Tuesday, 26 July 2011


I'm not that interested in the brain.




What is the brain doing?
It's clearly the case that the brain is up to something; it consumes something like 20% of our body's energy (Clark & Sokoloff, 1999), and our bodies are thoroughly innervated by projections to and from the nervous system. So what, precisely, is it up to?

Incorrect Answer: Mentally representing the world
Whatever it's up to, the brain is not representing the world. My main reason for thinking this is that this argument is firmly based in the assumption of poverty of stimulus, which is entirely incorrect. My very first post on this blog was about this;  modern cognitive psychology is committed to this assumption, and everything it thinks the brain is up to stems from this assumption. In actual fact, we are embedded in a rich information flow to which we have a great deal of direct access, so representation just isn't really required any more.

A note about what I think representations are for
Cognitive psychology is dedicated to the idea that the input to the system is insufficient to support the kinds of behaviour we see. Mental representations are mediating states which have content, and this content is used to supplement the input so as to make it good enough to produce the behaviour we actually observe. But they are only required if the input is impoverished, and the point of the ecological approach is that the input is rich and specific to the environment. If this is the case, then the brain doesn't need to be mentally representing anything.

There are a couple of reasons why people think the stimulus is poor. The tipping point for the cognitive revolution in the 1960s was Chomsky's review of Skinner's Verbal Behaviour, which claimed to show that the input for language simply wasn't structured enough to support a learning story. In perception, the poverty of stimulus assumption is rooted in studying the anatomy of the eye. The retina seems to only be capable to producing an upside-down, horribly noisy and pixelated image, and so clearly this input needs serious enriching in order to support the visual experience we are all familiar with. Gibson spent some time detailing why this is ridiculous; vision is not derived from static images and the retinal image is an invention of scientists, an analytic tool, not a fact of perception. Interestingly, while Chomsky's ideas are alive and well in linguistics, psychology is getting increasingly interested in the richness of the statistical structure of linguistic input and starting to tell interesting learning stories.

So what is the brain doing then?
The brain is not some isolated, abstract executive. The idea of the brain in a vat is incoherent and a distraction; real brains are utterly integrated with a wide variety of other systems as well as the world around it, via perception. Brains have an important job to do, but they just aren't the only player in the game and there's a lot of things they simply don't need to do because some other system takes care of it. (Done carefully, this is the point of embodied, enactivist theories of cognition). To work out what the brain needs to do, one thing we have to do is understand what everything else is up to. We need job descriptions for the brain and the bodies they're embedded in.

Here's what I believe to be true:
Our behaviour emerges over time as we respond to the flow of information in our environment. We are not general purpose systems - we are, instead, at any given moment, one kind of task specific device (and importantly, not another kind). The kind of device we currently are is a function of what we've been up to recently; the specifics of the device reflect the nature of multiple subsystems and how these respond to the flow of information we are currently embedded within (more on this in a moment; bear with me).

This information flow is surprisingly stable; we are not adrift in a 'blooming, buzzing confusion' (to use the regularly misused William James quote). One consequence of this stability is that we can rely on it to do a lot of work for us, and there is plenty of evidence that we do just this. My favourite example is change-blindness, a phenomenon in which people can be easily made to not notice dramatic changes in their environment by removing the information specifying that a change has occurred. The early versions of these studies involved 2-frame animations of, say an aeroplane. In one frame, the engine was present under the wing, and in the other frame it's missing. You play this animation over and over and ask people whether anything is changing, and people take ages to find it, if ever. There's a critical trick, however: the animation has to be filtered to remove what's called a transient signal: the abrupt change in pixel values creates a blip, if you like, in the signal. If you filter this out, but still have two very different frames, then people simply often do not see the change. There are more recent and ambitious demonstrations, including some great real life examples (see this video, for example) but they all also require that something covers up the information that something has changed (a camera cut, or an intervening event).

Change blindness tells us that we do not store a representation of a scene; instead, we simply perceive the flow of information about the scene and respond. We don't see the change because the information specifying that a change has occurred has been removed - no information, no basis for noticing the difference. We float along in a flow of information and our behaviour emerges as we interact with that flow.

We aren't passive blank slates, however. At any given moment in time we are very specific measurement devices, sensitive to some information variables and not others and capable of responding in some ways and not others. We spend our days being one device, then another, then another, in response to changes in the information flow and in accordance with our capacities. The information flow alters as our location in space and time changes, and is specific to the current environment; our capacities reflect the current state of multiple subsystems, of which the brain is only one.

