Predictive coding and how the dynamical Bayesian brain achieves specialization and integration

Authors note: this marks the first in a new series of journal-entry style posts in which I write freely about things I like to think about. The style is meant to be informal and off the cuff, building towards a sort of socratic dialogue. Please feel free to argue or debate any point you like. These are meant to serve as exercises in writing and thinking,  to improve the quality of both and lay groundwork for future papers. 

My wife Francesca and I are spending the winter holidays vacationing in the north Italian countryside with her family. Today in our free time our discussions turned to how predictive coding and generative models can accomplish the multimodal perception that characterizes the brain. To this end Francesca asked a question we found particularly thought provoking: if the brain at all levels is only communicating forward what is not predicted (prediction error), how can you explain the functional specialization that characterizes the different senses? For example, if each sensory hierarchy is only communicating prediction errors, what explains their unique specialization in terms of e.g. the frequency, intensity, or quality of sensory inputs? Put another way, how can the different sensations be represented, if the entire brain is only communicating in one format?

We found this quite interesting, as it seems straightforward and yet the answer lies at the very basis of predictive coding schemes. To arrive at an answer we first had to lay a little groundwork in terms of information theory and basic neurobiology. What follows is a grossly oversimplified account of the basic neurobiology of perception, which serves only as a kind of philosopher’s toy example to consider the question. Please feel free to correct any gross misunderstandings.

To begin, it is clear at least according to Shannon’s theory of information, that any sensory property can be encoded in a simple system of ones and zeros (or nerve impulses). Frequency, time, intensity, and so on can all be re-described in terms of a simplistic encoding scheme. If this were not the case then modern television wouldn’t work. Second, each sensory hierarchy presumably  begins with a sensory effector, which directly transduces physical fluctuations into a neuronal code. For example, in the auditory hierarchy the cochlea contains small hairs that vibrate only to a particular frequency of sound wave. This vibration, through a complex neuro-mechanic relay, results in a tonitopic depolarization of first order neurons in the spiral ganglion.

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The human cochlea, a fascinating neural-mechanic apparatus to directly transduce air vibrations into neural representations.

It is here at the first-order neuron where the hierarchy presumably begins, and also where functional specialization becomes possible. It seems to us that predictive coding should say that the first neuron is simply predicting a particular pattern of inputs, which correspond directly to an expected external physical property. To try and give a toy example, say we present the brain with a series of tones, which reliably increase in frequency at 1 Hz intervals. At the lowest level the neuron will fire at a constant rate if the frequency at interval n is 1 greater than the previous interval, and will fire more or less if the frequency is greater or less than this basic expectation, creating a positive or negative prediction error (remember that the neuron should only alter its firing pattern if something unexpected happens). Since frequency here is being signaled directly by the mechanical vibration of the cochlear hairs; the first order neuron is simply predicting which frequency will be signaled. More realistically, each sensory neuron is probably only predicting whether or not a particular frequency will be signaled – we know from neurobiology that low-level neurons are basically tuned to a particular sensory feature, whereas higher level neurons encode receptive fields across multiple neurons or features. All this is to say that the first-order neuron is specialized for frequency because all it can predict is frequency; the only afferent input is the direct result of sensory transduction. The point here is that specialization in each sensory system arises in virtue of the fact that the inputs correspond directly to a physical property.

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Presumably, first order neurons predict the presence or absence of a particular, specialized sensory feature owing to their input. Credit: wikipedia.

Now, as one ascends higher in the hierarchy, each subsequent level is predicting the activity of the previous. The first-order neuron predicts whether a given frequency is presented, the second perhaps predicts if a receptive field is activated across several similarly tuned neurons, the third predicts a particular temporal pattern across multiple receptive fields, and so on. Each subsequent level is predicting a “hyperprior” encoding a higher order feature of the previous level. Eventually we get to a level where the prediction is no longer bound to a single sensory domain, but instead has to do with complex, non-linear interactions between multiple features. A parietal neuron thus might predict that an object in the world is a bird if it sings at a particular frequency and has a particular bodily shape.

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The motif of hierarchical message passing which encompasses the nervous system, according the the Free Energy principle.

