wrong about our own minds. If I think that I’m feeling sad or anxious, then there is a sense in which I am feeling sad or anxious. We have untrammelled access to our own minds, so the argument goes, and this immediacy of introspection means that we are rarely wrong about ourselves.

This is known as the ‘privileged access’ view of self-knowledge, and has been dominant in philosophy in various guises for much of the 20th century. René Descartes relied on self-reflection in this way to reach his conclusion ‘I think, therefore I am,’ noting along the way that: ‘I know clearly that there is nothing that can be perceived by me more easily or more clearly than my own mind.’

An alternative view suggests that we infer what we think or believe from a variety of cues – just as we infer what others think or feel from observing their behaviour. This suggests that self-knowledge is not as immediate as it seems. For instance, I might infer that I am anxious about an upcoming presentation because my heart is racing and my breathing is heavier. But I might be wrong about this – perhaps I am just feeling excited. This kind of psychological reframing is often used by sports coaches to help athletes maintain composure under pressure.

The philosopher most often associated with the inferential view is Gilbert Ryle, who proposed in The Concept of Mind (1949) that we gain self-knowledge by applying the tools we use to understand other minds to ourselves: ‘The sorts of things that I can find out about myself are the same as the sorts of things that I can find out about other people, and the methods of finding them out are much the same.’ Ryle’s idea is neatly summarised by another New Yorker cartoon in which a husband says to his wife: ‘How should I know what I’m thinking? I’m not a mind reader.’

Many philosophers since Ryle have considered the strong inferential view as somewhat crazy, and written it off before it could even get going. The philosopher Quassim Cassam, author of Self-knowledge for Humans (2014), describes the situation:

Philosophers who defend inferentialism – Ryle is usually mentioned in this context – are then berated for defending a patently absurd view. The assumption that intentional self-knowledge is normally immediate … is rarely defended; it’s just seen as obviously correct.

But if we take a longer view of history, the idea that we have some sort of special, direct access to our minds is the exception, rather than the rule. For the ancient Greeks, self-knowledge was not all-encompassing, but a work in progress, and something to be striven toward, as captured by the exhortation to ‘know thyself’ carved on the Temple of Delphi. The implication is that most of us don’t know ourselves very well. This view persisted into medieval religious traditions: the Italian priest and philosopher Saint Thomas Aquinas suggested that, while God knows himself by default, we need to put in time and effort to know our own minds. And a similar notion of striving toward self-awareness is found in Eastern traditions, with the founder of Chinese Taoism, Lao Tzu, endorsing a similar goal: ‘To know that one does not know is best; not to know but to believe that one knows is a disease.’

Self-awareness is something that can be cultivated

Other aspects of the mind – most famously, perception – also appear to operate on the principles of an (often unconscious) inference. The idea is that the brain isn’t directly in touch with the outside world (it’s locked up in a dark skull, after all) – and instead has to ‘infer’ what is really out there by constructing and updating an internal model of the environment, based on noisy sensory data. For instance, you might know that your friend owns a Labrador, and so you expect to see a dog when you walk into her house, but don’t know exactly where in your visual field the dog will appear. This higher-level expectation – the spatially invariant concept of ‘dog’ – provides the relevant context for lower levels of the visual system to easily interpret dog-shaped blurs that rush toward you as you open the door.

Adelson’s checkerboard. Courtesy Wikipedia

Elegant evidence for this perception-as-inference view comes from a range of striking visual illusions. In one called Adelson’s checkerboard, two patches with the same objective luminance are perceived as lighter and darker because the brain assumes that, to reflect the same amount of light, the one in shadow must have started out brighter. Another powerful illusion is the ‘light from above’ effect – we have an automatic tendency to assume that natural light falls from above, whereas uplighting – such as when light from a fire illuminates the side of a cliff – is less common. This can lead the brain to interpret the same image as either bumps or dips in a surface, depending on whether the shadows are consistent with light falling from above. Other classic experiments show that information from one sensory modality, such as sight, can act as a constraint on how we perceive another, such as sound – an illusion used to great effect in ventriloquism. The real skill of ventriloquists is being able to talk without moving the mouth. Once this is achieved, the brains of the audience do the rest, pulling the sound to its next most likely source, the puppet.

These striking illusions are simply clever ways of exposing the workings of a system finely tuned for perceptual inference. And a powerful idea is that self-knowledge relies on similar principles – whereas perceiving the outside world relies on building a model of what is out there, we are also continuously building and updating a similar model of ourselves – our skills, abilities and characteristics. And just as we can sometimes be mistaken about what we perceive, sometimes the model of ourselves can also be wrong.

