Some thoughts on writing ‘Bayes Glaze’ theoretical papers.

[This was a twitter navel-gazing thread someone ‘unrolled’. I was really surprised that it read basically like a blog post, so I thought why not post it here directly! I’ve made a few edits for readability. So consider this an experiment in micro-blogging ….]

In the past few years, I’ve started and stopped a paper on metacognition, self-inference, and expected precision about a dozen times. I just feel conflicted about the nature of these papers and want to make a very circumspect argument without too much hype. As many of you frequently note, we have way too many ‘Bayes glaze’ review papers in glam mags making a bunch of claims for which there is no clear relationship to data or actual computational mechanisms.

It has gotten so bad, I sometimes see papers or talks where it feels like they took totally unrelated concepts and plastered “prediction” or “prediction error” in random places. This is unfortunate, and it’s largely driven by the fact that these shallow reviews generate a bonkers amount of citations. It is a land rush to publish the same story over and over again just changing the topic labels, planting a flag in an area and then publishing some quasi-related empirical stuff. I know people are excited about predictive processing, and I totally share that. And there is really excellent theoretical work being done, and I guess flag planting in some cases is not totally indefensible for early career researchers. But there is also a lot of cynical stuff, and I worry that this speaks so much more loudly than the good, careful stuff. The danger here is that we’re going to cause a blowback and be ultimately seen as ‘cargo cult computationalists’, which will drag all of our research down both good and otherwise.

In the past my theoretical papers in this area have been super dense and frankly a bit confusing in some aspects. I just wanted to try and really, really do due-diligence and not overstate my case. But I do have some very specific theoretical proposals that I think are unique. I’m not sure why i’m sharing all this, but I think because it is always useful to remind people that we feel imposter syndrome and conflict at all career levels. And I want to try and be more transparent in my own thinking – I feel that the earlier I get feedback the better. And these papers have been living in my head like demons, simultaneously too ashamed to be written and jealous at everyone else getting on with their sexy high impact review papers.

Specifically, I have some fairly straightforward ideas about how interoception and neural gain (precision) inter-relate, and also have a model i’ve been working on for years about how metacognition relates to expected precision. If you’ve seen any of my recent talks, you get the gist of these ideas.

Now, I’m *really* going to force myself to finally write these. I don’t really care where they are published, it doesn’t need to be a glamour review journal (as many have suggested I should aim for). Although at my career stage, I guess that is the thing to do. I think I will probably preprint them on my blog, or at least muse openly about them here, although i’m not sure if this is a great idea for theoretical work.

Further, I will try and hold to three key promises:

  1. Keep it simple. One key hypothesis/proposal per paper. Nothing grandiose.
  2. Specific, falsifiable predictions about behavioral & neurophysiological phenomenon, with no (minimal?) hand-waving
  3. Consider alternative models/views – it really gets my goat when someone slaps ‘prediction error’ on their otherwise straightforward story and then acts like it’s the only game in town. ‘Predictive processing’ tells you almost *nothing* about specific computational architectures, neurobiological mechanisms, or general process theories. I’ve said this until i’m blue in the face: there can be many, many competing models of any phenomenon, all of which utilize prediction errors.

These papers *won’t* be explicitly computational – although we have that work under preparation as well – but will just try to make a single key point that I want to build on. If I achieve my other three aims, it should be reasonably straight-forward to build computational models from these papers.

That is the idea. Now I need to go lock myself in a cabin-in-the-woods for a few weeks and finally get these papers off my plate. Otherwise these Bayesian demons are just gonna keep screaming.

So, where to submit? Don’t say Frontiers…

UPDATED WITH ANSWERS – summary of the major questions [and answers] asked at #LSEbrain about the Bayesian Brain Hypothesis

ok here are the answers! meant to release them last night but was a bit delayed by sleep 🙂

OK it is about 10pm here and I’ve got an HBM abstract to submit but given that the LSE wasn’t able to share the podcast, i’m just going to quickly summarize some of the major questions brought up either by the speakers or audience during the event.