If this general scheme is correct, then according to hierarchical predictive coding functional specialization primarily arises in virtue of the fact that at the lowest level each hierarchy is receiving inputs that strictly correspond to a particular feature. The cochlea is picking up fluctuations in air vibration (sound), the retina is picking up fluctuations in light frequency (light), and the skin is picking up changes in thermal amplitude and tactile frequency (touch). The specialization of each system is due to the fact that each is attempting to predict higher and higher order properties of those low-level inputs, which are by definition particular to a given sensory domain. Any further specialization in the hierarchy must then arise from the fact that higher levels of the brain predict inputs from multiple sensory systems – we might find multimodal object-related areas simply because the best hyper-prior governing nonlinear relationships between frequency and shape is an amodal or cross-model object. The actual etiology of higher-level modules is a bit more complicate than this, and requires an appeal to evolution to explain in detail, but we felt this was a generally sufficient explanation of specialization.

Nonlinearity of the world and perception: prediction as integration

At this point, we felt like we had some insight into how predictive coding can explain functional specialization without needing to appeal to special classes of cortical neurons for each sensation. Beyond the sensory effectors, the function of each system can be realized simply by means of a canonical, hierarchical prediction of each layered input, right down to the point of neurons which predict which frequency will be signaled. However, something still was missing, prompting Francesca to ask – how can this scheme explain the coherent, multi-modal, integrated perception, which characterizes conscious experience?

Indeed, we certainly do not experience perception as a series of nested predictions. All of the aforementioned machinery functions seamlessly beyond the point of awareness. In phenomenology a way to describe such influences is as being prenoetic (before knowing; see also prereflective); i.e. things that influence conscious experience without themselves appearing in experience. How then can predictive coding explain the transition from segregated, feature specific predictions to the unified percept we experience?

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When we arrange sensory hierarchies laterally, we see the “markov blanket” structure of the brain emerge. Each level predicts the control parameters of subsequent levels. In this way integration arises naturally from the predictive brain.

As you might guess, we already hinted at part of the answer. Imagine if instead of picturing each sensory hierarchy as an isolated pyramid, we instead arrange them such that each level is parallel to its equivalent in the ‘neighboring’ hierarchy. On this view, we can see that relatively early in each hierarchy you arrive at multi-sensory neurons that are predicting conjoint expectations over multiple sensory inputs. Conveniently, this observation matches what we actually know about the brain; audition, touch, and vision all converge in tempo-parietal association areas.

Perceptual integration is thus achieved as easily as specialization; it arises from the fact that each level predicts a hyperprior on the previous level. As one moves upwards through the hierarchy, this means that each level predicts more integrated, abstract, amodal entities. Association areas don’t predict just that a certain sight or sound will appear, but instead encode a joint expectation across both (or all) modalities. Just like the fusiform face area predicts complex, nonlinear conjunctions of lower-level visual features, multimodal areas predict nonlinear interactions between the senses.

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A half-cat half post, or a cat behind a post? The deep convolutional nature of the brain helps us solve this and similar nonlinear problems.

It is this nonlinearity that makes predictive schemes so powerful and attractive. To understand why, consider the task the brain must solve to be useful. Sensory impressions are not generated by simple linear inputs; certainly for perception to be useful to an organism it must process the world at a level that is relevant for that organism. This is the world of objects, persons, and things, not disjointed, individual sensory properties. When I watch a cat walk behind a fence, I don’t perceive it as two halves of a cat and a fence post, but rather as a cat hidden behind a fence. These kinds of nonlinear interactions between objects and properties of the world are ubiquitous in perception; the brain must solve not for the immediately available sensory inputs but rather the complex hidden causes underlying them. This is achieved in a similar manner to a deep convolutional network; each level performs the same canonical prediction, yet together the hierarchy will extract the best-hidden features to explain the complex interactions that produce physical sensations. In this way the predictive brain summersaults the binding problem of perception; perception is integrated precisely because conjoint hypothesis are better, more useful explanations than discrete ones. As long as the network has sufficient hierarchical depth, it will always arrive at these complex representations. It’s worth noting we can observe the flip-side of this process in common visual illusions, where the higher-order percept or prior “fills in” our actual sensory experience (e.g. when we perceive a convex circle as being lit from above).

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Our higher-level, integrative priors “fill in” our perception.

