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A Multidimensional Interdisciplinary Framework for Linguistics: The Lexicon as a Case Study*

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A Multidimensional Interdisciplinary Framework for Linguistics: The Lexicon as a Case Study*

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This paper contrasts the standard modular, uni-dimensional approach of linguistic studies with a more interdisciplinary, multi-dimensional approach, and claims that the latter has a better chance of fulfilling the cognitive objectives of the field. We sketch a research program that is intended to link the levels of the genome, connectome, dynome, cognome, and phenome, and illustrate some of the consequences of this approach by focusing on the nature of the lexicon. The shift of perspective advocated here casts doubt on the sort of primitives routinely entertained in generative circles, and favors approaches of the sort advocated among cognitive linguists. Our discussion also makes clear the need to view the linguistic enterprise as firmly rooted in our knowledge of the brain and its operations (specifically oscillations).
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A Multidimensional Interdisciplinary Framework for
Linguistics: The Lexicon as a Case Study*
Cedric Boeckx1,2 and Constantina Theofanopoulou2
1ICREA
cedric.boeckx@ub.edu
2Universitat de Barcelona
constantinaki@hotmail.com
This paper contrasts the standard modular, uni-dimensional approach of
linguistic studies with a more interdisciplinary, multi-dimensional approach,
and claims that the latter has a better chance of fulfilling the cognitive
objectives of the eld. We sketch a research program that is intended to link
the levels of the genome, connectome, dynome, cognome, and phenome,
and illustrate some of the consequences of this approach by focusing on the
nature of the lexicon. The shift of perspective advocated here casts doubt on
the sort of primitives routinely entertained in generative circles, and favors
approaches of the sort advocated among cognitive linguists. Our discussion
also makes clear the need to view the linguistic enterprise as rmly rooted in
our knowledge of the brain and its operations (specically oscillations).
Key words: lexicon, variation, parameter, genome, d ynome, connectome,
cognome, phenome
*This work was made possible in part by funds from a Marie Curie International
Reintegration Grant from the European Union (PIRG-GA-2009-256413) and grants
from the Generalitat de Catalunya (2014-SGR-200), and from the Spanish Ministry
of Economy and Competitiveness (FFI2013043823-P). We gratefully acknowledge
the useful comments we received from two anonymous reviewers.
Journal of Cognitive Science 15: 403-420, 2014
©2014 Institute for Cognitive Science, Seoul National University
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Cedric Boeckx and Constantina Theofanopoulou
1. Introduction
Most linguists (ourselves included) claim that linguistics finds its raison
d’être as a scientic discipline if not solely, at least primarily in the broader
context of cognitive science, and ultimately biology (hence our use of the
term “bio-linguistics” below). But what exactly do linguists mean when
they make this claim? How does it affect their actual work? How many of
their hypotheses really depend on the correctness of this assertion?
It is our intention to address these questions in this paper by focusing on
a particular issue pertaining to the nature of the lexicon and of linguistic
variation.
The histories of modern linguistics and modern cognitive science are
intimately intertwined, ever since the Chomskyan attack on behaviorism in
the 1950s. Much like we believe the ultimate goal of cognitive science to
be the development of biologically plausible accounts of human cognition
(“cognitive biology”), linguists repeatedly claim that the ultimate goal
of the language sciences is to uncover the biological foundations of the
language faculty (“biolinguistics”). But it is fair to say that the connection
between linguistics and the various disciplines that make up biology has
so far remained, for the most part, fairly abstract (and, for the most part,
rhetorical). Recently, though, there are signs that things are changing
for the better. At least some linguists are beginning to take active steps
towards engaging with adjacent disciplines and ensuring a more fruitful
integration with biology (for a review of this recent trend that began with
Hauser, Chomsky, and Fitch 2002, see Boeckx 2013a). We would like to
provide a concrete illustration of this recent turn in the pages that follow,
and in so doing contribute to this trend. We will do so in several steps.
First, we will sketch a general framework through which we think genuine
interdisciplinary can be achieved. Next we will argue that adopting this
framework not only enhances interdisciplinarity, it also favors a certain
rapprochement between research traditions within linguistics that many still
view as antagonistic and incompatible. In other words, our claim will be
that intradisciplinarity stands to benet from improved interd i s c i p l i narit y.
