Tag Archives: David Hays

Cognitivism and the Critic 2: Symbol Processing

It has long been obvious to me that the so-called cognitive revolution is what happened when computation – both the idea and the digital technology – hit the human sciences. But I’ve seen little reflection of that in the literary cognitivism of the last decade and a half. And that, I fear, is a mistake.

Thus, when I set out to write a long programmatic essay, Literary Morphology: Nine Propositions in a Naturalist Theory of Form, I argued that we think of literary text as a computational form. I submitted the essay and found that both reviewers were puzzled about what I meant by computation. While publication was not conditioned on providing such satisfaction, I did make some efforts to satisfy them, though I’d be surprised if they were completely satisfied by those efforts.

That was a few years ago.

Ever since then I pondered the issue: how do I talk about computation to a literary audience? You see, some of my graduate training was in computational linguistics, so I find it natural to think about language processing as entailing computation. As literature is constituted by language it too must involve computation. But without some background in computational linguistics or artificial intelligence, I’m not sure the notion is much more than a buzzword that’s been trendy for the last few decades – and that’s an awful long time for being trendy.

I’ve already written one post specifically on this issue: Cognitivism for the Critic, in Four & a Parable, where I write abstracts of four texts which, taken together, give a good feel for the computational side of cognitive science. Here’s another crack at it, from a different angle: symbol processing.

Operations on Symbols

I take it that ordinary arithmetic is most people’s ‘default’ case for what computation is. Not only have we all learned it, it’s fundamental to our knowledge, like reading and writing. Whatever we know, think, or intuit about computation is built on our practical knowledge of arithmetic.

As far as I can tell, we think of arithmetic as being about numbers. Numbers are different from words. And they’re different from literary texts. And not merely different. Some of us – many of whom study literature professionally – have learned that numbers and literature are deeply and utterly different to the point of being fundamentally in opposition to one another. From that point of view the notion that literary texts be understood computationally is little short of blasphemy.

Not so. Not quite.

The question of just what numbers are – metaphysically, ontologically – is well beyond the scope of this post. But what they are in arithmetic, that’s simple; they’re symbols. Words too are symbols; and literary texts are constituted of words. In this sense, perhaps superficial, but nonetheless real, the reading of literary texts and making arithmetic calculations are the same thing, operations on symbols. Continue reading

Statistics and Symbols in Mimicking the Mind

MIT recently held a symposium on the current status of AI, which apparently has seen precious little progress in recent decades. The discussion, it seems, ground down to a squabble over the prevalence of statistical techniques in AI and a call for a revival of work on the sorts of rule-governed models of symbolic processing that once dominated much of AI and its sibling, computational linguistics.

Briefly, from the early days in the 1950s up through the 1970s both disciplines used models built on carefully hand-crafted symbolic knowledge. The computational linguists built parsers and sentence generators and the AI folks modeled specific domains of knowledge (e.g. diagnosis in elected medical domains, naval ships, toy blocks). Initially these efforts worked like gang-busters. Not that they did much by Star Trek standards, but they actually did something and they did things never before done with computers. That’s exciting, and fun.

In time, alas, the excitement wore off and there was no more fun. Just systems that got too big and failed too often and they still didn’t do a whole heck of a lot.

Then, starting, I believe, in the 1980s, statistical models were developed that, yes, worked like gang-busters. And these models actually did practical tasks, like speech recognition and then machine translation. That was a blow to the symbolic methodology because these programs were “dumb.” They had no knowledge crafted into them, no rules of grammar, no semantics. Just routines the learned while gobbling up terabytes of example data. Thus, as Google’s Peter Norvig points out, machine translation is now dominated by statistical methods. No grammars and parsers carefully hand-crafted by linguists. No linguists needed.

What a bummer. For machine translation is THE prototype problem for computational linguistics. It’s the problem that set the field in motion and has been a constant arena for research and practical development. That’s where much of the handcrafted art was first tried, tested, and, in a measure, proved. For it to now be dominated by statistics . . . bummer.

So that’s where we are. And that’s what the symposium was chewing over.

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Where Are Memes?

This is more a public note to myself than anything else. It’s likely to seem a bit odd to those who haven’t been following my thinking on memes. Cross-posted at New Savanna.

Back in 1996 I published a long article, Culture as an Evolutionary Arena (link to downloadable PDF), in the, alas, now defunct, Journal of Social and Evolutionary Systems. In that article I introduced the notion of units of cultural inheritance with these paragraphs:

Following conversations with David Hays, I suggest that we regard the whole of physical culture as the genes: the pots and knives, the looms and cured hides, the utterances and written words, the ploughshares and transistors, the songs and painted images, the tents and stone fortifications, the dances and sculpted figures, all of it. For these are the things which people exchange with one another, through which they interact with one another. They can be counted and classified and variously studied.

What then of the ideas, desires, emotions, and attitudes behind these things? After all, as any college sophomore can point out, words on a page are just splotches unless apprehended by an appropriately prepared mind, one that knows the language. Pots and knives are not so ineffable as runes and ideograms, but they aren’t of much use to people who don’t know how to use them, that is, to people whose minds lack the appropriate neural “programs”. Surely, one might propose, these mental objects and processes are the stuff of culture.

What I in fact propose is that we think of these mental objects and processes as being analogous to the biologist’s phenotype just as the physical objects and processes are analogous to the genotype. Properly understood, these mental objects and processes are embodied in brain states (cf. Benzon and Hays 1988). Thus we have the whole of physical culture interacting with the inner cultural environment to produce the various mental objects and activities which are the substance of culture.

Richard Dawkins has proposed the term “meme” for the units of the cultural genotype, but proposes no special term for the cultural phenotype, though he recognizes the necessity of distinguishing the two (Dawkins 1982, pp. 109 ff., see also Dawkins 1989, pp. 189 ff.). Following more or less standard anthropological usage, I offer “psychological trait”, or just “trait”, as a term designating phenotypical units or features. Note, however, that Dawkins places memes in the brain and traits in the external world, which is just the opposite of what I am doing.

I have maintained that position until quite recently, say a week or two ago. I am now considering abandoning that conception. But first, a little more about how I further developed it.

In my 2001 book on music, Beethoven’s Anvil, I developed that idea with respect to music, arguing that the neural ‘trace’ (trajectory in neural state space) of musical performances is a cultural phenotype while the memes are those aspects of musical sound around which individuals coordinate their music-making activities. I further developed this idea only a few weeks ago in a series of posts I wrote as background to a post I did for the National Humanities Center on cultural evolution.

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