e value of uncertainty - Sussex Research Online - University of Sussex

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�e value ofuncertaintyIn fiction, it grips us. In life, it can unravel us. Howcan brains hooked on certainty put its opposite togood use?

by Mark Miller, Kathryn Nave, George Deane & Andy Clark

Mark Miller is a philosopher of cognition. His research explores what recent advances inneuroscience can tell us about human happiness and wellbeing, and what it means tolive well in our increasingly technologically mediated world. A member of the Horizon2020 European Union ERC project XSPECT, he is an assistant professor at the Centerfor Human Nature, Artificial Intelligence and Neuroscience at Hokkaido University inJapan.

Kathryn Nave is a PhD student in philosophy at the University of Edinburgh, where sheis working on Andy Clark’s European Research Council-funded project 'ExpectingOurselves'. She is also a contributing editor at WIRED Magazine.



I can live with doubt and uncertainty and not knowing. I think it is muchmore interesting to live not knowing than to have answers which might bewrong … In order to make progress, one must leave the door to theunknown ajar. – from ‘�e Uncertainty of Science’ (1963) by Richard P Feynman

[W]e demand rigidly defined areas of doubt and uncertainty! ― from �e Hitchhiker’s Guide to the Galaxy (1979) by Douglas Adams

Noonan’s North bar and grill is in an unassuming blue clapboard building, just offMain Street in the town of Holy Cross, Iowa (population 366). Locally, it’s popular forits BBQ ribs and all-you-can-eat shrimp nights. �is is not the kind of place a persontends to drive hundreds of miles to visit. But Max Hawkins’s phone had told him togo there – and so go there he did.

Hawkins is a computer scientist (turned artist) who spent more than two years ‘livingrandomly’. His story began when, working as a Google engineer (his dream job) inSan Francisco (his dream city), he realised that he had optimised his life to fit hispreferences to what he suddenly found to be an alarming degree. He started everyday at the stroke of 7am, went to the best coffeeshop, then cycled an optimal 15-minute route to work. A simple algorithm, fed with his GPS tracker data from oneweek, could predict with great accuracy his whereabouts and movements the nextweek at the same time of day. �is smacked, he felt, of a certain lack of personalautonomy.

Despite having fit his life almost exactly to his preferences, he felt trapped – as if hehad optimised his life to the point where his own role had become superseded.Hawkins responded by using new technologies to introduce greater variety into hislife. For two years, he lived his life according to a series of randomisation algorithms.A diet generator told him what to eat, an algorithmic travel agent picked out the citywhere – having gone freelance – he would live for the next two months, a randomSpotify playlist provided music for the journey, and a random Facebook-eventselector told an Uber driver where to take him when he got there.

George Deane is a PhD student in the School of Philosophy at the University ofEdinburgh, where he is working on Andy Clark's European Research Council-fundedproject 'Expecting Ourselves'.

Andy Clark professor of cognitive philosophy at the University of Sussex and a memberof the Horizon 2020 European Union ERC project XSPECT. His books include BeingThere: Putting Brain, Body And World Together Again (1997), Supersizing the Mind(2008), Mindware (2nd ed, 2014) and Surfing Uncertainty: Prediction, Action, and theEmbodied Mind (2016).



�e algorithms took him to acrobatic-yoga classes in Mumbai and to a goat farm inSlovenia, but they also took him to the small-town pub of Holy Cross, Iowa, and to aneighth-grade flute recital, and to a small family Christmas in Fresno, California.Anywhere that would break him out of the comfortably predictable rut of the affluentSan Franciscan tech worker. Reporting back from the frontiers of uncertainty in talkswith titles such as ‘Leaning In to Entropy’, Hawkins said that the algorithms dictatednot just where to go, what to eat and what leisure activities he should engage in, buteven what clothes and hairstyles (he ended up needing several wigs) he shouldadopt. He even has a chest tattoo selected randomly from images on the web.

Hawkins reported finding great fulfilment in multiple unexpected ways, and feeling(paradoxically) more present as a person as a result of escaping what he had come tosee as the dictatorship of his own preferences and preference-optimised lifestyle. Hetalked of escaping the tiny ‘bubbles’ of places to eat and things to do that kept ondragging him back time and time again.