Although it should be welcomed, such a consequence should not come
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as a surprise: given the complex nature of the language faculty, it stands
to reason that only a pluralistic and conciliatory approach has a fighting
chance of succeeding.
2. An interdisciplinary framework
Traditionally speaking, linguists working under the banner of
“biolinguistics” have approached language in modular terms, studying it
separately from other cognitive domains. This one-dimensional approach
has undoubtedly led to significant progress within linguistics, but it is
equally unquestionable that this approach has had the collateral effect of
isolating linguistic research from the rest of cognitive science, to the point
that the field of linguistics now occupies a fairly marginal place in the
context of cognitive studies.
Such a modular or one-dimensional approach is to be contrasted with
the modus operandi advocated by Marr (1982), who encouraged working
on cognitive issues across several dimensions or levels (for Marr, these
were the computational, algorithmic, and implementational levels). In this
section, we would like to advocate an approach that is very much in line of
Marr’s vision.
It’s long been clear to biologists, and (we hope) it is clear to cognitive
scientists as well, that there is no direct way from the genotype to the
phenotype. As Marcus (2004) put it, genes code for proteins, not for
cognition. To relate the genotype to the phenotype, several intermediate
steps will have to be taken, in the form of linking hypotheses. Work
at the genome level will have to be related (via the proteome and the
transcriptome) to what is now known as the connectome (Sporns et al.
2005), the set of all neural connections within an organism’s nervous system.
In turn, the connectome will have to be related to the “dynome” (Kopell et
al. 2014), linking brain connectivity with brain dynamics, specically brain
oscillations. In turn, the dynome will have to be connected to the “cognome”
(Poeppel 2012), understood as the comprehensive list of elementary mental
representations and operations. Finally, the cognome will have to be related
to the phenotypic level (phenome). This last step will show how elementary
mental representations and operations are able to provide the basis for the
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richness and diversity that has captured the attention of so many students of
language and cognition until now.
As this last sentence makes clear, work in linguistics has mainly taken
place at the level of the phenome, or at the border between the phenome
and the cognome. As a matter of fact, linguists would claim that most of the
work in the discipline has taken place at the cognome level, with attention
being placed on deducing a variety of linguistic phenomena to a narrow
set of ‘primitive’ properties. We would like to take issue with this claim.
Although we recognize the effort of linguists to formulate compact and
elegant theories, we believe that (rare exceptions aside) little attention has
been devoted to examining the bio-cognitive plausibility of the resulting
theoretical primitives. Indeed most primitives or atomic units formulated
by linguists have a modular, sui generis character, in accordance with
the standard uni-dimensional approach mentioned above. But along with
Poeppel (2005), we think that the constituents of the cognome will almost
certainly turn out to have a very generic character: they will not be specic
to a particular cognitive domain. Their combinations, or the context in
which they operate may be, but the operations themselves will not.
We think that this has the (so far) under-appreciated consequence of
bringing into contact two linguistic traditions, one (“Chomskyan”) seeking
Figure 1. One-dimensional and multi-dimensional approaches to mind/brain
contrasted.
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A Multidimensional Interdisciplinary Framework for Linguistics
to reduce linguistic complexity to a set of elementary primitives, and the
other (“Cognitive”) seeking to account for linguistic processes in terms of
general ‘cognitive’ mechanisms. The challenge ahead is to marry these two
traditions, showing that elementary primitives used by Chomskyan linguists
to explain various linguistic phenomena can be understood in terms of
generic processes that in turn can be translated into what we know about
how the brain works (dynome, connectome, and ultimately genome).
We think that this ‘translational research program at the heart of Figure 1
is the way to proceed for cognitive biology. Although in practice cognitive
studies cannot be expected to work at all levels at once, we would like to
stress the need to bear all these levels in mind when formulating specic
hypotheses at a given level, to avoid re-establishing disciplinary boundaries
that only serve to delay progress.
