Predictive Processing: one theory to rule them all…

After discussing some of the basic concepts behind the Predictive Processing (PP) framework, it’s time to explore why I think it was worth the effort. In short, the explanatory power that PP seems to have is, as far as I can tell, unprecedented in neuroscience. No theory that I’ve been exposed to has ever managed to get close to the width and depth encompassed by the PP proposal. One way to see why is to concentrate on one key element and briefly mention some of the phenomena it might explain. My choice is precision weighting (PW), a mechanism that suggests many possible implications. In this post, I will explore the ones that I find more striking.

[Note: this post is part of a series inspired by Andy Clark’s “Surfing Uncertainty” book. For a general introduction to the Predictive Brain idea, see this post and the literature cited therein. The concept of precision weighting and how it fits in the PP framework is discussed in a previous post.]

Many illusions can be explained in terms of PP. Image adapted from Flikr by Robson# CC BY 2.0

A short recap: when a sensory stimulus is transduced (collected and transformed in a nervous signal), PP hypothesises that it will reach a sequence of neural layers, each busy producing predictions that try to match the signal arriving from the layer below. [In this convention, lower levels are those situated closer to sensory organs.] Each layer will issue a prediction to the layer below, and will concurrently match the prediction it receives from above with the incoming signal from below. The matching will result in a “difference” signal (or, better, an Error Signal – ES) which is presumed to be the main/only signal that a given layer will send upwards. The ES thus carries upwards only the information that could not be predicted, or, if you prefer, only the surprising and newsworthy elements of the original raw sensory stimuli. We have explored before two additional ingredients:

  1. For such a system to work, it is necessary that whenever a signal passes from one layer to the other, it must carry some information about its expected precision/confidence. [We have also seen why it is reasonable to conflate precision and confidence into one single “measure”.] PW allows a given layer to dynamically give more importance to the error/sensory signal arriving from below or to the prediction issued from above. It is generally assumed that precision/confidence information is encoded as the gain (strength) of a given signal.
  2. Such an architecture is proposed to continue uninterrupted from layers that deal with sensory information, all the way to layers that are concerned with action selection and control. In this latter case, the ES will (or might) also be used to control muscles/effectors. Reducing the confidence (gain) of motor-related prediction signals will thus allow to “plan” actions, without triggering actual movements.

We have also seen before that, at levels concerned with integrating information coming from different senses, PW becomes important to deal with possible conflicts. For example, when watching TV, sounds will not seem to come from the TV speakers, but from the images themselves, as visual stimuli come with much higher spatial precision than acoustic ones. Thus, PW proposes to explain how sensory stimuli can be integrated, as well as why and how a perfect matching isn’t required.

When trying to understand how a complex system/mechanism works, it is often very useful to explore anomalies, especially when one is proposing a strictly mechanistic explanation of the inner workings of such systems. This makes perfect sense: any given mechanism must be constrained, and therefore it is reasonable to expect that it will not work particularly well under unusual circumstances. Moreover, particular idiosyncrasies will be specific to given mechanisms (different implementations will be characterised by different anomalies). This means that studying where things “go wrong” allows to match failures with hypothetical mechanisms: some mechanisms will be expected to fail in one way, some in an other. Thus, a theory of perception that happens to easily accommodate known (and hard to explain) perceptual anomalies (such as what happens when watching TV) and/or neurological conditions, will look more promising than one that doesn’t. For us, this consideration means that it makes sense to look at how PP proposes to explain some of such failings with the aid of PW.

One such anomaly is the rather spectacular rubber hand illusion:

To say it with Seth (2013):

[S]tatistical correlations among highly precision-weighted sensory signals (vision, touch) could overcome prediction errors in a different modality (proprioception)

In other words, proprioception isn’t very precise, or, more specifically, produces reliable signals about movement and changes in forces; thus, in the unusual experimental conditions (people are expected not to move their hidden hand), and given enough time, the relatively high precision signals coming from sight and touch can take precedence, forcing the overall system to explain them (that is: successfully predict them) by assuming the rubber hand is the real one.

Perhaps more interestingly, it’s also possible to relate PW to more natural anomalous conditions. One way to describe this line of thought it to ask: what would happen if the delicate balancing of precision versus confidence is systematically biased in one or the other direction?

On one extreme, we could imagine the situation where predictions tend to have too much weight. The result would be an overall system that relies too little on the supervision of sensory input and is therefore more likely to make systematic mistakes. If the imbalance is strong enough, the whole system will occasionally get flooded with abnormal errors (whenever the predictions happen to be very wrong, but issued with high confidence/gain), triggering an equally abnormal need to revise the predictions themselves, which could then realise a self-sustaining vicious cycle: more top-heavy, misinformed predictions will be issued, producing more floods of error signals, requiring even more revisions in the predictions themselves. The result would be the establishment of ungrounded expectations, which would then have visible impact on both perception (how the subject experiences the outside world) and on the overall understanding of the outside world itself (beliefs). Recall that according to PP, prior expectations are intrinsically able to shape perceptions themselves. Wrong perceptions, when they are indeed very wrong, are normally called hallucinations, while wrong beliefs can be seen as delusions. Sounds familiar? Indeed, the combination of both represents the “positive symptoms” of schizophrenia. In short, a systematic bias towards prediction confidence, if PP is broadly correct, would produce a system which is unable to self-correct.

On the opposite extreme, what would happen if issued predictions are not trusted enough? In such cases, prior knowledge would fail to help interpreting incoming signals, making it harder and harder to ‘explain away’ a given stimulus, as even the right predictions might struggle to quash out the incoming signals (which will then be interpreted incorrectly as a genuine ES). A subject afflicted with this condition will be able to react correctly to very familiar situations, where confidence in the prediction is highest and is therefore strong enough to reduce the ES. On the other hand, in new and ambiguous situations, predictions will systematically struggle to perform their function even when correct, and will therefore force the subject to attempt re-evaluating the current situation over and over. This would allow to gradually increase confidence in the issued predictions, and thus regain the ability of appropriately react to the outside world, at the cost of an abnormally high investment of time and attention. It’s easy to predict that such subjects will naturally tend to avoid unfamiliar circumstances and that they will also find it hard to correctly navigate the maze of ambiguities that we call natural language. In this case, an excess of error signal doesn’t lead to hallucinations and delusions because the “supervision” of sensory information happens to be too high (not too weak!), and thus only very precise predictions, i.e. those able to exactly match the stimuli, will have the best chance of reducing error signals to manageable levels. Once again, this kind of condition should also sound familiar: it is tantalisingly similar to autism. It’s worth noting that this approach is entirely compatible (indeed, I see it as a proposal of how the general principle might be implemented) with the well established view that autism is connected to an impaired ability to use Bayesian inference; for the details, see Pellicano and Burr (2012).