Let’s see how this might work in practice. If I need to remember something complicated, such as a shopping list, I might judge I will fail unless I write it down somewhere. This is a metacognitive judgment about how good my memory is. And this model can be updated – as I grow older, I might think to myself that my recall is not as good as it used to be (perhaps after experiencing myself forgetting things at the supermarket), and so I lean more heavily on list-writing. In extreme cases, this self-model can become completely decoupled from reality: in functional memory disorders, patients believe their memory is poor (and might worry they have dementia) when it is actually perfectly fine when assessed with objective tests.

We now know from laboratory research that metacognition, just like perception, is also subject to powerful illusions and distortions – lending credence to the inferential view. A standard measure here is whether people’s confidence tracks their performance on simple tests of perception, memory and decision-making. Even in otherwise healthy people, judgments of confidence are subject to systematic illusions – we might feel more confident about our decisions when we act more quickly, even if faster decisions are not associated with greater accuracy. In our research, we have also found surprisingly large and consistent differences between individuals on these measures – one person might have limited insight into how well they are doing from one moment to the next, while another might have good awareness of whether are likely to be right or wrong.

This metacognitive prowess is independent of general cognitive ability, and correlated with differences in the structure and function of the prefrontal and parietal cortex. In turn, people with disease or damage to these brain regions can suffer from what neurologists refer to as anosognosia – literally, the absence of knowing. For instance, in Alzheimer’s disease, patients can suffer a cruel double hit – the disease attacks not only brain regions supporting memory, but also those involved in metacognition, leaving people unable to understand what they have lost.

This all suggests – more in line with Socrates than Descartes – that self-awareness is something that can be cultivated, that it is not a given, and that it can fail in myriad interesting ways. And it also provides newfound impetus to seek to understand the computations that might support self-awareness. This is where Premack and Woodruff’s more expansive notion of theory of mind might be long overdue another look.

Saying that self-awareness depends on similar machinery to theory of mind is all well and good, but it begs the question – what is this machinery? What do we mean by a ‘model’ of a mind, exactly?

Some intriguing insights come from an unlikely quarter – spatial navigation. In classic studies, the psychologist Edward Tolman realised that the rats running in mazes were building a ‘map’ of the maze, rather than just learning which turns to make when. If the shortest route from a starting point towards the cheese is suddenly blocked, then rats readily take the next quickest route – without having to try all the remaining alternatives. This suggests that they have not just rote-learned the quickest path through the maze, but instead know something about its overall layout.

A few decades later, the neuroscientist John O’Keefe found that cells in the rodent hippocampus encoded this internal knowledge about physical space. Cells that fired in different locations became known as ‘place’ cells. Each place cell would have a preference for a specific position in the maze but, when combined together, could provide an internal ‘map’ or model of the maze as a whole. And then, in the early 2000s, the neuroscientists May-Britt Moser, Edvard Moser and their colleagues in Norway found an additional type of cell – ‘grid’ cells, which fire in multiple locations, in a way that tiles the environment with a hexagonal grid. The idea is that grid cells support a metric, or coordinate system, for space – their firing patterns tell the animal how far it has moved in different directions, a bit like an in-built GPS system.

There is now tantalising evidence that similar types of brain cell also encode abstract conceptual spaces. For instance, if I am thinking about buying a new car, then I might think about how environmentally friendly the car is, and how much it costs. These two properties map out a two-dimensional ‘space’ on which I can place different cars – for instance, a cheap diesel car will occupy one part of the space, and an expensive electric car another part of the space. The idea is that, when I am comparing these different options, my brain is relying on the same kind of systems that I use to navigate through physical space. In one experiment by Timothy Behrens and his team at the University of Oxford, people were asked to imagine morphing images of birds that could have different neck and leg lengths – forming a two-dimensional bird space. A grid-like signature was found in the fMRI data when people were thinking about the birds, even though they never saw them presented in 2D.

Clear overlap between brain activations involved in metacognition and mindreading was observed

So far, these lines of work – on abstract conceptual models of the world, and on how we think about other minds – have remained relatively disconnected, but they are coming together in fascinating ways. For instance, grid-like codes are also found for conceptual maps of the social world – whether other individuals are more or less competent or popular – suggesting that our thoughts about others seem to be derived from an internal model similar to those used to navigate physical space. And one of the brain regions involved in maintaining these models of other minds – the medial prefrontal cortex (PFC) – is also implicated in metacognition about our own beliefs and decisions. For instance, research in my group has discovered that medial prefrontal regions not only track confidence in individual decisions, but also ‘global’ metacognitive estimates of our abilities over longer timescales – exactly the kind of self-estimates that were distorted in the patients with functional memory problems.