The kinds of devices we are
If I measure my height with a ruler calibrated in inch units, I get the number 67 out. If I do it with a ruler calibrated in centimetre units, I get 170. It's the same amount of space, but my two measurements have produced different results because they are calibrated differently.  If I want to measure area and only have a straight ruler, I have to make several independent measurements and combine them in a computation; if I have a polar planimeter, I can measure the area directly, no computation required. The 'simple' unit depends on the device and can, actually, be quite higher order (see this old post for what I still think is an excellent explanation using right angled triangles).

The moral of those two points is this: perception is an act of measurement, and the result of an act of measurement depends on the device doing the measuring. The perception-action approach claims we perceptually measure the world using measurement devices calibrated in action-relevant units; the output is therefore action-scaled without any further manipulation required and can be plugged directly into the action system in question. 

The trick here is that the actions we are attempting to perform are constantly changing, and thus the device we measure the world with needs to be regularly re-calibrated to different scales. Proffit's work on distance perception clearly demonstrates that we perceive the 'same' distance differently depending on whether we intend to traverse it by walking or throwing. This is what I mean when I say we move through the day being one device, then another, then another - as the required actions change, so must our perceptual measurement systems.

Calibration & Subsystems
We are, in turns out, very flexible devices. Healthy adults can calibrate and recalibrate swiftly and efficiently, although it takes years to be fully competent at this (hence motor development is a long, slow process). Recalibration does take time (we are physical systems with inertia and delays) but then persists for a while without active maintenance (for roughly the same reason); it can, of course, be maintained more robustly if the required information remains present. 

We build these devices out of the locally available resource dynamics; the combination of the task-related incidental dynamics and our internally available inherent dynamics (definitions and details here). We couple ourselves to incidental dynamical resources via perception (e.g. tool use); we use perception in an identical fashion for our inherent dynamics (there's no privileged access, no peeking behind the curtain - we only know what perception tells us), but these are obviously special in one sense, namely that we take them from one task to the next. 

Our inherent dynamical resources come from a variety of subsystems. Each contributes different elements to a task-specific perception-action device. Most critically, I think, these systems work at different time-scales to provide a careful balance between stability and flexibility, and this is where the brain starts to enter back into things for me. Bingham (1988) describes the following basic divisions in the inherent dynamics. These subsystems are all coupled to one another, and their basic behaviours are non-linear, as are the couplings.
  1. The link-segment system: our skeleton is hooked together in a very specific way, and this arrangement enables some behaviour and rules some other behaviours out. This arrangement doesn't fundamentally change over time (barring injury, although the composition of the bones remodels in response to load-bearing exercise over long time scales), and thus it serves as a very stable basis for the other systems. The role of this system is to provide a physical substrate for the transmission of the forces involved in moving and interacting with the world, and it places useful constraints on the kind of motions that are possible.
  2. The musculotendon system: the size, shape and composition of this system is partly constrained by genetics, but is of course highly responsive to use. The time course is days and weeks, and so this system is highly stable over short periods of time but flexible and responsive to demand over longer time scales. The role of this system is to generate the forces required to move the link-segment system; it's organised in ways to solve many of the 'degrees of freedom' problems inherent in controlling a complex system.
  3. The circulatory system:  This system is highly responsive to current events: it only takes 10s of seconds for heart rate to accommodate current energy demands, and veins and arteries change size and shape in response. The role of this system is to deliver energy to the musculotendon system so it can generate the forces required to move the link-segment system.
  4. The respiratory system: This is another highly responsive system which adapts to current requirements on very short time-scales. It's role is to provide oxygen and remove carbon dioxide, to enable the continuing metabolic processes powering the muscles.
  5. Nutritional systems: this operates over longer time scales, and it's role is to provide the nutrients and energy required for the above systems. 
Finally, the nervous system. The brain and it's innervation of the body via the peripheral nervous system seems to me to be the fast response system in all this. The nervous system responds on microsecond time scales, and is in constant flux. It's connected to everything, and is, I think, the primary medium for the informational coupling between the various subsystems (not the only one; a lot of these systems have direct physical contact, for instance). 

