My answer: very, and getting more so all the time. This view will strike many as controversial.
For example Cedric Boeckx (here
and here)
and David Berlinsky (here)
(and most linguistics in discussions over beer) contend that linguistics is a
BINO (biology in name only). After all, there is little biochemistry, genetics,
or cellular biology in current linguistics, even of the Minimalist variety. Even
the evolang dimension is largely speculative (though, IMO, this does not
distinguish it from most of the “seripous” stuff in the field). And, as this is
what biology is/does nowadays, then, the argument goes, linguistic
pronouncements cannot have biological significance and so the “bio” in
biolinguistics is false advertising. That’s the common wisdom as best as I can
tell, and I believe it to be deeply (actually, shallowly) misguided. How so?
A domain of inquiry, on this view, is defined by its tools
and methods rather than its questions. Further, as the tools and methods of GG
are not similar to those found in your favorite domain of biology then there
cannot be much bio in biolinguistics. This is a very bad line of reasoning,
even if some very smart people are pushing it.
In my view, it rests on pernicious dualist assumptions which, had they been
allowed to infect earlier work in biology, would have left it far poorer than
it is today. Let me explain.
First, the data linguists use is biological data: we study patterns
which would be considered contenders for Nobel Prizes in Medicine and
Physiology (i.e. bio Nobels) were they emitted by non humans. Wait, would be? No, actually were. Unraveling the bee waggle dance was Nobel worthy. And what’s
the waggle dance? It’s the way a bee “articulates” (in a sign language sort of
way, but less sophisticated) how far and in what direction honey lies. In other
words, it is a way for bees to map AP expressions onto CI structures that
convey a specific kind of message. It’s quite complicated (see here), and describing it’s
figure 8 patterns (direction and size) and how they related to the position of
the sun and the food source is what won von Frisch the prize in Physiology and
Medicine. In other words, von Frisch won a bio Nobel for describing a grammar of the bee dance.
And it really was “just” a G, with very little “physiology”
or “medicine” implicated. Even at the present time, we appear to know very little
about either the neural or genetic basis of the dance or its evolutionary
history (or at least Wikipedia and a Google search seems to reveal little
beyond anodyne speculations like “Ancestors to
modern honeybees most likely performed excitatory movements to encourage other
nestmates to forage” or “The waggle dance is thought to have evolved to aid in
communicating information about a new nest site, rather than spatial
information about foraging sites” (Wikipedia)). Nonetheless, despite the dearth
of bee neurophysiology, genetics or evo-bee-dance evolutionary history, the bio
worthies granted it a bio Nobel! Now here is my possibly contentious claim:
describing kinds of patterns humans use to link articulations to meanings is no
less a biological project than is describing waggle dance patterns. Or, to
paraphrase my good and great friend Elan Dresher: if describing how a bunch of
bees dance is biology so too is describing how a bunch of Parisians speak
French.
Second, it’s not only bees! If you work on bird songs or
whale songs or other forms of vocalization or vervet monkey calls you are
described as doing biology (look at the journals that publish this stuff)! And
you are doing biology even if you are largely describing the patterns of these songs/calls. Of
course, you can also add a sprinkle of psychology to the mix and tentatively
describe how these calls/songs are acquired to cement your biological bona
fides. But, if you study non human
vocalizations and their acquisition then (apparently) you are doing biology,
but if you do the same thing in humans apparently you are not. Or, to be more
precise, describing work on human language as biolinguistics is taken to be
wildly inappropriate while doing much the same thing with mockingbirds is
biology. Bees, yes. Whales and birds, sure. Monkey calls, definitely. Italian
or Inuit; not on your life! Dualism anyone?
As may be evident, I think that
this line of reasoning is junk best reserved for academic bureaucrats
interested in figuring out how to demarcate the faculty of Arts from that of
Science. There is every reason to think that there is a biological basis for
human linguistic capacity and so studying manifestations of this capacity and
trying to figure out its limits (which is what GG has been doing for well over
60 years) is biology even if it fails to
make contact with other questions and methods that are currently central in
biology. To repeat, we still don’t know the neural basis or evolutionary
etiology of the waggle dance but nobody is lobbying for rescinding von Frisch’s
Nobel.