Viewed from a certain perspective, Hawkins’s unease can seem strange, evenparadoxical. Brains like ours are – according to a leading neuroscientific theoryknown as ‘predictive processing’ – designed to solve one basic puzzle: how tominimise their own long-term average surprise (prediction error) during ourembodied exchanges with the world. �e more volatile the environment, the less gripthis core strategy gets, resulting in anxiety, stress and feelings of loss of control. Yetthere was Hawkins, apparently adding huge doses of the unexpected into his life.

However, even from a predictive processing perspective, staying locally within thebounds of the expected is only one part of a much more complex story. For thosevery same predictive brains were designed to drive mobile, inquisitive creatures likeourselves. Such creatures must productively surf the waves of their own uncertainty.To do so, they probe and sample the world in ways that aim to reveal just where thekey uncertainties lie, so that by future actions they can resolve them and move on.�ey seek new information, and they engage in complex rituals such as art andscience whose role (we’ll argue) is in part at least to safely reveal and stress-test theirown deepest assumptions.

Hawkins was actually doing something rather similar – stress-testing his owndeepest assumptions about who he is and what he likes so as to more fully explore aspace of human possibility. His methods were extreme, but his general project is bothfamiliar and distinctively human. Creatures like us, it seems, have added some brand-new layers to our relationship with the space of our own predictions, errors anduncertainties, turning that space into a kind of concrete arena that affords deeperand more challenging explorations than those undertaken by most other livingorganisms. We have discovered ways of turning our own best models (including ourself-model) into objects apt for explicit questioning.



�e examined human life reflects, we suggest, a new kind of relationship with ourown expectations and uncertainty. Yet it is one that we have somehow constructedwithin the inviolable bounds of a biologically bedrock drive to minimise long-termprediction error. How is this neat trick possible?

Understanding our own relationship with uncertainty has never been moreimportant, for we live in unusually challenging times. Climate change, COVID-19 andthe new order of surveillance capitalism make it feel as if we are entering a new age ofglobal volatility. Where once for many in the West there were just pockets ofinstability (deep unpredictability) in a sea of reliability – albeit sometimes indisagreeable structures and expectations – it lately seems as if there are just pocketsof stability in a swirling sea of hard-to-master change. By better understanding boththe varieties and the value of uncertainty, and recognising the immense added valueof turning our own uncertainties and expectations into concrete objects apt for testand challenge, we become better able to leverage the power of our own predictivebrains.

Expected uncertainty

�e desire to escape a predictable life is a familiar theme in literature. Moby Dick’sIshmael takes to the ocean, and Steppenwolf’s Harry Haller asserts that he ‘wouldrather feel the very devil burn in me than this warmth of a well-heated room’. In thecounterculture classic �e Dice Man (1971), the bored psychiatrist Luke Rhinehart –pseudonymous author of this fictionalised autobiography – entrusts his decisions tothe roll of a dice, inspiring readers to do the same.

Yet the decision to open oneself up to total uncertainty, merely for its own sake, is farfrom the norm. Humans are creatures of habit. We eat the same thing for breakfast,take the same path to work, and meet the same people in the same pub for drinksafterwards. In 2010, a group of computer scientists analysed the mobility patterns of50,000 anonymous cellphone users, and found it was possible to predict their futurelocations with 93 per cent accuracy based on past behaviours. Yet as we negotiateeven our predictable daily worlds, we have no choice but to surf wave upon wave ofuncertainty.

Not all uncertainties, however, are equal.

A handy taxonomy distinguishes expected uncertainty, unexpected uncertainty, andvolatility. Expected uncertainty is task-salient uncertainty that is already predicted byan existing mental (generative) model – a set of structured knowledge that enablesus to generate local predictions in ways nuanced by context and current task.Unexpected uncertainty arises when – for example – an environmental changecauses us to become uncertain about our own generative model. Volatility is subtlydifferent: it names a situation in which the frequency of changes in the environmentalcontingencies are themselves rapidly changing. Volatility is thus the most potentially



anxiety-provoking species of uncertainty. It is uncertainty about the space ofuncertainty itself.