This leads me to the matter of attention. According to Friston and many of the PP proponents (see Feldman and Friston, 2010), attention is the perceivable result of highly weighted error signals. On the face of it, it makes perfect sense: what should we pay attention to? To whatever is news to us, and therefore, to what we struggle to predict. Moreover, we should be able to direct attention according to our current task: this can be readily done by reducing the confidence on the predictions we are making. By doing so, we’d amplify the residual error signals concerned with what we are paying attention to, making only very precise predictions (precise in the sense of being a perfect match of the incoming signal) able to reduce prediction error. This reinforces the view of autism sketched above: autistic individuals would thus be unable to command their attention and would instead be forced to attend any stimulus that isn’t readily explained away.

Conclusion

Predictive Processing, once enriched with the concept of Precision Weighting, is able to propose a preliminary sketch that includes reasonable explanations of how we manage to make sense of the world, learn from sensory information, plan, execute and control actions, pay attention to something and/or get our attention diverted by sudden and unexpected stimuli. Moreover, our abilities of dreaming and daydreaming are easily accommodated (in ways I might explore in the future). If this wasn’t enough, it also aspires to explain why and how certain well-known pathologies work, and is generally able to accommodate many perceptual illusions and anomalies. In other words, one single theory is proposing to explain much of what the brain does. This in a nutshell is why I’ve dedicated so much of my spare time to this subject: for the first time I get the impression that we might have some hope to understand how brains work – we now have a candidate theory which is potentially able to offer a unifying interpretative lens. Otherwise, without a set of general and encompassing principles, all our (increasing) understanding would be (has been) condemned to remain local, applicable only within a given restricted frame of reference (how neurons communicate, how edges are detected in vision, and so forth).
Given my background in neuroscience, I expect that my excitement comes with no surprise. Fair enough: but is my enthusiasm justified? Perhaps. To answer this question in the following posts I will look at what I find unconvincing or underdeveloped in the PP world. I might also use the occasion to err on the overconfidence side(!) and propose some of my ideas on how to tackle such difficulties.

Bibliography

ResearchBlogging.org

 

Clark, A (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind Oxford Scholarship DOI: 10.1093/acprof:oso/9780190217013.003.0011

Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in human neuroscience, 4.

Pellicano, E., & Burr, D. (2012). When the world becomes ‘too real’: a Bayesian explanation of autistic perception. Trends in cognitive sciences, 16(10), 504-510.

Seth, A. K. (2013). Interoceptive inference, emotion, and the embodied self. Trends in cognitive sciences, 17(11), 565-573. DOI: http://dx.doi.org/10.1016/j.tics.2013.09.007.

 

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Posted in Neuroscience, Psychology

Not quite wrong enough

In my last posts on politics I’ve made a few predictions. Wrong predictions! In this post, I want to acknowledge my errors, reflect on what they mean, and perhaps make a few more in the process. In a nutshell, the root of my mistakes is clear: the initial directions taken by both May’s government and Trump’s administration have been openly fascistic and seemed to encounter little resistance, especially in May’s case. This sent sending me down the path of the gloomiest predictions. Luckily, I was wrong (with my immense relief), but unfortunately, not quite wrong enough.

What I got wrong.

On the US side, keeping in mind that I only have second-hand knowledge of the situation, I had underestimated the strength of constitutional checks and balances (along with the volume of bottom-up dissent). I don’t think I had also overestimated Trump’s capacity to exploit the situation, if anything, I was expecting him to make more mistakes, driven by his massive ego. The specific prediction I got wrong was that Trump would exploit, if not facilitate, internal unrest, and use the consequent emergencies to suffocate the system of checks and balances that limit the executive powers of the presidency. I also predicted that this kind of scenario will unfold really quickly, and I can’t emphasise enough how happy I am to realise that I was wrong. Happier every day. I honestly have no good explanation on why I was wrong, but I do fear that the reasons why unrest might explode at any time are still valid. I am also still convinced that riots, or any form of civil unrest that is widespread enough to disrupt productivity, can still be exploited by Trump’s administration to undermine the democratic institutions of the country. Thus, I’m left in a state of fearful hope: what if I got only the timing wrong, while my worst fears are still valid? I can only hope I was entirely wrong!

On the UK side, my fear was that the authoritarian inclinations of Theresa May, and a good proportion of her Tory supporters, was backed by a decent amount of competence and that her fascistic aspirations could go unrecognised by both the main stream media and by a sizeable proportion of the electorate. Luckily, a crucial assumption was entirely wrong: despite the fact that May did have a reputation for high competence, Theresa May called a snap election without having a convincing reason to do so. She then ran the worst campaign I’ve ever witnessed, and in doing so, demonstrated to the country and the whole world how utterly incompetent she is (along with her whole team, one would think). I feel that my mistake was entirely justified: yes, you can never overestimate human stupidity, but assuming that your adversaries are a bunch of witless morons is a very obvious act of self-harm.

From the seminal mistake above (it appears that May herself, as well as her strategist, genuinely don’t have a clue), a second mistake followed. I have also predicted that “Corbyn and McDonnell are sleepwalking into their own obliteration“. Under the assumption that the Tories wouldn’t shoot themselves on the foot by their own initiative (an assumption that one is forced to make, when thinking about strategy), this could have been the case. However, I did underestimate two things: (1) Corbyn’s ability to appear genuine, along with the renewed appeal of his sensible domestic policies. (2) How well the deliberate ambiguity of Brexit would work.

There isn’t much to say about (1). Corbyn appears sincere, and he probably is, broadly speaking; I am 92% sure that he does mean well, although I can’t be persuaded that he genuinely believes in the open approach to decision-making he advocates (I can’t, because he never follows his own advice!). On point (2), there is much to be said, giving me the chance of making even more (hopefully wrong!) predictions.

Mistakes you need to make.

Along with problems that are good to have and problems that should not be solved, another mantra of mine is that some mistakes need to be made. The typical example is when there is a lesson to be learnt: sometimes making a mistake (preferably under controlled circumstances, where the consequences can be minimised) is the only effective way to permanently learn the lesson and reduce the likelihood of making the same mistake again, when stakes might be too high. [There is an interesting argument about the roles of parenting and education to be made here, perhaps something worth a separate discussion.] In the case of one of the wrong predictions I’ve made in the past 6-8 months, however, the mistakes I’ve made were mistakes that should not be avoided, which is different, and interesting in itself (to me, at least). It’s useful to learn to detect and react appropriately to these kind of counter-intuitive situations, so I’ll write down my reasoning here, doing so solidifies it (useful for me) and might be thought-provoking to my occasional readers. It is also very relevant to the current political situation, so please bear with me.