One can go further: Comparing
modern work in GG and early work in genetics leads to a similar conclusion. I
take it as evident that Mendel was doing biology when he sussed out the genetic
basis for the phenotypic patterns in his pea plant experiments. In other words,
Mendel was doing biogenetics (though
this may sound redundant to the modern ear). But note, this was biogenetics
without much bio beyond the objects of interest being pea plants and the
patterns you observe arising when you cross breed them. Mendel’s work involved
no biochemistry, no evolutionary theory, no plant neuro-anatomy or plant neuro-physiology.
There were observed phenotypic patterns and a proposed very abstract underlying mechanism (whose physical basis was a
complete mystery) that described how these might arise. As we know, it took the
rest of biology a very long time to catch up with Mendel’s genetics. It took
about 65 years for evolution to integrate these findings in the Modern
Synthesis and almost 90 years until biology (with the main work carried out by
itinerant physicists) figured out how to biochemically ground it in DNA. Of
course, Mendel’s genetics laid the groundwork for Watson and Crick and was
critical to making Darwinian evolution conceptually respectable. But, and this
is the important point here, when first proposed, its relation to other domains
of biology was quite remote. My point: if you think Mendel was doing biology
then there is little reason to think GGers aren’t. Just as Mendel identified
what later biology figured out how to embody, GG is identifying operations and
structures that the neurosciences should aim to incarnate. Moreover, as I discuss below, this melding of
GG with cog-neuro is currently enjoying a happy interaction somewhat analogous
to what happened with Mendel before.
Before saying more, let me make
clear that of course biolinguists
would love to make more robust contact with current work in biology. Indeed, I
think that this is happening and that Minimalism is one of the reasons for
this. But I will get to that. For now let’s stipulate that the more interaction
between apparent disparate domains of research the better. However, absence of
apparent contact and the presence of different methods does not mean that
subject matters differ. Human linguistic capacity is biologically grounded. As
such inquiry into linguistic patterns is reasonably considered a biological
inquiry about the cognitive capacities of a very specific animal; humans. It
appears that dualism is still with us enough to make this obvious claim
contentious.
The point of all of this? I
actually have two: (i) to note that the standard criticism of GG as not real biolinguistics at best rests on
unjustified dualist premises (ii) to note that one of the more interesting
features of modern Minimalist work has been to instigate tighter ties with
conventional biology, at least in the neuro realm. I ranted about (i) above. I
now want to focus on (ii), in particular a recent very interesting paper by the
group around Stan Dehaene. But first a little segue.
I have blogged before on Embick
and Poeppel’s worries about the conceptual mismatch between the core concepts
in cog-neuro and those of linguistics (here for some discussion). I have also suggested that
one of the nice features of Minimalism is that it has a neat way of bringing
the basic concepts closer together so that G structure and its bio substructure
might be more closely related. In particular, a Merge based conception of G
structure goes a long way towards reanimating a complexity measure with real
biological teeth. In fact, it is effectively a recycled version of the DTC,
which, it appears, has biological street cred once again.[1]
The cred is coming from work showing that one can take the neural complexity of a structure as roughly indexed by the number
of Merge operations required to construct it (see here).
A recent paper goes the earlier paper one better by embedding the discussion in
a reasonable parsing model based on a Merge based G. The PNAS paper (Henceforth
Dehaene-PNAS) (here)
has a formidable cast of authors, including two linguists (Hilda Koopman and
John Hale) orchestrated by Stan Dehaene. Here is the abstract:
Although
sentences unfold sequentially, one word at a time, most linguistic theories
propose that their underlying syntactic structure involves a tree of nested
phrases rather than a linear sequence of words. Whether and how the brain
builds such structures, however, remains largely unknown. Here, we used human
intracranial recordings and visual word-by-word presentation of sentences and
word lists to investigate how left-hemispheric brain activity varies during the
formation of phrase structures. In a broad set of language-related areas,
comprising multiple superior temporal and inferior frontal sites, high-gamma
power increased with each successive word in a sentence but decreased suddenly
whenever words could be merged into a phrase. Regression analyses showed that
each additional word or multiword phrase contributed a similar amount of
additional brain activity, providing evidence for a merge operation that
applies equally to linguistic objects of arbitrary complexity. More superficial
models of language, based solely on sequential transition probability over
lexical and syntactic categories, only captured activity in the posterior
middle temporal gyrus. Formal model comparison indicated that the model of
multiword phrase construction provided a better fit than probability- based
models at most sites in superior temporal and inferior frontal cortices.
