But Science Doesn't Work That Way : Miller and Chomsky (1963)

Sep 02 2010 Published by under Forget What You've Read!

In this post, our heroine -- spurred on by her godly pursuit of science and a bevy of caffeinated drinks -- compares the standard approach to language to intelligent design.  It might get noodly.

Pick one :

Does language "emerge" full-blown in children, guided by a hierarchy of inbuilt grammatical rules for sentence formation and comprehension?

Or is language better described as a learned system of conventions -- one that is grounded in statistical regularities that give the appearance of a rule-like architecture, but which belie a far more nuanced and intricate structure?

For over half a century now, many scientists have believed that the second of these possibilities is a non starter.


No one's quite sure -- but it might be because Chomsky told them it was impossible.

In a seminal 1963 paper, Miller and Chomsky argued that the computational problems facing any stochastic model of language were insoluble.  Without a hierarchical framework of rules already in place to lay the groundwork, they argued, language would be unlearnable from the available input.

Since 1963, language models have become increasingly sophisticated.  Problems that then seemed intractable in computational linguistics, no longer do.  Yet in psychology, there is still widespread belief that the ‘grammar’ of a language cannot be learned, and thus must be hardwired.  Indeed, the field is rife with arguments that it would be ‘logically impossible’ to learn language without such innate structures.

These arguments hinge on key assumptions that do not stand up to either empirical or theoretical scrutiny.   It's certainly possible that some piece of linguistic machinery  could be hardwired –  this is a legitimate  question, clearly worth investigating.  But there is absolutely no logical requirement that it be so.

Modeling Language

To begin to dismantle Miller and Chomsky’s critique, we first need to address its content.  To precis: the pair take a hypothetical stochastic model of language and then show that for that model to accurately and precisely estimate the probability of a reasonable proportion of English sentences, the model would need to be exposed to simply astronomical amounts of linguistic input, of the kind that a human language learner could never possibly hope to be exposed to in the span of a lifetime.  By dint of this, the pair conclude that language is unlearnable from the input.  This then becomes known – in the history of science  – as one of the classic 'poverty of the stimulus' arguments.

“Just how large must n and V be in order to give a satisfactory model? Consider a perfectly ordinary sentence: The people who called and wanted to rent your house when you go away next year are from California. In this sentence there is a grammatical dependency extending from the second word (the plural subject people) to the seventeenth word (the plural verb are). In order to reflect this particular dependency, therefore, n must be at least 15 words. We have not attempted to explore how far n can be pushed and still appear to stay within the bounds of common usage, but the limit is surely greater than 15 words; and the vocabulary must have at least 1000 words. Taking these conservative values of n and V, therefore, we have V^n = 1045 parameters to cope with, far more than we could estimate even with the fastest digital computers.”

The suggestion is that a functional language model would require exposure to an input of at least 10^45 words to parse even a simple sentence.  Miller and Chomsky emphasize that this is a highly conservative estimate, based on a language that had only 1000 words, which is, of course, only a small fraction of the vocabulary of an adult speaker.  But regardless, the figure is huge!  To put it into perspective, the average lifetime consists of 2.2 billion seconds.  For a person to hear all the words required within her lifespan, she would have to process about undecillion words a second.  The point is : it ain’t gonna happen.  Consequently -- conclude the daring duo -- language simply must be unlearnable from the input.

The numbers are impressive, no doubt.  But is the argument grounded in anything real?

Turns out – no, it’s not.  The problem is that Miller and Chomsky never consider the possibility that another, more psychologically realistic model of probabilistic learning might not suffer from the same problem.  The underlying –and unjustified—assumption is that learning could only work in this way.  But indeed, there are many reasons to think that human learning would not proceed in the way that their model does – namely, the last forty years of learning theory!

Before we get to that, however, let's waltz a step back for a moment, and consider how the argument is set up in the first place.   Step 1:  Miller and Chomsky describe a possible probabilistic model of language.  Step 2:  They show that it can't possibly account for how language is learned.  Step 3:  They conclude that language can't possibly be learned.

I'm sorry -- is it just me, or does this sound shockingly like an argument from intelligent design?  "This is too complex -- evolution couldn't possibly explain it" versus "This is too complex -- we couldn't possibly learn it."

Isn't the point of science to figure out whether our models can explain things?  And to build better models if they can't?  If I tried to model quantum mechanics with play-dough and failed, that wouldn't be any cut on quantum (Kidding.  Kind of).

