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Now you see it, now you don't: what optical illusions tell us about our brains

Illusions can offer insights into how the visual system processes images.

Maurits Escher: where do the staircases lead?

The human brain is a network of about 20 billion neurons – nerve cells – linked by several trillion connections. Not to mention glial cells, which scientists used to think were inactive scaffolding, but increasingly view as an essential part of how the brain works. Our brains give us movement, language, senses, memories, consciousness and personality. We know a lot more about the brain than we used to, but it still seems far too complicated for human understanding.

Fortunately, the brain contains many small networks of neurons that carry out some specific function: vision, hearing, movement. It makes sense to tackle these simple modules first. Moreover, we have good mathematical models of nerve cell behaviour. In 1952, Alan Hodgkin and Andrew Huxley wrote down the “Hodgkin-Huxley equations” for the transmission of a nerve impulse, which won them the 1963 Nobel Prize in Medicine. We also have effective techniques for understanding small networks’ components and how they are linked.

Many of these simple networks occur in the visual system. We used to think that the eye was like a camera, taking a “snapshot” of the outside world that was stored in the brain like a photo stuck in an album. It uses a lens to focus an image on to the retina at the back of the eye, which functions a bit like a roll of film – or, in today’s digital cameras, a charge-coupled device, storing an image pixel by pixel. But we now know that when the retina sends information to the brain’s visual cortex, the similarity to a camera ends.

Although we get a strong impression that what we are seeing is “out there” in front of us, what determines that perception resides inside our own heads. The brain decomposes images into simple pieces, works out what they are, “labels” them with that information, and reassembles them. When we see three sheep and two pigs in a field, we “know” which bits are sheep, which are pigs, and how many of each there are. If you try to program a computer to do that, you quickly realise how tricky the process is. Only very recently have computers been able to distinguish between faces, let alone sheep and pigs.

Probing the brain’s detailed activity is difficult. Rapid progress is being made, but it still takes a huge effort to get reliable information. But when science cannot observe something directly, it infers it, working indirectly. An effective way to infer how something functions is to see what it does when it goes wrong. It may be hard to understand a bridge while it stays up, but you can learn a lot about strength of materials when it collapses.

The visual system can “go wrong” in several interesting ways. Hallucinogenic drugs can change how neurons behave, producing dramatic images such as spinning spirals, which originate not in the eye, but in the brain. Some images even cause the brain to misinterpret what it’s seeing without outside help. We call them optical illusions.

One of the earliest was discovered in Renaissance Italy in the 16th century. Giambattista della Porta was the middle of three surviving sons of a wealthy merchant nobleman who became secretary to the Holy Roman emperor Charles V. The father was an intellectual, and Giambattista grew up in a house in Naples that hosted innumerable mathematicians, scientists, poets and musicians. He became an outstanding polymath, with publications on secret codes (including writing on the inside of eggshells), physiology, botany, agriculture, engineering, and much else. He wrote more than 20 plays.

Della Porta was particularly interested in the science of light. He made definitive improvements to the camera obscura, a device that projects an image of the outside world into a darkened room; he claimed to have invented the telescope before Galileo, and very likely did. His De refractione optices of 1593 contained the first report of a curious optical effect. He arranged two books so that one was visible to one eye only and the other to the other eye. Instead of seeing a combination of the two images, he perceived them alternately. He discovered that he could select either image at will by consciously switching his attention. This phenomenon is known today as binocular rivalry.

Two other distinct but related effects are impossible figures and visual illusions. In rivalry, each image appears unambiguous, but the eyes are shown conflicting images. In the other two phenomena, both eyes see the same image, but in one case it doesn’t make sense, and in the other it makes sense but is ambiguous.

Impossible figures at first sight seem to be entirely normal, but depict things that cannot exist – such as Roger Shepard’s 1990 drawing of an elephant in which everything above the knees makes sense, and everything below the knees makes sense, but the two regions do not fit together correctly. The Dutch artist Maurits Escher made frequent use of this kind of visual quirk.

In 1832, the Swiss crystallographer Louis Necker invented his “Necker cube” illusion, a skeletal cube that seems to switch its orientation repeatedly. An 1892 issue of the humorous German magazine Fliegende Blätter contains a picture with the caption “Which animals are most like each other?” and the answer “Rabbit and duck”. In a 1915 issue of the American magazine Puck, the cartoonist Ely William Hill published “My wife and my mother-in-law”, based on an 1888 German postcard. The image can be seen either as a young lady looking back over her shoulder, or as an elderly woman facing forwards. Several of Salvador Dalí’s paintings include illusions; especially Slave Market With the Apparition of the Invisible Bust of Voltaire, where a number of figures and everyday objects, carefully arranged, combine to give the impression of the French writer’s face.