Let’s start with a simple case of expected uncertainty. An agent who knows her wayhome from a certain location might navigate to that location even when aware thathome actually lies in another direction. By reaching the familiar location, shecollapses task-salient uncertainty and can proceed fluently to solve her real problem.�e intervening action, which was designed to improve her state of information, issometimes dubbed ‘epistemic’ (knowledge-seeking) as opposed to pragmatic(puzzle-solving).

When confronted with unexpected uncertainty,our brain reacts by increasing its learning rate

Work in predictive processing understands epistemic actions as actions selected tominimise expected future surprise (expected future prediction error). �is hints at adeep and unexpected continuity between simple strategies such as returning to thatfamiliar point, and much more complex, distinctively human, strategies, such asusing Google Maps to help us find our destination from wherever we happen to be.What unites these superficially very different strategies is that each can be seen, indifferent ways and at different timescales, as a way of reducing salient expecteduncertainty (hence future prediction error) by harvesting information from the localenvironment. �e ability of prediction-error minimising systems to find solutions ofthis kind has now been demonstrated in multiple simulation studies too, includingone in which simulated rats use a mixture of pragmatic and epistemic actions to findtheir way home, navigating first to familiar landmarks before exploiting their existingknowledge to find their way home.

More interesting cases of the same broad type arise when purely mental actions areperformed in order to ‘think things through’ in advance of acting. All such actions areepistemic, and seek to improve our state of information prior to acting – but this timeby making the best use of what we already know. For example, I can work out thenumber of radiators I need to buy to replace those in my living room by countingthem in my mind’s eye. Imagination, nuanced by the use of selective attention, is akey means by which we engage in ‘vicarious trial and error’, whose role is to minimiseexpected future uncertainty by pressing new information from the existing model.

Unexpected uncertainty

Expected uncertainty is experienced whenever we know the weak spots in our ownstate of information, and we can take remedial action accordingly. If I know that Idon’t know the way home from where I am, and know how to reach the spot fromwhich I can reliably succeed, then my uncertainty is fully expected, and (in that case)



remediable. Or if I know that the dice is fair, then I know that no player, myselfincluded, can know with confidence the outcome of an unbiased roll. �is is expectedand intransigent uncertainty of quite a familiar kind.

Unexpected uncertainty, by contrast, typically occurs when the environment (ineffect, the rule structure) changes without our being in a position to predict thatchange. For example, suppose someone (without my knowledge) swapped the fairdie for a loaded one, and play commenced. Now, my estimations of the other player’suncertainty is suddenly rendered incorrect, and I will start to lose some seriousmoney. Or suppose (to borrow an example from the neuroscientists Amy Bland andAlexandre Schaefer) I know that a certain restaurant has dishes I like about 80 percent of the time, so that eight out of 10 visits will tend to yield a happy outcome. �euncertainty about the offerings of the day is then an expected uncertainty: one that Ican work with as I plan my outings. By contrast, if the restaurant suddenly changesits chef, my estimates are immediately unreliable. I am thrust into the land ofunexpected uncertainty – the scary land that, remarkably, Hawkins found sosurprisingly satisfying.

Volatility means that there is little useful to learnat the target level except that things are apt tochange

When confronted with unexpected (salient) uncertainty, our brain reacts byincreasing its learning rate, encouraging the kinds of plastic change needed toupdate the predictive model – for example, by starting to learn about the typicalmenus created by that new chef. Over time, the upshot should be a revised model,one in which (let’s imagine) I expect dishes I like to be served only about five timesduring any 10 visits to the restaurant. �is might be my cue to move into exploratorymode and try another restaurant.

�is form of uncertainty can in fact be very beneficial for organisms like us. �is isjust the sort of uncertainty that can help us break out of bad habits and escape ‘localminima’ – good enough solutions that fall far short of what we might achieve by‘pushing on through’.