But in all seriousness, there are at least two goals of modeling in cognitive science : 1) to discover the best computational method of accounting for a given phenomena, 2) to discover the best account that is also psychologically plausible

The goal has never been to rule out a whole class of models on the basis of one ill-starred example.  Because -- quite frankly -- models don't deal in 'logical possibilities.'  They are not mathematical or logical proofs.  Step 3 in Miller and Chomsky's paper is a pseudo-scientific non sequitur.

The Trouble with the Model

I'm sure you're wondering -- what was wrong with the model Miller and Chomsky used?  And how do we know for certain that they were working with the wrong model and the wrong set of assumptions?

For starters, the Markov model they use automatically assigns a probability of zero to any sentence (or other string of words) that it hasn't encountered yet.  Which is to say : if the model hasn't been exposed to that particular string, then the string is deemed ‘ungrammatical.’  This is why such massive amounts of input are needed to guarantee that no potentially ‘grammatical’ sentences are ruled out.  As Chomsky and Miller point out:

“We know that the sequences produced by n-limited Markov sources cannot converge on the set of grammatical utterances as n increases because there are many grammatical sentences that are never uttered and so could not be represented in any estimation of transitional probabilities.”

Fair enough.  Markov models aren't up to snuff when it comes to explaining the whole of language learning.  It's not clear, however, what this is supposed to tell us -- for one, how psychologically plausible are these models to begin with?  Well -- if we take the model 'literally' -- i.e., if we assume that this is actually how human learning mechanisms work -- then our expectation should be that children will be stuck parroting back sentences they've heard before, since all other sentences will have been ruled 'ungrammatical' until proven otherwise.  But -- do we really think this model captures the extent of our learning capabilities ?  And is this actually the best a 'probabilistic' model can do?

No (and no again).  It's easy to see why : think of a non-linguistic skill of yours that you're really good at -- say, cooking.  Now imagine that as you were learning to cook, you were only able to learn precisely what you were taught, and never able to move outside those bounds.  For example, if you were given a recipe, you could only cook at that temperature, with those specific ingredients, in that order, and so on.  And you could only ever make the recipes you'd already made -- never anything else.

Does that seem like a fair way to describe what a top chef does?


Why then should we expect that language learning would be so limited?

Stimulus generalization is ubiquitous in nature. Even goldfish do it.  There are many reasons to think that a child can generalize from what she has already learned : for example, when learns how to use the word 'dog,' she might at the same time gain some insight into how to use words like 'cat.'

If you think about it, using one word like another isn't so different from substituting Tapatio for Tabasco in a recipe -- sure, they're not quite the same flavor, but until we really learn to distinguish the differences between them, we may use them interchangeably in our cooking.  Once we can tell the difference, of course, we'll know that Tapatio is a great sauce for salsa, while Tabasco is better for chips, but that takes time and -- well -- trial and error!

In a similar manner, children will, over the course of several years, learn that while both cats and dogs 'sit on mats' and 'walk outside,' only cats 'meow' and only dogs go 'woof woof.'  (Funny enough, very small children raised with dogs have a tendency to call just about every animal they encounter a "dog," just as those brought up in households with Subarus, may amusingly call every car they encounter a "Subaru")

I want to save a more in-depth treatment of early language learning for a later post, but the simple point here is : we're fully capable of both contextual generalization and discrimination learning in other domains.   Why would these powerful learning mechanisms not be available to us as we learn language?  And why should we trust in the failure of models that don't begin to approximate those general learning capabilities?

The bottom line is : we shouldn't.

Do we really want to say that phonemes are 'innate'?

I haven't yet addressed how we know -- with all but certainty -- that the model Miller and Chomsky used had to be a poor approximation of human learning capabilities.  It has to do with phonemes.

Experiments have shown that people are remarkably sensitive to the transitional probabilities between phonemes in their native languages, both when speaking and when listening to speech.  If Miller and Chomsky’s assessment of probabilistic learning is correct, then the problem of "parameter estimation" should apply not only to learning the probabilities between words, but also to learning the probabilities between phonemes.  Given that people do learn to predict phonemes, Miller and Chomsky's logic would force us to conclude that not only must ‘grammar’ be innate, but the particular distribution of phonemes in English (and every other language) must be innate as well.

