Syntax in a Dynamic Brain

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Syntax in a Dynamic BrainAuthor(s): James W. GarsonSource: Synthese, Vol. 110, No. 3 (Mar., 1997), pp. 343-355Published by: SpringerStable URL: http://www.jstor.org/stable/20117603

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JAMESW. GARSON

SYNTAX IN A DYNAMIC BRAIN

ABSTRACT. Proponents of the languageof thought (LOT) thesis are realistswhen itcomesto syntactically structured representations, and must defend their view against instrumentalists, who would claim that syntactic structures may be useful in describing cognition,but have nomore causal powers ingoverning cognition than do the equations of physicsin guiding the planets. This paper explores what itwill take to provide an argument forLOT that can defend its conclusion from instrumentalism. I illustrate a difficulty in thisproject by discussing arguments for LOT put forward by Horgan and Tienson. When theirevidence is viewed in the light of results inconnectionist research, it is hard to see how arealist conception of syntax can be formulated and defended.

1. INTRODUCTION

If the language of thought (LOT)hypothesis is right, then thebrain isgoverned by processes which are causally responsive to syntactically structured representations. On this view, syntax ismore than a convenient wayto describe mental processing; structures with syntactic form really controlthe brain. So proponents of LOT are realists when it comes to syntacticstructure and must defend their view against instrumentalists, who wouldclaim that syntax may be useful in describing cognition, but has no morecausal powers in governing cognition than do the equations of physics inguiding the planets.

The dynamical account of the brain sways us towards an instrumentalistvision of the role of syntax. On the dynamic model, the brain ismerely avector transformer, a device that converts the massively many activationvalues of sensory and internal neurons into new activations of internalandmotor neurons (Churchland 1988,Ch. 7.4). The dynamical picture isentirely procedural; it gets by without any mention of representations atall. If LOT is also right, then syntactic structures with causal powers arerealized in the dynamic brain at some level of description, and the fact oftheir realization there should help explain the brain's success.This paper explores the nature of that realization by raising two interlocking questions. First, what does LOT actually require about the dynamics of the brain ? what does realism with respect to syntax really amountto? Second, what kind of evidence should be sought in building a good

Synthese 110: 343-355, 1997.? 1997Kluwer Academic Publishers. Printed in theNetherlands.


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344 JAMESW. GARSONcase for or against LOT? One purpose of this paper will be to show whythese questions deserve more careful thought.

Connectionists suggest that styles of non-symbolic representation foundin neural net models challenges LOT. However, philosophers such as Hor

gan and Tienson (1989,1994) warmly endorse connectionist and dynamicmodels of cognition, while at the very same time defending the view thatcognition is governed by representations with "causally efficacious syntax". Whether room can be found for such a view depends crucially onexactly how evidence about representation in connectionist models affectstheLOT thesis.

In this paper, I argue that there is very little room for Horgan andTienson tomaneuver. They are hard pressed to formulate a criterion for thepresence of causally efficacious syntax in the brain that does not collapseto syntactic instrumentalism. Iwill illustrate this dilemma in the courseof examining their arguments for LOT The moral of the story will bethat anyone who takes connectionist research seriously will have difficultyformulating a d?fendable and empirically meaningful version of LOT.

2. NON-CONCATINATIVE CONSTITUENCY AND THE PHILOSOPHICALLANDSCAPE

What would entail that syntactic structure governs the brain? What evidence would be relevant to showing that this is so? Van Gelder (1990) hassuggested a criterion that seems clear enough. It is that the brain's syntacticconstituents be concatinative, i.e. tokening the representation entails thatits constituents are also tokened. An intuition behind this requirement isthat if the constituent structure of a representation is to play a causal role,then instances of constituents must be present to control the system.

Concatinative constituency appears to be a good choice for framingan argument for LOT, because itwould seem to be a necessary conditionfor successful linguistic processing. If constituent structure of languageis recognized, the intuition goes, then that structure must somehow berepresented, and how else than by having representations token their constituents?

However, connectionist research demonstrates that this intuition mayreflect the failure of our imagination, an imagination that is bewitched bythe metaphor of symbols arrayed on a printed page. It has been shown(Chalmers 1990; Smolensky 1988) that concatinative coding is not necessary for simple syntactic processing. The brain might use distributedrepresentations, which contain information about constituency, but wherethe constituents are not explicitly tokened when a representation is tokened.