Mistakes that should not be avoided are a specific case of mistaken predictions, which may happen when the act of issuing a prediction can influence the outcome. In my case, I’m living in a society that is manifesting numerous warning signs: there is a very visible drive towards authoritarianism/fascism. Making the prediction that other parts of society will counterbalance this drive automatically and inevitably weakens the defences is question: if you are confident there is no danger, you will not spend your energies resisting it. If everyone involved feels the same, they will not push in the other direction, leaving the original drive free to steer society in the wrong direction. Thus, anyone who recognises such an unusual feedback is faced with a choice. One option is to issue the prediction one would hope is right (or, more weakly, choose to remain silent, because of it): people will recognise and reject fascism. This prediction automatically undermines itself, so in terms of predictable effects, it helps brining about the undesired outcome. The other option is to sound the alarm, hoping to be wrong. Doing so makes it more likely that things will turn out well.
It is paradoxical: the act of expressing a prediction is bound to reduce the likelihood that the prediction is correct. I know very well that in the case of my own prediction, its own effect is tiny enough to be well below being detectable. I don’t care. If everyone chose to play it safe, fascism would encounter zero resistance; I am not going to be complicit.

Overall, the choice above is not really a choice, not if you care about the outcome more than about your own track record. The only reasonable thing to do is pick the second option and shout the alarm as loud as possible.

In short: I could not be happier to acknowledge that my specific prediction (there is an authoritarian drive in the UK and it is not being met by an appropriate backlash) was wrong. For now. The situation might change: for as long as the worrying signs are present I will continue to call for countermeasures.

There are self-fulfilling prophecies, but also self-undermining ones, one ought to recognise them and act accordingly.

Consequences

I’ve learned one lesson: I do not know enough about what is happening in the US. Situation still looks very alarming, I still think a shitstorm might explode anytime, but I know there are many forces at play, most of them unknown to me. This makes all of my predictions moot, so I may as well avoid making them.

In the case of the UK, I’m happy to keep getting it wrong: here is my assessment of the current situation.

  1. The macroscopic and unprecedented mistakes made by the Tories are certainly due, at least in part, to their own hubris. They thought Corbyn was a lame duck and underestimated their own weaknesses (see above: they relied on a self-undermining prediction, ha!). Assuming they will repeat the same mistake again would be utterly foolish.
  2. The strongest rhetorical weapon of the Tories has been somewhat weakened, but it is not neutralised. It is self-evident that some Tories have been betting on the failure of the Brexit negotiations. In such a case, there is little doubt that the plan was to put all the blame onto the evil (undemocratic, unaccountable, etc.) European bureaucracy. To make this move effective, the Tories need to re-establish their own credibility, which isn’t easy, but I am not ready to bet that it’s impossible.
  3. Corbyn and McDonnell might still be sleepwalking into their own obliteration. If the Tories will find a way to neutralise their own hubris, they will automatically expose the blind self-righteousness of Corbyn and the Labour left (see below). In other words, the outcome of the 2017 General Election makes it more likely that Labour will fall on the same hubristic trap that has almost destroyed the current Tory leadership. We must try to compensate for this, which requires to actively push in the opposite direction.
  4. As far as Brexit goes, it would be a mistake to assume that it is now likely that Brexit will not happen. Once again, making this prediction inherently undermines it. Thus, the only reasonable strategy is to keep fighting against Brexit. The best way to do so hasn’t changed one inch (for some of my ideas, see this post and the preceding ones).

One entirely positive effect of the last election is that it is now visibly wrong to assume that the neoliberal overreach (links to an excellent article by Simon Wren-Lewis, see also this equally good one by Simon Tilford) is the only kind of rhetoric that chimes with the public. The importance of this change cannot be overestimated (by Dougald Hine) and is due to the relentless efforts of Corbyn and co. (as well as many con-causes, obviously). Yes, while acknowledging my own mistakes I also want to highlight what they did do well! Specifically, this historic change of mood is happening also because Corbyn and his team have forcefully ignored all advice intended to move them towards the so-called centre ground. I applaud their resilience, with all my heart. I also worry that the same resilience will mean they will keep favouring Brexit, and do so in a covert and oblique way (as they are doing now).

Taking an ambiguous stance while working towards a covert objective will inevitably backfire (only question is when and how). Most of Corbyn’s capital is in the form of personal credibility. He appears genuine and trustworthy, probably for good reasons. However, this capital can be destroyed in the blink of an eye: it will disappear instantly, if the electorate will conclude that Brexit was a bad idea and that Corbyn backed it all along. Moreover, sooner or later, Corbyn will have to abandon the current ambiguity, he will need to choose between an act of national self-harm (implicitly affirming that he doesn’t care for the well-being of his electors, not if that means compromising on his ideals), or to revise his world-view and accept that the EU is a problem that is worth having (see here and here). Depending on his previous actions, Corbyn might find himself already forced to pick the first option, which would be catastrophic.

Brexit is bad for the country and worse for the international scene. Backing it means backing the wrong forces of history. Anyone who cares for peace, international stability and development should be busy managing or fixing the many problems that afflict the EU. Choosing to help destroying the most effective peace-making project in the history of humanity is inexcusable and foolish.

For us single individuals, the course of action is therefore obvious.
We need to keep saying that Brexit is the worst decision the UK could take. We need to point out that it was taken on the basis of false information, the public was systematically misled, we need to remind everyone that the choice of 37.47% of the electorate cannot be misrepresented as “the will of the people”. We also need to keep asking Labour to stop backing Brexit. Brexit is self-destructive, contrary to all the values shared across the party (admittedly, it is not entirely incompatible with the values that distinguish the Labour’s left); but above all, it is morally indefensible.

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Posted in Ethics, Politics, Stupidity

Predictive processing: action and action control.

In our exploration of the Predictive Processing (PP) framework, it is time to complete the overall theoretical sketch by discussing how action and action control fit in the overall picture. This will allow to finally appreciate the astonishing explanatory power that precision weighting is proposed to carry.

Tasks that appear to be simple, such as walking, are not simple at all.
Image by Vanillase  [CC BY-SA 3.0].

[Note: this series of posts concentrates on Clark’s “Surfing Uncertainty” book. For a general introduction to the idea, see this post and the literature cited therein. The concept of precision weighting and how it fits in the PP framework is discussed in the previous post of this series.]

So far, we’ve seen that sensory input is processed by trying to anticipate it across a series of hierarchical layers which compare mini top-down predictions with the bottom up signal coming from sensory pathways. One concept that I find important to fully grasp is that, when a sensory organ transduces a stimulus into a nervous signal, only the first PP layer will actually receive what we can easily consider as the nervous representation of the original stimulus (probably including the expected precision of the signal itself), the next level up will receive only the prediction error, meaning that if the prediction was spot-on, no further signal will be sent to the higher levels at all. The absence of an error signal then must be considered as a signal in itself, meaning: “prediction was correct”. In terms of action and action control, this special quality of PP signalling pattern will play a crucial role, which we are about to explore.

Clark discusses the problem of action control and the solution proposed by PP in a biological-centric way, he does not ignore the engineering perspective (i.e.  action-control of manufactured robots and effectors), but doesn’t quite put it into the centre stage. Clark’s approach makes a lot of sense, of course. However, I found that in order to appreciate it in full one needs to be armed with a large amount of multidisciplinary knowledge, which I wouldn’t be able to summarise here. For this post, I will try to explore the same topic starting from an engineering point of view, which I hope will make the subject easier to follow, even for non-specialists.