Volatility

�is next (and final) kind of uncertainty is the most challenging of all. To creep up onit, notice that expected and unexpected uncertainties come in first- and second-orderforms. First-order forms are about simple targets, such as the chances of getting thefood we like. Second-order forms are about the first-order estimations themselves. Imight be confident or unconfident about my estimation that most visits will yield a



meal I like – for example, if I have been only a few times, or if others with similartastes to my own seem to have had very different experiences.

But there are also environments that offer a special challenge to our predictive minds.Such environments make the second-order estimations unreliable. �ese are the so-called ‘volatile environments’ in which the statistical regularities themselves, whenprobed in my usual way, are unstable. In June 2020, COVID-19-related issuesafflicting the world were joined by mass unrest and protests following the killing ofGeorge Floyd in Minneapolis – sudden dramatic shifts that are a paradigm of highvolatility. Bland and Schaefer’s example is less dramatic, of a restaurant manager whodecides to change the menu many times every season, even while the chef remainsthe same. �e learning that ensues – either when I start to notice that my favouritedishes are less frequently available, or when I start to adapt to the COVID-19 crisis –is itself unreliable.

�is is like playing dice with pieces that are sometimes fair, sometimes loaded oneway, sometimes loaded another way, sometimes loaded yet another way, and so on. Itis a world in which the frequency of change in the underlying ‘rule structure’ is high.�is is a world that is highly resistant to informative learning, apart from learning thatit is indeed such a world – and hence assigning lower confidence to all our estimatesof target states.

Such environments are especially challenging. �e failure to get a grip on expected(first order) uncertainty initially wants to drive us into plasticity and learning, andperhaps into an exploratory mode as we seek better and more stable environments.But the volatility means that this strategy is itself suspect, as there is little useful tolearn at the target level except that things are apt to change. �is is a situation thathuman and animal minds typically find extremely uncomfortable. It is, in fact, notunlike how the standard environment (and especially the social environment) mightseem to those with autism spectrum condition, which has been theorised to involveoverestimating the importance of small sensory deviations from expected patterns,hence estimating that the world itself is highly volatile and hard to predict.

Human experience, we believe, reflects nothing so much as the operation ofpredictions and uncertainty estimations along many dimensions and at many levelsof processing. When all goes well, a wide range of predictions and estimations oftheir reliability (uncertainty) allow us to leverage everything we have been through, awhole life of experience and learning, to quickly detect those sensory patterns thatmatter to us, assess the reliability of our own expectations relative to the currentsensory evidence, and (hence) to behave in ways that help bring about desired andbeneficial patterns.

But there are dangers here too. Our predictions about the world can be mistaken ormisled in various ways. Our hidden biases can sculpt how we perceive and behave inthe world in ways that result in the world conforming to our mistaken view. In effect,



making our mistake into a reality, which only reinforces our belief in that bias.Vicious cycles, such as these, in fact characterise many forms of functional(‘psychogenic’) illness and some forms of psychosis.

Hunger, homelessness, loneliness and chronic pain are all examples of situations andstates that continually produce volatility (difficult-to-manage negative surprises).Sustained exposure to such volatile situations and environments – where theoutcomes of actions appear inherently unpredictable – leads to an inevitabledecrease in confidence in one’s ability to bring about the outcomes they expect. Atthat point, our predictive brains begin to infer an inability to exert successful control,and this then forms a damaging part of the model that guides our future actions.

�is can result in a form of learned helplessness. Learned helplessness occurs whenan animal is exposed to an adversive outcome that’s unavoidable. Many studies(rather cruelly) involve rats receiving electric shocks without the chance of escape.What is striking is that, even if an avenue of escape (a door) is made available, after acertain point the rat won’t even try to escape. It has learned that it is helpless, that itis unable to behave in ways that will avoid these adversive outcomes. �ecomputational basis of states such as learned helplessness is understood in predictiveprocessing (in the field of ‘computational psychiatry’) as an ingrained expectation ofbeing unable to control outcomes, due to the environment being too volatile topredict and navigate successfully. Beliefs about levels of control and ability to avoidadverse outcomes that might underpin adaptive behaviour in one environment(when the door is closed) don’t necessarily carry over to other environments (when itopens).