Beating the homunculus: the dynamic, enactive Bayesian brain

Feeling satisfied with this, Francesca and I concluded our fun holiday discussion by thinking about some common misunderstandings this scheme might lead one into. For example, the notion of hierarchical prediction explored above might lead one to expect that there has to be a “top” level, a kind of super-homunculus who sits in the prefrontal cortex, predicting the entire sensorium. This would be an impossible solution; how could any subsystem of the brain possibly predict the entire activity of the rest? And wouldn’t that level itself need to be predicted, to be realised in perception, leading to infinite regress? Luckily the intuition that these myriad hypotheses must “come together” fundamentally misunderstands the Bayesian brain.

Remember that each level is only predicting the activity of that before it. The integrative parietal neuron is not predicting the exact sensory input at the retina; rather it is only predicting what pattern of inputs it should receive if the sensory input is an apple, or a bat, or whatever. The entire scheme is linked up this way; the individual units are just stupid predictors of immediate input. It is only when you link them all up together in a deep network, that the brain can recapitulate the complex web of causal interactions that make up the world.

This point cannot be stressed enough: predictive coding is not a localizationist enterprise. Perception does not come about because a magical brain area inverts an entire world model. It comes about in virtue of the distributed, dynamic activity of the entire brain as it constantly attempts to minimize prediction error across all levels. Ultimately the “model” is not contained “anywhere” in the brain; the entire brain itself, and the full network of connection weights, is itself the model of the world. The power to predict complex nonlinear sensory causes arises because the best overall pattern of interactions will be that which most accurately (or usefully) explains sensory inputs and the complex web of interactions which causes them. You might rephrase the famous saying as “the brain is it’s own best model of the world”.

As a final consideration, it is worth noting some misconceptions may arise from the way we ourselves perform Bayesian statistics. As an experimenter, I formalize a discrete hypothesis (or set of hypotheses) about something and then invert that model to explain data in a single step. In the brain however the “inversion” is just the constant interplay of input and feedback across the nervous system at all levels. In fact, under this distributed view (at least according to the Free Energy Principle), neural computation is deeply embodied, as actions themselves complete the inferential flow to minimize error. Thus just like neural feedback, actions function as  ‘predictions’, generated by the inferential mechanism to render the world more sensible to our predictions. This ultimately minimises prediction error just as internal model updates do, albeit in a different ‘direction of fit’ (world to model, instead of model to world). In this way the ‘model’ is distributed across the brain and body; actions themselves are as much a part of the computation as the brain itself and constitute a form of “active inference”. In fact, if one extends their view to evolution, the morphological shape of the organism is itself a kind of prior, predicting the kinds of sensations, environments, and actions the agent is likely to inhabit. This intriguing idea will be the subject of a future blog post.

Conclusion

We feel this is an extremely exciting view of the brain. The idea that an organism can achieve complex intelligence simply by embedding a simple repetitive motif within a dynamical body seems to us to be a fundamentally novel approach to the mind. In future posts and papers, we hope to further explore the notions introduced here, considering questions about “where” these embodied priors come from and what they mean for the brain, as well as the role of precision in integration.

Questions? Comments? Feel like i’m an idiot? Sound off in the comments!

Further Reading:

Brown, H., Adams, R. A., Parees, I., Edwards, M., & Friston, K. (2013). Active inference, sensory attenuation and illusions. Cognitive Processing, 14(4), 411–427. http://doi.org/10.1007/s10339-013-0571-3
Feldman, H., & Friston, K. J. (2010). Attention, Uncertainty, and Free-Energy. Frontiers in Human Neuroscience, 4. http://doi.org/10.3389/fnhum.2010.00215
Friston, K., Adams, R. A., Perrinet, L., & Breakspear, M. (2012). Perceptions as Hypotheses: Saccades as Experiments. Frontiers in Psychology, 3. http://doi.org/10.3389/fpsyg.2012.00151
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 364(1521), 1211–1221. http://doi.org/10.1098/rstb.2008.0300
Friston, K., Thornton, C., & Clark, A. (2012). Free-Energy Minimization and the Dark-Room Problem. Frontiers in Psychology, 3. http://doi.org/10.3389/fpsyg.2012.00130
Moran, R. J., Campo, P., Symmonds, M., Stephan, K. E., Dolan, R. J., & Friston, K. J. (2013). Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation. The Journal of Neuroscience, 33(19), 8227–8236. http://doi.org/10.1523/JNEUROSCI.4255-12.2013

 

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.