As we saw for the case of measuring instruments, also action control is a problem that has been extensively studied by engineers. It turns out that allowing mechanical artefacts to autonomously act on the world is a hard problem to solve, especially if high precision is a requirement. Since the world is noisy, even in a highly controlled environment (such as an automated factory), noise, in the form of random deviations from the perfect “action” (as idealised in the engineers’ “plan”) will interfere with the movements enacted by a given robot/effector. This poses the problem of detecting such deviations and correcting them in real-time. The intuitively sensible way to allow machines to interact with the world with high precision is to allow feedback loops, where the robot finely readjusts its movements according to the aforementioned “plan”. This strategy is potentially very powerful, but it is extremely difficult to implement in practice, as it requires to design complex control systems: these define how the robot will  detect each possible deviation and how it should dynamically readjust its actions while they are already occurring. The standard way to tackle this problem would be to have a long sequence of logic “if/then” steps. In the real world, this approach becomes quickly impractical as it entails an explosion of interacting possibilities; it is really hard to produce robots that are able to run the program quickly enough to intervene on their actions in a timely fashion. Moreover, the situation becomes unmanageable once one realises that changing the action plan while it is executed inherently changes what should count as new anomalies. If the “plan” itself keeps changing, also the systems used to detect deviations need to dynamically readjust accordingly, while what would be an appropriate reaction to further departures from an already changing plan would also change at the same time! If you sensed the dangers posed by bottom-less recursion, I’d say that you have grasped the computational difficulty that is inherent in action-control.

Realising how hard this problem is has a direct consequence in our context: it is self-evident that animals in general and humans in particular are very good at the what we have just found to be computationally difficult (to put it mildly). The questions that should therefore puzzle more or less every neuroscientist interested in action control are:

How can nervous systems achieve what seems to be almost impossible?

Or:

How does it happen that extremely complex dynamic actions such as walking along a hiking trail (where the surface is uneven and each step requires different and fine adjustments) are normally fluid and feel effortless?

As you’re probably guessing, PP promises to solve this particular conundrum. Let’s see how.

It is well known that proprioception (the ensemble of sensory signals that report about position of our movable parts along with forces applied to them) follows its own sensory pathways, which, in somewhat surprising ways, are still hard to fully understand. In PP, sensory “prediction-based” architecture is expected to apply also to proprioception, with the added expectation that proprioception error signals are also used to control effectors (muscles). In this context, predictions represent, as before, the best guess the organism can produce for what a given sensory signal should be in the current context. Importantly, the last sentence implicitly contains a major twist in our story: in the proprioceptive arena, the context necessarily includes what the body is doing, or, if you prefer, it includes action. Better still, action (how the sensed body is moving) is inevitably a major ingredient of what signals are produced by proprioceptive organs. This means that context-dependent predictions have to be heavily influenced by what the organism is doing; it is a clearly strict requirement for the PP model to even apply to proprioception as a whole. Thus, according to PP, at any given level in a proprioceptive pathway, a higher PP layer would produce a prediction of what the proprioceptive signals would be if the body was moving in the expected way.

As a consequence, if PP does apply to proprioception, the relevant prediction error signals become concise descriptions of what isn’t moving according to the “original plan” (the prediction). PP theorists therefore propose that the prediction error, besides participating in the usual PP pattern, can also be recycled to control muscles. The key element here is that error signals are the “distilled” representations of the deviations from the expected action plan: they are inherently the exact kind of information that is required to readjust and can therefore be used more or less directly to control muscles **[Update 01/07/2017: following Clark’s kind feedback, please see note below for two important addenda]. Moreover, because the same error signals also participate in the multilayered PP pathway, large deviations will get a chance to travel upwards to higher level layers, and will thus be able to influence the overall plan and/or trigger a radical re-evaluation of the current high-level hypotheses. In this way, the overall PP architecture is able to directly explain how finely tuned control is even possible, as well as the role that proprioception is expected to have in our ability to understand what is going on in the real world. Depending on the strength and amount of prediction errors, error signals may trigger fine movement readjustments, and/or a change of plan, and/or force the organism to realise that the current best hypothesis about the state of the world was wrong and needs to be re-evaluated.

Naturally, real-time control must be supported, and this is inherently included, for the lower layers will be able to produce quick and small adjustments (with minimal impact on the overall plan), while big prediction errors will fail to be ironed out by the lower layers and will keep travelling upwards, where, if necessary, the original plan itself might change in more significant ways (which would, unsurprisingly, require more time). If even major action plan changes would fail to minimise proprioceptive prediction errors, the overall increase of error signals would force a re-evaluation of the context itself, as this condition inevitably occurs if/when the current state of affairs is likely to be quite different from the currently active “best explanation” computed by the overall PP system.

Going back to our engineering perspective, it is worth noting that, for control problems that include more that one linear degree of freedom (applies to virtually all action-control issues encountered by complex organisms), common artificial controllers end up being error-minimising feedback circuits (see for example Proportional-Integral-Derivative controllers / PID-controllers), which are at the very least analogous to a single PP layer.

For single PP action-controlling (proprioceptive) layers (as well as PID-controllers), if the computed error signal is entirely cancelled, it means that “everything is proceeding according to plan” and therefore the effectors receive no new control signal and can continue operating as planned. This chimes with the observation I’ve reported above: the absence of an error signal becomes a signal than means “all is well, no adjustment is needed”.

To complete the “action-control” picture I’ve tried to summarise, one element is still missing: the role of precision weighting. As per “passive” sensory pathways, proprioception organs will also have their inherent precision, thus, the initial sensory stimulus will still have an associated precision which can be weighted against top-down confidence. In terms of action planning and control (fine tuning of an action plan can be conceptualised as action planning and control at a high spatio-temporal resolution), the confidence that we have on a given action plan would be a direct function of how confident we are on our assessment of the current situation, as well as “how robust” the current plan seems to be. In PP, this confidence measure can be obtained by recycling the residual error produced by whichever PP layer is issuing the relevant “prediction for action”. Since all PP layers are expected to report a prediction error along with its precision/confidence weighting, this information is always available, making it theoretically possible for any PP layer to control action. This is important, but requires a long digression which I plan to follow separately. For now, I will concentrate on the proposed function of the precision weighting signal in action control.

At one level, it is obvious: low confidence in a given action plan (justified either by low confidence on our current evaluation of the external state of affairs, or by a low confidence on the effectiveness of the plan itself), means that deviations from the plan will have higher relative importance. Thus, error signals will have a bigger chance of travelling towards higher level PP layers and less propensity of being “explained away” by adjustments to the action itself. This mechanism follows the general PP architecture without any ad-hoc change and seems entirely appropriate: the lower the confidence on our action plan, the higher our propensity of radically changing the plan should be. Moreover, one interesting case is what happens when confidence is minimal (I don’t think it can be zero*). In this case, the possibility that such “extremely low confidence action predictions” will have of actually controlling action will be minimal, perhaps to the point of having no chance of initiating and/or influencing any movement at all. Thus, such predictions will remain output-less: they should be understood as action-plans that are not expected to be acted upon.