Activity in those regions was consistent with a neural implementation of a
bottom-up or left-corner parser of the incoming language stream. Our results
provide initial intracranial evidence for the neurophysiological reality of the
merge operation postulated by linguists and suggest that the brain compresses
syntactically well-formed sequences of words into a hierarchy of nested
phrases.
A few comments, starting with a point of disagreement: Whether the brain builds hierarchical
structures is not really an open question. We have tons of evidence that it
does, evidence that linguists a.o. have amassed over the last 60 years. How
quickly the brain builds such structure (on line, or in some delayed fashion)
and how the brain parses incoming strings in order to build such structure is
still opaque. So it is misleading to say that what Dehaene-PNAS shows is both that the brain does this and how.
Putting things this way suggests that until we had such neural data these
issues were in doubt. What the paper does is provide neural measures of this
structure building processes and provides a nice piece of cog-neuro inquiry
where the cog is provided by contemporary Minimalism in the context of a parser
and the neuro is provided by brain activity in the gamma range.
Second, the paper demonstrates a nice connection between a
Merge based syntax and measures of brain activity. Here is the interesting bit
(for me, my emphasis):
Regression analyses showed that
each additional word or multiword phrase contributed a similar amount of additional brain activity, providing evidence for a merge operation that applies equally to
linguistic objects of arbitrary complexity.
Merged based Gs treat all combinations as equal regardless
of the complexity of the combinations or differences among the items being combined.
If Merge is the only operation, then
it is easy to sum the operations that provide the linguistic complexity. It’s
just the same thing happening again
and again and on the (reasonable) assumption that doing the same thing incurs
the same cost we can (reasonably) surmise that we can index the complexity of
the task by adding up the required Merges. Moreover, this hunch seems to have
paid off in this case. The merges seem to map linearly onto brain activity as
expected if complexity generated by Merge were a good index of the brain
activity required to create such structures. To put this another way: A virtue
of Merge (maybe the main virtue for
the cog-neuro types) is that it simplifies the mapping from syntactic structure
to brain activity by providing a common combinatory operation that underlies
all syntactic complexity.[2]
Here is Dehaene-PNAS paper (4):
A
parsimonious explanation of the activation profiles in these left temporal
regions is that brain activity following each word is a monotonic function of
the current number of open nodes at that point in the sentence (i.e., the
number of words or phrases that remain to be merged).
This makes for a limpid trading relation between complexity
as measured cognitively and as measured brain-wise transparent when implemented
in a simple parser (note the weight carried by “parsimonious” in the quote
above). What the paper argues is that this simple transparent mapping has surprising
empirical virtues and part of what makes it simple is the simplicity of Merge as
the basic combinatoric operation.
There is lots more in this paper. Here are a few things I
found most intriguing.
A key assumption of the model is that combining the words
into phrases occurs after the word at the left edge of the constituent boundary
(2-3):
…we
reasoned that a merge operation should occur shortly after the last word of
each syntactic constituent (i.e., each phrase). When this occurs, all of the
unmerged nodes in the tree comprising a phrase (which we refer to as “open
nodes”) should be reduced to a single hierarchically higher node, which becomes
available for future merges into more complex phrases.
This assumption drives the empirical results. Note that it
indicates that structure is being built bottom-up.
And this assumption is a key feature of a Merge based G that assumes something
like Extension. As Dehaene-PNAS puts it (4):
The
above regressions, using “total number of open nodes” as an independent
variable, were motivated by our hypothesis that a single word and a multiword
phrase, once merged, contribute the same amount to total brain activity. This
hypothesis is in line with the notion of a single merge operation that applies
recursively to linguistic objects of arbitrary complexity, from words to
phrases, thus accounting for the generative power of language
If the parsing respects the G principle of Extension then it
will have to build structure in this bottom up fashion. This means holding the
“open” nodes on a stack/memory until this bottom up building can occur. The
Dehaene-PNAS paper provides evidence that this is indeed what happens.
What kind of evidence? The following (3) (my emphasis):
We
expected the items available to be merged (open nodes) to be actively
maintained in working memory. Populations of neurons coding for the open nodes
should therefore have an activation profile that
builds up for successive words, dips following each merge, and rises again as
new words are presented. Such an activation profile could follow if words
and phrases in a sentence are encoded by sparse overlapping vectors of activity
over a population of neurons (27, 28). Populations of neurons involved in
enacting the merge operation would be expected to show activation at the end of constituents, proportional to the number
of nodes being merged. Thus, we searched for systematic increases and
decreases in brain activity as a function of the number of words inside phrases
and at phrasal boundaries.