We only get to this absurdist conclusion because Miller & Chomsky's argument mistakes philosophical logic for science (which is, of course, exactly what intelligent design does).  So what's the difference between philosophical logic and science? Here's the answer, in Einstein's words, "No amount of experimentation can ever prove me right; a single experiment can prove me wrong."

(Rule #264 : Models don't make for good premises.)

Attributions and Amusements

In line with Einstein's comment, Scholz & Pullum (2006) have this to say about what the nativist research program should amount to :

“…The research program of linguistic nativism aims to show, proposition by proposition and mechanism by mechanism, that very little knowledge of syntactic structure is acquired or learned from sensory stimuli. Thus the discovery of one (or even a few) language-specialized cognitive mechanisms does not resolve the partisan nativist/non-nativist dispute.” (in Irrational Nativist Exuberance)

This post draws heavily on the literature reviews in Yarlett (2007), Ramscar, Yarlett, Dye, Denny & Thorpe (2010) and Yarlett, Ramscar & Dye (in submission).  Much of the original scholarship on Miller and Chomsky is Dan Yarlett's.  If you would like a copy of the first or the third paper, please email me.

Suggested Reading

Rescorla RA (1988). Pavlovian conditioning. It's not what you think it is. American Psychologist, 43 (3), 151-60 PMID: 3364852

Bannard C, Lieven E, & Tomasello M (2009). Modeling children's early grammatical knowledge. Proceedings of the National Academy of Sciences of the United States of America, 106 (41), 17284-9 PMID: 19805057

Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010). The Effects of Feature-Label-Order and their Implications for Symbolic Learning Cognitive Science, 34 (7), 909-957 : 10.1111/j.1551-6709.2009.01092.x

Scholz, B., & Pullum, G. (2006). Irrational Nativist Exuberance Contemporary Debates in Cognitive Science, 59-80

29 responses so far

  • Firionel says:

    Whoa, what happened to the comments?

    Anyways, I just wanted to say a quick thank you for a couple of hints on literature I got while they still where there. It took me about a week to even skim them, and it's all way over my head, but that's a different matter altogether, I guess.

    • melodye says:

      I don't know :( I'm still hoping that maybe they can be restored... (They got lost in the server switch). It's one reason I was waiting on resuming blogging.

  • Mark says:

    Wow! Who the hell wrote this? That was well worth reading.

  • Mark says:

    Well it seems to me you have a have a damm good analytical process; quite focussed on the simple concepts not wrapped up in too much jargon.

    I actually followed what you were saying, even though basically ignorant of the matters of which you spoke. It challenged my brain but was sufficiently well thought out for me to follow. A pleasure to read.

    What do we (science) know about the evolution of the brain itself?
    What do we know about the evolution of language (as a function) and where it sits in that context?

  • John in Michigan, USA says:

    I studied Cognitive Science for my undergraduate degree in the late 80's -- there aren't many who can say that. We were stuck with the Turing Test for Artificial Intelligence, the functional paradigm and ATMs (Augmented Transition Networks, not cash machines). It was all fun to program on a computer, but it didn't really go anywhere -- it turns out that human language just doesn't work that way.

    I very much like your analogy to Intelligent Design. In an odd way, it gives me some sympathy for the Intelligent Design folks. They are, of course,
    wrong about the origin of life, but in their defense, they are a bit like someone acquiring a new language; they are actually *correctly* imitating other patterns of thought that are quite common in science; what they lack is a deeper sense of the idiom. Oh, and of course they lack data...just like Chomsky.

    Perhaps that explains some of their sense of victimization...I imagine them thinking to themselves, "I'm only doing what Chomsky did, why are my ideas
    summarily dismissed and often mocked?"

    As I go through life, I am staggered my how many branches of science utterly fail to keep up with the basic developments outside of their field, and instead plod along wrestling with dead ideas. I blame the legacy of Continental philosophy which, for the most part, has proven to be useless, and even counter-productive, in the pursuit of true, skeptical, empirical

    • The difference between Chomsky and ID is that the ID people just seem to think that life is too complicated to have emerged by consistent operation of the laws of nature (basically saying that God isn't good enough at math to do it that way, if He wanted to), while Chomsky wants to propose specific principles that will fill the gap between what he think can be 'learned' (according to some vague and limited concept of what learning is, which I've never understood), vs what is evidently acquired.