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SYNTAX INA DYNAMIC BRAIN 345Furthermore, neural nets trained to recognize syntax do so not by implementing concatinative coding, but by exploiting the rich space of possibletime trajectories the system takes in response to various inputs (Elman1991; Port 1990). These nets appear to adopt a procedural rather than a

declarative solution to the problem of keeping track of syntactic form.It is now accepted all around, even among staunch proponents of LOT,that it is at least possible that human language and thought depends uponprocesses that use non-concatinative coding. Smolensky et al. (1992) haveeven proven that nets using nonconcatinative coding have computationalpower that appears adequate for language. In the face of this evidence, one

might expect that philosophers should leave the resolution of the LOT issueto future advances in cognitive science. However, this is not the positiontaken by Fodor, Horgan and Tienson. They believe that there is alreadygood evidence for LOT, despite what connectionists have shown. One ofFodor's arguments cites the systematicity of language and thought. Thesystematicity argument has been thoroughly thrashed out in the literature,

where I have already taken my stand (Garson, 1994,1995). So that will notbe the topic of this paper. Horgan and Tienson, however, present evidenceof a different kind, and itwill be my task to evaluate ithere. In the course ofthat discussion, we will also develop evidence against a second argumentof Fodor's that appeals towhat he calls Principle P.

Defenders of LOT who hope to prevail in the face of connectionist counterevidence dare not hitch the fate of LOT to the presence of concatinativerepresentation. Those who take the connectionist evidence to heart mustopt for some weaker condition for the presence of causally active syntax.

Horgan, agrees (in personal communication) that this is exactly what LOTtheorists must do. But can a realist yet non-concatinative account of syntaxbe found? I believe the prospects are bleak. The point can be brought intofocus by examining an instability in arguments for LOT, an instability thatis exemplified by Horgan and Tienson's reasoning.

3. INSTABILITY INARGUMENTS FORLOT

The issue as to what should count as evidence for LOT is clouded bytensions in the argument schema typically used by LOT proponents.

LOT ARGUMENT SCHEMA(1) To accomplish its tasks, thebrain needs (to do) X.(2) (Doing) X entails LOT.

Therefore LOT holds.


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346 JAMESW. GARSONThe X in premise (1) describes evidence (for example, the systematicityof language and thought)which will be cited infavor of LOT. Premise (2)helps fix the content of LOT in claiming that this evidence is a sufficientcondition for LOT. The instability inLOT arises in the following way.Given the novel forms of processing revealed by connectionist research,and our ignorance about the brain, it is hard to locate necessary conditionsfor human cognition. So to satisfy premise (1), there is pressure to pick anX which is fairly weak. However, a weakened X will not bear the burdenof providing a sufficient condition for LOT in premise (2), unless LOT isweakened in parallel. The result is an anemic version of LOT ? one that

may be indistinguishable from an instrumentalist view of syntax.In this paper, I will discuss choices for X drawn from Horgan andTienson (1989,1994). Our purpose is not so much to argue against LOT asto illustrate the tensions in the LOT schema that enfeeble its conclusion.This will help define thedifficult taskahead for thosewho would defend ameaningful version of LOT.

4. REPRESENTATION OF LOGICAL FORM

To illustrate these problems, let us consider first a familiar version ofthe LOT schema (Horgan and Tienson 1989, Section 5.iii; Fodor andPylyshyn 1988, section 3.4). We can all distinguish valid from invalidarguments, at least in simple cases, given time to reflect. So letX be thoseabilities. Premise (1) now claims that those abilities entail representationof logical form. Premise (2) asserts that this in turn supports LOT, becauserepresentations could be of

useonly if the brain responds to their logicalform. So syntactic structure plays a causal role in the brain.

However, this version of the LOT argument trades on an ambiguity in'representation of logical form'. In fact, we know virtually nothing abouthow logical form is distinguished by the brain. The mere presence of this

ability tells us little about themethods used to accomplish it.Connectionistresearch shows that our previous intuition that representing constituentsrequires tokening them is just plain wrong. In the face of counter-intuitivestyles of processing revealed by connectionists, we should be wary ofintuitions of the form: the brain can manage only if it does X. Our intuitions leap too readily from representation of logical form conceived as a

process or task the brain can accomplish, to the presence of representations with structures controlling a process (Garson 1994). Taking the leapfor granted simply begs the question in favor of LOT. If the first premiseof the LOT argument is to be plausible, 'representation of logical form'means a process that allows the brain to recognize logical form. If rep


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348 JAMESW. GARSONLOT, for it claims that combinatory coding is all that it takes. Combinatorycoding of propositions does not entail coding for the familiar syntacticconstituents such as subject and predicate. The code could be profoundlysubsymbolic. The idea that coding by neural patterns establishes a kindof syntactic structure with causal role is a desperate non-starter. The constituents of representations in this "syntax" are individual neural firings.It is true that these "constituents" have causal role, but granting this addsnothing new to the dynamic conception of the brain. We knew all alongthat neural activations have causal powers. A version of LOT that supportssyntactic realism must say something more.