Recently, the psychologist Anthony G Vaccaro and I surveyed the accumulating literature on theory of mind and metacognition, and created a brain map that aggregated the patterns of activations reported across multiple papers. Clear overlap between brain activations involved in metacognition and mindreading was observed in the medial PFC. This is what we would expect if there was a common system building models not only about other people, but also of ourselves – and perhaps about ourselves in relation to other people. Tantalisingly, this very same region has been shown to carry grid-like signatures of abstract, conceptual spaces.

At the same time, computational models are being built that can mimic features of both theory of mind and metacognition. These models suggest that a key part of the solution is the learning of second-order parameters – those that encode information about how our minds are working, for instance whether our percepts or memories tend to be more or less accurate. Sometimes, this system can become confused. In work led by the neuroscientist Marco Wittmann at the University of Oxford, people were asked to play a game involving tracking the colour or duration of simple stimuli. They were then given feedback about both their own performance and that of other people. Strikingly, people tended to ‘merge’ their feedback with those of others – if others were performing better, they tended to think they themselves were performing a bit better too, and vice-versa. This intertwining of our models of self-performance and other-performance was associated with differences in activity in the dorsomedial PFC. Disrupting activity in this area using transcranial magnetic stimulation (TMS) led to more self-other mergence – suggesting that one function of this brain region is not only to create models of ourselves and others, but also to keep these models apart.

Another implication of a symmetry between metacognition and mindreading is that both abilities should emerge around the same time in childhood. By the time that children become adept at solving false-belief tasks – around the age of four – they are also more likely to engage in self-doubt, and recognise when they themselves were wrong about something. In one study, children were first presented with ‘trick’ objects: a rock that turned out to be a sponge, or a box of Smarties that actually contained not sweets but pencils. When asked what they first thought the object was, three-year-olds said that they knew all along that the rock was a sponge and that the Smarties box was full of pencils. But by the age of five, most children recognised that their first impression of the object was false – they could recognise they had been in error.

Indeed, when Simon Baron-Cohen, Alan Leslie and Uta Frith outlined their influential theory of autism in the 1980s, they proposed that theory of mind was only ‘one of the manifestations of a basic metarepresentational capacity’. The implication is that there should also be noticeable differences in metacognition that are linked to changes in theory of mind. In line with this idea, several recent studies have shown that autistic individuals also show differences in metacognition. And in a recent study of more than 450 people, Elisa van der Plas, a PhD student in my group, has shown that theory of mind ability (measured by people’s ability to track the feelings of characters in simple animations) and metacognition (measured by the degree to which their confidence tracks their task performance) are significantly correlated with each other. People who were better at theory of mind also formed their confidence differently – they were more sensitive to subtle cues, such as their response times, that indicated whether they had made a good or bad decision.

Recognising a symmetry between self-awareness and theory of mind might even help us understand why human self-awareness emerged in the first place. The need to coordinate and collaborate with others in large social groups is likely to have prized the abilities for metacognition and mindreading. The neuroscientist Suzana Herculano-Houzel has proposed that primates have unusually efficient ways of cramming neurons into a given brain volume – meaning there is simply more processing power devoted to so-called higher-order functions – those that, like theory of mind, go above and beyond the maintenance of homeostasis, perception and action. This idea fits with what we know about the areas of the brain involved in theory of mind, which tend to be the most distant in terms of their connections to primary sensory and motor areas.

A symmetry between self-awareness and other-awareness also offers a subversive take on what it means for other agents such as animals and robots to be self-aware. In the film Her (2013), Joaquin Phoenix’s character Theodore falls in love with his virtual assistant, Samantha, who is so human-like that he is convinced she is conscious. If the inferential view of self-awareness is correct, there is a sense in which Theodore’s belief that Samantha is aware is sufficient to make her aware, in his eyes at least. This is not quite true, of course, because the ultimate test is if she is able to also recursively model Theodore’s mind, and create a similar model of herself. But being convincing enough to share an intimate connection with another conscious agent (as Theodore does with Samantha), replete with mindreading and reciprocal modelling, might be possible only if both agents have similar recursive capabilities firmly in place. In other words, attributing awareness to ourselves and to others might be what makes them, and us, conscious.