The brain is dynamically stable, possibly (probably?) ideally edge-of-chaos stable. It exhibits a great deal of structural stability (visual cortex is in the same place in everyone), but even this stability is being actively maintained by the stability of the informational flow. Change that flow (for example, have someone pick up a tool, or provide information that you actually have three hands) and that structure smoothly alters in response. The reason visual cortex is where it is and organised the way it is, therefore, is because that's what a nervous system looks like when it is exposed to that kind of informational flow. Change the flow, change the organisation (as happens when people become blind, for instance: that cortex is 'colonised' by neighbouring functionality because it's structure as visual cortex is no longer being maintained).

Besides this actively maintained stability, the brain also ebbs and flows in fast response to changes in that flow. I remember taking Olaf Sporn's neuroscience class and watching microscope video of neurons extending and retracting axons on millisecond timescales; neurons aren't static at all, they are alive and frankly they are busy as fuck. The brain is alive and on the go and what it looks like at any given moment is a function of the information flow it is currently embedded in.

Trying to get a rigorous handle on this kind of dynamic behaviour is a daunting task. What makes me think it's possible is the fact that people are trying seriously to do it. I mentioned Olaf Sporns; he's a neuroscience professor at IU and he's about the only neuroscientist I've ever seen in person grapple seriously with the dynamic nature of the brain. He gave a talk once to the cognitive science reading group when I was there, and I came away thinking that 'yes, that's it, that's the way to do business'. I'm slowly reading his excellent book Networks of the Brain, and the purpose of that book is to connect neuroscientists with the modern science of network analysis so they can give up on 'biologically plausible neural networks' (you know, the little PDP toys we all got impressed with in the 80s and 90s) and actually try to cope somehow with the multi-scale structure of the brain. 

(One of the ideas I like a lot already is that of topological neighbours. Topology is a flavour of geometry that describes the set of possible transformations and relations possible when you relax the rules as far as you can; no need to preserve metric distances, for example. Networks can contain regions which are not physically next to one another but that are informationally coupled so that they are effectively part of the same network This informational coupling can then ebb and flow over time, so a given bit of cortex can be involved in multiple systems doing different things at different times. Of course, not all combinations are possible; there are anatomical limits on who is connected to whom and by how many steps.  It's not clear to me that fMRI based neuroscience can cope with this kind of data, and wouldn't know it was seeing it if it saw it.)

So what is the brain doing? With our clearer understanding of what's going into the total system, we can start making sensible guesses.

I think the nervous system is the fast switching system that enables us to functionally wall off resource dynamics to form task specific devices, a walling off which lasts only as long as it's supported by the flow of information. I think the nervous system routes action scaled information to the systems that can use it. In network geometry terms, I think the nervous system is a high dimensional shape that constantly changes configuration in response to information, preserving some aspects and transforming others. I think the notion of invariance-over-transformation is a geometric tool that applies equally to this network as to the optic flow Gibson applied it too; there is flux, but critically there is invariance and that invariance is information. I also think these are all very different from what most people seem to think goes on in the brain.

I don't think the brain is performing all the computations supposedly required to move a limb, given a) many of those 'computations' are actually solved by the architecture of, say, the hand, and b) not enough people seem to know about the way the nervous system actually moves limbs, namely via equilibrium point control. I don't think the nervous system is the place to look to explain stability in behaviour; the globally stable environment we live in and have perceptual contact with solves that problem.

Now all I need to do is make these slightly mad sounding ideas make more sense and hold together better. Annoying questions welcome :)

Bingham, G. (1988). Task-specific devices and the perceptual bottleneck. Human Movement Science, 7 (2-4), 225-264 DOI: 10.1016/0167-9457(88)90013-9  Download

Clarke, D.D., & Sokoloff, L. (1999). Circulation and energy metabolism of the brain. In Basic Neurochemistry, Molecular, Cellular and Medical Aspects (6th ed) (Agranoff,   B.W., Siegel, G. J, eds), pp. 637-670. Lippincott-Raven

Sporns, O. (2010) Networks of the Brain. Cambridge, MA: MIT Press. Amazon.co.uk


  1. I think the question of representation is interesting. And its used differently by different sorts of psychologists. So to a cognitive psychologist "representation" will mean something quite different to a traditional animal learning theorist.
    Where do you stand on things like Rescorla-Wagner model? There have to be learned responses to environmental stimuli - because otherwise we wouldn't know how to react in the world. And there is reasonable neuroscience research suggesting that the delta term in RW is represented by activity of dopamine neurons in striatum.
    What about things like Pearce-Hall? Given that stimulus rich environment, we need to be able to decide which stimulus representations to assign value to, and attend - and Pearce-Hall (and Mackintosh) 'attentional' mechanisms are one way of doing this. (Though attention is another problematic concept imo). And again, there is good neuroscience evidence showing that the brain impacts on Pearce-Hall alpha - eg. effects of lesions of central nucleus.
    Maybe you need to get away from cognitive neuroscience and look at behavioural neuroscience?