For those that don’t know, the LSE hosted a brief event tonight exploring the question: “is the brain a predictive machine”, with panelists Paul Fletcher, Karl Friston, Demis Hassabis, Richard Holton and chaired by Benedetto De Martino. I enjoyed the event as it was about the right length and the discussion was lively. For those familiar with Bayesian brain/predictive coding/FEP there wasn’t much new information, but it was cool to see an outside audience react.

These were the principle questions that came up in the course of the event. Keep in mind these are just reproduced from my (fallible) memory:

  • What does it mean if someone acts, thinks, or otherwise behaves irrationally/non-optimally. Can their brain still be Bayesian at a sub-personal level?
    • There were a variety of answers to this question, with the most basic being that optimal behavior depends on ones prior, so someone with a mental disorder or poor behavior may be acting optimally with respect to their priors. Karl pointed out that that this means optimal behavior really is different for every organism and person, rendering the notion of optimal trivial.
  • Instead of changing the model, is it possible for the brain to change the world so it fits with our model of it?
    • Yes, Karl calls this active inference and it is a central part of his formulation of the Bayesian brain. Active inference allows you to either re-sample or adjust the world such that it fits with your model, and brings in a kind of strong embodiment to the Bayesian brain. This is because the kinds of actions  (and perceptions) one can engage in are shaped by the body and internal states,
  • Where do the priors come from?
    • Again the answer from Karl – evolution. According to the FEP, organisms who survive do so in virtue of their ability to minimize free energy (prediction error). This means that for Karl evolution ‘just is the refinement and inheritance of our models of the world’; our brains reflect the structure of the world which is then passed on through natural selection and epigenetic mechanisms.
  • Is the theory falsifiable and if so, what kind of data would disprove it?
    • From Karl – ‘No. The theory is not falsifiable in the same sense that Natural Selection is not falsifiable’. At this there were some roars from the crowd and philosopher Richard Holton was asked how he felt about this statement. Richard said he would be very hesitant to endorse a theory that claimed to be non-falsifiable.
  • Is it possible for the brain to ‘over-fit’ the world/sensory data?
    • Yes, from Paul we heard that this is a good description of what happens in psychotic or other mental disorders, where an overly precise belief might resist any attempts to dislodge it or evidence to the contrary. This lead back into more discussion of what it means for an organism to behave in a way that is not ‘objectively optimal’.
  • If we could make a Bayesian deep learning machine would it be conscious, and if so what rights should we give it?
    • I didn’t quite catch Demis response to this as it was quite quick and there was a general laugh about these types of questions coming up.
  • How exactly is the brain Bayesian? Does it follow a predictive coding, approximate, or variational Bayesian implementation?
    • Here there was some interesting discussion from all sides, with Karl saying it may actually be a combination of these methods or via approximations we don’t yet understand. There was a lot of discussion about why Deep Brain doesn’t implement a Bayesian scheme in their networks, and it was revealed that it is because hierarchical Bayesian inference is currently too computationally demanding for such applications. Karl picked up on this point to say that the same is true of the human brain; the FEP outlines some general principles but we are still far from understanding how the brain actually approximates Bayesian inference.
  • Can conscious beliefs, or decisions in the way we typically think of them, be thought of in a probabilistic way?’
    • Karl: ‘Yes’
    • Holton: Less sure
    • Panel: this may call for multiple models, binary vs discrete, etc
    • Karl redux: isn’t it interesting how now we are increasingly reshaping the world to better model our predictions, i.e. using external tools in place of memory, navigation, planning, etc (i.e. extended cognition)

There were other small bits of discussion, particularly concerning what it means for an agent to be optimal or not, and the relation of explicit/conscious states to a subpersonal Bayesian brain, but I’m afraid I can’t recall them in enough detail to accurately report them. Overall the discussion was interesting and lively, and I presume there will be some strong opinions about some of these. There was also a nice moment where Karl repeatedly said that the future of neuroscience was extended and enactive cognition. Some of the discussion between the panelist was quite interesting, particularly Paul’s views on mental disorders and Demis talking about why the brain might engage in long-term predictions and imagination (because collecting real data is expensive/dangerous).

Please write in the comments if I missed anything. I’d love to hear what everyone thinks about these. I’ve got my opinions particularly about the falsification question, but I’ll let others discuss before stating them.

PubPeer – A universal comment and review layer for scholarly papers?

