Guest post by former student, William Matchin:
It’s been almost 10 years since the Society for the
Neurobiology of Language conference (SNL) began, and it is always one of my
favorite events of the year, where I catch up with old friends and see and
discuss much of the research that interests me in a compact form. This year’s
meeting was no exception. The opening night talk about dolphin communication by
Diana Reiss was fun and interesting, and the reception at the Baltimore
aquarium was spectacular and well organized. I was impressed with the high
quality of many of the talks and posters. This year’s conference was
particularly interesting to me in terms of the major trending ideas that were
circulating at the conference (particularly the keynote lectures by Yoshua
Bengio & Edward Chang), so I thought I would write some of my impressions
down and hear what others think. I also have some thoughts about Society for
Neuroscience (SfN), in particular one keynote lecture: Erich Jarvis, who
discussed the evolution of language, with the major claim that human language
is continuous with vocal learning in non-human organisms. Paško Rakić, who gave a history of his research in neuroscience, also had an
interesting comment on the tradeoff between empirical research and theoretical development
and speculation, which I will also discuss briefly.
The notions of abstractness, innateness, and
modality-independence of language loomed large at both conferences; much of
this post is devoted to these issues. The number of times that I heard a
neuroscientist or computer scientist make a logical point that reminded me of
Generative Grammar was shocking. In all, I had an awesome conference season,
one that gives me great hope and anticipation for the future of our field,
including much closer interaction between biologists & linguists. I
encourage you to visit the Faculty of Language blog, which often discusses similar issues,
mostly in the context of psychology and linguistics.
1. Abstractness &
combinatoriality in the brain
Much of the work at the conference this year touched on some
very interesting topics, ones that linguists have been addressing for a long
time. It seemed that for a while embodied cognition and the motor theory of
speech perception were dominant topics, but now it seems as though the table
has turned. There were many presentations showing how the brain processes
information and converts raw sensory signals into abstract representations. For
instance, Neal Fox presented ECoG data on a speech perception task,
illustrating that particular electrodes in the superior temporal gyrus (STG) dynamically
encode voice onset time as well as categorical voicing perception. Then there
was Edward Chang’s talk. I should think that everyone at SNL this year would agree
that his talk was masterful. He clearly illustrated how distinct locations in STG
have responses to speech that are abstract and combinatorial. The results
regarding prosody were quite novel to me, and nicely illustrate the abstract
and combinatorial properties of the STG, so I shall review them briefly here.
Prosodic contours can be dramatically different in frequency
space for different speakers and utterances, yet they share an underlying
abstract structure (for instance, rising question intonation at the end of a
sentence). It appears that certain portions of the STG are selectively
interested in particular prosodic contours independently of the particular
sentence or speaker; i.e., they encode abstract prosodic information. How can a
brain region encode information about prosodic contour independently of speaker
identity? The frequency range of speech among speakers can vary quite
dramatically, such that the entire range for one speaker (say, a female) can be
completely non-overlapping with another speaker (say, a male) in frequency
space. This means that the prosodic contour cannot be defined physically, but
must be converted into some kind of psychological
(abstract) space. Chang reviewed literature suggesting that speakers
normalize pitch information by the speaker’s fundamental frequency, thus
resulting in an abstract pitch contour that is independent of speaker identity.
This is similar to work by Phil Monahan and colleagues (Monahan & Idsardi,
2010) who showed that vowel normalization can be obtained by dividing F1 and F2
by F3.
From Tang, Hamilton & Chang (2017). Different speakers
can have dramatically different absolute frequency ranges, posing a problem for
how common underlying prosodic contours (e.g., a Question contour) can be
identified independently of speaker identity.
Chang showed that the STG also encodes abstract responses to
speaker identity (the same response regardless of the particular sentence or
prosodic contour) and phonetic features (the same response to a particular sentence
regardless of speaker identity or pitch contour). Thus, it is not the case that
there are some features that are abstract and others are not; it seems that all of the relevant features are abstract.
From Tang, Hamilton & Chang (2017). Column 1 shows the responses
for a prosody-encoding electrode. The electrode distinguishes among different
prosodic contours, but not different sentences (i.e., different phonetic
representations) or speakers.
Why do I care about this so much? Because linguists (among
other cognitive scientists) have been talking for decades about abstract
representations, and I think that there has often been skepticism placed about
how the brain could encode abstractness. But the new work in ECoG by Chang and
others illustrates that much of the organization of the speech cortex centers
around abstraction – in other words, it seems that abstraction is the thing the
brain cares most about, doing so
rapidly and robustly in sensory cortex.