      So for example what is sometimes called the `Uniform Theta-role Assignment Hypothesis' (UTAH) says that a predicate needs to find its arguments in the same grammatical relationships to it in all occurrences, which implies that in a sentence such as "I want to leave", if 'too leave' is a subordinate clause, it must have an unpronounced syntactic subject, something for which a reasonable amount of evidence can be found (more in Icelandic than in English, it turns out).

      It is however very hard to work out what these theories allow and what they don't allow, so in effect they're not as empirical as people would like them to be.

  • Miguel says:

    Lack data? Let's have a little honesty here, shall we? Try Legate and Yang's "Empirical reassessment of stimulus poverty arguments," The Linguistic Review 19: 151-162.

    Or try the extensive reviews which support nativism in The Chomsky Notebook (such as Charles Gallistel's "Learning Organs"). Is it a problem to suggest that bees have a learning organ which allows them to calculate distances based on the zenith of the sun (else it be a miracle that they forage based on those calculations)?

    • I don't think it's useful to say that somebody's being 'dishonest' when they ignore something that you think is important: maybe they missed it, maybe they haven't had time to think about it, maybe they have some well-thought out reason for not believing it, or a badly thought one that they believe ATM.

      GB/MP also has a long history of ignoring problematic results, such as for example the fact that Icelandic was known to refute the PRO theorem several years before it was formulated (Andrews (1976), reprinted in Maling & Zaenen (1990) _Modern Icelandic Syntax_). This can't have been due to general obscurity because the talk was delivered at NELS, with many future GB-ers in attendance. And then there have been numerous Icelanders doing their PhD's in the USA who've applied GB and then MP to Icelandic syntax in various interesting ways, but have afaik ever been able to rescue the PRO theorem, but it's still sitting there in an implausibly modified form in the Understanding Minimalism book, with no mention of Icelandic, even tho 2 of the authors have been publishing on Icelandic recently, and even involved in producing a possible solution to the problem (there is no PRO, apparent PRO is movement).

      The logic of the non-discussion of inconvenient results is all and all a very interesting topic, but accusing people of dishonesty really isn't helpful.

      • Miguel says:


        You are correct. I should not assume dishonesty. I do know quite a few people in the connectionist school who refuse to address the "inconvenient results." They are somehow exempt from evidence, but I should start off in good faith.

        That said, it isn't hard to find out what the other side says. A simple "chomsky poverty of the stimulus" search on Google Scholar does wonders. I follow this debate as a hobbyist, and yet I know of the POS research! You would think that before writing a huge essay on the topic, you would at least take the time to address what the exponents of your subject have to say before you passionately denounce and belittle them (unless it is prima facie absurd). This took me a few hours to do.

        • There's a long history of daftness and confusion (from all directions) in this area, so it's not so easy to pick out the sane bits from any of the established positions.

          How for example is an innocent psychologist supposed to react to a bunch of people who appear to take seriously Fodor and Piatelli-Palmerini's absurd claims about the lexicon, whereby all lexical items are innate, including 'carburetor' and the five terms Canberra-area children have for ants (ants, meat ants, sugar ants, bull ants and jumping ants) of these, only the last has any sort of compositionality, in that they are ants that jump (relatives of the bull ants, I believe), but is still a species name, not a compositionally defined type.

          • Miguel says:

            Yeah but a lot of "daftness and confusion" comes out of the connectionist/associationist schools of CogPsy. Eg, my friend getting his PhD for memory, language, & neuroscience in a top-notch program, thinks, with the rest of his dept. (including some big-whigs), that language is learned through syntactic weighting. It's unreal.

            In any event, I thought u might be interested in Chomsky's comments on 'carburetor,' March 20, 2002 (I understand 'carburetor' is Fodor's in LOT):


            "MODERATOR: We have a number of questions that we'd like to put to you. First of these is, when you say that language is a matter of computation, do you mean in the observer-relative or the observer-independent sense? And do humans really start off in the world with the concepts to express things like “carburetor” or “bureaucrat?” [59:25]

            CHOMSKY: Well, as far as the first part is concerned, we could ask the same question about insect navigation. Take a look at, say, the study of insects by scientists. You find that they attribute to the insect highly intricate computational systems which enable, say, bees or ants to do things that are far out of our range: we can't determine the azimuth of the sun as a function of the time of year and day or do dead reckoning the way an ant can and so on and so forth. The explanations of these that are offered in the literature are computational systems. [1:00:10]