Lately I’ve had a plethora of discussions with colleagues concerning the possible benefits of a reddit-like “democratic review layer”, which would index all scholarly papers and let authenticated users post reviews subject to karma. We’ve navel-gazed about various implementations ranging from a full out reddit clone, a wiki, or even a full blown torrent tracker with rated comments and mass piracy. So you can imagine I was pleasantly surprised to see someone actually went ahead and put together a simple app to do exactly that.

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Pubpeer states that it’s mission is to “create an online community that uses the publication of scientific results as an opening for fruitful discussion.” Users create accounts using an academic email address and must have at least one first-author publication to join. Once registered any user can leave anonymous comments on any article, which are themselves subject to up/down votes and replies.

My first action was of course to search for my own name:

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Hmm, no comments. Let’s fix that:

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Hah! Peer review is easy! Just kidding, I deleted this comment after testing to see if it was possible. Ostensibly this is so authors can reply to comments, but it does raise some concerns that one can just leave whatever ratings you like on your own papers. In theory with enough users, good comments will be quickly distinguished from bad, regardless of who makes them.  In theory… 

This is what an article looks like in PubPeer with a few comments:

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Pretty simple- any paper can be found in the database and users then leave comments associated with those papers. On the one hand I really like the simplicity and usability of PubPeer. I think any endeavor along these lines must very much follow the twitter design mentality of doing one (and only one) thing really well. I also like the use of threaded comments and upvote/downvotes but I would like to see child comments being subject to votes. I’m not sure if I favor the anonymous approach the developers went for- but I can see costs and benefits to both public and anonymous comments, so I don’t have any real suggestions there.

What I found really interesting was just to see this idea in practice. While I’ve discussed it endlessly, a few previously unforeseen worries leaped out right away. After browsing a few articles it seems (somewhat unsurprisingly) that most of the comments are pretty negative and nit-picky. Considering that most early adopters of such a system are likely to be graduate students, this isn’t too surprising. For one thing there is no such entity as a perfect paper, and graduate students are often fans of these kind of boilerplate nit-picks that form the ticks and fleas of any paper. If comments add mostly doubt and negativity to papers, it seems like the whole commenting process would become a lot of extra work for little author pay-off, since no matter what your article is going to end up looking bad.

In a traditional review, a paper’s flaws and merits are assessed privately and then the final (if accepted) paper is generally put forth as a polished piece of research that stands on it’s on merits. If a system like PubPeer were popular, becoming highly commented would almost certainly mean having tons of nitpicky and highly negative comments associated to that manuscript. This could manipulate reader perceptions- highly commented PubPeer articles would receive fewer citations regardless of their actual quality.

So that bit seems very counter-productive to me and I am not sure of the solution. It might be something similar to establishing light top-down comment moderation and a sort of “reddiquette” or user code of conduct that emphasizes fair and balanced comments (no sniping). Or, perhaps my “worry” isn’t actually troubling at all. Maybe such a system would be substantially self-policing and refreshing, shifting us from an obsession with ‘perfect papers’ to an understanding that no paper (or review) should be judged on anything but it’s own merits. Given the popularity of pun threads on reddit, i’m not convinced the wholly democratic solution will work. Whatever the result, as with most solutions to scholarly publishing, it seems clear that if PubPeer is to add substantial value to peer review then a critical mass of active users is the crucial missing ingredient.

What do you think? I’d love to hear your thoughts in the comments.

Enactive Bayesians? Response to “the brain as an enactive system” by Gallagher et al

Shaun Gallagher has a short new piece out with Hutto, Slaby, and Cole and I felt compelled to comment on it. Shaun was my first mentor and is to thank for my understanding of what is at stake in a phenomenological cognitive science. I jumped on this piece when it came out because, as I’ve said before, enactivists often  leave a lot to be desired when talking about the brain. That is to say, they more often than not leave it out entirely and focus instead on bodies, cultural practices, and other parts of our extra-neural milieu. As a neuroscientist who is enthusiastically sympathetic to the embodied, enactive approach to cognition, I find this worrisome. Which is to say that when I’ve tried to conduct “neurophenomenological” experiments, I often feel a bit left in the rain when it comes time construct, analyze, and interpret the data.