Illusions offer insights into how the visual system processes images. The first few stages are fairly well understood. The top layer in the visual cortex detects edges of objects and the direction in which they are pointing. This information is passed to lower layers, which detect places where the direction suddenly changes, such as corners. Eventually some region in the cortex detects that you are looking at a human face and that it belongs to Aunt Matilda. Other parts of the brain are alerted, and you belatedly remember that tomorrow is her birthday and hurry off to buy a present.

These things don’t happen by magic. They have a very definite rationale, and that’s where the mathematics comes in. The top layer of the visual cortex contains innumerable tiny stacks of nerve cells. Each stack is like a pile of pancakes, and each pancake is a network of neurons that is sensitive to edges that point in one specific direction: one o’clock, two o’clock and so on.

For simplicity, call this network a cell; it does no harm to think of it as a single neuron. Roughly speaking, the cell at the top of the stack senses edges at the one o’clock position, the next one down corresponds to the two o’clock angle, and so on. If one cell receives a suitable input signal, it “fires”, telling all the other cells in its stack: “I’ve seen a boundary in the five o’clock direction.” However, another cell in the same stack might disagree, claiming the direction is at seven o’clock. How to resolve this conflict?

Neurons are linked by two kinds of connection, excitatory and inhibitory. If a neuron activates an excitatory connection, those at the other end of it are more likely to fire themselves. An inhibitory connection makes them less likely to fire. The cortex uses inhibitory connections to reach a definite decision. When a cell fires, it sends inhibitory signals to all of the other cells in its stack. These signals compete for attention. If the five o’clock signal is stronger than the seven o’clock one, for instance, the seven o’clock one gets shut down. The cells in effect “vote” on which direction they are detecting and the winner takes all.

Many neuroscientists think that something very similar is going on in visual illusions and rivalry. Think of the duck and rabbit with two possible interpretations. Hugh R Wilson, a neuroscientist at the Centre for Vision Research at York University, Toronto, proposed the simplest model, one stack with just two cells. Rodica Curtu, a mathematician at the University of Iowa, John Rinzel, a biomathematician then at the National Institutes of Health, and several other scientists have analysed this model in more detail. The basic idea is that one cell fires if the picture looks like a duck, the other if it resembles a rabbit. Because of the inhibitory connections, the winner should take all. Except that, in this illusion, it doesn’t quite work, because the two choices are equally plausible. That’s what makes it an illusion. So both cells want to fire. But they can’t, because of those inhibitory connections. Yet neither can they both remain quiescent, because the incoming signals encourage them to fire.

One possibility is that random signals coming from elsewhere in the brain might introduce a bias of perception, so that one cell still wins. However, the mathematical model predicts that, even without such bias, the signals in both cells should oscillate from active to inactive and back again, each becoming active when the other is not. It’s as if the network is dithering: the two cells take turns to fire and the network perceives the image as a duck, then as a rabbit, and keeps switching from one to the other. Which is what happens in reality.

Generalising from this observation, Wilson proposed a similar type of network that can model decision-making in the brain – which political party to support, for instance. But now the network consists of several stacks. Maybe one stack represents immigration policy, another unemployment, a third financial regulation, and so on. Each stack consists of cells that “recognise” a distinct policy feature. So the financial regulation stack has cells that recognise state regulation by law, self-regulation by the industry, or free-market economics.

The overall political stance of any given political party is a choice of one cell from each stack – one policy decision on each issue. Each prospective voter has his or her preferences, and these might not match those of any particular party. If these choices are used as inputs to the network, it will identify the party that most closely fits what the voter prefers. That decision can then be passed to other areas of the brain. Some voters may find themselves in a state akin to a visual illusion, vacillating between Labour and Liberal Democrat, or Conservative and Ukip.

This idea is speculative and it is not intended to be a literal description of how we decide whom to vote for. It is a schematic outline of something more complex, involving many regions of the brain. However, it provides a simple and flexible model for decision-making by a neural network, and in particular it shows that simple networks can do the job quite well. Martin Golubitsky of the Mathematical Biosciences Institute at Ohio State University and Casey O Diekman of the University of Michigan wondered whether Wilson’s networks could be used to model more complex examples of rivalry and illusions. Crucially, the resulting models allow specific predictions about experiments that have not yet been performed, making the whole idea scientifically testable.