A simple route for improving self-awareness is to take a third-person perspective on ourselves

Finally, a symmetry between self-awareness and other-awareness also suggests novel routes towards boosting our own self-awareness. In a clever experiment conducted by the psychologists and metacognition experts Rakefet Ackerman and Asher Koriat in Israel, students were asked to judge both how well they had learned a topic, and how well other students had learned the same material, by watching a video of them studying. When judging themselves, they fell into a trap – they believed that spending less time studying was a signal of being confident in knowing the material. But when judging others, this relationship was reversed: they (correctly) judged that spending longer on a topic would lead to better learning. These results suggest that a simple route for improving self-awareness is to take a third-person perspective on ourselves. In a similar way, literary novels (and soap operas) encourage us to think about the minds of others, and in turn might shed light on our own lives.

There is still much to learn about the relationship between theory of mind and metacognition. Most current research on metacognition focuses on the ability to think about our experiences and mental states – such as being confident in what we see or hear. But this aspect of metacognition might be distinct from how we come to know our own, or others’, character and preferences – aspects that are often the focus of research on theory of mind. New and creative experiments will be needed to cross this divide. But it seems safe to say that Descartes’s classical notion of introspection is increasingly at odds with what we know of how the brain works. Instead, our knowledge of ourselves is (meta)knowledge like any other – hard-won, and always subject to revision. Realising this is perhaps particularly useful in an online world deluged with information and opinion, when it’s often hard to gain a check and balance on what we think and believe. In such situations, the benefits of accurate metacognition are myriad – helping us recognise our faults and collaborate effectively with others. As the poet Robert Burns tells us:

O wad some Power the giftie gie us
To see oursels as ithers see us!
It wad frae mony a blunder free us…

(Oh, would some Power give us the gift
To see ourselves as others see us!
It would from many a blunder free us )

Greater than the sum of our parts: The evolution of collective intelligence (EurekaAlert!)

News Release 15-Jun-2021

University of Cambridge

Research News

The period preceding the emergence of behaviourally modern humans was characterised by dramatic climatic and environmental variability – it is these pressures, occurring over hundreds of thousands of years that shaped human evolution.

New research published today in the Cambridge Archaeological Journal proposes a new theory of human cognitive evolution entitled ‘Complementary Cognition’ which suggests that in adapting to dramatic environmental and climactic variabilities our ancestors evolved to specialise in different, but complementary, ways of thinking.

Lead author Dr Helen Taylor, Research Associate at the University of Strathclyde and Affiliated Scholar at the McDonald Institute for Archaeological Research, University of Cambridge, explained: “This system of complementary cognition functions in a way that is similar to evolution at the genetic level but instead of underlying physical adaptation, may underlay our species’ immense ability to create behavioural, cultural and technological adaptations. It provides insights into the evolution of uniquely human adaptations like language suggesting that this evolved in concert with specialisation in human cognition.”

The theory of complementary cognition proposes that our species cooperatively adapt and evolve culturally through a system of collective cognitive search alongside genetic search which enables phenotypic adaptation (Darwin’s theory of evolution through natural selection can be interpreted as a ‘search’ process) and cognitive search which enables behavioural adaptation.

Dr Taylor continued, “Each of these search systems is essentially a way of adapting using a mixture of building on and exploiting past solutions and exploring to update them; as a consequence, we see evolution in those solutions over time. This is the first study to explore the notion that individual members of our species are neurocognitively specialised in complementary cognitive search strategies.”

Complementary cognition could lie at the core of explaining the exceptional level of cultural adaptation in our species and provides an explanatory framework for the emergence of language. Language can be viewed as evolving both as a means of facilitating cooperative search and as an inheritance mechanism for sharing the more complex results of complementary cognitive search. Language is viewed as an integral part of the system of complementary cognition.

The theory of complementary cognition brings together observations from disparate disciplines, showing that they can be viewed as various faces of the same underlying phenomenon.

Dr Taylor continued: “For example, a form of cognition currently viewed as a disorder, dyslexia, is shown to be a neurocognitive specialisation whose nature in turn predicts that our species evolved in a highly variable environment. This concurs with the conclusions of many other disciplines including palaeoarchaeological evidence confirming that the crucible of our species’ evolution was highly variable.”

Nick Posford, CEO, British Dyslexia Association said, “As the leading charity for dyslexia, we welcome Dr Helen Taylor’s ground-breaking research on the evolution of complementary cognition. Whilst our current education and work environments are often not designed to make the most of dyslexia-associated thinking, we hope this research provides a starting point for further exploration of the economic, cultural and social benefits the whole of society can gain from the unique abilities of people with dyslexia.”

At the same time, this may also provide insights into understanding the kind of cumulative cultural evolution seen in our species. Specialisation in complementary search strategies and cooperatively adapting would have vastly increased the ability of human groups to produce adaptive knowledge, enabling us to continually adapt to highly variable conditions. But in periods of greater stability and abundance when adaptive knowledge did not become obsolete at such a rate, it would have instead accumulated, and as such Complementary Cognition may also be a key factor in explaining cumulative cultural evolution.