As an “enactive” neuroscientist, I often find the de-emphasis of brains a bit troubling. For one thing, the radically phenomenological crew tends to make a lot of claims to altering the foundations of neuroscience. Things like information processing and mental representation are said to be stale, Cartesian constructs that lack ontological validity and want to be replaced. This is fine- I’m totally open to the limitations of our current explanatory framework. However as I’ve argued here, I believe neuroscience still has great need of these tools and that dynamical systems theory is not ready for prime time neuroscience. We need a strong positive account of what we should replace them with, and that account needs to act as a practical and theoretical guide to discovery.

One worry I have is that enactivism quickly begins to look like a constructivist version of behaviorism, focusing exclusively on behavior to the exclusion of the brain. Of course I understand that this is a bit unfair; enactivism is about taking a dynamical, encultured, phenomenological view of the human being seriously. Yet I believe to accomplish this we must also understand the function of the nervous system. While enactivists will often give token credit to the brain- affirming that is indeed an ‘important part’ of the cognitive apparatus, they seem quick to value things like clothing and social status over gray matter. Call me old fashioned but, you could strip me of job, titles, and clothing tomorrow and I’d still be capable of 80% of whatever I was before. Granted my cognitive system would undergo a good deal of strain, but I’d still be fully capable of vision, memory, speech, and even consciousness. The same can’t be said of me if you start magnetically stimulating my brain in interesting and devious ways.

I don’t want to get derailed arguing about the explanatory locus of cognition, as I think one’s stances on the matter largely comes down to whatever your intuitive pump tells you is important.  We could argue about it all day; what matters more than where in the explanatory hierarchy we place the brain, is how that framework lets us predict and explain neural function and behavior. This is where I think enactivism often fails; it’s all fire and bluster (and rightfully so!) when it comes to the philosophical weaknesses of empirical cognitive science, yet mumbles and missteps when it comes to giving positive advice to scientists. I’m all for throwing out the dogma and getting phenomenological, but only if there’s something useful ready to replace the methodological bathwater.

Gallagher et al’s piece starts:

 “… we see an unresolved tension in their account. Specifically, their questions about how the brain functions during interaction continue to reflect the conservative nature of ‘normal science’ (in the Kuhnian sense), invoking classical computational models, representationalism, localization of function, etc.”

This is quite true and an important tension throughout much of the empirical work done under the heading of enactivism. In my own group we’ve struggled to go from the inspiring war cries of anti-representationalism and interaction theory to the hard constraints of neuroscience. It often happens that while the story or theoretical grounding is suitably phenomenological and enactive, the methodology and their interpretation are necessarily cognitivist in nature.

Yet I think this difficulty points to the more difficult task ahead if enactivism is to succeed. Science is fundamentally about methodology, and methodology reflects and is constrained by one’s ontological/explanatory framework. We measure reaction times and neural signal lags precisely because we buy into a cognitivist framework of cognition, which essentially argues for computations that take longer to process with increasing complexity, recruiting greater neural resources. The catch is, without these things it’s not at all clear how we are to construct, analyze, and interpret our data.  As Gallagher et al correctly point out, when you set out to explain behavior with these tools (reaction times and brain scanners), you can’t really claim to be doing some kind of radical enactivism:

 “Yet, in proposing an enactive interpretation of the MNS Schilbach et al. point beyond this orthodox framework to the possibility of rethinking, not just the neural correlates of social cognition, but the very notion of neural correlate, and how the brain itself works.”

We’re all in agreement there: I want nothing more than to understand exactly how it is our cerebral organ accomplishes the impressive feats of locomotion, perception, homeostasis, and so on right up to consciousness and social cognition. Yet I’m a scientist and no matter what I write in my introduction I must measure something- and what I measure largely defines my explanatory scope. So what do Gallagher et al offer me?

 “The enactive interpretation is not simply a reinterpretation of what happens extra-neurally, out in the intersubjective world of action where we anticipate and respond to social affordances. More than this, it suggests a different way of conceiving brain function, specifically in non-representational, integrative and dynamical terms (see e.g., Hutto and Myin, in press).”