Addictive substances exploit a different vulnerability, again allowing suboptimalcycles to take hold. Opioids ‘hijack’ reward circuitry in the brain that predictiveprocessing researchers take to compute estimations of the rate of prediction-errorminimisation – that is, the brain’s estimation of how well it is doing at reducingprediction error compared with its expected rate of reduction in the current context.Hijacking this process means that the brain gets fooled into estimating that it isdoing far better than expected at reducing salient (high precision) error. Importantly,any context where this occurs gets marked as one that we will seek to inhabit. Overtime, the repeated ability of the drug to induce this effect leads to an (aptly named)‘habit’ of use – a habit whose grip is not loosened simply by the person realising thatthe drug is not, in fact, delivering pleasure, success or fulfilment. �is is because,despite that high-level judgment, in the moment of use all those hidden estimationsof unexpectedly good error-reduction become active. It’s important to note that thepredictive brain here is not malfunctioning, it is doing just what it has evolved to do –reduce uncertainty. However, the brain didn’t evolve to manage the sorts of signalspresented by drugs of addiction.



Addicted predictors can create a personal nichein which elements incompatible with their modelare excluded

It might seem hard to understand how such clearly pernicious habits could sustainthemselves indefinitely. Opioid addiction is manifestly not conducive to humanflourishing. How can a (non-malfunctioning) predictive brain continue to sustain amodel in which feeding this habit is, at any level, cast as positive, in defiance of awealth of evidence to the contrary? What is the evolutionary utility of the predictivestrategy if it allows our models to remain so persistently disconnected from ourexternal situation?

To fully understand the self-reinforcing power of such habits, we need to look oncemore beyond the brain. We need to attend to how the process of acting to minimisesurprise ensnares our environment into the overarching error-minimising process. Atthe simplest level, such actions might just involve ignoring immediate sources oferror – as when alcoholics preserve the belief that they are functioning well by notlooking at how much they’re regularly spending on drink. But our actions can alsohave a lasting effect on the structure of our environment itself, by moulding it into theshape of our cognitive model. �rough this process, addicted predictors can create apersonal niche in which elements incompatible with their model are excludedaltogether – for instance, by associating only with others who similarly engage in,and thus do not challenge, their addictive behaviours.

�is mutually reinforcing circularity of habit and habitat is not a unique feature ofsubstance addiction. In 2010, the internet activist Eli Pariser introduced the term‘filter bubble’ to describe the growing fragmentation of the internet as individualsincreasingly interact only with a limited subset of sources that fit their pre-existingbiases. Pariser laid the blame for these bubbles on the increasing use of predictivealgorithms by big tech companies to deliver up exactly the sort of content anindividual has interacted with in the past. From the perspective of the predictivebrain, such personalisation technologies look less like a radical new developmentthan an extension to the predictive algorithms we subconsciously run on ourselves inan effort to keep our environmental interactions within easily anticipatable bounds.

Indeed, the 21st century’s proliferation of digital enclaves might be less attributableto new technological impositions than to how the weakening of such geographical,social and political constraints has freed each individual to construct a personalenvironment in ever more specific ways. As the journalist Bill Bishop argued in �eBig Sort (2008), by charting the movement of US citizens into increasingly like-minded neighbourhoods over the past century, this homophilic drive has longdirected our movements through physical space. In the online world, it now occurs



through more than a million subreddits and innumerable Tumblr communitiesserving everyone from queer skateboarders to incels, flat-Earthers and furries.

�e environment that each surprise-minimising brain interacts with is not someshared and stable domain of regularities that can be relied upon as a neutral check tokeep all of our predictive models in line. Instead, it is recast as a flexible resource forextracting and creating just those regularities that we predict. Perversely, the moreflexible the environment, the more it allows for the creation of self-protective bubblesand micro-niches, and hence affords the entrenchment of rigid models.