We also would like to take this opportunity to emphasize the fact that
linguistic, or more generally cognitive studies, should take the brain as
their focus, for this is the real nexus in Figure 1. For too long linguists have
disregarded what we know about the brain, claiming that we still know
so little about it that it is pointless to try to relate mind and brain now. We
think this view must be retired. We readily concede that not everything is
known about the brain, but we think that enough is known to guide us in
our formulation of the cognome. Specifically, it is fairly well-established
that information processing at the brain level is achieved by a meaningful
interaction of oscillations at various frequencies generating by populations
of neurons (Buzsaki 2006). We therefore urge linguists to frame their
discussion in light of this working hypothesis about brain rhythms and offer
concrete proposals at the cognome level that can be translated in rhythmic
terms. Short of that, the slash between mind and brain (“mind/brain”) is
there to remain.
3. An illustration
As an example of this shift of perspective from domain-specific atomic
units to neurally more plausible generic primitives, we’d like to focus on the
nature and structure of the lexicon (for another example, see Appendix). In
generative studies of the past 25 years or so, this issue has been intimately
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Cedric Boeckx and Constantina Theofanopoulou
related to the nature and structure of what is known as the parametric
space (Chomsky 1981), taken to be the set of options the child must choose
from to converge on the language of the environment. Our question in
this section is how to understand this “parametrome”. Traditionally, this
parametric space has been defined in terms of “parameters”. These are
conceived of as the atoms of linguistic variation. They are domain-specic,
and informationally encapsulated, as expected from the orthodox modular
stance in linguistics. As far their biological nature is concerned, they are
claimed to be “part of the child’s genetic makeup”, never mind the fact that
genes don’t code for parameters. When pressed, linguists such as David
Lightfoot point to a certain parallelism with the visual system, as in the
following passage:
“The grammar is one subcomponent of the mind, which interacts
with other cognitive capacities or modules. Like the grammar, each of
the other modules may develop in time and have distinct initial and
mature states. So the visual system recognizes triangles, circles, and
squares through the structure of the circuits that lter and recompose
the retinal image (Hubel & Wiesel 1962). Certain nerve cells respond
only to a straight line sloping downward from left to right, other nerve
cells to lines sloped in different directions. The range of angles that
an individual neuron can register is set by the genetic program, but
experience is needed to fix the precise orientation specificity (Sperry
1968). In the mid 1960s David Hubel, Torsten Wiesel, and their
colleagues devised an ingenious technique to identify how individual
neurons in an animal’s visual system react to specific patterns in the
visual field (including horizontal and vertical lines, moving spots,
and sharp angles). They found that particular nerve cells were set
within a few hours of birth to react only to certain visual stimuli, and,
furthermore, that if a nerve cell is not stimulated within a few hours, it
becomes inert. In several experiments on kittens, it was shown that if a
kitten spent its rst few days in a deprived optical environment (a tall
cylinder painted only with vertical stripes), only the neurons stimulated
by that environment remained active; all other optical neurons became
inactive because the relevant synapses degenerated, and the kitten
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never learned to see horizontal lines or moving spots in a normal way.
In this view, learning is a selective process: parameters are provided by
the genetic equipment and relevant experience xes those parameters.”
(Lightfoot 2005: 52-53)
The problem we have with this perspective is that grammatical options
(such as Verb rst or Verb last) cannot be seriously taken to be on par with
visual orientation cues. The latter have had millions of years, and very good
adaptationist reasons, to be coded for, biologically speaking. Options like
left/right, vertical/horizontal are plausibly primitive, in a way that “subject”,
“object”, “verb”, etc. aren’t. It is somewhat foolish to believe that these two
sets of options are coded for in the same way, and that the innateness of one
can justify the innate status of the other. This seems to us to be the strongest
argument against taking parameters to be primitives. (For a detailed list
of reasons why ‘parametric options’ cannot reasonably be taken to be part
of our “genetic make-up”, we refer the interested reader to Longa and
Lorenzo, 2008, 2012, and Lorenzo & Longa, 2009.)
To drive the point home, let us stress also that unlike visual orientation
cues that provide the basics for low-level visual perception, parameters are
typically conceived of at a much higher level of ‘abstraction (in the sense
of remoteness from biological substrate). In this sense, our take differs
sharply from Chomsky’s recent appeal to “three factors” in language design.