Two last points. First, Edward also showed that any of these
properties identified in the left STG are also found in the right STG,
consistent with the claim that speech perception is bilateral rather than
unilateral (Hickok & Poeppel, 2000). Thus, it does not seem that speech
perception is the key to language laterality in humans (but maybe syntax – see
section 3). Second, the two of us also had a nice chat about what his results
mean for innateness and development of these functional properties of the STG.
And he had the opinion that the STG innately encodes these mechanisms, and that
different languages make different use of this pre-existing phonetic toolbox. This
brings me to the next topic, which centers on the issue of what is innate about
language.
2. Deep learning and
poverty of the stimulus
Yoshua Bengio gave one of the keynote lectures at this
year’s SNL. For the uninitiated (such as myself), Yoshua Bengio is one of the
leading figures in the field of deep learning. He stayed the course during the
dark ages of connectionist neural network modeling, thinking that there would
eventually be a breakthrough (he was right). Deep learning is the next phase of
connectionist neural network modeling, centered on the use of massive amounts
of training data and hidden network layers. Such computer models can correctly
generate descriptions of pictures, translate between languages; in sum, things
for which people are willing to pay money. Given this background, I expected to
hear him say something like this in his keynote address: deep learning is awesome, we can do all the things that we hoped to be
able to do in the past, Chomsky is wrong about humans requiring innate
knowledge of language.
Instead, Bengio made a poverty of the stimulus argument (POS) in favor of Universal
Grammar (UG). Not in those words. But the logic was identical.
For those unfamiliar with POS, the logic is that human
knowledge, for instance language, is underdetermined by the input. Question:
You never hear ungrammatical sentences (such as *who did you see Mary and _), so how do you know that they are
ungrammatical? Answer: Your mind innately contains the relevant knowledge to
make these discriminations (such as a principle like Subjacency), making learning them unnecessary. POS arguments are
central to generative grammar, as they provide much of the motivation for a
theory of UG, UG being whatever is in encoded in your genome that enables you
to acquire a language, and what is lacking in things that do not learn language
(such as kittens and rocks). I will not belabor the point here, and there are
many accessible
articles on the Faculty of Language
blog that discuss these issues in great detail.
What is interesting to me is that Bengio made a strong POS
argument perhaps without realizing that he was following Chomsky’s logic almost
to the letter. Bengio’s main point was that while deep learning has had a lot
of successes, such computer models make strange mistakes that children would
never make. For instance, the model would name a picture of an animal correctly
on one trial, but with an extremely subtle change to the stimulus on the next
trial (a change imperceptible to humans), the model might make a wildly wrong
answer. This is directly analogous to Chomsky’s point that children never make
certain errors, such as formulating grammatical rules that use linear rather
than structural representations (see Berwick et al., 2011 for discussion). Bengio
extended this argument, adding that children have access to dramatically less
data than deep learning computer models do, which shows that the issue is not
the amount or quality of data (very similar to arguments made repeatedly by
Chomsky, for instance, this
interview from 1977). For these reasons, Bengio suggested the following
solution: build in some innate knowledge that guides the model to the correct
generalizations. In other words, he made a strong POS argument for the
existence of UG. I nearly fell out of my seat.
People often misinterpret what UG means. The claim really
boils down to the fact that humans have some innate capacity for language that
other things do not have. It seems that everyone, even leading figures in
connectionist deep learning, can agree on this point. It only gets interesting
when figuring out the details, which often include specific POS arguments. And
in order to determine the details about what kinds of innate knowledge should be
encoded in genomes and brains, and how, it would certainly be helpful to invite
some linguists to the party (see part 5).
3. What is the phenotype
of language? The importance of modality-independence to discussions of biology
and evolution.
The central question that Erich Jarvis addressed during his
Presidential Address at this year’s SfN on its opening night was whether human
language is an elaborate form of vocal learning seen in other animals or rather
a horse of a different color altogether. Jarvis is an expert of the biology of
birdsong, and he argued that human language is continuous with vocal learning in
non-human organisms both genetically and neurobiologically. He presented a wide
array of evidence to support his claim, mostly along the lines of showing how
the genes and parts of the brain that do vocal learning in other animals have
closely related correlates in humans. However, there are three main challenges
to a continuity hypothesis that were either entirely omitted or extravagantly
minimized: syntax, semantics, and sign language. It is remiss to discuss
biology and evolution of a trait without clearly specifying the key phenotypic
properties of that trait, which for human language includes the ability to
generate an unbounded array of hierarchical expressions that have both a
meaning and a sensory-motor expression, which can be auditory-vocal or
visual-manual (and
perhaps even tactile, Carol Chomsky, 1986). If somebody had only the modest
aim of discussing the evolution of vocal learning, I would understand omitting
these topics. But Jarvis clearly had the aim of discussing language more
broadly, and his second slide included a figure by Hauser Chomsky & Fitch
(2002), which served as the bull’s-eye for his arguments. Consider the following
a short response to his talk, elaborating on why it is important to discuss the
important phenotypic traits of syntax, semantics, and modality-independence.
