Calvin, W.
H. (1987). The brain as a
Darwin
machine. Nature 330:33-34 (5 November).
Available at
WilliamCalvin.com/1980s/1987Nature.pdf
AMIDST all the hyperbole about thinking machines that has accompanied the emergence of large-scale parallel computers from their serial predecessors, we have begun to contemplate the prospect of simulating some of our brain's massive parallelism. But one immediately runs into a role reversal worthy of a Mozart opera: the most distinctively human higher brain functions are surprisingly serial. Human beings are perpetually stringing things together: phonemes into words, words into sentences, concepts into scenarios - and then fussing about getting them in the right order. Our brain uses word-order rules to create a very productive language, with an infinite number of novel messages, rather than the several dozen standard interpretations Dissociated with the several dozen cries and grunts of any other primate species. It is not our mellifluous voices that constitute a significant advance but rather our arrangement rules, the meaningful order in which we chain our utterances. Further, talking-to-ourselves consciousness is, among other things, particularly concerned with trying to chain together memory schemata to explain the past and forecast the future. As literary critic Peter Brooks has said1 :
It is our ability to choose between such scenarios that constitutes our free will -- though, of course. our choices are only as good as our imagination in constructing a wide range of candidate scenarios. Logical reasoning also seems dependent upon the rules of reliable sequencing. Our sophisticated projection abilities are very sequential: a chess master, for example, tends to see each board configuration not just after the next move but a half-dozen moves ahead, as several different scenarios. Not that you could get much human-like language or scenario-spinning consciousness out of the ordinary serial computer -- what we are probably talking about is parallel architecture being used to create a lot of serial paths from which to choose. And, perverse though it may seem, we are also likely to make intentional use of noise, good old randomness ('stochastic process' is the polite euphemism). I can hear an incredulous voice already: "Not only does he want us to waste our precious parallel computing power simulating an old-fashioned serial device, but he wants us to make our machine intentionally noisy?". Yet abandoning low-level reliability (and achieving overall reliability via stable superstructures) is very useful. Noise can be creative. Every time that you think of sex, you should remember that it is all about guaranteeing some randomness -- shuffling the DNA deck during crossing over when making sperm and ova. The invention of eukaryotic sex a thousand million years ago probably prompted the great Precambrian diversification of complex life forms into the familiar tree of species. The brain's construction of chained memories and actions is probably another tree, though a more functional metaphor might be the candelabra-shaped railroad marshaling yard, with words for cars: imagine that many trains are randomly constructed on the parallel tracks. but only the best is selected to be let loose on the 'main track' of consciousness and speech. Best is determined by memories of the fate of somewhat similar sequences in the past, and one presumes a series of selection steps2 that shape up candidates into increasingly more realistic sequences. This selection among stochastic sequences is more analogous to the ways of darwinian evolutionary biology than to the 'von Neumann machine' serial computer. One might call it a Darwin Machine3 instead: it shapes up thoughts in milliseconds rather than millennia, and uses innocuous remembered environments rather than the noxious real-life ones.Before pursuing such intracerebral Darwin Machines, consider some nonbiological examples. Daniel Hillis has been using massive parallelism to create some competing computer programs. They mutate, surviving on the basis of how fast they can put a list of names into alphabetical order. Just using random variations on a basic program loop, his parallel computer has re-discovered many of the known sorting algorithms 4. Similarly, the artist Harold Cohen's computerized drawing machine AARON makes aesthetically pleasing paintings using random variations and some general selection rules5.Toolmaking can operate the same way. and perhaps did so even two million years ago when hominids had ape-sized brains. The late Glynn Isaac used to demonstrate early toolmaking techniques during his archaeology lectures by pounding together two potato-sized rocks, not delicately but furiously: chips would soon he scattered all over the floor. After a minute, he would stop and sort through the dozens of stone flakes. And he would pick up some excellent analogues of the single-edged razor blade, just the thing for incising the tough hide of a savannah animal, or amputating a leg at the joint. This stochastic toolmaking is one round of a Darwin Machine: make lots of random variants by brute hashing about, then select the good ones. Perhaps another round of bashing resulted in a flake splitting, two sharp edges intersecting in a point. Careful craftsmanship probably developed where the raw materials were scarce. 6 (some of which have the successive rounds of randomness plus shaping-up selection that mark them as members of the class I am calling Darwin Machines) have also emerged as partial explanations for:
The Darwin Machines of particular interest here are the ones associated with chaining together actions (sequencing). Although they are often useful, command queues for detailed preplanning are seldom essential: goal-plus-feedback usually suffices, as when raising a cup to one's lips and getting progress reports from the joints and muscles. Where planned chains become essential, and thus likely to evolve rapidly, is where feedback becomes impossible, yet a linked series of moves must be precisely executed. Reaction time becomes a problem in brief ballistic movements such as hammering, throwing, clubbing or kicking: the progress reports will usually arrive too late for corrections to be made. For organisms that need to be both large (metres of conduction distance) and fast, one often needs the neural equivalent of an old-fashioned roll for a player piano. During 'get set', we carefully plan to act without feedback.