Whereas Chomsky continues to insist on the role of a rst factor (genetic
endowment) that he sees as domain specic, we want to emphasize that for
us genes cannot be so construed.
What must be done is nd a level of description at the cognome layer of
which one can then ask how biology codes for it. As we will now show, this
level necessarily implies the abandonment of atomic units like parameters,
and favors implementational solutions that are much closer in spirit to
the theoretical vocabulary employed by cognitive linguists, as well as
representational options that are plausibly shared across species.
A first step in this direction was taken by linguists like Yang (2002
et seq.), who sought to combine the setting of parametric options with
the gradual consolidation of rules on the basis of statistical learning in
light of the revival of Bayesian learning studies (Saffran et al. 1996). An
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Cedric Boeckx and Constantina Theofanopoulou
equally important step was taken by cognitive scientists like Mehler’s team
(Endress et al., 2009; Gervain & Mehler, 2010), who relied on attested and
cognitively ancient perceptual and memory constraints to structure the
data input (‘data intake’). This type of work led to the formulation of an
explicit learning algorithm constructing the lexicon in Boeckx and Leivada
2014. This algorithm has the advantage of relying on mechanisms and
initial representations that are plausibly shared across cognitive domains.
The algorithm rests on the ability for ‘reasoning under uncertainty’ at the
heart of Bayesian learning. A central aspect of this reasoning is the ability
to entertain overhypotheses and constraints on hypotheses at the same time,
that is to work at both levels at the same time: while trying to generalize
(overhypotheses), paying attention to singularities (exceptions) (Kemp et al.
2007). In other words, tracking down both types and tokens.
In this context, the efficient learner should be able to integrate in the
process of acquisition some conflicting tendencies, such as the need to
formulate generalizations over input, without however generalizing so much
as to require subsequent backtracking in light of numerous exceptions.
More specically, the efcient learner internalizes linguistic knowledge by
making use of biases that simultaneously allow for both overgeneralizing
hypotheses (e.g., Boeckx’s 2011 Superset Bias), leading to the formulation
of ever more general types, but also for adequately constraining
overgeneralizations. This is fully in line with Briscoe & Feldman’s (2011)
Bias/Variance Trade-off, according to which learners adopt an intermediate
point on the bias/variance continuum in order to refrain from over-tting,
backtracking and reanalyzing data.
Another property of the efcient learner is the ability to pay attention to
statistical distributions. Many studies point out that humans are powerful
statistical learners (e.g., Saffran et al., 1996). Yang (2005) suggests that
productivity of hypothesized rules is subject to the Tolerance Principle,
which seeks to dene how many exceptions to a hypothesized rule can be
tolerated without the learner deciding to abandon the rule as unproductive.
One of the more recent formal representations of the Tolerance Principle
holds that Rule R is productive if T(ime)(N,M) < T(N,N), with (N-M)
being the rule-following items and M the exceptions (Yang, 2005; Legate
& Yang, 2012). If T(N,N) < T(N,M), then R is not productive and all items
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are listed as exceptions (M=N, all items are stored as exceptions). This
principle accurately predicts rule productivity and inference in the course of
acquisition in terms of overhypotheses formulated by the learner.
The upshot of this discussion is that frequency and the type/token
distinction refined over the learning processes starting with generic
categories are at the heart of the construction of the lexicon, a claim that is
much more congenial to work in cognitive linguistics than to work in the
Chomskyan tradition. The key point is that the primitive representation
one starts with progressively gets rened and entrenched (in pretty much
the sense of Langacker 1999) as a result of usage (see Bybee 2010). Rather
than seeing the acquisition of the lexicon as the setting of pre-specified
parametric options, the lexicon is constructed. The grammar progressively
grammaticalizes, and is not xed ab initio.
The representations produced by the algorithm have a lot in common
with Inheritance hierarchies found in many studies in Construction
Grammar. Such Inheritance hierarchies offer concise representations of how
specic languages (nal states of the language faculty) organize themselves
in various families of increasingly abstract constructions (see Goldberg
and Jackendoff 2004; also Jackendoff 2010). The grammar/lexicon divide
dissolves in our perspective in much the same way it does for work in the
cognitive tradition.