… !!! …

Yes, what you are thinking is what I meant! Adding precision weighting to the proposed PP action control mechanism immediately explains how brains may become able to produce and evaluate alternative action plans and, by extension, allows to start building an explanation of how imagination and day-dreaming can be implemented. Along the way, the basic mechanism underpinning actual dreams is also implied. QED, if you are reading this, I hope you are starting to understand the huge explanatory potential of PP in general and of precision weighting in particular.

Bibliography and notes

* In mainstream PP implementations, precision weighting is encoded as the gain of a given signal (irrespective of its direction). Thus, a prediction issued with zero confidence would be implemented as a signal with zero gain, which means “no signal at all”.

** [Update] Andy Clark has very kindly (thanks!) made me realise that it may be useful to make two additional points explicit.
1. In the special case when action is being initiated, the error signal will be maximal (the prediction would be entirely wrong, as the expected movement isn’t happening at all). In this situation, the error signal itself would contain precisely the information needed to get the planned action started. To be translated into actual movement, precision weighting must, in this case, markedly favour the prediction itself. In this way, PP becomes a unified framework which may be able to encompass perception, action selection (issuing the prediction in question), action control, and learning (see below).
2. Importantly, the proposed architecture is also able to learn. The whole idea is that error signals that can’t be cancelled by issuing more accurate predictions will ignite additional mechanisms dedicated to finding new and better predictions. I confess that I don’t have a clear idea of how such mechanisms are expected to operate (in terms of precise neurophysiological mechanisms, I might tackle this point in a later post), but in this context, it is important to note that the multilayered architecture allows for a concurrent “search” of more apt predictions across the whole stack, from perception to action control, passing through action planning/initiation. This allows to dynamically accommodate deviations that are due to noise, as well as bigger changes (say, in the case of a damaged limb, extreme tiredness, or a change of situation – swimming, for example).
The proposed architecture actually (/theoretically) allows to bootstrap action control itself: in fact, this view directly affects how we might interpret the uncoordinated movements of newborns. The main purpose of such relatively random (or apparently aimless) movements might in fact be to allow the whole stack of layers to search for and select appropriate predictions, based on the feedback signals that are triggered by the movements themselves.

ResearchBlogging.org

Clark, A (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind Oxford Scholarship DOI: 10.1093/acprof:oso/9780190217013.003.0011

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Posted in Neuroscience, Philosophy, Psychology

Predictive Processing: the role of confidence and precision

This is the second post in a series inspired by Andy Clark’s book “Surfing Uncertainty“. In the previous post I’ve mentioned that an important concept in the Predictive Processing (PP) framework is the role of confidence. Confidence (in a prediction) is inevitably linked to a similar, but distinct idea: precision. In this post I will discuss both, trying to summarise/synthesise the role that precision and confidence play in the proposed brain architecture. I will be doing this for a few reasons: first and foremost, much of the appeal of PP becomes evident only after integrating these concepts in the overall interpretative framework. Secondarily, Clark does an excellent job in linking together the vast number of phenomena where precision and confidence are thought to play a crucial role, thus an overview is necessary in order to allow enumerating them (in a follow-up post). Finally, reading the book allowed me to pinpoint what doesn’t quite convince me as much as I’d like. This post will thus allow me to summarise what I plan to criticise later on.

Image adapted from Kanai et al. 2015 © CC BY 4.0

[Note: this series of posts concentrates on Clark’s book, as it proposes a comprehensive and compelling picture of (mostly human) brains as prediction engines, from perception to action. For a general introduction to the idea, see this post and the literature cited therein. As usual, I’ll try to avoid highly abstract maths, as I’d like my writing to be as accessible as possible.]

Precision and confidence: definitions.

Precision is a common concept in contexts such as measurement, signal detection and processing. Instruments that measure something (or receive/relay some signal) can never produce exact measures: on different occurrences of the same quantity (whatever it is that it’s being measured/transmitted), the resulting reaction of the device will change slightly. To be honest, it’s more complicated than that: in discussing precision, one should also mention accuracy and how both values are needed to characterise a measurement system – as usual, Wikipedia does a good job at describing the two, allowing me to gloss over the details, for now.

The point where we first encounter precision is when dealing with perception: it goes without saying that perceptions rely on sensory stimuli, and these can be captured in ways that are more or less precise. For example, eyesight can be more or less precise in different people, but for all, the precision will drastically drop when looking underwater with our naked eyes. Our vision underwater becomes heavily blurred, and I think that we can all agree to describe this situation as a marked drop of precision in the detected visual signals.

Confidence is more slippery concept: the term itself is loaded because it presupposes an interpreter. Someone must have a given degree of confidence in something else: “confidence” itself cannot exist without an agent. I’ll come to this thorny philosophical issue (and others) in later posts. For now, we can discuss how Clark uses the concept (which is typical of PP frameworks). The general idea is that perception is an active business. Brains don’t passively receive input and then try to interpret it. In PP, brains are constantly busy trying to predict the signals that are arriving; when a prediction is successful, it will also count as a valid interpretation of the collected stimulus (one attractive feature of this architecture is that it allows to collapse certain powerful forms of learning along with active interpretation of sensory input: if PP is roughly correct, they happen within the same mechanism). In mainstream PP theories, prediction happens continuously at multiple layers within the brain architecture and is organised hierarchically, different layers will be busy predicting different aspects of incoming signals.
Within this general view, the idea of multiple layers allows to avoid positing a central interpreter that collects predictions: at any given time, each layer will be busy producing predictions for the layer below, while also receiving predictions from above. Thus, having dispensed of the dreaded homunculus (a central, human-like interpreter), the concept of confidence becomes more tractable: a given prediction is now a bundle of nervous signals, which can come encoded with some associated confidence (indicating the estimated likelihood that the prediction is correct), without having to sneak-in a fully fledged interpreter. The encoded confidence can have systematic effects on the receiving layer and exert such effects in a purely mechanistic way.

Thus, we can generally expect incoming (sensory) signals to arrive along with their evaluated precision (a mix of precision and accuracy, to be fair) while the downward predictions travel with a corresponding (but distinct!) property which looks at least analogous to what we normally call confidence.

What counts, and what is proposed to explain a fantastically diverse range of phenomena (from attention to psychosis, from imagination to action), is the interplay between precision (coming up, arriving in) and confidence (going down, from centre towards the sensory periphery). Let’s see a general overview, which will allow to refine the current sketch.

Interplay and conflation between precision and confidence.

In PP, any given layer would receive two inputs, one is arriving from the sensory periphery, the other is the prediction issued by higher-level layer(s). The general schema posits that the two inputs are compared. If the two signals match perfectly, the layer will remain silent (a sign of a successful prediction), otherwise the difference will be sent back to the higher level layer, signalling a prediction error. What precision and confidence do, in the PP flavour generally espoused by Clark, is change the relative importance of the two inputs (within a layer) and the importance of each signal in general, across all layers. Thus, a very precise signal will, in a sense, overpower a not-so-confident prediction; a very confident prediction will in turn be able to override a not so precise signal. Simple, uh? Perhaps an example can help clarifying. Our eyesight is quite precise in detecting where the source of a given signal is: we can use sight to locate objects in space with very good precision. Not being bats, the same does not apply to our auditory abilities. We can roughly localise where a noise comes from, but can’t pinpoint exactly where. Thus, vision has high spacial precision, hearing does not.