Complementary cognition has enabled us to adapt to different environments, and may be at the heart of our species’ success, enabling us to adapt much faster and more effectively than any other highly complex organism. However, this may also be our species’ greatest vulnerability.

Dr Taylor concluded: “The impact of human activity on the environment is the most pressing and stark example of this. The challenge of collaborating and cooperatively adapting at scale creates many difficulties and we may have unwittingly put in place a number of cultural systems and practices, particularly in education, which are undermining our ability to adapt. These self-imposed limitations disrupt our complementary cognitive search capability and may restrict our capacity to find and act upon innovative and creative solutions.”

“Complementary cognition should be seen as a starting point in exploring a rich area of human evolution and as a valuable tool in helping to create an adaptive and sustainable society. Our species may owe our spectacular technological and cultural achievements to neurocognitive specialisation and cooperative cognitive search, but our adaptive success so far may belie the importance of attaining an equilibrium of approaches. If this system becomes maladjusted, it can quickly lead to equally spectacular failures to adapt – and to survive, it is critical that this system be explored and understood further.”

Newly Identified Social Trait Could Explain Why Some People Are Particularly Tribal (Science Alert)


Having strong, biased opinions may say more about your own individual way of behaving in group situations than it does about your level of identification with the values or ideals of any particular group, new research suggests.

This behavioural trait – which researchers call ‘groupiness’ – could mean that individuals will consistently demonstrate ‘groupy’ behaviour across different kinds of social situations, with their thoughts and actions influenced by simply being in a group setting, whereas ‘non-groupy’ people aren’t affected in the same way.

“It’s not the political group that matters, it’s whether an individual just generally seems to like being in a group,” says economist and lead researcher Rachel Kranton from Duke University.

“Some people are ‘groupy’ – they join a political party, for example. And if you put those people in any arbitrary setting, they’ll act in a more biased way than somebody who has the same political opinions, but doesn’t join a political party.”

In an experiment with 141 people, participants were surveyed on their political affiliations, which identified them as self-declared Democrats or Republicans, or as subjects who leaned more Democrat or Republican in terms of their political beliefs (called Independents, for the purposes of the study).

They also took part in a survey that asked them a number of seemingly neutral questions about their aesthetic preferences in relation to a series of artworks, choosing favourites among similar-looking paintings or different lines of poetry.

After these exercises, the participants took part in tests where they were placed in groups –  either based around political affiliations (Democrats or Republicans), or more neutral categorisations reflecting their answers about which artworks they preferred. In a third test, the groups were random.

While in these groups, the participants ran through an income allocation exercise, in which they could choose to allocate various amounts of money to themselves, to fellow group members, or to members of the other group.

The researchers expected to find bias in terms of these income allocations based around political mindsets, with people giving themselves more money, along with people who shared their political persuasion. But they also found something else.

“We compare Democrats with D-Independents and find that party members do show more in-group bias; on average, their choices led to higher income for in-group participants,” the authors explain in their study.

“Yet, these party-member participants also show more in-group bias in a second nonpolitical setting. Hence, identification with the group is not necessarily the driver of in-group bias, and the analysis reveals a set of subjects who consistently shows in-group bias, while another does not.”

According to the data, there exists a subpopulation of ‘groupy’ people and a subpopulation of ‘non-groupy’ people – actions of the former type are influenced by being in group settings, in which case they are more likely to demonstrate bias against others outside their group.

By contrast, the latter type, non-groupy individuals, don’t display this kind of tendency, and are more likely to act the same way, regardless of whether or not they’re in a group setting. These non-groupy individuals also seem to make faster decisions than groupy people, the team found.

“We don’t know if non-groupy people are faster generally,” Kranton says.

“It could be they’re making decisions faster because they’re not paying attention to whether somebody is in their group or not each time they have to make a decision.”

Of course, as illuminating as the discovery of this apparent trait is, we need a lot more research to be sure we’ve identified something discrete here.

After all, this is a pretty small study all told, and the researchers acknowledge the need to conduct the same kind of experiments with participants in several settings, to support the foundations of their groupiness concept, and to try to identify what it is that predisposes people to this kind of groupy or non-groupy mindset.

“There’s some feature of a person that causes them to be sensitive to these group divisions and use them in their behaviour across at least two very different contexts,” one of the team, Duke University psychologist Scott Huettel, explains.

“We didn’t test every possible way in which people differentiate themselves; we can’t show you that all group-minded identities behave this way. But this is a compelling first step.”

The findings are reported in PNAS.