Posts tagged ‘global workspace’

Picture: CorticothalamicThis paper on ‘Biology of Consciousness’ embodies a remarkable alliance: authored by Gerald Edelman, Joseph Gally, and Bernard Baars, it brings together Edelman’s Neural Darwinism and Baars’ Global Workspace into a single united framework. In this field we’re used to the idea that for every two authors there are three theories, so when a union occurs between two highly-respected theories there must be something interesting going on.

As the title suggests, the paper aims to take a biologically-based view, and one that deals with primary consciousness. In human beings the presence of language among other factors adds further layers of complexity to consciousness; here we’re dealing with the more basic form which, it is implied, other vertebrates can reasonably be assumed to share at least in some degree. Research suggests that consciousness of this kind is present when certain kinds of connection between thalamus and cortex are active: other parts of the brain can be excised without eradicating consciousness. In fact, we can take slices out of the cortex and thalamus without banishing the phenomenon either: the really crucial part of the brain appears to be the thalamic intralaminar nuclei.  Why them in particular? Their axons radiate out to all areas of the cortex, so it seems highly likely that the crucial element is indeed the connections between thalamus and cortex.

The proposal in a nutshell is that dynamically variable groups of neurons in cortex and thalamus, dispersed but re-entrantly connected constitute a flexible Global Workspace where different inputs can be brought together, and that this is the physical basis of consciousness. Given the extreme diversity and variation of the inputs, the process cannot be effectively ring-mastered by a central control; instead the contents and interactions are determined by a selective process – Edelman’s neural Darwinism (or neural group selection): developmental selection (‘fire together, wire together’), experiential selection, and co-ordination through re-entry.

This all seems to stack up very well  (it seems almost too sensible to be the explanation for anything as strange as consciousness). The authors note that this theory helps explain the unity of consciousness.  It might seem that it would be useful for a vertebrate to be able to pay attention to several different inputs at once, thinking separately about different potential sources of food, for example: but it doesn’t seem to work that way – in practice there seems to be only one subject of attention at once; perhaps that’s because there is only one ‘Dynamic Core’.  This constraint must have compensating advantages, and the authors suggest that these may lie in the ability of a single piece of data to be reflected quickly across a whole raft of different sub-systems. I don’t know whether that is the explanation, but I suspect a good reason for unity has to do with outputs rather than inputs. It might seem useful to deal with more than one input at a time, but having more than one plan of action in response has obvious negative survival value. It seems plausible that part of the value of a Global Workspace would come from its role in filtering down multiple stimuli towards a single coherent set of actions. And indeed, the authors reckon that linked changes in the core could give rise to a coherent flow of discriminations which could account for the ‘stream of consciousness’.  I’m not altogether sure about that – without saying it’s impossible a selective process without central control can give rise to the kind of intelligible flow we experience in our mental processes, I don’t quite see how the trick is done. Darwin’s original brand of evolution, after all, gave rise to speciation, not coherence of development. But no doubt much more could be said about this.

Thus far, we seem on pretty solid ground. The authors note that they haven’t accounted for certain key features of consciousness, in particular subjective experience and the sense of self: they also mention intentionality, or meaningfulness.  These are, as they say, non-trivial matters and I think honour would have been satisfied if the paper concluded there: instead however, the authors gird their loins and give us a quick view of how these problems might in their view be vanquished.

They start out by emphasising the importance of embodiment and the context of the ‘behavioural trinity’ of brain, body, and world. By integrating sensory and motor signal with stored memories, the ‘Dynamic Core’ can, they suggest, generate conceptual content and provide the basis for intentionality. This might be on the right track, but it doesn’t really tell us what concepts are or how intentionality works: it’s really only an indication of the kind of theory of intentionality which, in a full account, might occupy this space.

On subjective experience, or qualia, the authors point out that neural and bodily responses are by their nature private, and that no third-person description is powerful enough to convey the actual experience. They go on to deny that consciousness is causal: it is, they say, the underlying neural events that have causal power.  This seems like a clear endorsement of epiphenomenalism, but I’m not clear how radical they mean to be. One interpretation is that they’re saying consciousness is like the billows: what makes the billows smooth and bright? Well, billows may be things we want to talk about when looking at the surface of the sea, but really if we want to understand them there’s no theory of billows independent of the underlying hydrodynamics. Billows in themselves have no particular explanatory power. On the other hand, we might be talking about the Hepplewhiteness of a table. This particular table may be Hepplewhite, or it may be fake. Its Hepplewhiteness does not affect its ability to hold up cups; all that kind of thing is down to its physical properties. But at a higher level of interpretation Hepplewhiteness may be the thing that caused you to buy it for a decent sum of money.  I’m not clear where on this spectrum the authors are placing consciousness – they seem to be leaning towards the ‘nothing but’ end, but personally I think it’s to hard to reconcile our intuitive sense of agency without Hepplewhite or better.

On the self, the authors suggest that neural signals about one’s own responses and proprioception generate a sense of oneself as a separate entity: but they do not address the question of whether and in what sense we can be said to possess real agency: the tenor of the discussion seems sceptical, but doesn’t really go into great depth. This is a little surprising, because the Global Workspace offers a natural locus in which to repose the self. It would be easy, for example, to develop a compatibilist theory of free will in which free acts were defined as those which stem from processes in the workspace but that option is not explored.