The many ways that we can fall prey to our own predictive brains correspond to thevarious ways in which we can become trapped by our own estimations of thereliability of different predictions. Hawkins felt trapped by his own optimisations –precise high-level predictions that he would live a life of a certain kind. But bynoticing the restricted shape of that life, he was eventually able to break the grip ofhis own self-model. �is suggests that there are ways to hack our own predictiveminds so as to escape at least some of the traps we have been examining. �is is truein cases where people yearn for a more varied and engaging life. But similarprinciples underpin some of the most devastating psychopathologies, where, just likein the case of learned helplessness, ingrained expectations about the volatility andunpredictably of the environment are resistant to revision even when agents findthemselves in more favourable environmental circumstances. A host of psychologicaland affective disorders such as major depression, anxiety, addiction and post-traumatic stress disorder (PTSD) can be broadly understood within those terms.

A more radical means of loosening these entrenched predictive models, which hasbeen used for thousands of years in cultures across the world, is psychedelic drugs.After years of suppression of psychedelic research – documented by Michael Pollanin How to Change Your Mind (2018) – it’s only in the past decade that such researchhas really taken off. Evidence is emerging that psychedelics could offer a powerfulnew means to treat a range of affective disorders, including addiction, obsessive-compulsive disorder, PTSD, treatment-resistant depression, as well as being effectiveat easing ‘existential distress’ in end-of-life care. Non-clinical populations stand tobenefit too – with feelings of increased ‘nature-relatedness’, ecological awareness andreduced anxiety.

�ese drugs are known to induce profound alterations in phenomenology, fromsensory perception, mood and thought (including the perception of reality), andeven ‘dissolving’ the usual sense of self in a way that strikingly resembles the loss ofself in the Buddhist notion of nirvana. �e writer, philosopher and psychedelicpioneer Aldous Huxley puts it like this in �e Doors of Perception (1954):

To be shaken out of the ruts of ordinary perception, to be shown for a fewtimeless hours the outer and the inner world, not as they appear to an



animal obsessed with survival or to a human being obsessed with words andnotions … [this is] an experience of inestimable value …

Huxley conceptualised psychedelics using a metaphor that now seems strikinglyprescient. He thought of the brain as a ‘reducing valve’ – stripping out the enormousamount of information in sensory input down to just a trickle that allows for adaptiveinterface with the environment. Under psychedelics, the valve is opened, and thisfiltration process is temporarily suspended. Predictive processing formalises thisintuition.

On the predictive processing view, predictions are essentially compressive, wherehigher levels of the hierarchy – dealing with more abstract or invariant features ofreality, and tracking longer timescales – strip the sensory signal of redundancies: thatis, anything that’s not relevant to adaptive action. Under psychedelics, the ‘reducingvalve’ is opened. From a predictive processing perspective, these drugs can beunderstood as loosening the grip of engrained and rigid expectations about howsensory signals are caused, allowing the brain to create novel hypotheses about theworld and how we relate to it.

Psychedelics can be seen as putting the braininto a ‘hot state’ of temporary malleability

�ere is burgeoning evidence that these drugs, when used properly – with ampleattention to ‘set and setting’ – could offer a lasting means of cultivating a new modeof interfacing with the world for both clinical and nonclinical populations. �e‘relaxation’ of high-level beliefs that constrain and construct ordinary perceptionresults in lower levels being, to some extent, ‘freed’ from their constraining influenceof compressive predictions. �e striking perceptual effects (‘tripping’) can beunderstood as lower-levels rampantly overfitting best guesses to this rich yet newlypuzzling sensory stream as a result of being less constrained by the high-levelinfluence – it’s as if the brain cycles through many more hypotheses to explain awaythe incoming sensory signal and make sense of the current sensory influx.

Metaphorically, then, psychedelics can be seen as putting the brain into a temporary‘hot state’. An analogy can be made to annealing a metal – heating up so as tointroduce a state of temporary malleability. When put in the hot state bypsychedelics, the brain becomes malleable enough for its prediction-generatingmodels to be remoulded. Under the right conditions – with proper consideration ofcontext and post-experience integration – this has the potential to be profoundlytherapeutic. �inking of this in terms of putting the brain into a highly plastic,sensitive state gives an idea of how psychedelics can underpin profoundlytherapeutic experiences, at the same time as it emphasises the crucial importance ofresponsible usage, set and setting.