It is a cliché that sentences are not simply sequences of
words, but rather hierarchical structures. Hierarchical structure was a central
component of Hauser, Chomsky & Fitch’s (2002) proposal that syntax may be
the only component of human language that is specific to it, as part of the
general Minimalist approach to try and reduce UG to a conceptual minimum (note
that Bengio, Jarvis and Chomsky all agree on this point – none of them want to have a rich,
linguistically-specific UG, and all of them argue against it). Jarvis is not an
expert on birdsong syntax, so it is perhaps unfair of him to discuss syntax in
detail. However, Jarvis merely mentioned that some have claimed to identify
recursion in birdsong (Gentner et al., 2006), feeling that to be sufficient to
dispatch syntax. However, he did not mention the work debating this issue
(Berwick et al., 2012), which illustrates that birdsong has syntax that is
roughly equivalent to phonology, but not human sentence-level syntax. This work
suggests that birdsong may be quite relevant to human language as a precursor
system to human phonology (fascinating if true), but it does not appear capable
of accounting for sentence-level syntax. In addition, the main interesting
thing about syntax is that it combines words to produce new meanings, unlike birdsong, which does
not.
With respect to semantics, Jarvis showed that dogs can learn
to respond to our commands, such as sitting when we say “sit”. He suggested
that because dogs can “comprehend” human speech, they have a precursor to human
semantics. But natural language semantics is way more than this. We combine
words that denote concepts into sentences which denote events (Parsons, 1990). We
do not have very good models of animal semantics, but a stimulus-response
pairing is probably a poor one. It may very well be true that non-human
primates have a similar semantic system as we do – desirable from a Minimalist
point of view – but it needs to be explored beyond pointing out that animals
learn responses to stimuli. Many organisms learn stimulus response pairing,
probably including insects – do we want to claim that they have a similar semantic
system as us?
The most important issue for me was sign language. I do not
think Jarvis mentioned sign language
once during the entire talk (I believe he briefly mentioned gestures in
non-human animals). As somebody who works on the neurobiology of American Sign
Language (ASL), this was extraordinarily frustrating (I cannot imagine the
reaction of my Deaf colleagues). I believe that one of the most significant
observations about human language is that it is modality-independent. As
linguists have repeatedly shown, all of the relevant properties of linguistic
organization found in spoken languages are found in sign languages: phonology,
morphology, syntax, semantics (Sandler & Lillo-Martin, 2006). Deaf children
raised by deaf parents learn sign language in the same way that hearing
children spoken language, without instruction, including a babbling stage (Petitto
& Martentette, 1991). Sign languages show syntactic priming just like
spoken languages (Hall et al., 2015). Aphasia is similarly left-lateralized in
sign and spoken languages (Hickok et al., 1996), and neuroimaging studies show
that sign and spoken language activate the same brain areas when sensory-motor
differences are factored out (Leonard et al., 2012; Matchin et al., 2017a). For
instance, in the Mayberry and Halgren labs at UCSD we showed using fMRI that left
hemisphere language areas in the superior temporal sulcus (aSTS and pSTS) show
a correlation between constituent structure size and brain activation in deaf
native signers of ASL (6W: six word lists; 2S: sequences of three two word
phrases; 6S: six word sentences) (Matchin et al., 2017a). When I overlap these
effects with similar structural contrasts in English (Matchin et al., 2017b) or
French (Pallier et al., 2011), there is almost perfect overlap in the STS.
Thus, both signed and spoken languages involve a left-lateralized combinatorial
response to structured sentences in the STS. This consistent with reports of a
human-unique hemispheric asymmetry in the morphology of the STS (Leroy et al.,
2015).
TOP: Matchin et al., in prep (ASL). BOTTOM: Pallier et al.,
2011 (French).
Leonard et al. (2012), also from the Mayberry and Halgren
labs, show that semantically modulated activity in MEG for auditory speech and
sign language activates pSTS is nearly identical in space and time.
