“The real question is not whether machines think but whether men do. The mystery which surrounds a thinking machine already surrounds a thinking man.”–B. F. Skinner.
The study of mind begins with a metaphor.
In the 20th century (and now on into the 21st) the metaphor that has dominated our study of mind is the computational metaphor. The mind, they say, is like a computer.
But in what way like a computer? In what respect, and in which dimensions?
The answer that has often been given – by Chomsky, say, and others – is that the mind is like a spreadsheet: a prefabricated architecture that follows a strict, rule-based program, that rigidly structures its inputs and outputs in just such a way.
If you should open a spreadsheet on your computer, you will find that all the functions they perform have already been coded in by a team of programmers. Once you, as the user, have learned what these functions are, you can type in a series of inputs, press a button (or three), and the spreadsheet will spit you back an output.
There is nothing unpredictable about a spreadsheet. To the contrary, everything is determinate. Should you ask the spreadsheet to calculuate a square root for you, or give you a statistical mean, it will give you the precise answer every time. But, should you accidentally type “w” when you meant to type “2,” the program will fail. (#EH?!#)
To read Chomsky, is to understand that he envisions human language in just such a way.
“It is fair to say,” he writes, “that in any domain in which we have any understanding about the matter, specific and often highly structured capacities enter into the acquisition and use of belief and knowledge.”
The parallel is even clearer when he writes on universal grammar:
“We may think of the language faculty as a complex and intricate network of some sort associated with a switch box consisting of an array of switches that can be in one of two positions. Unless the switches are set one way or another, the system does not function. When they are set in one of the permissible ways, then the system functions in accordance with its nature, but differently, depending on how the switches are set. The fixed network is the system of principles of universal grammar; the switches are the parameters to be fixed by experience.”
Human language, as so described, is underpinned by a universal structure; a pre-programmed grammar that organizes its inputs accordingly. As a child, the process of learning a language is akin to flipping a series of switches – or defining a set of variables for a spreadsheet. The spreadsheet already contains the logical structure; all that is being input are the particulars.
By this suggestion, when we hear or read language, the computational principles of this innate grammar conduct a series of logical operations, which parse the incoming stream according to its component parts, and so yield understanding.
But what if this is simply the wrong metaphor?
What if – say – language is more like a search engine?
A search engine is a probabilistic, predictive learning machine. Unlike spreadsheets, search engines do not engage with their input in a determinate, preprogrammed manner. Instead of rigidly structuring incoming information according to some prefabricated set of rules, they discover structure within information.
To use a search engine, you need not have memorized a laundry-list of rules. Instead, you can learn to better your search over time, by narrowing down which inputs are likely to yield the results you want. At the same time, a search engine learns to optimize outputs for its users by determining (probabilistically) what results are expected or desired given a particular search term. For example, should you search for “mldy dye,” Google will still find me for you.
A search algorithm is powerful because of its ability to learn from – and sift through – a massive stream of data. But it relies on relatively simple learning algorithms to accomplish this, rather than a sophisticated, rule-governed architecture. And it produces probabilistic, rather than determinate output.
This metaphor gives rise to a fundamentally different view of language: one in which language acquisition relies on relatively simple – yet powerful – learning mechanisms, and in which language comprehension and production is fundamentally predictive, rather than determinate. In marked contrast to the traditional metaphor of mind, it does not suggest that a complex innate hardware (a “universal grammar” or “language acquisition device”) must be assumed to account for how children learn language. Rather, it suggests that structure resides in the linguistic environment and is there to be discovered.
Of course, as Michael readily notes – which metaphor works best cannot be resolved by fiat – it is an open empirical question.
Even posing the question, however, goes against the grain of the last five decades in linguistic and psychological research. Since the dawn of the “cognitive revolution” led by Chomsky in the fifties, many psychologists and linguists have assumed that a universal grammar (UG) is necessary to account for language acquisition, development and use. Popular books such as “The Language Instinct,” would lead you to believe that the debate is long since up and finished. But this is far from the case.