  2. You argue that the brain doesn't "represent" because the environment is rich enough to support behaviour. This may work for perception-action type behaviours but it doesn't really cover all cognition or brain function in my view. E.g., episodic memory allows me to retrieve information about who I sat next to at my sister's wedding over a year ago. Hard to see how this is supported by the environment.

    "Representation" is sometimes taken as a loaded word, but I don't think it should be controversial. The central nervous system is (in my view) in the business of transforming information from one form to another. For example, the pattern of light on the retina is transformed into a pattern of firing in primary visual cortex. It seems reasonable to call this transformed information a representation.

    Whatever you call it the brain is transforming information into different forms. Some can support action fairly directly in line with the ideas you put forward (e.g., some parietal representations seem to mediate perception-action transformations) but others fit less well.

    I still take the view that neural network models are quite a good way of understanding real neural computation, albeit hugely simplified. In this framework it is hard to define discrete "computations" but a given neuron or population of neuron retains the same computation so long as its connections are unchanged. To extrapolate a given part of the brain is doing something, and it is our job to find out what. If the same part of the brain is active in more than one task, it is nonetheless doing the same thing so we should infer that the tasks can entail the common computations. This is what fMRI is useful for.

    It is clear that the brain is a very dynamic system, but it is not so obvious (to me) that it can only be understood through its dynamics. It seems to me that fMRI gives very useful information about the nature of processing/representation/computation in different parts of the brain. Brain imaging is still in its infancy and I don't think we yet understand this very well. It seems possible or likely that the functional organization of these regions might be understood in terms of some simple principles reflecting brain topology and connectivity, and I think fMRI can play an important role in understanding the spatial constraints, while other techniques (DTI, EEG, MEG etc) might be more useful for understanding dynamics and connectivity. Both will be important.

  3. An interesting post but I don't understand why you equate representations with the 'poverty of the stimulus' argument. I am quite happy with the idea that the world provides a rich, detailed, dynamic & statistically structured input, but I still find it helpful to think about how cognition might transform and represent this input to accomplish different tasks.

  4. Nice Post!

    I think you are correct that brain-aversion has been a detriment to Ecological Psychology and, I would add, to behaviorists. If we could articulate a better vision of the role of the brain as a component of the active organism, rather than as the control system for a Cartesian robot, we would be much better off. It is ridiculously challenging, and while there is much good work being done, it is not well enough understood yet to provide a simple, usable alternative to the cognitive vocabulary. One interesting place we might find help with this is the budding interest in "Neuropragmatism" (e.g. http://neuropragmatism.com/). I think an increasing number of neuroscientists are becoming disillusioned with the cognitive framework, so the time is ripe. Any chance you want to draft some notes, or let me steal from your blog for a chapter in the nascent Eco-Psych textbook project?

    To other posters: The problems with the representational model of the mind are not obvious in themselves. The problems are that the representational model derives from a faulty understanding of the mind-body relationship, and a faulty model of what it means to be an actor in the world. Thus, I am increasingly convinced that arguing about the existence or non-existence of representations in the brain is not productive. To be productive, the discussion has to focus on more basic issues. The fundamental function of the brain is to be part of the system whereby people act in the world. That actions are directed at objects and events spread out over space and time doesn't really change anything. Redefining the word representation to not mean re-presentation doesn't help, if you keep all the baggage that the word brings with it. Similarly, the term 'information processing' is now so ambiguous that it has become virtually meaningless except for inevitably carrying some bit of supposedly disavowed baggage. (Information about what? Information to whom?)

    I'm one of those crazy people who think you need to understand something better to explain it more simply. If you cannot think of a way to explain what the brain does that does not use the words "representation" or "information processing", then they are crutches, and not convenient short-hands. If you can explain what the brain does without those words, ask yourself seriously what is value-added about using them. If most of the apparent value is illusory (e.g. it makes it sound like we are talking about re-presenting, when we really are not), then we should probably ditch the terms.


    P.S. Really, the textbook project is started, you want to draft something?