6. SYNTACTIC ENCODING

In describing what it takes for syntax to be active in the brain, Horgan andTienson suggest what that something more might be, and so provide a different candidate for X in the LOT argument. They believe that the brain'slinguistic capacities depend on the following feature: "When different representational states predicate the same property or relation of differentindividuals, the fact that the same property or relation is predicated isencoded in the structure of the representations. And when different representational states predicate different properties or relations to the sameindividuals, that fact is encoded in the structure of the representations.Similarly for more complex propositional content involving connectivesand quantifiers. Finally these facts are encoded in a manner that makes therepresentations systematically susceptible to suitable structure-sensitiveprocessing" (Horgan and Tienson, 1994, p. 5, emphasis theirs).

When thisholds, let us say the brain uses a syntactic encoding.

One problem with syntactic encoding is that it leaves us hanging. It isstill not clear what itmeans for the same item to be encoded in the structureof two different representations, especially once we abandon the idea thatencoded representations token their constituents. Given that brain state Jrepresents that John is happy, and M thatMary is happy, what further factabout J andM must hold if the property of happiness is encoded in theirstructures? Since we have abandoned the requirement of concatinativecoding, we cannot demand that there is a token for happiness common toJ and M. So the encoding of happiness must amount to something weaker,presumably that the information that J and M both predicate happiness canbe somehow extracted from J and M, even though it is not there explicitly.But that information can be extracted trivially from themere fact that J and

M represent what they do represent. Whatever it is that makes it true that Jrepresents John is happy and thatM represents Mary is happy, insures that


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SYNTAX INA DYNAMICBRAIN 349J and M have something in common, namely to represent propositions witha common predicate. It follows that the information that J and M predicatehappiness can be extracted from their structure. Furthermore, the very factsthat make J and M about happiness are presumably the ones that undergirdthe causal success of J and M in the brain's cognitive economy, so itwon'tdo to object that the information we have extracted is not relevant to thecausal roles of J and M.

Our complaint about syntactic encoding is, in a way, a cheap shot. By"encoding

the property of happinessin the structure of J and M", Horganand Tienson surely do not mean merely that J and M represent propositions

that both predicate happiness. They are referring to some property therepresentations have in common that explains their causal roles. But whatnon-trivial property can they cite that works in the LOT schema? Untilthey provide some conceptual tools for fighting off trivialization of theirthesis, this version of the LOT argument does not even get off the ground.

7. CAUSAL ROLEOne strategy for meeting this challenge is to try to forge a criterion for asyntactic encoding from one's theory of the individuation of mental states.One might (for example) adopt a functionalist account for individuatingsyntactic form. The idea is that mental states individuated by the causalroles they play in the (potential as well as actual) activities of the brain.If we find that the causal roles of brain states mimic the formal roles ofsyntactically structured symbol strings, then the syntax of those strings isencoded in those brain states.1 LOT would be established provided thatthere are brain state types playing the right causal roles.

However, there is a serious problem with this suggestion. Having theright causal roles does not (on its own) insure the presence of syntacticallystructured representations. For example, if the brain represents propositions with arbitrarily assigned neuron firing patterns, the representations

would lack any obvious signs of syntactic constituents, but the representations might still set up the right causal roles. Functionalists, despite theirhistorical alliance with LOT, can not provide the right tools for forging asound criterion for syntactic realism. (Cummins (1992, p. 115) makes thesame point).

8. PHYSICAL TRACKING AND PRINCIPLEPLOT theorists may object that such an arbitrary coding is not practical. Ifthe brain is to give representations J and M the causal roles appropriate


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350 JAMESW. GARSON

for representing something about happiness, then there must be somethingcommon to the physical structures of J and M that explains this similarityof role. In other words, physical properties of the representations have gotto track their syntactic properties, otherwise itwould be a sheer miracle ifthe representations were to behave properly. Physical tracking means thatthere is a mapping between the syntactic structures we attribute to J and

M on the basis of their causal roles, and (non-relational) physical structureof J and M. This suggests that physical tracking is a good choice for Xin the LOT argument. Premise (1) would claim that physical tracking isnecessary for the brain to place representations in the right causal roles,and premise (2) would claim that physical tracking entails the truth of LOT.