The first success of this approach helped to explain an experiment that had already been carried out, with puzzling results. When the brain reassembles the separate bits of an image, it is said to “bind” these pieces. Rivalry provides evidence that binding occurs, by making it go wrong. In a rivalry experiment carried out in 2006 by S W Hong and S K Shevell, the subject’s left eye is shown a horizontal grid of grey and pink lines while the right eye sees a vertical grid of grey and green lines. Many subjects perceive an alternation between the images, just as della Porta did with his books. But some see two different images alternating: pink and green vertical lines, and pink and green horizontal lines – images shown to neither eye. This effect is called colour misbinding; it tells us that the reassembly process has matched colour to grid direction incorrectly. It is as if della Porta had ended up seeing another book altogether.

Golubitsky and Diekman studied the simplest Wilson network corresponding to this experiment. It has two stacks: one for colour, one for grid direction. Each stack has two cells. In the “colour” stack one cell detects pink and the other green; in the “orientation” stack one cell detects vertical and the other horizontal. As usual, there are inhibitory connections within each stack to ensure a winner-takes-all decision.

Following Wilson’s general scheme, they also added excitatory connections between cells in distinct stacks, representing the combinations of colour and direction that occur in the two “learned” images – those actually presented to the two eyes. Then they used recent mathematical techniques to list the patterns that arise in such a network. They found two types of oscillatory pattern. One corresponds to alternation between the two learned images. The other corresponds precisely to alternation between the two images seen in colour misbinding.

Colour misbinding is therefore a natural feature of the dynamics of Wilson networks. Although the network is “set up” to detect the two learned images, its structure produces an unexpected side effect: two images that were not learned. The rivalry experiment reveals hints of the brain’s hidden wiring. The same techniques apply to many other experiments, including some that have not yet been performed. They lead to very specific predictions, including more circumstances in which subjects will observe patterns that were not presented to either eye.

Similar models also apply to illusions. However, the excitatory connections cannot be determined by the images shown to the two eyes, because both eyes see the same image. One suggestion is that the connections may be determined by what your visual system already “knows” about real objects.

Take the celebrated moving illusion called “the spinning dancer”. Some observers see the solid silhouette of a dancer spinning anticlockwise, others clockwise. Sometimes, the direction of spin seems to switch suddenly.

We know that the top half of a spinning dancer can spin either clockwise or anticlockwise. Ditto for the bottom half. In principle, if the top half spins one way but the bottom half spins the other way, you would see the same silhouette, as if both were moving together. When people are shown “the spinning dancer”, no one sees the halves moving independently. If the top half spins clockwise, so does the bottom half.

Why do our brains do this? We can model that information using a series of stacks that correspond to different parts of the dancer’s body. The brain’s prior knowledge sets up a set of excitatory connections between all cells that sense clockwise motion, and another set of excitatory connections between all “anticlockwise” cells. We can also add inhibitory connections between the “clockwise” and the “anticlockwise” cells. These connections collectively tell the network that all parts of the object being perceived must spin in the same direction at any instant. Our brains don’t allow for a “half and half” interpretation.

When we analyse this network mathematically, it turns out that the cells switch repeatedly between a state in which all clockwise cells are firing but the anticlockwise ones are quiescent, and a state in which all anticlockwise cells are firing but the clockwise ones are quiescent. The upshot is that we perceive the whole figure of the dancer switching directions. Similar networks provide sensible models for many other illusions, including some in which there are three different inputs.

These models provide a common framework for both rivalry and illusion, and they unify many experiments, explain otherwise puzzling results and make new predictions that can be tested. They also tell us that in principle the brain can carry out some apparently complex tasks using simple networks. (What it does in practice is probably different in detail, but could well follow the same general lines.)

This could help make sense of a real brain, as new experiments improve our ability to observe its “wiring diagram”. It might not be as ambitious as trying to model the whole thing on a computer, but modesty can be a virtue. Since simple networks behave in strange and unexpected ways, what incomprehensible quirks might a complicated network have?

Perhaps Dalí, and Escher, and the spinning dancer can help us find out. 