But the algorithm proposed by Boeckx and Leivada 2014 cannot be taken
to be fully adequate until it can be translated into brain terms, in accordance
with the research program sketched in the previous section. This is what we
will focus on in the rest of this section.
As we pointed out above in the context of Lightfoot’s quote, the classical
parametric model offers no plausible brain mechanism for the formation
of the lexicon. Instead, an algorithm that relies on frequency tracking can
already draw on the literature identifying the neural basis of statistical
learning. This literature (see Karuza et al. 2013, Schapiro et al. 2014)
highlights the role of usual suspects such as Broca’s area and the temporal
lobe, but crucially also implicates subcortical structures such as the basal
ganglia and the hippocampus. We would like to point out, however, that
we think that it would be a mistake to reduce these findings to the now
well-established procedural vs. declarative memory system (Ullman 2004).
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Cedric Boeckx and Constantina Theofanopoulou
Although we believe this distinction to be useful, it is often taken as a way
to cash out at the brain level the lexicon/grammar divide (Ullman et al.
1997). Since we are skeptical of this distinction, we want to caution against
multiplying memory systems.
To this literature on the neural basis of statistical learning, which tends to
be focused on linguistic issues and give the impression of identifying neural
structures dedicated to specic cognitive domains, we would like to draw
attention to recent work on how the brain makes categorical decisions about
ambiguous stimuli that is, how it learns categories. Work by Miller and
colleagues (Roy et al. 2014, Antzoulatos and Miller 2014, Buschman and
Miller 2014) has highlighted the importance of the functional connectivity
between the prefrontal cortex and the striatum in this task. This work
shows two systems working in concert. A basal ganglia-centred system
that quickly learns simple, xed goal-directed behaviors (think of these as
“tokens”), and a prefrontal cortex-centred system that gradually learns more
complex, more abstract goal-directed behaviors (think of them as types
or over hypotheses). As Miller and colleagues show, interactions between
these two systems, mediated by the thalamus, allow top-down control
mechanisms to learn how to direct behavior towards a goal but also how
to guide behavior when faced with a novel situation. This interaction takes
the form of a combination of neural oscillations at various frequencies,
especially gamma, beta, alpha, and theta. These form the neural ‘language’
of what we referred to earlier as the bias/variance trade-off, entrenchment,
and ultimately grammaticalization (at the level of ontogeny).
Buschman and Miller observe that because interactions between the basal
ganglia and the pre-frontal cortex work in both directions, this functional
connectivity provides a powerful computational mechanism that allows
for new experiences to be compared to expectations from previous ones,
and in so doing find the optimal balance between (general) “rules” and
“exceptions”.
As Buschman and Miller 2014 point out, this dual architecture—”fast-
learning in more primitive, non- cortical structures training the slower,
more advanced, cortex”—may be a general brain strategy, and in our view
complements the procedural/declarative duality that is better known to
cognitive scientists. If we are correct about the basal ganglia-pre-frontal
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cortex recurrent network to constitute the backbone of the lexicon, our
line of argument leads us to anticipate neuroimaging findings where the
acquisition of the lexicon is shown to implicate both cortical and subcortical
structures, in particular the basal ganglia and the thalamus, and is thus fully
in line with the reports in Mestres-Missé et al. 2008 and Ripollés et al. 2014.
Being a general strategy the dual architecture advocated here has the
advantage of meeting Poeppel’s genericity desideratum. Our point here is
that this genericity must be matched at the cognome level as well. It is for
this reason that lexical atomic units like parameters have to be abandoned
and replaced by a much more dynamic, and ‘constructivist’ system that
allows for the lexicon to grow. We think that this is the message that the
brain ndings reported on here are giving us, and we would be wrong not to
take this ‘data point’ seriously in our attempt to formulate adequate theories
of linguistic knowledge.
