One of the things that seems to bug many about FL/UG is the
supposition that it is a domain specific module dedicated to ferreting out
specifically linguistic information. Even those that have been reconciled to
the possibility that minds/brains come chock full of pre-packaging, seem loath
to assume that these natively provided innards are linguistically
dedicated. This antipathy has even come
to afflict generativists of the MP stripe for there is believed to be an
inconsistency between domain specificity and the minimalist ambition to
simplify UG by removing large parts of its linguistically idiosyncratic
structure. I have suggested elsewhere (here)
that this tension is only apparent, and that it is entirely possible to pursue
the MP cognitive leveling strategy without abandoning the idea that there is a
dedicated FL/UG as part of human biological endowment. In a new paper,
Gallistel and Matzel (G&M) (here) argue that domain specificity is the biological default
once one rejects associationism and adopts an information processing model of
cognition. Put more crudely, allergy to domain specificity is just another
symptom of latent empiricism (i.e. a sad legacy of associationism).
I, of course, endorse G&M’s
position that there is nothing inconsistent between accepting functionally
differentiated modules and the
assumption that these are largely constructed using common basic operations. And,
I of course love G&M’s position that once one drops any associationist
sympathies (and I urge you all to immediately do this for your own intellectual
well being!), then the hunt for general learning mechanisms looks, at least in
biological domains, ill-advised. Or put
more positively: once one adopts an information processing perspective then
domain specificity seems obviously correct. Let’s consider G&M’s points in
a little detail.
G&M contrasts associationist (A) and information
processing (IP) models of learning and memory. The G&M paper is divided
into two parts, more or less. The first several pages comprise a concise
critique of associationist/neural net models in which learning is “the rewiring
of a plastic nervous system by experience, and memory resides in the changed
wiring (170).” The second part develops the evidence for an IP perspective on
neural computations. The IP models contrast with A-models in distinguishing the
mechanisms for learning (whose function is to “extract potentially useful
information from experience”) and those for memory (whose function is to
“carr[y] the acquired information forward in time in a computationally
accessible form that is acted upon by the animal at the time of retrieval”)
(170). Here are some of their central
points.
A-models are “recapitulative.” What G&M (170) intend
here is that learning consists in finding the pattern in the data (see here):
“An input that is part of the training input, or similar to it, evokes the
trained output, or an output similar to it.”
IP models are “in no way recapitulations of the mappings (if any) that
occurred during the learning.” This is the classical difference between
rationalist vs empiricist conceptions of learning. A- models conceive of
environmental input as adapting “behavior” to environmental circumstances. IP-models
conceive of learning as building a “representation of important aspects of the
experienced world.”
A-models gain a lot of their purchase within neuro-science
(and psychology) by appearing to link so directly to a possible neural
mechanism; long-term potentiation (LTP). However, G&M argue vigorously that
LTP support for A-models entirely evaporates when the evidence linking LTP to A-models
is carefully evaluated. G&M walk us
slowly through the various disconnects between standard A-processes of learning
and LTP function; their time scales are completely different (“…temporal
properties of LTP do not explain the temporal properties of behaviorally
measured association formation (172).”), their persistence (i.e. how long the
changes in LTP vs associations last) is entirely different and so “LTP does not
explain the persistence of associative learning (172),” their reactivation
schedules are entirely different (i.e. If L is learned and then extinguished, L
is reacquired more quickly, but “LTP is neither more easily nor more persistent
than it was after previous inductions.”), nor do LTP models provide any
mechanism for solving the encoding problem (viz. A-learning is mediated by the
comparison of different kinds of temporal intervals and there is no obvious way
for LTP nets to do this) except by noting that what gets encoded is emergent
(and this amounts to punting on the encoding problem, rather than addressing it).
In short, there is no
support for A-models from neural LTP models. Indeed, the latter seem entirely
out of synch with what’s needed to explain memory and learning. As G&M put
it: “…if synaptic LTP is the mechanism of associative learning- and more
generally, of memory- then it is disappointing that its properties explain
neither the basic properties of associative learning nor the essential
properties of a memory mechanism (173).” So much for the oft insinuated claim
that connectionist models are preferable because they are neurally plausible
(indeed, obvious!).
General conclusion: A-models have no obvious support from
standard LTP models and these standard LTP models are inadequate for handling
the simplest behavioral data. In effect, A- and LTP- accounts are the wrong kinds of theories (not wrong in detail,
but in conception and hence without much (if any) redeeming scientific value)
if one is interested in understanding the neural bases of cognition.
So what’s the right approach? IP-models. In the last parts
of the paper G&M go over some examples.
They note that biologically plausible IP models will all share some
important features:
1. They
will involve domain specific computations. Why? “Because no general purpose
computation could serve the demands of all types of learning (175),” i.e.
domain specificity is the natural expectation for IP models of neuro-cognition.
2. The
different computations will apply the same “primitive operations” in achieving functionally
different results (175).[1]
3. The
IP approach to learning mechanisms “requires an understanding of the rudiments
of the different domains in which the different learning mechanisms operate”
(175). So, for example, figuring out if A is cause of B, or A is the edge of B
will involve different computations from each other and from those that mediate
the pairing of meanings and/with sounds.
4. Though
the neuro-science is at a primitive stage right now, “…if learning is the
result of domain-specific computations, then studying the mechanism of learning
is indistinguishable from studying the neural mechanisms that implement
computations (175).”
Note that this will hold as much in the domain of language
as in navigation and spatial representation.
In other words, once one dumps Associationism (as one must as it is
empirically completely inadequate and intellectually toxic) then domain
specificity is virtually ineluctable. There exist no interesting general purpose learning systems (just
as there it no general sensing mechanism,
as Gallistel has been wont to observe). That’s the G&M message. Cognitive
computation, if it’s to be neurally based, will be quite specifically tailored
to the cognitive tasks at hand, even if built from common primitive circuits.
The most interesting part of G&M, at least to me, was
the review of the specific neural cells implicated in animal capacities for locating
oneself in space and moving around within it. It seems that neuro-scientists
are finding “functionally specialized neurons [that] signal abstract properties
of the animal’s relation to its spatial environment (185).” These are
genetically controlled and, as G&M note, their functional specialization provide
“compelling evidence for problem-specific mechanisms.”
Note that the points G&M make above fit very snugly with
standard assumptions within the Chomsky version of the generative tradition. In
other words, the assumptions that generative linguists make concerning domain
specific computations and mechanisms (though not necessarily primitive
operations) simply reflects what is, or at least should be, the standard
assumption in the study of cognition once Associationism is dumped (may it’s
baneful influence soon disappear). If G&M are right, then there are no good
reasons from neuro-biology for thinking that the standard assumptions
concerning native domain specific structures for language are exotic or
untoward. They are neither. The problem
is not with these assumptions, but with the unholy alliance between some parts
of contemporary neuroscience and the A-models of learning and cognition that
neuro types have uncritically accepted.
If you read the whole G&M paper (some parts involve some
heavy lifting) and translate it into a linguistics framework, it is very hard
to avoid the conclusion that if G&M are correct, (and, in case you’ve
missed it, IMO they are) then the Chomskyan conception of language, mind, and
brain is both anodyne and the only plausible game in cog-neuro town.