The problem with this version of the LOT argument is that both premises are in doubt. Connectionist research weighs against premise (1). Forexample, Servan-Schreiber et al. (1991) investigated nets that were successfully trained to predict legal continuations of symbol strings generatedby a simple grammar. Once trained, the nets were able to distinguish wellformed from ill-formed strings in the following way. The symbols of astring were presented to the net one after another, and the neural patternfor the subsequent symbol of the string was compared with the patternpredicted by the net. In the case of well-formed strings, the predicted andactual patterns were very similar, whereas in ill-formed strings the difference was quite large at the point in the string where the symbol sequenceviolated the rules of the grammar.

When a symbol string is processed by such a net, we can think of thesequence of patterns appearing on its neurons as a trajectory in neuralphase space. Servan-Schreiber et al. examined whether strings which aresimilar in syntactic structure correspond to similar (nearby) trajectories inthat space. They discovered that there was a fairly good correspondencein the case of neural nets with small numbers of neurons. This wouldsupport the physical tracking hypothesis. However, the correspondencebecame weaker as the same experiment was tried on larger nets. Largernets learned the task faster, and were less prone to error. But the cost ofthese advantages was that syntactic structure of the strings could not be aseasily discerned in the processing history of the nets.

These experiments suggest that large nets may carry out syntacticprocessing without generalizing cleanly to physical structures that mirror the set of syntactic categories we would use to describe the task. Asnets get larger, they may treat representations more and more as a collection of special cases. At the end of this continuum, in the hundred billionneurons of the brain, the coding from representations to physical properties

might be nearly arbitrary. Our intuitions rule that such processing would


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352 JAMESW. GARSONsyntactic information is somehow encoded in the brain will not do, forthis is compatible with dynamic instrumentalism. LOT realism requiressecuring two things: first, representations must be found to exist in thebrain, and second these representations must have constituents with causalrole. But the concept of the presence of a representation is parasitic on

making sense of the difference between coded data on one hand, and theprocedures that "read off" that code on the other. To secure the presence ofrepresentations, LOT theorists need tomake sense of the ?/ata-proceduredistinction in the brain. Choices for X in the LOT argument (like causalrole or physical tracking) that merely advert toprocedural features of thebrain are doomed at the start, because they do not secure that divide.This point isnot new; it iscarefully argued by aLOT theorist.Pylyshyn(1984,26-32) points out that the distinction between data and proceduresthat are guided by data is exactly what distinguishes a dynamic or purely

procedural processor from a symbolic one. A machine that evolves fromone state to another by a set of rules is not symbolic unless it also containsan analog of the Turing machine's tape where data can be stored andread. The presence of this potentially infinite memory is not only centralto the symbolic processor's computational power,3 it is what defines itas symbolic in the first place. It makes no sense to talk of the presenceof symbolic representations in a system that lacks a divide between datastorage and procedures that are sensitive to that data.

So any LOT thesis worth its salt must entail the existence of data storageon one hand, and mechanisms that read off that data on the other. Thereforea successful X in the LOT argument must establish the same thing. Theproblem with Horgan and Tienson's versions of the LOT argument isthat none of the evidence they cite is relevant to establishing the dataprocessing divide. The facts they cite are compatible with purely procedural

processing.In (1994a, especially section 5) Horgan and Tienson provide more

details on how they view syntactic processing in a dynamic brain. Althoughtheir picture is an attractive one, the considerations we have just givenshould make it clear that (if anything) theirs is an instrumentalist ratherthan realist theory of the role of syntactic structure. As they point out,the classicist presumes a kind of isomorphism between cognitive structureand physical structure. On Horgan and Tienson's view, the relationshipbetween syntactic structure and physical structure ismuch more abstract.Their account adopts a variant of Marr's three levels: the physical, themathematical or computational, and the cognitive. The computational level

is described using themathematics of dynamical systems theory, rather thanthe concepts of the theory of symbolic processing. In this tri-level view, the


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SYNTAX INA DYNAMICBRAIN 353relationship between the cognitive and the physical involves two realizationrelations ? one from the cognitive to the dynamical systems level, and theother from that level to the physical level. What is most telling for thepoint we are making in this paper is that each of these realization relationsis dispositional. Structure encoded at the dynamic level is found in the

dispositions of the system to evolve from one state to another, that is, inits potentialities for temporal evolution. Since the dynamic level mediatesthe relationship between the cognitive and the physical, the relationshipbetween structure at the cognitive and physical levels is very complex.There is no hope of locating constituency in any physical properties ofthe system. Horgan and Tienson make it clear that syntactic processing

viewed at the dynamical level is procedural; it is entirely characterized bythe systems transition behavior. So they believe that the presence of syntax,and its causal powers are underwritten by the way in which the dynamicalbrain visits the various regions in its space of possible states. This theoryattempts to rest realism for syntax on the functional role of dynamicalstates. But we have already explained why functional role theories cannotsupport realism. There is not the slightest hint in Horgan and Tienson'saccount of what really is needed tomake a good case for realism, namelya way to craft the data-processing divide.