Let us conclude this section with a few remarks. First, we wish you dispel
an impression that we may have given when describing the role of the basal
ganglia in the preceding paragraphs. Readers of Miller’s work on which we
draw may be tempted to view the role of the basal ganglia in behavioristic
terms (simple stimulus-response learning mechanisms). But we think
this would be mistaken. Attention to the basal ganglia in recent years
has certainly revived behaviorist concepts such as operant self-learning
(Mendoza et al. 2014), but it is important to recognize that even the very
fast, token-based learning for which the basal ganglia plays a crucial role is
nonetheless quite abstract, and quite distinct from what Skinner would have
been willing to endorse.
The second remark we want to make concerns the context in which
our appeal to the “goal-directed learning” model of Miller and is made
colleagues. Whereas this model captures the basics of the type-token
distinction, it is necessary to view it as part of a much broader “learning”
brain. A full description of this broader system would take us too far aeld
here, but we want to highlight some of the key components that interact
with the basal ganglia-prefrontal cortex loop system. One such component
will be the (multi-sensorial) attention network, especially the dorsal
attention network made up of fronto-parietal connections modulated by
the higher-order thalamic nuclei (see Boeckx and Benitez-Burraco 2014
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Cedric Boeckx and Constantina Theofanopoulou
for references). Another component, crucial to capture the entrenchment of
learned items, will be the long-term memory system, primarily consisting
of the hippocampus, but also other brain structures such as the precuneus,
involved in memory retrieval, as well as the amygdala, known to modulate
hippocampal activity. A third component will be the so-called working
memory system, which not only involves parts of the pre-frontal cortex but
also implicates other cortical (parietal and temporal) and subcortical (basal
ganglia, and possibly as well, cerebellum) structures that work in tandem
with the attention network, as well as with the long-term memory system.
This working memory component provides a buffer space that is not only
important in the context of parsing, but also in the context of keeping active
the various options from which to choose, and eventually learn.
All these networks must be seen as interacting with the components of
goal-directed learning discussed above to generate the proper ‘symphony’ of
oscillations through which the brain will focus on, select, and retain lexical
items, as it constructs the lexicon. We cannot emphasize enough the need to
think of language-related tasks as ‘whole-brain’ affairs. Even if the modular
organization of the mind and the brain still dominates, it is crucial to view
complex tasks, such as constructing the lexicon, as operations that recruit
numerous networks, none of which domain-specific, to arrive at mature
domain-specic representations. Such representations, we believe, lie at the
intersection of embedded oscillations, generated by dynamically forming
neural networks. At the level of the connectome, it is clear that structures
like the thalamus are likely to play an important role in coordinating these
multiple networks, and in future work we hope to return to this issue in
the context of the dynome, and show how the thalamus may tune in all
these interacting oscillation ensembles (for initial steps in this direction, see
Boeckx and Theofanopoulou to appear).
The third remark pertains to the vexed issue of ‘innateness’. As a reviewer
points out, readers may well wonder whether our claim that “the grammar
progressively grammaticalizes, and is not fixed ab initio” casts doubt on
the idea that ‘language is innate’. We think it does, and this is why we
believe it is important to distinguish between a ‘language-ready brain’ (the
set of innate abilities, none language-specic, allowing for the acquisition
of linguistic systems) and a mental grammar. The latter certainly relies
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on innate abilities, but its content is the result of growth and interaction
with the environment. In this context it is worth pointing out that our
‘constructivist’ approach stands to benefit from a closer examination of
neurodevelopmental data as well as data from developmental cognitive
disorders to strengthen our claim that the lexicon is not ‘pre-formed’. As we
discuss in related work (Boeckx and Theofanopoulou to appear), our model
leads us to expect cognitive disorders to translate in terms of deviant brain
rhythms (‘oscillopathies’).