For example, there is now a large body of evidence showing that psychedelic drugssuch as lysergic acid diethylamide (LSD), psilocybin (the active factor in magicmushrooms) and even dimethyltryptamine (DMT– the ‘spirit molecule’ found inayahuasca) can, when used carefully, play an important role in addressing manyforms of treatment-resistant chronic depression. To see how this fits in with ourpicture, notice that people with severe depression have often formed a preciseexpectation of their own behaviours and responses – a predictive self-model thatactively inhibits the joyful or playful exploration of their worlds, and which acts as aself-fulfilling prophecy of powerlessness and retreat. By inducing a temporary ‘hot’state, psychedelic drugs seem able to intervene our own high-level self-model, freeingus up to encounter the world and our sense of being in new and often helpful ways.�ese windows on other ways of being are not lost when the immediate effects of thedrug subside but can instead mediate an empowering re-examination of our own life,goals and sense of self, other and nature.

Psychedelics offer a means of remoulding ingrained expectations about – forinstance – volatility in our environment. Other ways of hacking our own predictivebrains includes the deliberate installation of helpful expectations, as seen in the useof so-called ‘honest placebos’ (when patients know that the drug they’re taking is aplacebo). In these cases, predictions of relief seem to be activated regardless of theperson knowing perfectly well that there is no active ingredient present. Honest (or‘open-label’) placebos have proven effective in cases ranging from irritable bowelsyndrome to cancer-related fatigue. And the higher the estimate of their power, thegreater the effect – inert substances delivered by syringe are typically more effectivethan those delivered by pill, presumably because we automatically estimate this as amore powerful form of intervention.

All these are, however, fairly blunt interventions on our own predictions and (self)expectations. Less blunt interventions include structured practices such asmeditation and mindfulness, which serve to retrain our own constrainingexpectations (eg, of permanence). A useful tool here is training attention to activelydrop our sampling towards the sensory edge, and away from our set beliefs,disengaging the expectation of high-level stability itself. When this succeeds, we canlive in the moment while still dealing with the changing contingencies of daily life.Such practices reflect something important yet easy to miss – our very human abilityto turn our own mental states into objects for reflection and action. �is brings usfull-circle to Hawkins, and that key thread in the process that led to his extremeattempt to see beyond his normal web of expectations about his daily life.

Hawkins’s algorithms were designer tools pushing him out of his routine envelopeand allowing new patterns and experiences to emerge. He deliberately crafted themto achieve a certain goal – the goal of breaking the mould of his highly optimisedlifestyle. �e key to this kind of radical action lies in a process that, though seldomremarked upon, seems to us to lie at the very heart of much that is distinctively



human about our relationship with the space of uncertainty. �e two-step processstarts by making our own predictive models and associated expectations visible,turning them into objects inspectable by ourselves and others. It then proceeds bydevising tricks, schemes and ploys that can stress, challenge and sometimesproductively break those models.

�e first step comes ‘for free’ with symbolic culture. Spoken language, written textand a host of associated practices turn aspects of our own generative models intopublic (material) objects – words, books, diagrams – apt for sharing, refinement andmultigenerational transmission. When Hawkins speaks of feeling trapped by thepredictability of his own highly optimised weekly routines, he makes visible the waythat his own model of ‘a good life’ is delivering a stream of choices and experiencesthat he now finds unsatisfying. �is is a remarkable feat, and it is worth dwelling on.

Flexible symbolic codes, once in place, enable us to step back from our owngenerative models and model-based predictions, turning them into public objects aptfor questioning, stress-testing and deliberate ‘breaking’. It strikes us as extremelyunlikely that the majority of nonhuman animals ever succeed at making their ownlife-models visible to themselves. �eir models guide actions, but they are notthemselves the objects of actions. But once in command of symbolic codes, thefloodgates are open, and our own models and model-based expectations can becomeobjects of scrutiny. �is might be the single most transformative epistemic bonusconferred by material culture.

