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ARTICLE

Broca’s Arrow: Evolution, Prediction, andLanguage in the BrainDAVID L. COOPER*

Brodmann’s areas 44 and 45 in the human brain, also known as Broca’s area, have long been associated with

language functions, especially in the left hemisphere. However, the precise role Broca’s area plays in human language

has not been established with certainty. Broca’s area has homologs in the great apes and in area F5 in monkeys,

which suggests that its original function was not linguistic at all. In fact, great ape and hominid brains show very

similar left-over-right asymmetries in Broca’s area homologs as well as in other areas, such as homologs to

Wernicke’s area, that are normally associated with language in modern humans. Moreover, the so-called mirror

neurons are located in Broca’s area in great apes and area F5 in monkeys, which seem to provide a representation

of cause and effect in a primate’s environment, particularly its social environment. Humans appear to have these

mirror neurons in Broca’s area as well. Similarly, genetic evidence related to the FOXP2 gene implicates Broca’s area

in linguistic function and dysfunction, but the gene itself is a highly conserved developmental gene in vertebrates and

is shared with only two or three differences between humans and great apes, five between humans and mice, and

eight between humans and songbirds. Taking neurons and portions of the brain as discrete computational segments

in the sense of constituting specific Turing machines, this evidence points to a predictive motor and conceptual

function for Broca’s area in primates, especially for social concepts. In human language, this is consistent with

evidence from typological and cognitive linguistics. Anat Rec (Part B: New Anat) 289B:9–24, 2006.

© 2006 Wiley-Liss, Inc.

KEY WORDS: Broca; FOXP2; Brodmann; Wernicke; mirror neurons; predictive computation; bird song; human language;

neuroscience

INTRODUCTION

Paul Broca’s 1861 demonstration of linguistic specialization in the lefthemisphere of the human brain(Broca, 1861a, 1861b, 1861c, 1861d)created a sensation in the medical andscientific world, no doubt enhancedby his presentation of key findings on-stage to an audience of fellow scien-

tists. His association of stroke patient

Tan’s difficulties with the posterior

half of the second and third left fron-

tal gyri (circonvolutions) was subse-quently refined to Brodmann areas 44and 45, a refinement very likely as-sisted by the patient’s brain preserva-tion: it can be examined to this day.His clear and compelling correlationof specific symptoms to identifiablestructures has likewise been refinedand expanded considerably, but re-mains justifiably famous as a criticaltipping point in the history of neuro-science.

Tan’s difficulties, which Broca calledan aphemia (lack of speech), are nowknown as Broca’s aphasia (also lack of speech, but taken from the verb stemand not the noun). Patients with Bro-ca’s aphasia have deficits both in lan-guage production and in languagecomprehension. Tan himself gener-ally only spoke the phrase “tan tan,”which was the source of his nick-

name, while simultaneously demon-

strating by gestures that he could

comprehend the language of others.

Higher-performing patients generally

produce speech telegraphically, in

very short bursts, and delivered with a

great deal of effort. In terms of com-

prehension, they have difficulty with

repeating or understanding phrases

with unusual or complex word order,

such as the English passive, and they

also have difficulty repeating long andcomplex words accurately (Gazzanigaet al., 2002). Broca ascribed theseproblems to “the motor image of theword” (Kolb and Whishaw, 1990: p.580).

While evidence from patients withBroca’s aphasia as well as evidencefrom brain activations indicate thatBroca’s area is important for process-ing syntactic information (Caplan etal., 2000), other areas in the brain,such as Wernicke’s area, includingportions of Brodmann areas 22, 41,

Mr. Cooper is a former UniversityScholar at Princeton University, a re-tired U.S. Army officer, a senior federalbureaucrat, and a PhD candidate in neu-roscience at George Mason Universityin Fairfax, VA. He is the author of Lin-

guistic Attractors: The Cognitive Dy- namics of Language Acquisition and Change.*Correspondence to: David Cooper,Krasnow Institute for Advanced Study,Mail Stop 2A1, George Mason Univer-sity, Fairfax, VA 22030. Fax: 703-993-4325; E-mail: [email protected]

DOI 10.1002/ar.b.20088Published online in Wiley InterScience(www.interscience.wiley.com).