Ok, so I can’t talk about representations. Presumably we’ll call them “processes” or something like that. Whatever we call them, neurons are still doing something, and that something is important in producing behavior. Integrative- I’m not sure what that means, but I presume it means that whatever neurons do, they do it across sensory and cognitive modalities. Finally we come to dynamical- here is where it gets really tricky. Dynamical systems theory (DST) is an incredibly complex mathematical framework dealing with topology, fluid dynamics, and chaos theory. Can DST guide neuroscientific discovery?

This is a tough question. My own limited exposure to DST prevents me from making hard conclusions here. For now let’s set it aside- we’ll come back to this in a moment. First I want to get a better idea of how Gallagher et al characterize contemporary neuroscience, the source of this tension in Schillbach et al:

Functional MRI technology goes hand in hand with orthodox computational models. Standard use of fMRI provides an excellent tool to answer precisely the kinds of questions that can be asked within this approach. Yet at the limits of this science, a variety of studies challenge accepted views about anatomical and functional segregation (e.g., Shackman et al. 2011; Shuler and Bear 2006), the adequacy of short-term task- based fMRI experiments to provide an adequate conception of brain function (Gonzalez-Castillo et al. 2012), and individual differences in BOLD signal activation in subjects performing the same cognitive task (Miller et al. 2012). Such studies point to embodied phenomena (e.g., pain, emotion, hedonic aspects) that are not appropriately characterized in representational terms but are dynamically integrated with their central elaboration.

Claim one is what I’ve just argued above, that fMRI and similar tools presuppose computational cognitivism. What follows I feel is a mischaracterization of cognitive neuroscience. First we have the typical bit about functional segregation being extremely limited. It surely is and I think most neuroscientists today would agree that segregation is far from the whole story of the brain. Which is precisely why the field is undeniably and swiftly moving towards connectivity and functional integration, rather than segregation. I’d wager that for a few years now the majority of published cogneuro papers focus on connectivity rather than blobology.

Next we have a sort of critique of the use of focal cognitive tasks. This almost seems like a critique of science itself; while certainly not without limits, neuroscientists rely on such tasks in order to make controlled assessments of phenomena. There is nothing a priori that says a controlled experiment is necessarily cognitivist anymore so than a controlled physics experiment must necessarily be Newtonian rather than relativistic. And again, I’d characterize contemporary neuroscience as being positively in love with “task-free” resting state fMRI. So I’m not sure at what this criticism is aimed.

Finally there is this bit about individual differences in BOLD activation. This one I think is really a red herring; there is nothing in fMRI methodology that prevents scientists from assessing individual differences in neural function and architecture. The group I’m working with in London specializes in exactly this kind of analysis, which is essentially just creating regression models with neural and behavioral independent and dependent variables. There certainly is a lot of variability in brains, and neuroscience is working hard and making strides towards understanding those phenomena.

 “Consider also recent challenges to the idea that so-called “mentalizing” areas (“cortical midline structures”) are dedicated to any one function. Are such areas activated for mindreading (Frith and Frith 2008; Vogeley et al. 2001), or folk psychological narrative (Perner et al. 2006; Saxe & Kanwisher 2003); a default mode (e.g., Raichle et al. 2001), or other functions such as autobiographical memory, navigation, and future planning (see Buckner and Carroll 2006; 2007; Spreng, Mar and Kim 2008); or self -related tasks(Northoff & Bermpohl 2004); or, more general reflective problem solving (Legrand andRuby 2010); or are they trained up for joint attention in social interaction, as Schilbach etal. suggest; or all of the above and others yet to be discovered.

I guess this paragraph is supposed to get us thinking that these seem really different, so clearly the localizationist account of the MPFC fails. But as I’ve just said, this is for one a bit of a red herring- most neuroscientists no longer believe exclusively in a localizationist account. In fact more and more I hear top neuroscientists disparaging overly blobological accounts and referring to prefrontal cortex as a whole. Functional integration is here to stay. Further, I’m not sure I buy their argument that these functions are so disparate- it seems clear to me that they all share a social, self-related core probably related to the default mode network.