All of these observations tell us that there is nothing
important about language that must be
expressed in the auditory-vocal modality. In fact, it is conceptually possible
to imagine that in an alternate universe, humans predominantly communicate through
sign languages, and blind communities sometimes develop strange “spoken
languages” in order to communicate with each other. Modality-independence has
enormous ramifications for our understanding of the evolution of language, as
Chomsky has repeatedly noted (Berwick & Chomsky, 2015; this talk,
starting at 3:00). In order to make the argument that human language is
continuous with vocal learning in other animals, sign language must be satisfactorily
accounted for, and it’s not clear to me how it can. This has social
ramifications too. Deaf people still struggle for appropriate educational and
healthcare resources, which I think stems in large part from ignorance about how
sign languages are fully equivalent to spoken languages among the scientific
and medical community.
When I tweeted at Jarvis pointing out the issues I saw with
his talk, he responded skeptically:
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At my invitation, he stopped by our poster, and we discussed
our neuroimaging research on ASL. He appears to be shifting his opinion:
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This reaffirms to me how important sign language is to our
understanding of language in general, and how friendly debate is useful to make
progress in understanding scientific problems. I greatly appreciate that Erich
took the time to politely respond to my questions, come to our poster, and
discuss the issues.
If you are interested in learning more about some of the issues
facing the Deaf community in the United States, please visit Marla Hatrak’s
blog: http://mhatrak.blogspot.com/,
or Gallaudet University’s Deaf Education resources: http://www3.gallaudet.edu/clerc-center/info-to-go/deaf-education.html.
4. Speculative science
Paško Rakić is a famous neuroscientist, and his
keynote lecture at SfN gave a history of his work throughout the last several
decades. I will only give one observation about the content of his work: he
thinks that it is necessary to posit innate mechanisms when trying to
understand the development of the nervous system. One of his major findings is
that cortical maps are not emergent, but rather are derived from precursor
“protomaps” that encode the topographical organization that ends up on the
cortical surface (Rakić, 1988).
Again, it seems as though some of the most serious and groundbreaking
neuroscientists, both old and new, are thoroughly comfortable discussing innate
and abstract properties of the nervous system, which means that Generative
Grammar is in good company.
Rakić also
made an interesting commentary on the current sociological state of affairs in
the sciences. He discussed a previous researcher (I believe from the late
1800s) who performed purely qualitative work speculating about how certain
properties of the nervous system developed. He said that this research, serving
as a foundation for his own work, probably would be rejected today because it
would be seen as too “speculative”. He mentioned how the term speculative used to be perceived as a
compliment, as it meant that the researcher went usefully beyond the data,
thinking about how the world is organized and developing a theory that would
make predictions for future research (he had a personal example of this, in
that he predicted the existence of a particular molecule that he didn’t
discover for 35 years).
This comment resonated with me. I am always puzzled about the
lack of interest in theory and the extreme interest in data collection and analysis:
if science isn’t about theory, also known as understanding the world, then what
is it about? I get the feeling that people are afraid to postulate theories
because they are afraid to be wrong. But every
scientific theory that has ever been proposed is wrong, or will eventually be
shown to be wrong, at least with respect to certain details. The point of a
theory is not to be right, it’s to be
right enough. Then it can provide some
insight into how the world works which serves as a guide to future empirical
work. Theory is a problem when it becomes misguiding dogma; we shouldn’t be
afraid of proposing, criticizing, and modifying or replacing theories.
The best way to do this is to have debates that are civil
but vigorous. My interaction with Erich Jarvis regarding sign language is a
good example of this. One of the things I greatly missed about this year’s SNL
was the debate. I enjoy these debates, because they provide the best
opportunity to critically assess a theory by finding a person with a different
perspective who we can count on to find all of the evidence against a theory,
saving us the initial work of finding this evidence ourselves. This is largely why
we have peer review, even with its serious flaws – the reviewer acts in part as
a debater, bringing up evidence or other considerations that the author hasn’t thought
of, hopefully leading to a better paper. I hope that next year’s SNL has a good
debate about an interesting topic. I also feel that the conference could do
well to encourage junior researchers to debate, as there is nothing better for
personal improvement in science than interacting with an opposing view to
sharpen one’s knowledge and logical arguments. It might be helpful to establish
ground rules for these debates, in order to ensure that they do not cross the
line from debate to contentious argument.
5. Society for the
Neurobiology of …
I have pretty much given up on hoping that the “Language”
part of the Society for the Neurobiology of Language conference will live up to
its moniker. This is not to say that SNL does not have a lot of fine quality
research on the neurobiology of language – in fact, it has this in spades. What
I mean is that there is little focus in the conference on integrating our work
with people who spend their lives trying to figure out what language is:
linguists and psycholinguists. I take great value in these fields, as language
theory provides a very useful guide for my own research. I don’t always take
the letter of language theory in detail, but rather as inspiration for the
kinds of things one might find in the brain.