THE ANATOMICAL RECORD (PART B: NEW ANAT.) 289B:9–24, 2006

© 2006 Wiley-Liss, Inc.



and 42 (Just et al., 1996), or the ante-rior portion of the superior temporalgyrus, Brodmann area 22 (Dronkers,1996), are also implicated. These ar-eas lie along the perisylvian area of the brain, which is a highly conservedarea in terms of human genetic ex-pression (Thompson et al., 2000).Their linkage overall has been takenas support for the classic Lichtheim-Geschwind triangle model with a mo-tor processing area (Broca’s), an audi-tory processing area (Wernicke’s), and

an unlocated conceptual area (Lich-theim, 1885; Geschwind, 1967). How-ever, their common association withthe task of processing syntactic infor-mation may also indicate that lan-guage processing is a very complexand multifaceted task, and that thetriangle model is too simple.

In fact, despite being the first sucharea identified and probably the onemost widely studied, the precise cor-relation of Broca’s area to languageand language processing is still a mat-

ter of some controversy. This is exac-erbated considerably by the fact thatBroca’s area in humans correspondsto similar brain regions in primates.Moreover, the famous FOXP2 muta-tion in humans that reproduces symp-toms very similar to Broca’s aphasiaalso affects much wider areas in thehuman brain, while FOXP2 has nearlyidentical homologs not only in pri-mates, but in vertebrates generally(Vargha-Khadem et al., 2005).

This article will trace these variousstructural, functional, and genetic

connections in an effort to see if thereare indeed any underlying unifyingthreads and to bound the contributionof Broca’s area as precisely as possi-ble. To do that, it will organize theavailable anatomical, genetic, behav-ioral, and functional evidence withina computational framework very sim-ilar to the one David Marr used togreat effect in his study of visionnearly 3 decades ago. This is summa-rized at Table 1. This will perhapshelp to bring some of the earlier issues

into focus and define precisely whatBroca’s area is “for.”

Marr employed a general frame-work to help focus his work on human

visual processing, but its computa-tional focus was not on the detailedimplementation within a neural net-work but on the transformation of im-age intensity maps into three-dimen-sional object representations usingthe full panoply of mathematics anddigital computational power availableat the time. While human brains de-

veloped all these things, in their natu-ral state none of this would be avail-able, nor is any of it available to theother species that share homologousstructures. Like all other brain areas,Broca’s area must acquire and refineits functions solely from the organisminteracting with its environment.

Table 1 attempts to reformulateMarr’s framework in a way that iscloser to Turing’s initial computa-tional framework (Turing, 1936). Thegoal of the computation will remain

very much the same—a strategy to

handle inputs and transform them tothe appropriate outputs—but repre-sentations, algorithms, and physicalimplementation in Turing’s terms re-quire that we specify initial and finalconfiguration states, a system of sig-nals (Turing used the word “symbol”but that will have a different implica-tion below), and a set of operationsthat act on the configurations in thecontext of the signal at hand. If theseconditions are met, it is possible tospecify an initial configuration (the

initial state and an active signal) andpredict the subsequent behavior (anoperation bringing about a finalstate). This depiction in terms of states, signals, and operations can beused within a neuron, as well asacross collections of them—looking atchannel distributions, skeletal pro-teins, and reactions to electrical orchemical signals at the cellular level,and at connectivity properties, prim-ing, resonance, firing, and connectiv-ity adjustments for neural ensembles.

This permits valid inferences aboutcomputation within the brain withoutimputing activity to elaborate pro-cesses or even explicit knowledge thatmay not exist except as an artifact of human civilization.

In this framework, the first questionabout Broca’s area then becomes thefinal one of identifying its role withinan implicit computational strategy. Inthe discussion below, the nature of theevidence is best suited to workingthrough these issues from right to left,from hardware to strategy.