  5. Eric's actually covered a lot of the ground I was going to hit. Let me just add a couple of things;

    1. My main goal here isn't actually to argue about what representations are. I have many reasons to think they are the wrong formalism for understanding cognition, and I'm now trying to front up to the question 'if not representation, then what is the brain doing?'. So I don't want to spend too much time on representation here; Sabrina and I have posted about these things throughout the blog, and Sabrina has more coming sparking off this post; feel free to comment!

    Pam: I have no particular beef with behaviourist learning models, other than they also trade in fairly simplistic notions about what counts as input. I also don't think the kind of associational mechanisms they entail are sufficient; any two things can be associated just by virtue of co-occurance, but what you need is meaning. I prefer the Gibsons 'differentiation of invariant information' approach.

    Anon: representations, as used in cognitive psychology, are only necessary if you have a gap to fill between information and behaviour. See below for the issue here.

    This is a standard reply, and it certainly needs an answer. Sabrina's plotting on this front, it's something she's especially interested in.

    Here's my potted reply: I think cognitive science jumped too far upstream too early. Instead of starting at the bottom and working up, cognitive science started studying things like 'episodic memory' and 'imagination' and all those complex things. They then looked to see how these things might be supported, and because they assumed poverty of stimulus and hadn't done any work at this critical, lower level, they identified a gap between the world and the behaviour they observed. They filled this gap with representations, and proceeded to interpret everything they did next in those terms.

    What I think is required is for psychology to go back to the drawing board a bit, and get a clearer picture of what is actually available to support behaviour. If you go back to the start, and work your way back up carefully, you actually end up in a different place. My hunch, my bet, is that if cognitive/neuro-science did this then we would actually end up studying different things, and talking about them in different ways. So your difficulty in seeing that it could be something else is a) a product of an historical process we need to rerun and b) therefore not my problem, just yours.

    Gibson reran this process, and ended up in a fundamentally different (and I think, better) place. On this blog I've spent a lot of time talking about why I think this approach is better and viable. I now want to spend some time resting on this foundation without defending it further and taking the next step: if not representation, then what?

    What I can't do much of is rest on modern neuroscience, because that data was collected and interpreted within a framework I think is incoherent and a dead end, and that is incommensurate with the re-evaluation inspired by Gibson. I don't think the current tools and methods are suitable to answer the questions I'm asking, and I'm trying to develop a feel for what might be required.

    This is helping, thanks all - keep it coming! I'll try to refine all this into a post next week as well, but this conversation is helping :)

  6. Contemporary learning theory has moved on significantly from the 'co-occurance = association' notion - so that's a straw man.

    What do you mean by 'meaning'?

    I still think you might find the behavioural neuroscience approach less frustrating than the cognitive one. Although there tends to be an assumption that stimuli form invariant representations (obviously not true!), we certainly don't rely on magic such as 'face processing' or 'episodic memory'.

  7. Pam,
    "Meaning" is a funny word. It is an awkward conversation, because the Gibson stuff comes out of Peirce and James and Pragmatism, which is poorly understood. (With the caveat that, in my experience, the Europeans and South Americans have a much better handle on this stuff than those in North America). A more neutral way to have the discussion is to say that the Ecological Approach to perception handles with 'perception' many things that traditional 'continental' philosophy has told us cannot be handled without a dualistic concept of mental meaning-making. At the most basic level 'affordances' blur the traditional boundary between 'objective object properties' and 'subjective meaning' as traditionally understood. There is more, but that is the core.

    You are also correct that Eco-Psych should talk more with behavioral neuroscientists (and I would add behaviorists in general). Surely, much opportunity for cross-fertilization of ideas and good collaboration has been stymied by an overstatement of the differences between the two movements.

    Would you have a suggestion for an easy reading or two to understand how modern behaviorists treat neuronal processes while staying true to their roots?


  8. To add to that:roughly, the difference I had in mind on terms of models is laid out here and here. To me, the goal is models that contain implementations of actual mechanism, rather than abstract parameters. (This isn't a specific critique of learning theories, it's a wide problem.)

  9. I think I get what you mean. One of the problems of learning theory is probably that we don't actually care how a rat presses a lever/carries out the behaviour.
    This is not to say that the identity of the behaviour is unimportant (though to many theorists it is).
    Interesting discussion, its making me think of many of the problems in our approach (though I still think its better than the cognitive approach!).
    This is some of the sort of work that is carried out:
    B. Reynolds. (2001). A cellular mechanism of reward related learning. Nature, 413, 64-67.
    Matell, M. S., Meck, W. H., & Nicolelis, M. A. L. (2003). Interval timing and the encoding of signal duration by ensembles of cortical and striatal neurons. Behavioral neuroscience, 117(4), 760-772.

    and here is some current thinking on the nature of stimulus representations in pavlovian conditioning - no brains contained within.