But there is more to come. For once our own best models are encountered as objects,we can do more than simply scrutinise them. We can take actions designed to breakand rebuild the models themselves. �e deliberate use of psychedelic drugs to easethe grip of our own high-level self-model belongs in this category. So do certainmeditative practices that explicitly aim to relax the tyranny of our expectations ofpermanence. So does Hawkins’s creation of all those randomising life-algorithms.

Perhaps most notably of all, our own artistic, engineering and scientific practicesoften play just this kind of role. For example, diagrams, descriptions and scalemodels enable us independently to manipulate different aspects of a design, and toselectively attend to the different elements. �is enables us to explore differentoutcomes as conditioned upon different choices in ways that ease the bonds of ourown model-based expectations – much like physically shuffling a bunch of Scrabbletiles so as to help uncover new words. To enable such operations, my current beliefsand models need to exist as more than probabilistic trends in the way that I navigatethe world on the basis of stored knowledge. �ey need to exist as concrete items aptfor attention, sharing and questioning.

When uncertainty management goes awry, wecan too easily lose our grip on self, world and



other

Art is often in this model-revealing/model-breaking business too. It can be a way ofmaterialising and confronting our own high-level assumptions, about self, world andother, but doing so in a framework that steps back from daily concerns (think ofbeing in a theatre watching the play Death of a Salesman) and hence is not usuallyexperienced as genuinely threatening, even if it is subversive. �eoretical science,even more clearly, aims to codify our own best models of how minds and worldswork, delivering up these as objects for sharing, scrutiny and ‘productive breaking’.

Whatever the story, human minds became able to go where no animal minds hadgone before. We became able to encounter our own predictive models as objects.Hawkins set out to break the grip of his own life-model. But it is notable that there isstill a predictable regime in play, and one that he himself understands (indeed, onethat he designed). For example, he knew that the algorithm would send himsomewhere new every two months, and that it wouldn’t first send him suddenly to anew town or city every week, then (randomly) every day, then (randomly) not for 10years.

It is interesting to speculate that it was this lack of volatility that enabled him to gainso much from his experiment, while avoiding the kinds of anxiety and fear that manyof us recently felt as COVID-19 first began to turn the pattern of our lives upsidedown. Predictive brains expect control and, if it fails, they drive learning. Normally,the detection of high volatility in the environment should drive learning andexploration. Yet, under lockdown conditions, we were (rightly) told to stay put anddo nothing.

�is is very odd for us. One response was to take control of small worlds – baking,jigsaws, exercise. �is is very similar to a response already seen in individuals withautism spectrum condition, which is to generate and inhabit a more controlledenvironment. And it is a good response, a way of restoring some sense of mastery inthe face of wider volatility. Impressive bodies of work in ‘computational psychiatry’are now devoted to better understanding our relationship with uncertainty, and themany ways it can go wrong. We humans are, it seems, uncertainty managementsystems – and when uncertainty management goes awry, whether due to external orinternal perturbations, we can all too easily lose our grip on self, world and other.

Perhaps the most revealing comment in Hawkins’s many talks is one made towardsthe end of ‘Leaning In to Entropy’. He remarks on how rapidly the strangest andmost ‘non-him’ situations and places became the ‘new normal’, so much so that hecould easily imagine life as that person in that once-alien place. �is, we conjecture, isthe predictive brain reasserting itself, reforming aspects of our own high-level self-model so as to get a grip on the new normal.



Hawkins’s takeaway message was simple: don’t let your own preferences become atrap. Yet on a kind of meta level, he remained trapped (in a good way) – hisrandomising algorithm simply fulfilling his new top-level preference for selecting inways that sidestepped his first-order preference structure. We can’t help but intuitsome kind of value in this experiment. Like art and science, it makes the invisibleconcrete, revealing the strong gravitational force of our own expectations.

It is also an object lesson in the surprising value of controlled uncertainty.

To read more about the workings of the human mind, visit Aeon’s sister site, Psyche, a newdigital magazine that illuminates the human condition through three prisms: mental health;the perennial question of ‘how to live’; and the artistic and transcendent facets of life.

25 September 2020aeon.co

e value of uncertainty - Sussex Research Online - University of Sussex

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