Finally, Gallagher and company set out to define what we should be explaining- behavior as “a dynamic relation between organisms, which include brains, but also their own structural features that enable specific perception-action loops involving social and physical environments, which in turn effect statistical regularities that shape the structure of the nervous system.” So we do want to explain brains, but we want to understand that their setting configures both neural structure and function. Fair enough, I think you would be hard pressed to find a neuroscientist who doesn’t agree that factors like environment and physiology shape the brain. [edit: thanks to Bryan Patton for pointing out in the comments that Gallagher’s description of behavior here is strikingly similar to accounts given by Friston’s Free Energy Principle predictive coding account of biological organisms]

Gallagher asks then, “what do brains do in the complex and dynamic mix of interactions that involve full-out moving bodies, with eyes and faces and hands and voices; bodies that are gendered and raced, and dressed to attract, or to work or play…?” I am glad to see that my former mentor and I agree at least on the question at stake, which seems to be, what exactly is it brains do? And we’re lucky in that we’re given an answer by Gallagher et al:

“The answer is that brains are part of a system, along with eyes and face and hands and voice, and so on, that enactively anticipates and responds to its environment.”

 Me reading this bit: “yep, ok, brains, eyeballs, face, hands, all the good bits. Wait- what?” The answer is “… a system that … anticipates and responds to its environment.” Did Karl Friston just enter the room? Because it seems to me like Gallagher et al are advocating a predictive coding account of the brain [note: see clarifying comment by Gallagher, and my response below]! If brains anticipate their environment then that means they are constructing a forward model of their inputs. A forward model is a Bayesian statistical model that estimates posterior probabilities of a stimulus from prior predictions about its nature. We could argue all day about what to call that model, but clearly what we’ve got here is a brain using strong internal models to make predictions about the world. Now what is “enactive” about these forward models seems like an extremely ambiguous notion.

To this extent, Gallagher includes “How an agent responds will depend to some degree on the overall dynamical state of the brain and the various, specific and relevant neuronal processes that have been attuned by evolutionary pressures, but also by personal experiences” as a description of how a prediction can be enactive. But none of this is precluded by the predictive coding account of the brain. The overall dynamical state (intrinsic connectivity?) of the brain amounts to noise that must be controlled through increasing neural gain and precision. I.e., a Bayesian model presupposes that the brain is undergoing exactly these kinds of fluctuations and makes steps to produce optimal behavior in the face of such noise.

Likewise the Bayesian model is fully hierarchical- at all levels of the system the local neural function is constrained and configured by predictions and error signals from the levels above and below it. In this sense, global dynamical phenomena like neuromodulation structure prediction in ways that constrain local dynamics.  These relationships can be fully non-linear and dynamical in nature (See Friston 2009 for review). Of the other bits –  evolution and individual differences, Karl would surely say that the former leads to variation in first priors and the latter is the product of agents optimizing their behavior in a variable world.

So there you have it- enactivist cognitive neuroscience is essentially Bayesian neuroscience. If I want to fulfill Gallagher et al’s prescriptions, I need merely use resting state, connectivity, and predictive coding analysis schemes. Yet somehow I think this isn’t quite what they meant- and there for me, lies the true tension in ‘enactive’ cognitive neuroscience. But maybe it is- Andy Clark recently went Bayesian, claiming that extended cognition and predictive coding are totally compatible. Maybe it’s time to put away the knives and stop arguing about representations. Yet I think an important tension remains: can we explain all the things Gallagher et al list as important using prior and posterior probabilities? I’m not totally sure, but I do know one thing- these concepts make it a hell of a lot easier to actually analyze and interpret my data.

fake edit:

I said I’d discuss DST, but ran out of space and time. My problem with DST boils down to this: it’s descriptive, not predictive. As a scientist it is not clear to me how one actually applies DST to a given experiment. I don’t see any kind of functional ontology emerging by which to apply the myriad of DST measures in a principled way. Mental chronometry may be hokey and old fashioned, but it’s easy to understand and can be applied to data and interpreted readily. This is a huge limitation for a field as complex as neuroscience, and as rife with bad data. A leading dynamicist once told me that in his entire career “not one prediction he’d made about (a DST measure/experiment) had come true, and that to apply DST one just needed to “collect tons of data and then apply every measure possible until one seemed interesting”. To me this is a data fishing nightmare and does not represent a reliable guide to empirical discovery.