Ian Stewart is Emeritus Professor of Mathematics and Digital Media Fellow at the University of Warwick

JOHN DEVOLLE/GETTY IMAGES
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Fitter, dumber, more productive

How the craze for Apple Watches, Fitbits and other wearable tech devices revives the old and discredited science of behaviourism.

When Tim Cook unveiled the latest operating system for the Apple Watch in June, he described the product in a remarkable way. This is no longer just a wrist-mounted gadget for checking your email and social media notifications; it is now “the ultimate device for a healthy life”.

With the watch’s fitness-tracking and heart rate-sensor features to the fore, Cook explained how its Activity and Workout apps have been retooled to provide greater “motivation”. A new Breathe app encourages the user to take time out during the day for deep breathing sessions. Oh yes, this watch has an app that notifies you when it’s time to breathe. The paradox is that if you have zero motivation and don’t know when to breathe in the first place, you probably won’t survive long enough to buy an Apple Watch.

The watch and its marketing are emblematic of how the tech trend is moving beyond mere fitness tracking into what might one call quality-of-life tracking and algorithmic hacking of the quality of consciousness. A couple of years ago I road-tested a brainwave-sensing headband, called the Muse, which promises to help you quiet your mind and achieve “focus” by concentrating on your breathing as it provides aural feedback over earphones, in the form of the sound of wind at a beach. I found it turned me, for a while, into a kind of placid zombie with no useful “focus” at all.

A newer product even aims to hack sleep – that productivity wasteland, which, according to the art historian and essayist Jonathan Crary’s book 24/7: Late Capitalism and the Ends of Sleep, is an affront to the foundations of capitalism. So buy an “intelligent sleep mask” called the Neuroon to analyse the quality of your sleep at night and help you perform more productively come morning. “Knowledge is power!” it promises. “Sleep analytics gathers your body’s sleep data and uses it to help you sleep smarter!” (But isn’t one of the great things about sleep that, while you’re asleep, you are perfectly stupid?)

The Neuroon will also help you enjoy technologically assisted “power naps” during the day to combat “lack of energy”, “fatigue”, “mental exhaustion” and “insomnia”. When it comes to quality of sleep, of course, numerous studies suggest that late-night smartphone use is very bad, but if you can’t stop yourself using your phone, at least you can now connect it to a sleep-enhancing gadget.

So comes a brand new wave of devices that encourage users to outsource not only their basic bodily functions but – as with the Apple Watch’s emphasis on providing “motivation” – their very willpower.  These are thrillingly innovative technologies and yet, in the way they encourage us to think about ourselves, they implicitly revive an old and discarded school of ­thinking in psychology. Are we all neo-­behaviourists now?

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The school of behaviourism arose in the early 20th century out of a virtuous scientific caution. Experimenters wished to avoid anthropomorphising animals such as rats and pigeons by attributing to them mental capacities for belief, reasoning, and so forth. This kind of description seemed woolly and impossible to verify.

The behaviourists discovered that the actions of laboratory animals could, in effect, be predicted and guided by careful “conditioning”, involving stimulus and reinforcement. They then applied Ockham’s razor: there was no reason, they argued, to believe in elaborate mental equipment in a small mammal or bird; at bottom, all behaviour was just a response to external stimulus. The idea that a rat had a complex mentality was an unnecessary hypothesis and so could be discarded. The psychologist John B Watson declared in 1913 that behaviour, and behaviour alone, should be the whole subject matter of psychology: to project “psychical” attributes on to animals, he and his followers thought, was not permissible.

The problem with Ockham’s razor, though, is that sometimes it is difficult to know when to stop cutting. And so more radical behaviourists sought to apply the same lesson to human beings. What you and I think of as thinking was, for radical behaviourists such as the Yale psychologist Clark L Hull, just another pattern of conditioned reflexes. A human being was merely a more complex knot of stimulus responses than a pigeon. Once perfected, some scientists believed, behaviourist science would supply a reliable method to “predict and control” the behaviour of human beings, and thus all social problems would be overcome.

It was a kind of optimistic, progressive version of Nineteen Eighty-Four. But it fell sharply from favour after the 1960s, and the subsequent “cognitive revolution” in psychology emphasised the causal role of conscious thinking. What became cognitive behavioural therapy, for instance, owed its impressive clinical success to focusing on a person’s cognition – the thoughts and the beliefs that radical behaviourism treated as mythical. As CBT’s name suggests, however, it mixes cognitive strategies (analyse one’s thoughts in order to break destructive patterns) with behavioural techniques (act a certain way so as to affect one’s feelings). And the deliberate conditioning of behaviour is still a valuable technique outside the therapy room.