    Harris, J. a. (2006). Elemental representations of stimuli in associative learning. Psychological review, 113(3), 584-605. doi:10.1037/0033-295X.113.3.584

    This is it.

    The ecological approach has behaviourist roots, so we have plenty in common, but less Skinner and more Watson.

  11. I'm looking forward to reading this post in more detail. I hope my contribution here is not too far off topic, or torquing; if it is, I would not be offended to have it not be posted here. I am a writer, in that the written word has always been a major part both of my vocation and my avocational interests. Since retiring, I've been writing on topics that have nothing to do with my former vocation (sustainability research and policy).

    I am one of those individuals who seems to have been hard wired for written language from toddlerhood. So I've spent my entire life observing, practicing, learning about, and teaching writing and methods, no matter what my actual jobs have been. In some cases I was hired for one thing and pushed over to the wordsmithing/communications area because of these capacities.

    I've been off of my usual daily writing schedule for some three months--a concatenation of major household projects and family visits/issues. Now getting back to my routines, I am trying to document (for myself) the (generally amusing) sense that I'm sharing a skull with an alien.

    I have been observing that half a dozen writing projects, in process, that were parked this spring as I went about the more immediate tasks seem to have been "getting written" without my actual presence. The upshot is that I am feeling that I'm taking dictation on them--whole paragraphs and lines of reasoning, association, and memory are coming to me fully formed. If I have the time and focus to "transcribe" them, I do--but there are many daily interruptions.

    I am not aware of having "thought about" any of these topics in the interim, and can't see how that would have happened, given the tight scheduling and pace of these past few months when my conscious attention was consumed 18 hours per day.

    It's oddly like keeping a dream journal, where, for me, in order to transcribe the sleep visions I have to pause before coming to full waking/external oriented (perceptual) consciousness and "replay the tape" of the dreams while they are still "fresh." Then I can write them down, or talk about/remember them. If I fail to "replay the tape," they are lost.

    In the case of the "emergent writing," I'm not replaying them before writing them; the transcription IS the replaying. But the overall sense of "having to get it while it's fresh" is very similar. In my professional writing, I never needed to do this, but it wasn't so automatic, either; every word was sweated out and felt very different, somehow.

    This...crossover...of...I don't even know how to say it...mental cybernetics seems to have shifted considerably regarding writing since what I suspect to be menopause (I have no uterus, so am just guessing; I still seem to have monthly hormonal cycles). It reopens my intent to do some searching/reading on the interaction of hormones and the brain's memory and language centers.

    The present experience of my "brain" having been off on its own, writing even though "I" couldn't, is startling, oddly charming, and something I'd like to understand better. I've had things like this happen, and even have worked with my own mind to strengthen it, around design of processes or products (my partner, the engineer, frequently solves technical problems while he sleeps), invention of tools/processes, or other problem solving. I've experienced it with kinesic movement/learning (for instance, dance moves that just will not click…then, overnight, the body/mind somehow gets all the pieces in place, and next day, wham, there it is.)

    This is the first time that I have experienced it in a purely linguistic form of this sort. I have always drafted very fluently, and only once in 35 years had a "writer's block."

  12. Oh, one more thing. What I'm transcribing is literally an "inner voice." It is my voice, it is speaking in "my" syntax and tones, as though I were extemporaneously speaking, say at a professional gathering. "So, Mikke, what do you think about Policy Issue X?" Except that this inner voice is coming up with things I've never consciously considered. It is in there "writing on its own" inasmuch as writing = shaping, honing, developing, associating facts and lines of memory and reason, as well as various strategies for communicating those in various ways to various audiences.

    So either my brain has suddenly sprouted Write-o-Matic modules at an exponentially more complex, new, level. Or I've always had them and they've just never worked this well before. Or I'm going to start hearing Ralph Wiggum's leprechaun. ;D

    Thank you for reading this, and for preparing this blog, which will be a rich starting point for me as I do some digging on the issue of what the brain is doing while it's busy counting basketballs and ignoring the gorilla.

  13. "highly responsive to use."

    This link is dead.