            Are they independent of the observer? Well, in the sense that any science is independent of the observer. So, when you have a model of the, say, planetary system, in a sense it's relative to the observer. That is, we can't get out of our skins: that's hopeless. But the enterprise of science is an effort to get out of our skins as much as we can. You try to construct an account of the world that is as observer-independent as we can manage. (There are limits to this, for example, studied in quantum physics, but we can put that aside). [1:00:47]

            So yes, it's observer-independent in the sense in which any scientific construct is observer-independent: no different. It's true whether it's computational systems of insects, whether it's computational systems of humans, whether it's the planetary system, or anything else that science tries to understand. [1:01:13]

            As for “carburetor” and “bureaucrat,” I have to say that my friend Jerry Fodor was a little offended by the fact that the statement you quoted was attributed to me in a recent article (actually it's his). His proposal, and he wrote to me that he's sorry that it came up because he can prove that the notion carburetor isn't innate since he doesn't even know to spell it. But the fact is that he was making a very serious point. His point was that we have to somehow account for the fact that terms like “carburetor” and “bureaucrat” we do understand, just as we understand “river,” and “tree,” and “person,” and very simple words, and we understand them on the basis of very limited evidence. And we have a rich and complex understanding of them. [1:02:09]

            So we're back to the problem of why we grow arms rather than wings. You can't just waive your hand about it and say, “well, it's the culture” because then we have to ask, “How did we acquire the culture?” And as I said, it's not by taking a pill. The culture is a construction of the mind based on scattered experience. So the answer that Fodor (Fodor takes a stronger position that I do; I don't want to defend his position); but in general, this is the problem: What is it about the intrinsic nature of our minds that allows us to acquire concepts like “river,” “person,” “tree,” “water,” “book,” “carburetor,” “bureaucrat,” even though we have very scattered experience. And that's the problem of developmental biology. And the only answer is the only one that anyone knows is the one I just quoted from Hume, talking about our moral nature 250 years ago, and the one that's assumed by every biologist for everything except human mental qualities. It's got to come somehow from the intrinsic nature of our minds. And in that respect, it's innate. The fact that it's difficult to accept is a sign of our irrationality, because it's got to be that way, unless it's a miracle. It's either a miracle or it's pretty much along the lines that Fodor suggested, maybe not as extreme as his position. [1:03:40]

          • Avery Andrews says:

            Replying to Miguel above: that's a more nuanced position that Chomsky is attributing to Fodor than I got from reading Fodor once upon a time. But anyway, it seems to me that vocab acquisition might involve very little, or even nothing it all, that is both language-specific and innate.

            On the one hand, a vocab learner needs to be able to learn to notice differences between things. For example, the difference between vaguely finger-shaped insects with big hind legs for jumping (grasshoppers in English, 'inteltye' in various Arandic languages in Central Australia), and bugs with a similar body shape and walking legs (praying mantises and stick insects in English, 'eltyweltywe' in Arandic (kinda unpronouncible without a lot of training). There's also the non-overap' principle for child vocab, that they don't like to have two words for one kind of thing, such as 'dog' and 'animal' (I can't remember it's correct name ATM).

            So if you look at an assortment of grasshoppers, and hear 'inteltye', and a collection of mantisses and stick insects and here 'eltyweltye', *and* assume non-overlap and that concepts are typically formed by conjunction of properties ('and' rocks, 'or' sux), it's probably not too hard to pick up the relevant shape categories. Rather amazingly, the Arandic speakers don't seem to notice the difference between the mantises and the stick insects until you point it out to them, but of course little boys in the USA who care about bugs do, since they want to know how to apply the terms to the critters.

            More generally, with 'no overlap' and 'and rocks', and some kind of scheme for shape classification (Marr's 1982 ideas seem to me like a good start) you can easily pick up lots of unfamiliar bug terminology by collecting them, showing them to people, and asking what the individuals are, without making what I would consider to be strong innatist assumptions. The concept and-lattice is probably not language-specific, since it seems like it would be useful for learning to survive, no so sure about no-overlap, but it's a soft bias, since kids do eventually learn that animal is a superordinate concept over dog, it just seems to take them a while.

            Psychologists and syntax I'll leave for another day.

        • And I just found that this poster by Prof Plumb and one of the students makes a case (looks prima facie good to me) that the no-overlap is not correct for children:


          I think the learning theory proposed there would work fine for Central Australian bugs as I understood them. But I think that account implicitly uses 'and rocks-or sux'.