The supposition that there is a universal grammar has been repeatedly challenged by findings in comparative linguistics, in computational linguistics (and in particular, natural language processing), in corpus analyses, in philosophy, in neural network modeling, in work on learning theory, and in experimental psychology. And yet, the approach and assumptions that characterize the Chomskian approach to language, and the old computational metaphor, continue to pervade research into language and learning, particularly in psychology. The old guard has not given up the ghost.
Why is this important?
The ways in which we set up the problems that we research is governed by the assumptions that we bring to bear on them. And when we set up the problems in fundamentally the wrong way, it becomes near impossible to make substantive progress. Think, for a moment, about how hard it would become to prepare a tasty meal if you completely disregarded quality and freshness of ingredients when cooking. Even with the best of the recipes, it’s hard to get around the fact that you’re working with rotten meat and spoiled milk. Similarly, we can throw all the math and all the money at research we want, but if we’re tooling around with bad ideas, it’s unclear what we’ve set ourselves out to accomplish.
The decades of research into the supposed mental architecture of language has yielded little in the way of practical import. As Chomsky himself was quick to admit, he didn’t think that “modern linguistics [could] tell you very much of practical utility.” And yet, it is clear that applying the simple principles of learning theory to language cannot only yield great insight into the workings of language – which may be of interest to philosophers and linguists – but can also substantially contribute to our understanding of how children learn language and point to practical interventions to speed language learning.
For example, in the lab I work in at Stanford with Michael, we have found how a simple property of word-learning can help children rapidly learn color and number words. Both colors and numbers are notoriously hard for children to grasp, and typically take children many years to master. Yet in both studies, we found that we could effect improvements that would normally take place over a time scale of months within a fifteen-minute training period. We have applied the same principles to help three-year olds learn pass the DCCS and tests of false belief understanding, and have shown how learning models can explain how infants come to succeed at the A-not-B task. We have similarly applied these principles to show why perfect pitch is so rare in the general population; why adults have so much difficulty learning new languages; and why children go through a stage in which they tend to over-regularize irregular plurals. If all of that sounds like “so much Greek” to you, let’s just say that we’ve found answers to and solutions for a number of seemingly insoluble learning problems that have long vexed developmental psychologists. And it's not just because we're clever: we’ve done it with simple models and simple behavioral interventions that anyone could adopt.
In theory, the models we use could be applied to a near-endless list of problems and questions in language learning. And yet – we are one of the only labs in the states to apply learning theory to language. For the most part, learning theory has been completely forgotten by modern developmental psychology. This is a shame, given that its practical utility is undeniable. Claims that language as such is “unlearnable,” continue to dissuade most researchers from looking into the question. And yet, these claims stem from a computational metaphor that is outdated, and for the most part, wholly ignored by modern research into natural language processing (when you dial GOOG-411 or any other automated voice processor, you can bet they’re not using generative, rule-based models of language to parse what you’re saying).
If you're interested in the debate that's been raging, I've listed some introductory reading below. But in the upcoming weeks, I'm less interested in covering the blow by blow of nativist-empiricist arguments, than in introducing you to what I think are some of the exciting new research avenues in child language research. The practical import of these discoveries is far more compelling than even the most persuasive arguments I might think to elucidate.
Chomsky, N. (2000). New horizons in the study of language and mind. Cambridge, England: Cambridge University Press.
Cowie, F. (2008) Innateness and Language. Stanford Encyclopedia of Philosophy.
Evans N, & Levinson SC (2009). The myth of language universals: language diversity and its importance for cognitive science. Behavioral and Brain sciences, 32 (5) PMID: 19857320
Ramscar, M. (2010). Computing Machinery and Understanding (PDF). Cognitive Science, 34 (6), 966-971.
Rescorla, R. (1988). Pavlovian conditioning: It's not what you think it is. American Psychologist, 43 (3), 151-160 DOI: 10.1037//0003-066X.43.3.151
Roediger, R. (2004) What Happened to Behaviorism? APS Observer.
Scholz, Barbara C. and Geoffrey K. Pullum (2006) Irrational nativist exuberance. In Robert Stainton (ed.), Contemporary Debates in Cognitive Science, 59-80. Oxford: Basil Blackwell.