Furthermore, developing the kind of evidence Horgan and Tienson needto supportLOT would appear tobe very difficult. Their projectmust be tolocate the data-processing distinction within the connectionist dynamicalsystems they favor as likely models of the brain. In all likelihood, theywill need to rebut Schwarz (1992) who argues that the data-processingdistinction makes no sense in standard connectionist models. Any LOTtheorists who take the evidence from connectionism to heart must tacklethis problem or face trivialization of their thesis.

Suppose I am wrong, and this challenge can be met. Then a secondchallenge awaits proponents of LOT. They need a criterion for the presence of causally active syntactic structure inbrain representations for whichevidence can be supplied. This will not be easy. In the case of classical computers, the data-processor divide is easily defined because the processorand the data inmemory are (roughly) laid out in spatially distinct locations.

Once representations are identified by spatial position, constituents can beidentified as their spatial parts. However, the same strategy will not workfor LOT realists. They dare not risk their thesis on the implausible assumption that constituents of brain representations are written out in physicalspace. Therefore they owe us some other account of the empirical contentof the claim that constituents play a causal role in the brain.


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354 JAMESW. GARSONBut then how can syntactic structures with causal powers be defined?

As I already said, a functionalist individuation won't work, because causalrole, by its very nature, has no leverage for prying causally active representations out of the activities of a dynamic brain. Something more isneeded for robust realism, and LOT theorists have yet to tell us what it is.Their project is a delicate one. First, criteria must be given for drawingthe data-procedure boundary in the brain, and evidence supplied that thesecriteria are actually met. Second, criteria must be given for the presenceof causally active syntactic constituents in the brain's representations, andevidence supplied for this. These criteria cannot be too strong. For examplea LOT theorist who locates the data/processor divide by spatial position, or

who defines constituency by concatinative coding, faces the problem thatevidence from neural net theory and neurology makes it likely that LOT

would be false on this construal. The problem is how toweaken the criteriawithout trivializing LOT. What is needed is a way to locate syntactic constituents with an odd set of properties. These constituents are not tokened

when their representations are tokened, and yet they still count as partsof the symbolic data that causally guide processing, and not proceduralfeatures of brain activity. If you are optimistic about this project, then thevalue of this paper will be to define the task ahead for those who wouldestablish a causal role for syntax in a dynamic brain.

NOTES

Horgan and Tienson believe that these causal roles are defined by ceteris paribus laws,laws that cannot be formulated simply without appeal to unspecified exceptions. Ido notsee how syntactical structurecould be individuated by appeal to such laws (Garson 1994a).2See Egan (1991) for a criticism of the use of Principle P to argue for LOT. She arrives ata conclusion parallel to ours, namely that the principle is too weak to support LOT.3 Pylyshyn argues that this computational power is crucial to the brain's success. Thedynamic model treats the brain as a finite statemachine. But finite statemachines haveknown computational limitations.They are incapable of displaying the infiniteproductivecapacity Pylyshyn assumes is found in language and thought.There are (at least) twowaysto respond to this reasoning. One is to challenge the assumption that humans actually dodisplay infiniteproductive capacity (Rummelhart et al. 1986, 119).Another is to note thatinfinite productive capacity is possible in dynamic systems operating near the transitionto chaos (Langton 1991). I leave the detailed discussion of the productivity argument toanother paper.

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of Philosophy 21, 379-88.Elman, J. L.: 1991, 'Distributed Representations, Simple Recurrent Networks, and Grammatical Structure', inTouretzky ( 1991), pp. 91-122.Fodor, J.: 1987, Psychosemantics, MIT Press.Fodor, J. and Z. Pylyshyn: 1988, 'Connectionism and Cognitive Architecture: A Critical

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17, 147-74.Horgan, T. and J. Tienson: 1994, 'Representations Don't Need Rules: Reply to James

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Department of PhilosophyUniversity of HoustonHouston Texas 77204-3785U.S.A.e-mail: [email protected]

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Syntax in a Dynamic Brain