TABLE 1. An implicit computational framework

Computational Theory

Representation and

Algorithm Hardware Implementation

Implicit Turing Computation What is the goal of the

computation in terms of

Turing machineconfigurations and

operations?

What are the states and

possible transformations

available to the neuronor neural network cell

assembly Turing

machine? Discuss

developmental stages as

well as functions of the

mature organism.

What are the structural and

procedural elements used

by the cell or neural networkcell assembly?

This is inspired by the computational framework in Marr (1982: 25) Marr’s categories appear in the title row. Corresponding

questions from the perspective of implicit computational steps appear in the second row. This perspective looks at neurons,

their subcomponents, and their ensembles within neural networks as states within a Turing computational process. The

transitions between states, equivalent to Turing operational steps, are performed by cellular and network operations. This

implicit computational perspective seeks to construct and perform Turing machine-like functions without an explicit program

written beforehand.

10 THE ANATOMICAL RECORD (PART B: NEW ANAT.) ARTICLE



Figure 1 provides a context to provide more focus to this series of ques-tions. It exploits the ideas of symbolicrepresentations from Peirce (1868)and cognitive linguistics from Lan-gacker (1998) and others and tries tolay out the steps necessary for thefunctions of learning, inference, andsymbolic representations that we as-sociate with language. The followingdiscussion will attempt to identify

where the computations made by Bro-ca’s area fall, and how much of thediagram they cover.

Figure 1 depicts entities that can beexternal or internal to the organism. In-ternal entities are representations. In-dexes are internal representations thatdistinguish between external entities.When external entities form a system of some sort, the corresponding indexesprovide the basis for a reference frame-work for them. When the internal rep-resentations form a corresponding sys-tem of rules and relationships to the

external entities, we have symbolic rep-resentations, following Peirce. Cogni-tive linguistics posits that language isbuilt up from symbolic ensembles,which tend to be expressed as sets of constructions rooted in external experi-ence and reinforced by intensive andfocused interactions between adultsand children as language is acquired(Tomasello, 2003). The diagram showsboth nonlinguistic inference (in solid

lines) and linguistic processes (indashed lines). Both pathways rely oncharacterizations of external entities bymeans of measurements, organizedinto data structures. Both pathwaysfeed an inferential cycle and ultimatelyresult in the updated representationsthat amount to learning.

HARDWARE: COMPARATIVE

ANATOMY

In Marr’s framework, the first step inunderstanding a given computational

role is to establish what the computa-

tion is “for.” However, for Broca’s

area, that was the puzzle Broca firstbrought up, and the issue that has yetto be resolved. Instead, the processmust be inductive and begin with the“hardware,” or the neural anatomy

underlying what Broca’s area does.The best line of evidence to determinethis comes from primates, beginningwith humans, as they are the primateswho speak.

In humans, Broca’s area is corre-lated with Brodmann areas 44 and 45,including portions of the pars triangu-laris and pars opercularis of the infe-rior frontal gyrus of the brain. Thislies across the anterior ascending ra-mus of the lateral, or Sylvian fissure-opposite the temporal lobe, which is

an auditory association area. It is adjacent to the premotor cortex, appro-priately near the areas that controloral and facial features, particularlythe lips and tongue. Many descrip-tions refer to the perisylvian area of the brain, which is a shorthand termfor the areas on either side of the lat-eral fissure, including Broca’s area to-ward the front and above the fissure,and Wernicke’s area toward the backand below it. When studying geneticexpression in fraternal and identicaltwins, Thompson et al. (2001) showed

very clearly that the perisylvian area ishighly conserved in both groups,whereas many other regions coex-pressed in identical twins show diver-gent genetic expression in the frater-nal twins. Clearly, Broca’s area isappropriately placed for a linguisticfunction, is adjacent to other areasthat are also appropriately placed forother aspects of linguistic processing,and seems to be part of the brainwhere the genetic code is highly con-served, indicating an important role

in individual survival.Wernicke himself, while describinganother kind of aphasia that is thepolar opposite of Broca’s, also notedthe direct connection of the area nowknown as Wernicke’s area to Broca’sarea by way of the angular gyrus andthe arcuate fasciculus. Because Wer-nicke’s area is adjacent to the auditorycortex in Heschl’s gyri, he ascribed anauditory memory role to it. Wer-nicke’s aphasia describes a conditionwhere the patient is fluent, but pro-duces nonsensical insertions of word