When I’m slumped on the sofa watching TV, the sounds I’ll perceive will come out from the speakers; however, I’ll perceive voices as if they were coming from the images of talking people within the screen. Why? According to PP, there will be a layer in my brain that combines auditory and visual “channels”. The visual one will be producing a prediction that a given sound comes from (the image of) a given mouth, the auditory channel will suggest otherwise (sound comes from where the speakers are). Thus, combining the two is a symmetric business: it could be that a given layer (driven by vision) produces the “source of sound” prediction and sends it to a layer which receives auditory data (from below). Otherwise the reverse could be the case, and the upcoming signal is visual, while the descending prediction is informed by the auditory channel. Either way, the visual channel (when discerning location) will have high precision (if upcoming) or high confidence (when issuing a prediction), while the auditory has low precision or confidence. When the two are combined to produce the prediction error (one that applies specifically to the combination of these two channels!), the visual signal will matter more, as it’s more precise/confident. Thus, if the prediction is visual, the error signal will be somewhat suppressed, signalling that the expectation (sound should come from where the mouth is seen) is likely to be correct. Vice-versa, if the prediction comes from the auditory channel, the error signal will be enhanced (signalling that the expectation is likely to be wrong). Either way, the end result doesn’t change: because vision is spatially more accurate than hearing, the final hypothesis produced by the brain will be that the voice is coming from where the mouth is seen, and the discrepancy across the two channels will be superseded.

This (oversimplified) example is interesting for a number of reasons. First of all, allows me to introduce another fundamental concept, which I’ll enunciate for completeness’ sake (I will not explain it in this post). In PP, what we end up perceiving at the conscious level is the most successful overall hypothesis: the combination of what all the layers produced, or the one hypothesis that is able to better suppress the error signals globally (within a single brain). There is a lot to unpack about this concept, so much so that even a full book can’t hope to explore all implications (more will follow!); for now, I will need my readers to take the statement above at face value.

The second interesting point is that the description above shows a peculiar symmetry: it doesn’t matter whether auditory information is used to produce a prediction, which is then matched to what is arriving via the visual pathway (in PP, this will be itself a residual prediction error), or vice-versa. In either case, we’ll perceive the sound as if it was coming from the viewed mouth. In turn, this means that the confidence of predictions (flowing down) and the precision of sensory signals (which are, after the very first layer, always in the form of residual errors!) are always combined, and can be modelled in terms of relative weight (higher weight is given more importance). In other words, the two values matter only relatively to one another; at a given layer, the effect of precision and confidence is determined by relative importance alone. That’s quantifiable in a single number, or, if you prefer, by a unidimensional, single variable.

Third observation is that, in view of the last point, the conflation of precision and confidence espoused by Clark and most of the PP theorists (for a paradigmatic example, see Kanai et al. 2015, where precision and confidence are described as a single variable, encoded by the strength of neural signals) is justified – at least, it is justified at this level of analysis. Because of how PP is supposed to work, it seems reasonable to conflate the two and sum them up in a single measure. In practical terms, the move is sensible: to describe the effects of precision and confidence on a single PP layer, all we need is a single measure of relative weight. Conceptually, it also makes sense: after the first layer, the upcoming signal (what I’ve described so far as incoming, sensory, information) is in fact a prediction error, which is in itself heavily influenced by the predictions that shaped it along the way. Thus, upcoming (incoming) signals cannot be said to encode their own precision (as they aren’t measurements any more), they de-facto encode a precision-cum-confidence signal. Overall, to fully embrace the PP hypothesis we are asked to collapse the (usually) distinct concepts of precision and confidence (at least for the upcoming signal); failing to do so would count as an a-priori rejection of the whole paradigm.

The above might look preposterous and over-complicated, however, I would like to remind my readers that brains are the most complex objects known to humanity (How complex? Beyond our ability to comprehend!). Thus, it would be unreasonable to expect that we could make sense of how they work via a single approach that also happens to be simple. Moreover, it’s relevant to note that both the concepts of perception (intended as mere signal detection) and prediction include their respective evaluation of reliability: any system described via one of the two concepts requires to treat either precision or confidence, in order to be fully functional (as commonly understood). What use is a weather forecast if it doesn’t at least implicitly come with an assurance that what it predicts is more accurate than pure guesswork? Would you use a measuring instrument that returns random numbers? Thus, I’d argue that a discussion of precision and confidence is necessary for any serious PP model, it is not a secondary hypothesis (or ingredient), it is as fundamental as the idea of prediction itself.

Finally, in the next post we’ll see that indeed, the proposed role of the interplay between precision and confidence is also the reason why PP is such an attractive proposition: the potential explanatory power of this orchestration is indeed stunning, to the point of being, perhaps, too good to be true.

Bibliography

ResearchBlogging.org
Clark, A (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind Oxford Scholarship DOI: 10.1093/acprof:oso/9780190217013.003.0011

Kanai R, Komura Y, Shipp S, & Friston K (2015). Cerebral hierarchies: predictive processing, precision and the pulvinar. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 370 (1668) PMID: 25823866

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Posted in Consciousness, Neuroscience, Philosophy

Partisan Review: “Surfing Uncertainty”, by Andy Clark.

Sometimes it happens that reading a book ignites a seemingly unstoppable whirlpool of ideas. The book in question is “Surfing Uncertainty: Prediction, Action, and the Embodied Mind” by Andy Clark.
Why is this a partisan review? Because Clark himself had already convinced me that the general idea is worth pursuing, well before writing the book. To use a famous expression: I want to believe. However, since I keep obsessing about my own biases, I also want to be as critical as possible – call it overcompensation, if you must.
In this post I will briefly review the book in general terms, the whirlpool of ideas mentioned above is mostly about the criticism I have to offer, which will come later (with the vague hope it might be useful).

As the title suggests, Clark’s book is about a subject I’ve touched before: the brain as a prediction engine (for an introduction, see previous posts: The Predictive Brain, part 1 and part 2). Clark himself summarised the arguments developed in the book via a series of posts published at the brains blog (starts here); for a review that also describes how the book is organised, see Andrew Buskell.
For my part, my previous discussion (links above) did not even mention a fundamental concept: confidence. When issuing a prediction, or even when detecting a signal, one important element that should never be overlooked is the evaluation of how much confidence can be attributed to the result. I’m using the word “confidence” here in order to keep the idea fairly general, for the time being. Clark does an exceptionally good job at explaining why the concept is indeed foundational, and how introducing it allows to move the generic predictive approach from the level of being a nice idea, all the way up to a research framework in the making. I mention this because, after appreciating the crucial role that confidence estimations play, my older presentation of the matter starts looking incomplete to the point of being misleading.