Grammar
Sequencing may involve much of the left cerebral hemisphere in mammals12
Comparison of grammars shows that the typical subject-verb-object word order of an English
sentence is not biologically determined: Japanese syntax uses subject-object-verb, while classical
Arabic puts the verb first. What the biology may provide is the serial buffer to hold the phrase while
it is analysed according to the learned rules (though more subtle grammatical linkages are perhaps
constrained by buffer branchings, corresponding to Chomsky's deep structure). There are some
suggestions that the capacity of one important serial buffer is about a half-dozen items, judging from
phenomena such as chunking of memory
Yet there is surely more than one serial buffer: the human brain seems to orchestrate many sequences
in parallel. Most are subconscious, with only one entering our 'main line' awareness (as in the
railroad yard's parallel-to-serial bottleneck); traditional lines of evidence are scene-shifting in
dreams, subconscious problem-solving and how subconscious scenarios sometimes pathologically
intrude into speech.
Another suggestion of parallel sequencers has arisen from a very different direction. Human beings
often hunt with projectiles; faster and farther throws are always better, provided accuracy can be
maintained. A biophysical model for throwing
Thus there must be many sequencers which, at least temporarily, can be ganged in parallel (imagine
multiple columns of horses pulling a single wagon) during the occasions demanding peak
performance in one-shot timing. To hit a rabbit-sized target reliably from twice the distance requires
that the jitter in rock release time must be narrowed by a factor of eight, and the only known way of
accomplishing this feat is, as one gets set to throw each time, to assign 64 times as many noisy
neurons to the task and then average their recommendations for the release time.
Technology treats noise as an unwanted impediment, darwinism as a means of exploring new
avenues. But here we see it as a stimulus to evolve redundant machinery - whose secondary uses
may be revolutionary. There may even have been a 'noise window' in hominid evolution: lacking
sufficient neuron noise to overcome, Ice Age hominids might have become proficient projectile
predators without the massively serial scheme
If the separate tracks can also be unhitched to operate independently, then one might expect a Darwin
Machine to emerge. By providing many candidate queues, it might foster stringing words together
into more sophisticated sentences, or schemata into more credible scenarios. Rather than our
productive language and planning-for-the-future consciousness arising gradually through their own
selective advantages, they could have emerged as novel spare-time uses of neural machinery
originally under selection for more mundane forelimb movements -- much as a novelty called bird
flight probably emerged willy-nilly as a consequence of natural selection for keeping warm via
forelimb feathers (because it takes a lot of feathers to begin flying).
Serendipity
Neural-like networks19-21
1. Brooks, P. Reading for the Plot (Knopf, New York, 1984).[to text]
2. Dawkins, R. The Blind Watchmaker, (Longman. London, 1986).[to text]
3. Calvin, W.H. Whole Earth Review 55. 22-28 (1987). [to text]
4. Hillis, W. D. personal communication 1987. [to text]
5. McCorduck. P. Whole Earth Review 55:45-51 (1987). [to text]
6. Young. J. Z. J. Roy. Soc. Med. 72:801-814 (1979). [to text]
7. Segall, J.E., Block, S.M. & Berg, H. C. Proc. Nat. Acad. Sci U. S. A. 83, 8987-8991 (1986). [to text]
8. Calvin, W.H. & Ojemann, G.A. Inside the Brain 65-67 (NAL, New York, 1980). [to text]
9. Edelman, G. M., in How We Know 24 (Nobel Conference, 1985). [to text]
10. Rakic, P., Bourgeois, J.-P., Eckenhoff, M. F ., Zecevic, N., & Goldman-Rakic, P.S. Science 232, 232-235 (1986). [to text]
11. Campbell, D.T. in The Philosophy of Karl Popper (ed. Schilpp, P.A.) 413-463 (Open Court, La Salle, Illinois, 1974). [to text]
12. Bradshaw, J.L., & Nettleton, N.C. Behavioral and Brain Sciences 4, 51-91 (1981). [to text]
13. Kimura, D. Phil. Trans. R. Soc. B292. 135-149 (1982). [to text]
14. Ojemann, G. A. Behav. Brain Sci. 6, 189-230 (1983). [to text]
15. Simon, H.A. Models of Thought 41 (Yale University Press, New Haven, 1979). [to text]
16. Calvin, W. H. J. Theoret. Biol. 104, 121-135 (1983). [to text]
17. Calvin, W.H. & Stevens, C.F. J. Neurophysiol. 31, 574-587 (1968). [to text]
18. Calvin, W.H. The River That Flows Uphill: A Journey from the Big Bang to the Big Brain 407 (Macmillan. New York, 1986). [to text]
19. Rumelhart, D.E., McClelland, J.L. & the PDP Research Group Parallel Distributed Processing (MIT Press 1986). [to text]
20. Dehaene, S., Changeux, J.-P. & Nadal, J. P. Proc. natn. Acad. Sci. U.S.A. 84, 2727-2731(1987). [to text]
21. Kleinfeld, D. Proc. natn. Acad. Sci. U S A 93, 9464-9473 (1986). [to text] |
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