The effective “behavioural modification programme” first publicised by Weight Watchers in the 1970s is based on reinforcement and support techniques suggested by the behaviourist school. Recent research suggests that clever conditioning – associating the taking of a medicine with a certain smell – can boost the body’s immune response later when a patient detects the smell, even without a dose of medicine.

Radical behaviourism that denies a subject’s consciousness and agency, however, is now completely dead as a science. Yet it is being smuggled back into the mainstream by the latest life-enhancing gadgets from Silicon Valley. The difference is that, now, we are encouraged to outsource the “prediction and control” of our own behaviour not to a benign team of psychological experts, but to algorithms.

It begins with measurement and analysis of bodily data using wearable instruments such as Fitbit wristbands, the first wave of which came under the rubric of the “quantified self”. (The Victorian polymath and founder of eugenics, Francis Galton, asked: “When shall we have anthropometric laboratories, where a man may, when he pleases, get himself and his children weighed, measured, and rightly photographed, and have their bodily faculties tested by the best methods known to modern science?” He has his answer: one may now wear such laboratories about one’s person.) But simply recording and hoarding data is of limited use. To adapt what Marx said about philosophers: the sensors only interpret the body, in various ways; the point is to change it.

And the new technology offers to help with precisely that, offering such externally applied “motivation” as the Apple Watch. So the reasoning, striving mind is vacated (perhaps with the help of a mindfulness app) and usurped by a cybernetic system to optimise the organism’s functioning. Electronic stimulus produces a physiological response, as in the behaviourist laboratory. The human being herself just needs to get out of the way. The customer of such devices is merely an opaquely functioning machine to be tinkered with. The desired outputs can be invoked by the correct inputs from a technological prosthesis. Our physical behaviour and even our moods are manipulated by algorithmic number-crunching in corporate data farms, and, as a result, we may dream of becoming fitter, happier and more productive.

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The broad current of behaviourism was not homogeneous in its theories, and nor are its modern technological avatars. The physiologist Ivan Pavlov induced dogs to salivate at the sound of a bell, which they had learned to associate with food. Here, stimulus (the bell) produces an involuntary response (salivation). This is called “classical conditioning”, and it is advertised as the scientific mechanism behind a new device called the Pavlok, a wristband that delivers mild electric shocks to the user in order, so it promises, to help break bad habits such as overeating or smoking.

The explicit behaviourist-revival sell here is interesting, though it is arguably predicated on the wrong kind of conditioning. In classical conditioning, the stimulus evokes the response; but the Pavlok’s painful electric shock is a stimulus that comes after a (voluntary) action. This is what the psychologist who became the best-known behaviourist theoretician, B F Skinner, called “operant conditioning”.

By associating certain actions with positive or negative reinforcement, an animal is led to change its behaviour. The user of a Pavlok treats herself, too, just like an animal, helplessly suffering the gadget’s painful negative reinforcement. “Pavlok associates a mild zap with your bad habit,” its marketing material promises, “training your brain to stop liking the habit.” The use of the word “brain” instead of “mind” here is revealing. The Pavlok user is encouraged to bypass her reflective faculties and perform pain-led conditioning directly on her grey matter, in order to get from it the behaviour that she prefers. And so modern behaviourist technologies act as though the cognitive revolution in psychology never happened, encouraging us to believe that thinking just gets in the way.

Technologically assisted attempts to defeat weakness of will or concentration are not new. In 1925 the inventor Hugo Gernsback announced, in the pages of his magazine Science and Invention, an invention called the Isolator. It was a metal, full-face hood, somewhat like a diving helmet, connected by a rubber hose to an oxygen tank. The Isolator, too, was designed to defeat distractions and assist mental focus.

The problem with modern life, Gernsback wrote, was that the ringing of a telephone or a doorbell “is sufficient, in nearly all cases, to stop the flow of thoughts”. Inside the Isolator, however, sounds are muffled, and the small eyeholes prevent you from seeing anything except what is directly in front of you. Gernsback provided a salutary photograph of himself wearing the Isolator while sitting at his desk, looking like one of the Cybermen from Doctor Who. “The author at work in his private study aided by the Isolator,” the caption reads. “Outside noises being eliminated, the worker can concentrate with ease upon the subject at hand.”