  • Miguel says:

    For those interested, Chomsky's recent take:


    Sponsored by The Neuoroscience of Language Laboratory at NYU.

  • Mark says:


    Regarding your comment...

    "The difference between Chomsky and ID is that the ID people just seem to think that life is too complicated to have emerged by consistent operation of the laws of nature (basically saying that God isn’t good enough at math to do it that way, if He wanted to), .."

    What a priceless metaphor. Did you come up with that?

    • Avery Andrews says:

      I think there's a general problem with the Chomsky pieces, which is that he starts out sensible (according to me, probably not according to Melody), but then eventually heads off in the extremely speculative Minimalist direction ('why' questions), without anywhere near a solid enough basis in 'descriptive adequacy' (the 'what' questions). I see Jackendoff and Culicover as having given an excellent critique of this at the beginning of their Simpler Syntax book.

      Minimalism is not necessarily wrong (at least moreso than any other syntactic framework); the Icelanders find it useful, and it fits in nicely with a general theory of clause structure that was invented much earlier in Scandinavia, and more generally seems helpful with respect to a lot of subtleties of word order in European languages and more), but it is very speculative, and not very well defined: almost every practitioner has their own version. Ash Asudeh and Ida Toivonen address this issue in their review of some Minimalist textooks, 'Symptomatic Imperfections'

      Connectionists seem to talk about tensors when pressed about phrase-structure and recursion, but I haven't managed to figure out whether these models manage to explain the behavior. There are some people doing possibly relevant work at Bob Coecke's lab in the UK, as discussed here:
      (ultra highbrow blog, I probably understand somewhere between 5 and 2 percent of what goes on there, tending towards the 2-ish end)

      • Miguel says:

        I've been told by connectionists (again, my soon-to-be-PhD buddy in a major super-funded program, big-wigs and all) that "synaptic weights changing between neurons" can account for anything from simple tasks and memory to all of consciousness itself (one big-wig is convinced of the latter). Language, according to my connectionist friend, is simply the impulse to communicate coupled with associations wired through experience. The models they construct can supposedly account for language as a whole. It's an entirely associationist account of language acquisition.

        • Mark says:

          I've developed a theory of the brain that sounds a bit like that. Where could I hear more about it?


        • Avery Andrews says:

          But can your connectionist friend's model manage concord in Kayardild? He/she can read about it and similar phenomena in Plank (ed) _Double Case_. Or the agreement around nested possessors in Ancient Greek that I've mentioned here (twice, but the first mention got nuked in the server move).

  • John in Michigan, USA says:

    @Miguel: My phrase "lack data" wasn't dishonesty, but it was hyperbole and a bit unfair. There are clearly some data for Chomsky-style hypotheses, while there are virtually no data for ID.

    I suppose instead of "lack data", I should have written, "science doesn't work that way", but someone beat me to it!

    • Miguel says:

      @John in Michigan: Science doesn't work what way? Obviously there's a poverty of stimulus, and that's the whole point in Chomsky's writings: poverty of the stimulus is the standard procedure in the sciences. Nobody knows how, for example, the genetic endowment gives rise to an organism's traits. We know that genes code for proteins, but we can't even account for the development of relatively simple organisms, like the c. elegans worm, in terms of the genetic code. Obviously there's a poverty of stimulus when we consider that some things develop into worms, some into dogs, and some into humans. When some organisms, like dogs and humans, hear the same input, but only one (humans) acquires language, obviously it is genetics that account for the differences (some properties of which are [as far as we know] unique to the human mind, like the lack of a one-to-one correlation b/w words and objects).

      I'm no expert on the specifics of the issues, and I don't pretend to be. But there have been NO reasons to doubt Legate & Yang's study that I know of, which I cited above, and await challengers to address. Chomsky doesn't bring up the empirical aspect that Legate & Yang do in his speeches from 2010, but he reviewed the study.

      @Avery: I enjoy your comments. I wish I had more time to follow up on them, but I'm not sophisticated enough for time to permit. How do you take Legate & Yang's study?

  • Mark says:

    Great post, Melody! You might be interested to know that Fiona Cowie says similar things in "What's Within"! See especially p. 60, section 3.4 which begins, "The idea that I want to explore is that 'everything is innate' is a statement of non-naturalism."

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