The fourth remark touches on another issue raised by a reviewer. It
concerns the status of work trying to deconstruct semantic processing in
terms of brain activity (e.g. Event Related Potentials) in the context of
the framework we have sketched here. What are we to make of indices
like the well-known N400 signal, routinely related to the processing
of semantic information (N400)? Concerning this issue, we share the
position advocated in Lau, Phillips and Poeppel 2008. After reviewing the
literature on the N400 signal, these authors conclude that “at least some
substantial part of the N400 effect reflects facilitated lexical access”, but
they are quick to point out that this result, as interesting as it is, can only
be taken as a starting point. It requires a deeper “understanding of the
neuronal architecture” generating the signal, and (fully in line with the
approach advocated here), it requires a deeper characterization of notions
like “semantic integration” that the N400 signal indexes. In the words of
Lau et al., “the notion of integration requires specication both at the level
of cognitive models and at the level of neuronal mechanisms.We could
not agree more with them concerning the fact that theoretical notions like
“semantic integration” “cover multiple sub-mechanisms”: [in this specic
case] “encoding compositional structure, generating expectations, linking
input with world knowledge, and controlling communication between
different information sources.” Our approach deems it necessary to offer
specific technical implementation of these mechanisms in terms of the
dynamics of neuronal populations.
The fth remark we would like to make concerns the shift of perspective
that we have called for and that is captured Figure 1. What this shift is
intended to do is change not so much the landscape of questions, the “puzzle”
if you wish, that linguists should be interested in; the big questions that
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Cedric Boeckx and Constantina Theofanopoulou
Chomsky 1986 listed, such as Plato’s problem, are there to stay. Rather it
changes the nature of the pieces with which to solve the puzzle. It asks
linguists to use pieces that can be made to t with pieces other scientists
working at other levels have discovered. For future students of the language
sciences, we hope that this way of approaching the linguistic puzzle will
lead them to more realistic representations of the biological nature of
linguistic knowledge.
Appendix
In this paper we have focused on the lexicon and specifically on the
notion of ‘parameter’ to illustrate the sort of interdisciplinary approach
we advocate. A reviewer asks if we are already in a position to provide
an example of how brain oscillations may explain certain phenomena at
the computational/cognome level. This is work we have begun to carry
out and we would like to briey touch on some tentative results here (for
additional discussion, we refer the reader to Boeckx 2013b and Boeckx and
Theofanopoulou to appear).
Boeckx (2013b) propose that we view syntax as an unbounded merge
operation, regulated by cyclic applications of a process called “Spell-
Out”. Spell-Out-regulated Merge amounts to an iterative application of a
generic combinatorial operation (set-formation), coupled with a periodic
forgetting of material already combined. This is not the place to justify
this model. Suffice it to say that all it needs are elements that can freely
com- bine (so-called lexical items, the precursors of “words”), an active
memory buffer (technically known as the division between the “phase
edge” and “the phase complement” in the recent generative literature), and
the right balance between a process of combination (Merge) and a process
of deactivation (De-Merge or Spell-Out). The latter balance is fairly close to
an optimal chunking strategy of the sort familiar to cognitive scientists.
Interestingly, in the literature on brain rhythms, it has been claimed that
flexible frequency control of cortical oscillations enables computations
required for working memory. In particular, Dipoppa and Gutkin (2013)
provide a model where individual frequency bands implement elementary
computations constituent of cognitive tasks. When looked at with the eyes
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A Multidimensional Interdisciplinary Framework for Linguistics
of a linguist, such elementary computations look a lot like those discussed
in the previous paragraph for syntactic computation. Dipoppa and Gutkin
(2013) claim that rapid memory access and load is enabled by the beta/
gamma oscillations, maintaining a memory while ignoring distractors by the
theta, rapid memory clearance by the alpha. Think of memory access and
load as accessing lexical items and merging them; think of maintaining a
memory in terms of the syntactician’s memory buffer, and think of memory
clearance as Spell-Out.
What this suggests to us is that if one is willing to decompose specific
linguistic operations like Merge in more generic terms, one can already take
advantage of the existing literature to translate these operations in terms of
neuronal dynamics.
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... The second component is a combinatorial device merge, whose role is to create complex expressions with elements extracted from the lexicon. The operation of "merge" (Chomsky 1957(Chomsky , 1995(Chomsky , 2013Moro 1997; discussed as a biological primitive in Dehaene et al. 2015; see also Boeckx and Theofanopoulou 2014;Murphy 2015), forms a minimal binary tree by combining two elements in order to create a new linguistic object. Finally, a hypothetical (set of ) working space(s) is required to apply reiterated operations of merge. ...
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