An important feature of the book is the emphasis it places on embodiment. However, it’s worth noting that the book starts by adopting mainstream assumptions of the computational kind – indeed, an overarching aim of the book is to bridge the gap between computational and embodied approaches. I was a little surprised to find that the computational assumptions are not really discussed: the fact that brains and neurons process information is taken as self-evident (Note: I agree!). The book proposes the “Predictive Processing” label (PP) as an overarching definition, able to point to a whole family of separate approaches. The “processing” word is telling: we are dealing with a solidly computational outlook. As the book proceeds, however, the ’embodiment’ promise in the title gets gradually fulfilled: PP isn’t merely applied to perception, instead, the book shows how the same framework can be used to model action and action control. The result is a continuum, from perception to action, which cannot possibly brush aside the fact that behaviours are physical: actual body parts move. Yes, brains have a controlling role over action, but, alas, much of vanilla cognitive science has been historically happy to study brain function without giving much attention to the role that bodies have in shaping what the brain does and, crucially, in defining what count as successful strategies.
Clark’s treatment has the refreshing quality of restoring the due balance in a field that has been characterised by unjustified prevalence of opposing extremes. Once upon a time, uncompromising behaviourism of the Skinner kind was accepted as the default assumption, only to get superseded by the opposite (and symmetrically wrong) stance of computational cognitivism.

Needless to say, in my view, this is one reason why this book was necessary (there are more!), and finds me in full agreement. [For those interested in this general debate, leaving aside the specific view offered by PP, I have explored (also) the relation between computationalism and embodiment in two posts at Conscious Entities: part 1, part 2. Moreover, I have recently produced my own Twitter-storm, commenting on Krakauer et. al. (2016) [Highly recommended reading!]. From the other side of the fence, my discussions (see also) with Golonka and Wilson might provide some insight on why and how classic cognitive approaches are being challenged by the school(s) of (more or less) Radical Embodiment.]

So far, so good: the assumptions on which the book rests look more than reasonable to my eyes, which made my reading sympathetic from the start. Additionally, one of the overarching aims of the book, reconciling computational and embodied approaches fits my own agenda perfectly. What’s not to like? Well, I must also report that some my own expectations did not find satisfaction.
Clark is a philosopher, so I was hoping to find a book that revolved around known philosophical issues, and showed how PP helps surpassing them. Pretty much like Hohwy’s “The Predictive Mind“, Clark’s book isn’t taking this approach. Instead, it begins from general considerations or observable phenomena, and methodically ties them to existing scientific literature. The result is a book that organises and summarises an astonishing amount of scientific (empirical, theoretical and frequently pretty arduous) work. Not what I was hoping for, but it turns out that sometimes you do get what you need. The works cited by Clark are usually scientific papers: as such, the vast majority isn’t suited to discuss the big picture at length, and is even less able to provide an overview of how different pieces fit together. Thus, we needed Clark to do this hard work, and indeed, it’s hard to imagine how it could have been done better.

If you are trying (like me) to gain a better understanding of where PP-centric research is heading, what it assumes, and what are the main conceptual pillars on which it rests, this book will satisfy your hunger and might even leave you feeling bloated. [Personal note: while reading the second half of the book I found that I was studiously slowing down. In part, this was to avoid overload and to make sure I was assimilating as much content as possible. In part, I just didn’t want the book to finish, as reading it was a genuine pleasure throughout.]

What of my aversion to standard academic publishing? [I.e.: my claim that peer-reviewed monographs frequently spoil the joy of reading by being overly cautious and pedantic.] Once again, my expectations proved to be misplaced (I love to be surprised! 😉 ).
On caution: the need to be fairly uncontroversial does transpire in many ways, you could say that it is ubiquitous. For example, Clark keeps adding caveats like “if the story I’ve been constructing is on the right track“, which went duly noticed and appreciated. We are, after all, dealing with an emerging, still to be established, research programme. The field is solidifying quickly, but we are far away from having a fairly complete and coherent jigsaw. On the other hand, treatments of thorny issues such as consciousness itself (why do we perceive some predictions?) are cautious to the point of being disappointingly sketchy, if not overlooked.

The flip side is how Clark leverages support from existing literature, which did surprise me in a good way. While reading, a recurring pattern characterised my reactions: judging on the primary text itself, I was frequently inclined to conclude “yes, the argument is promising, but I’m not convinced that it is strong/watertight enough to abandon due scepticism”. However, when the book relied on a body of evidence that I did happen to be familiar with, my initial reaction was regularly overridden (sometimes after checking the references): I ended up realising that Clark’s arguments are backed by wide and deep evidence (empirical and/or theoretical), to a point that wasn’t immediately evident by reading the text itself. Thus, my recommendation for future readers is obvious, but important: if you find yourself unconvinced, do read the relevant references. It is likely that you’ll find plenty of reasons to be convinced in the supporting bibliography. Needless to say: despite my bias against academic (peer-reviewed) monographs, Clark’s book struck me as an example that it is possible to get it right – chapeau.

Another pleasing consequence of how the book is organised is that, by offering a clear overview of the research field, it helped me in identifying the areas that failed to convince me in full. Since I know that I want to believe, I am very determined to use my resources to find and explore the reasons why the PP framework might not hold water. I found some, technical, which I plan to discuss separately. I also found what might be summarised as “gaps”, both at the beginning of the story (in the form of philosophical foundations as well as the lack of a convincing evolutionary perspective), and at the end (mostly in the lack of explanation of what we experience as our mental life).

The first consideration is a little worrisome; it seems to me that the research field is at risk of doing the usual mistake: oversimplifying. As for what I perceived as gaps, I see no reason for concern: if I’m right, these gaps should be treated as opportunities, to be seized by theorists coming from both philosophical and scientific perspectives.

In follow-up posts, I will try my own luck, and see where my criticism might lead – with apologies for being so openly and unjustifiably haughty.

Bibliography

ResearchBlogging.org
Clark, A (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind Oxford Scholarship DOI: 10.1093/acprof:oso/9780190217013.003.0011

Hohwy, J (2013). The Predictive Mind Oxford University Press DOI: 10.1093/acprof:oso/9780199682737.001.0001

Krakauer, J., Ghazanfar, A., Gomez-Marin, A., MacIver, M., & Poeppel, D. (2017). Neuroscience Needs Behavior: Correcting a Reductionist Bias Neuron, 93 (3), 480-490 DOI: 10.1016/j.neuron.2016.12.041

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Posted in Neuroscience, Philosophy, Psychology

Perspectives…

In the past few months I’ve spent some time looking for trouble on Twitter. I’ve found some (mild and polite), which translated into plenty food for thought, and eventually allowed me to put some order in my thoughts. The matter stems from the urgency of finding ways of having a positive (no matter how small!) effect on the political landscape, but inevitably extends far and wide, touching philosophy, psychology and more generally, how (one may try) not to be a jerk. The gist: nobody can ever fully grasp someone else’s point of view, that’s why dialogues are useful, but also the reason why getting it wrong is so easy.