The paper concludes with a call to arms: if all this is right, then the best way to vindicate it might be to develop a conscious artefact: a machine built on this model which displays signs of consciousness – a benchmark might be clear signs of the ability to rotate an image or hold a simulation. The authors acknowledge that there might be technical constraints, but I think they an afford to be optimistic. I believe Henry Markram, of the Blue Brain project, is now pressing for the construction of a supercomputer able to simulate an entire brain in full detail, so the construction of a mere Global Dynamic Core Workspace ought to be within the bounds of possibility – if there are any takers..?

Picture: global workspace. Global Workspace theories have been popular ever since Bernard Baars put forward the idea back in the eighties; in ‘Applying global workspace theory to the frame problem’*,  Murray Shanahan and Baars suggest that among its other virtues, the global workspace provides a convenient solution to that old bugbear, the frame problem.

What is the frame problem, anyway? Initially, it was a problem that arose when early AI programs were attempting simple tasks like moving blocks around. It became clear that when they  moved a block, they not only had to update their database to correct the position of the block, they had to update every other piece of information to say it had not been changed. This led to unexpected demands on memory and processing. In the AI world, this problem never seemed too overwhelming, but philosophers got hold of it and gave it a new twist. Fodor, and in a memorable exposition, Dennett, suggested that there was a fundamental problem here. Humans had the ability to pick out what was relevant and ignore everything else, but there didn’t seem to be any way of giving computers the same capacity. Dennett’s version featured three robots: the first happily pulled a trolley out of a room to save it from a bomb, without noticing that the bomb was on the trolley, and came too; the second attempted to work out all the implications of pulling the trolley out of the room; but there were so many logical implications that it was stuck working through them when the bomb went off. The third was designed to ignore irrelevant implications, but it was still working on the task of identifying all the many irrelevant implications when again the bomb exploded.

Shanahan and Baars explain this background and rightly point out that the original frame problem arose in systems which used formal logic as their only means of drawing conclusions about things, no longer an approach that many people would expect to succeed. They don’t really believe that the case for the insolubility of the problem has been convincingly made. What exactly is the nature of the problem, they ask: is it combinatorial explosion? Or is it just that the number of propositions the AI has to sort through to find the relevant one is very large (and by the way, aren’t there better ways of finding it than searching every item in order?). Neither of those is really all that frightening; we have techniques to deal with them.

I think Shanahan and Baars, understandably enough, under-rate the task a bit here. The set of sentences we’re asking the AI to sort through is not just very large; it’s infinite. One of the absurd deductions Dennett assigns to his robots is that the number of revolutions the wheels of trolley will perform in being pulled out of the room is less than the number of walls in the room. This is clearly just one member of a set of valid deductions which goes on forever; the number of revolutions is also less than the number of walls plus one; it’s less than the number of walls plus two… It may be obvious that these deductions are uninteresting; but what is the algorithm that tells us so? More fundamentally, the superficial problems are proxies for a deeper concern; that the real world isn’t reducible to a set of propositions at all, that, as Borges put it

“it is clear that there is no classification of the Universe that is not arbitrary and full of conjectures. The reason for this is very simple: we do not know what thing the universe is.”

There’s no encyclopaedia which can contain all possible facts about any situation. You may have good heuristics and terrific search algorithms, but when you’re up against an uncategorisable domain of infinite extent, you’re surely still going to have problems.

However, the solution proposed by Shanahan and Baars is interesting. Instead of the mind having to search through a large set of sentences, it has a global workspace where things are decided and a series of specialised modules which compete to feed in information (there’s an issue here about how radically different inputs from different modules manage to talk to each other: Shanahan and Baars mention a couple of options and then say rather loftily that the details don’t matter for their current purposes. It’s true that in context we don’t need to know exactly what the solution is – but we do need to be left believing that there is one).

Anyway, the idea is that while the global workspace is going about its business each module is looking out for just one thing. When eventually the bomb-is-coming-too module gets stimulated, it begins sending very vigorously and that information gets into the workspace. Instead of having to identify relevant developments, the workspace is automatically fed with them.

That looks good on the face of it; instead of spending time endlessly sorting through propositions, we’ll just be alerted when it’s necessary. Notice, however, that instead of requiring an indefinitely large amount of time, we now need an indefinitely large number of specialised modules. Moreover, if we really cover all the bases, many of those modules are going to be firing off all the time. So when the bomb-is-coming-too module begins to signal frantically, it will be competing with the number-of-rotations-is-less-than-the-number-of-walls module and all the others, and will be drowned out. If we only want to have relevant modules, or only listen to relevant signals, we’re back with the original problem of determining just what is relevant.

Still, let’s not dismiss the whole thing too glibly. It reminded me to some degree of Edelman’s analogy with the immune system, which in a way really does work like that. The immune system cannot know in advance what antibodies it will need to produce, so instead it produces lots of random variations; then when one gets triggered it is quickly reproduced in large numbers. Perhaps we can imagine that if the global workspace were served by modules which were not pre-defined, but arose randomly out of chance neural linkages, it might work something like that. However, the immune system has the advantage of knowing that it has to react against anything foreign, whereas we need relevant responses for relevant stimuli. I don’t think we have the answer yet.

*Thanks to Lloyd for the reference.