So, a Merge based parser that encodes Extension should show
a certain brain activity rhythm indexed to the number of open nodes in memory
and the number of Merge operations executed. And this is what the paper found.
Last, and this is very important: the paper notes that Gs
can be implemented in different kinds of parsers and tries to see which one
best fits the data in their study. There is no confusion here between G and
parser. Rather, it is recognized that the effects of a G in the context of a
parser can be investigated, as can the details of the parser itself. It seems
that for this particular linguistic task, the results are consistent either a
bottom-up or left corner parser, with the latter being a better fit for this data (7):
Model
comparison supported bottom-up and left-corner parsing as significantly
superior to top-down parsing in fitting activation in most regions in this left-hemisphere
language network…
Those
findings support bottom-up and/or left-corner parsing as tentative models of
how human subjects process the simple sentence structures used here, with some
evidence in favor of bottom-up over left-corner parsing. Indeed, the open-node
model that we proposed here, where phrase structures are closed at the moment
when the last word of a phrase is received, closely par- allels the operation
of a bottom-up parser.
This should not
be that surprising a result given the data that the paper investigates. The
sentences of interest contain no visible examples where left context might be
useful for downstream parsing (e.g. Wh element on the left edge (see Berwick
and Weinberg for discussion of this)). We have here standard right branching
phrase structure and for these kinds of sentences non-local left context will
be largely irrelevant. As the paper notes (8), the results do “not question the
notion that predictability effects play a major role in language processing”
and as it further notes there are various kinds of parsers that can implement a
Merge based model, including those where “prediction” plays a more important
role (e.g. left-corner parsers). That said, the interest of Dehaene-PNAS lies not only in the conclusion (or maybe not even mainly there), but in the fact that it provides a useful and usable model for how to investigate these computational models in neuro terms. That’s the big payoff, or IMO, the one that will pay dividends in the future. In this, it joins the earlier Pallier et al and the Ding et al papers. They are providing templates for how to integrate linguistic work with neuro work fruitfully. And in doing so, they indicate the utility of Minimalist thinking.
Let me say a word about this: what cog-neuro types want are simple usable models that have accessible testable implications. This is what Minimalism provides. We have noted the simplicity that Merge based models afford to the investigations above; a simple linear index of complexity. Simple models are what cog-neuro types want, and for the right reasons. Happily, this is what Minimalism is providing and we are seeing its effects in this kind of work.
An aside: let’s hear it for stacks! The paper revives classical theories of parsing and revives the idea that brains have stacks important for the parsing of hierarchical structures. This idea has been out of favor for a long time. One of the major contributions of the Dehaene-PNAS paper is to show that dumping it was a bad idea, at least for language, and, most likely, other domain where hierarchical organization is essential.
Let me end: there is a lot more in the Dehaene-PNAS paper. There are localization issues (where the operations happen) and arguments showing that simple probability based models cannot survive the data reviewed. But for current purposes there is a further important message: Minimalism is making it easier to put a lot more run of the mill everyday bio into biolinguistics. The skepticism about the biological relevance of GG and Minimalism for more bio investigation is being put paid by the efflorescence of intriguing work that combines them. This is what we should have expected. It is happening. Don’t let anyone tell you that linguistics is biologically inert. At least in the brain sciences, it’s coming into its own, at last![3]
[1]
Alec Marantz argued that the DTC is really the only game in town. Here’s a
quote:
…the more complex a
representation- the longer and more complex the linguistic computations
necessary to generate the representation- the longer it should take for a
subject to perform any task involving the representation and the more activity
should be observed in the subject’s brain in areas associated with creating or
accessing the representation or performing the task.
[2]
Note that this does not say that only a Merge based syntax would do this.
It’s just that Merge systems are particularly svelt systems and so using them
is easy. Of course many Gs will have Mergish properties and so will also serve
to ground the results.
[3]
IMO, it is also the only game in town when it comes to evolang. This is also
the conclusion of Tatersall in his review of Berwick and Chomsky’s book. So,
yes, there is more than enough run of the mill bio to license the
biolinguistics honorific.