This song is OT, I’m adding it here as a collective hat tip to the many wonderful women who enrich my life and keep me on my toes. Thank you all, I owe you a lot! © Morcheeba

To get started, I’ll draw from two twitter episodes that didn’t actually involve yours truly. The first went globally viral at a serendipitous time, you can read it here. It reports an all too common history of sexism and implicit bias. For my current purpose, the relevant observation is that Martin R. Schneider (the author of the twitter thread) clearly isn’t (and wasn’t, at the time) your typical douchebag, he most likely was already well aware of widespread sexism and the problems it creates. Nevertheless, he was taken by surprise when he finally got to experience sexism in first person. I suspect the thread got shared so widely because the story did surprise many (including, I confess, myself). Question is: why? Personally, I try hard to be aware of these issues, I work in an environment that is perhaps among the best places to rise awareness, and yet, my reaction was disappointing, all I could think was: “D’oh, I shouldn’t be surprised”.

To get closer to an explanation, another (related) Twitter thread might help: Eve Forster tried a similar experiment (summary here, while this is a Twitter search encompassing the length of the experiment), and guess what? She managed to surprise herself. Pretending to be a male made her self-image and attitude change in ways she didn’t expect. [Update, May 13 2017: Eve has now written a thoughtful piece about her experiment on Vox, well worth your time!]

Finally, here is an important report (HT Zara Bain) by the late Harriet McBryde Johnson on her encounters with Peter Singer. [Side Note: when I wrote this article I was willing to give Singer the benefit of doubt, reading Johnson’s article convinced me that he is indeed culpable of epistemic arrogance.] Why is this relevant? Because it makes the crucial point obvious: we can try to put ourselves in somebody else’s shoes, but our consequent understanding will usually be miles away from the real thing.

Obvious? Yes. Important? Obviously. Neglected? You bet! It’s the neglect that interests me.

We can only reason by leveraging the cognitive resources we already possess; when trying to understand what it’s like to be someone else, we may amplify the weight of some experiences, transpose some others to a different context, suppress a given family of feelings and so on. But no matter how we try, whatever happens to be qualitatively different from our past experience will be forever out of reach (echoes of Mary). However, try we must, and because we do, our exercise will usually produce some results. The trouble happens when one remembers that, to say it with Kahneman (2011), our brains easily produce the impression that what you get is all there is. In other words, if the imaginative exercise produces an impression that feels coherent, we are inclined to believe it – what else could we do? Thus, Singer is inherently unable to grasp why and how Johnson’s life feels unquestionably worthwhile from within (including why this feeling matters!), and Johnson has her own trouble in understanding how could Singer be so blind. Similarly, merely acting on the pretence of being someone else produces experiences that, being qualitatively new, Forster herself could not predict. In Schneider’s case, knowing that sexism is vicious and ubiquitous wasn’t an adequate substitute of experiencing it first hand.

This whole picture chimes extremely well with the growing interest in the idea that our brains are best understood as prediction engines, especially when modelled as Bayesian engines (for a gentle, very broad introduction, see here). Clark (2015, footnote #1, Chapter 10, p328), makes the point perfectly:

[A]t the very heart of human experience, PP [Predictive Processing] suggests, lie the massed strata of our own (mostly unconscious) expectations. This means that we must carefully consider the shape of the worlds to which we (and our children) are exposed. If PP is correct, our percepts may be deeply informed by non-conscious expectations acquired through the statistical lens of our own past experience. So if […] the world that tunes those expectations is thoroughly sexist or racist, that will structure the subterranean prediction machinery that actively constructs our own future perceptions – a potent recipe for tainted ‘evidence’, unjust reactions, and self-fulfilling, negative prophecies.

I took the liberty of transcribing the note almost in full because it highlights the core intuition that I’m wishing to put in writing. What we experience, and importantly, how we interpret it, is necessarily shaped by what we have and haven’t experienced already (this is tautological, that’s why it matters). Thus, it is not sufficient to realise that we are blind to our own systematic mistakes, doing so is just the first step. What is important is to realise that different world-views are mutually blind to each other’s differences and then blind to their own blindness. Thus, we finally reach my own misdeeds.

I have been exploring this train of thought for quite a while; in my efforts, I try to do as I preach, and actively sought criticism on Twitter. I was not surprised to find it easily, but on calmer reflection, it is surprising that I did manage to enact the kind of mistake I was trying to uncover. Surprising and ironically beautiful. The first conversation happened here, the second (sub)thread is this (apologies for the length). In both cases, I failed miserably (I’ve selected two sub-threads, picking the ones that showed my failings clearly). In the first case, I failed to deliver my main message (I’m trying to explain it better in this post), in the second, I got it wrong in more complex (and somewhat sinister) ways. Why? My conclusion is once more the same: I failed to fully grasp how my blabbing would be perceived, and I concurrently failed to spot the first failure. Failure and Meta-failure, hurray!

Conclusion

Hopefully, the direct and indirect experiences I’ve summarised here all point in the same direction. Bridging different points of views is hard, especially because each point of view would (usually) feel both complete and coherent from within – even when we are imagining someone else’s perspective. These differences are typically the direct consequence of different life experiences (cfr. Clark’s quote above), as such, they are in and of themselves entirely justified. Moreover, because two people can never share the same experiential trajectory, imagining someone else’s point of view is hard and frequently misleading. That’s why dialogue is important, but also why it is always dangerous to assume the other is simply mistaken. Reason does not help in bridging the gap, because it usually can’t: what is needed is an experiential bridge.

Why does this matter? Because politics. It’s no mystery that Western societies are dangerously veering to the right. What is less visible is that right-wing propaganda is exceptionally good at building and exploiting  experiential bridges. Conversely, the progressive side is spectacularly bad at winning hearts, and unsurprisingly, mostly blind to its own failure (do I need to mention Corbyn?). Thus, trying to understand what we’re doing wrong isn’t simply important, it’s an existential matter. Right now, we should all try to help people realise how profoundly they are being misled. Our game should not be about winning arguments, much less sneering at the gullibility of the masses. We should be busy changing minds, and to do so, we must begin by erasing our false sense of superiority.

Bibliography and further reading.

Kahneman, D (2011). Thinking, Fast and Slow Farrar, Straus and Giroux ISBN: 978-0374275631

Clark, A (2016). Surfing Uncertainty: Prediction, Action, and the Embodied Mind Oxford Scholarship DOI: 10.1093/acprof:oso/9780190217013.003.0011

ResearchBlogging.org
Abeba Birhane recently published a relevant article: Descartes was wrong: ‘a person is a person through other persons’, which explores, from a very different point of view (!), some of the themes I’m developing here. Highly recommended.
For those interested in my own trajectory, one of my earliest articles here also discusses a connected mechanism, which I’ve called ‘cognitive attractors‘.

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Posted in Ethics, Philosophy, Politics, Psychology, Stupidity
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