Modern anti-distraction tools such as computer software that disables your internet connection, or word processors that imitate an old-fashioned DOS screen, with nothing but green text on a black background, as well as the brain-measuring Muse headband – these are just the latest versions of what seems an age-old desire for technologically imposed calm. But what do we lose if we come to rely on such gadgets, unable to impose calm on ourselves? What do we become when we need machines to motivate us?

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It was B F Skinner who supplied what became the paradigmatic image of ­behaviourist science with his “Skinner Box”, formally known as an “operant conditioning chamber”. Skinner Boxes come in different flavours but a classic example is a box with an electrified floor and two levers. A rat is trapped in the box and must press the correct lever when a certain light comes on. If the rat gets it right, food is delivered. If the rat presses the wrong lever, it receives a painful electric shock through the booby-trapped floor. The rat soon learns to press the right lever all the time. But if the levers’ functions are changed unpredictably by the experimenters, the rat becomes confused, withdrawn and depressed.

Skinner Boxes have been used with success not only on rats but on birds and primates, too. So what, after all, are we doing if we sign up to technologically enhanced self-improvement through gadgets and apps? As we manipulate our screens for ­reassurance and encouragement, or wince at a painful failure to be better today than we were yesterday, we are treating ourselves similarly as objects to be improved through operant conditioning. We are climbing willingly into a virtual Skinner Box.

As Carl Cederström and André Spicer point out in their book The Wellness Syndrome, published last year: “Surrendering to an authoritarian agency, which is not just telling you what to do, but also handing out rewards and punishments to shape your behaviour more effectively, seems like undermining your own agency and autonomy.” What’s worse is that, increasingly, we will have no choice in the matter anyway. Gernsback’s Isolator was explicitly designed to improve the concentration of the “worker”, and so are its digital-age descendants. Corporate employee “wellness” programmes increasingly encourage or even mandate the use of fitness trackers and other behavioural gadgets in order to ensure an ideally efficient and compliant workforce.

There are many political reasons to resist the pitiless transfer of responsibility for well-being on to the individual in this way. And, in such cases, it is important to point out that the new idea is a repackaging of a controversial old idea, because that challenges its proponents to defend it explicitly. The Apple Watch and its cousins promise an utterly novel form of technologically enhanced self-mastery. But it is also merely the latest way in which modernity invites us to perform operant conditioning on ourselves, to cleanse away anxiety and dissatisfaction and become more streamlined citizen-consumers. Perhaps we will decide, after all, that tech-powered behaviourism is good. But we should know what we are arguing about. The rethinking should take place out in the open.

In 1987, three years before he died, B F Skinner published a scholarly paper entitled Whatever Happened to Psychology as the Science of Behaviour?, reiterating his now-unfashionable arguments against psychological talk about states of mind. For him, the “prediction and control” of behaviour was not merely a theoretical preference; it was a necessity for global social justice. “To feed the hungry and clothe the naked are ­remedial acts,” he wrote. “We can easily see what is wrong and what needs to be done. It is much harder to see and do something about the fact that world agriculture must feed and clothe billions of people, most of them yet unborn. It is not enough to advise people how to behave in ways that will make a future possible; they must be given effective reasons for behaving in those ways, and that means effective contingencies of reinforcement now.” In other words, mere arguments won’t equip the world to support an increasing population; strategies of behavioural control must be designed for the good of all.

Arguably, this authoritarian strand of behaviourist thinking is what morphed into the subtly reinforcing “choice architecture” of nudge politics, which seeks gently to compel citizens to do the right thing (eat healthy foods, sign up for pension plans) by altering the ways in which such alternatives are presented.

By contrast, the Apple Watch, the Pavlok and their ilk revive a behaviourism evacuated of all social concern and designed solely to optimise the individual customer. By ­using such devices, we voluntarily offer ourselves up to a denial of our voluntary selves, becoming atomised lab rats, to be manipulated electronically through the corporate cloud. It is perhaps no surprise that when the founder of American behaviourism, John B Watson, left academia in 1920, he went into a field that would come to profit very handsomely indeed from his skills of manipulation – advertising. Today’s neo-behaviourist technologies promise to usher in a world that is one giant Skinner Box in its own right: a world where thinking just gets in the way, and we all mechanically press levers for food pellets.

This article first appeared in the 18 August 2016 issue of the New Statesman, Corbyn’s revenge