This year, there were some individual exceptions to this
general rule of linguistic omission at the conference. I was pleased to see
some posters and talks that incorporated language theory, particularly John
Hale’s talk on syntax, computational modeling, and neuroimaging. He showed that
anterior and posterior temporal lobe are good candidates for basic structural
processes, but not the IFG – no surprise but good to see converging evidence (see
Brennan et al., 2016 for details). But, my interest in Hale’s talk only
highlighted the trend towards omission of language theory at SNL that can be
well illustrated by looking at the keynote lectures and invited speakers at the
conference over the years.
There are essentially three kinds of talks: (i) talks about
the neurobiology of language, (ii) talks about (neuro)biology, and (iii) talks
about non-language communication, cognition, or information processing. What’s
missing? Language theory. Given that
the whole point of our conference is about the nature of human language, one
would think that this is an important topic to cover. Yet I don’t think there
has ever been a keynote talk at SNL
about psycholinguistics or linguistics. I love dolphins and birds and monkeys,
but doesn’t it seem a bit strange that we hear more about basic properties of
non-human animal communication than human language? Here’s the full list of
keynote speakers at SNL for every conference in the past 9 years – not a single
talk that is clearly about language theory (with the possible exception of
Tomasello, although his talk was about very general properties of language with
a lot of non-human primate data).
2009
Michael Petrides: Recent insights into the anatomical
pathways for language
Charles Schroeder: Neuronal oscillations as instruments of
brain operation and perception
Kate Watkins: What can brain imaging tell us about
developmental disorders of speech and language?
Simon Fisher: Building bridges between genes, brains and language
2010
Karl Deisseroth: Optogenetics: Development and application
Daniel Margoliash: Evaluating the strengths and limitations
of birdsong as a model for speech and language
2011
Troy Hackett: Primate auditory cortex: principles of
organization and future directions
Katrin Amunts: Broca’s region -- architecture and novel
organizational principles
2012
Barbara Finlay: Beyond columns and areas: developmental
gradients and reorganization of the neocortex and their likely consequences for
functional organization
Nikos Logothetis: In vivo connectivity: paramagnetic
tracers, electrical stimulation & neural-event triggered fMRI
2013
Janet Werker: Initial biases and experiential influences on
infant speech perception development
Terry Sejnowski: The dynamic brain
Robert Knight: Language viewed from direct cortical
recordings
2014
Willem Levelt: Localism versus holism. The historical
origins of studying language in the brain
Constance Scharff: Singing in the (b)rain
Pascal Fries: Brain rhythms for bottom-up and top-down
signaling
Michael Tomasello: Communication without conventions
2015
Susan Goldin-Meadow: Gestures as a mechanism of change
Peter Strick: A tale of two primary motor areas: “old” and
“new” M1
Marsel Mesulam: Revisiting Wernicke’s area
Marcus Raichle: The restless brain: how intrinsic activity
organizes brain function
2016
Mairéad MacSweeney: Insights into the neurobiology of
language processing from deafness and sign language
David Attwell: The energetic design of the brain
Anne-Lise Giraud: Modelling neuronal oscillations to
understand language neurodevelopmental disorders
2017
Argye Hillis: Road blocks in brain maps: learning about
language from lesions
Yoshua Bengio: Bridging the gap between brains, cognition
and deep learning
Ghislaine Dehaene-Lambertz: The human infant brain: A neural
architecture able to learn language
Edward Chang: Dissecting the functional representations of
human speech cortex
I was at most of these talks; most of them were great, and
at least entertaining. But it seems to me that the great advantage of keynote
lectures is to learn about something outside of one’s field that is relevant to
it, and it seems to me that both neurobiology AND language fit this
description. This is particularly striking given the importance of theory to
much of the scientific work I described in this post. And I can think of many
linguists and psycholinguists who would give interesting and relevant talks,
and who are also interested in neurobiology and want to chat with us. At the
very least, they would be entertaining. Here are just some that I am thinking
of off the top of my head: Norbert Hornstein, Fernanda Ferreira, Colin
Phillips, Vic Ferreira, Andrea Moro, Ray Jackendoff, and Lyn Frazier. And if
you disagree with their views on language, well, I’m sure they’d be happy to
have a respectful debate with you.
All told, this was a great conference season, and I’m
looking forward to what the future holds for the neurobiology of language.
Please let me know your thoughts on these conferences, and what I missed. I
look forward to seeing you at SNL 2018, in
Quebec City!
-William
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