Figure 1. Conceptual Sketch of the Computations Involved in Learning and Inference.Learning, and its computational underpinnings, begins with external entities, which can bedistinguished by the senses. External entities may be parts of a system, or a bounded set ofentities and the rules that relate them to each other. Representations of external entities arealso entities, which are distinguishable internal states. The internal entities that representexternal entities are indices. When these indices are combined to reflect—or represent—the same rules that bind the external entities, they become symbols. This means thatsymbols form internal representational systems. These sets of relations are encoded aslinguistic construction, which are further explained in Figure 2. For computational purposes,each index is characterized by a sensory measurement, and organized into data sets.Algorithms performed on data provide inferences, which, when validated, become knowl-edge. Learning occurs when this knowledge changes the set of indices. There is a parallellinguistic chain (depicted by dashed lines), where the data is interpreted (now calledinformation), and this information is encoded as description or concepts, which can formhypotheses, which can be validated and become knowledge as well. Information is also

the set of interpreted data that provides the validation in the linguistic chain.

ARTICLE THE ANATOMICAL RECORD (PART B: NEW ANAT.) 11



forms (Wernicke, 1874). The two

kinds of aphasia are contrasted here

in examples drawn from Gazzaniga etal. (2002: p. 385–387):

Broca’s aphasia:Spontaneous speech: “Son . . . uni-

versity . . . smart . . . boy . . . good . . .

good.”Listening—Prompt: “The boy was

hit by the girl. Who hit whom?”Response: “Boy hit girl.”Repeating—Prompt: “Chrysanthe-

mum.”Response: “Chrysa . . . mum . . .

mum . . .”Wernicke’s aphasia:“I called my mother on the televi-

sion and did not understand thedoor.”

Thus, we have physically connected

areas in the brain, where damage tothe endpoints corresponds to polaropposite dysfunction. At one end, Bro-ca’s, the patient makes sense but hasmuch difficulty with even short se-quences, while at the other end, Wer-nicke’s, the patient is completely flu-ent but makes no sense. This highlyconnected region corresponds to theperisylvian region as well.

Using a new statistical methodcalled voxel-based lesion-symptommapping, Bates et al. (2003) providesstrong support for a functional con-

nection along this physical pathway.Summing across 101 patients with le-sions on the left side of their brains,they found that those who had diffi-culty with fluency in language showeda very high correlation between thesymptoms and their associated le-sions along the gray matter connec-tions in the insula and the arcuate fas-ciculus lying between Wernicke’s andBroca’s areas. Similarly, they foundthe highest correlation of symptomsto lesions for language comprehen-

sion in the middle temporal gyrus, inthe vicinity of Wernicke’s area. Theseobservations essentially confirm theassociation of Broca’s aphasia to flu-ency and Wernicke’s aphasia to wordcomprehension, but it is very interest-ing to note that the highest symptom-location correlations for fluency lie onthe pathway itself and posterior toBroca’s area in the cortex, while thecorrelation of cortical lesions to Wer-nicke’s aphasia is much stronger. Thispattern pointing to the pathways andconnections to Broca’s area as well as

the cortical area itself will be repeated

when we turn to the FOXP2 gene later.

As for Broca’s area itself, using anovel observer-independent methodfor measuring cell densities, Amuntset al. (1999) focused on the detailedstructure of the cortical layers in 10

human brains, taking thousands of profiles across five male and five fe-male subjects. They reported a num-ber of key observations, confirming,for example, the left-biased lateraliza-tion that is normally imputed to lan-guage functions. Interestingly, whileall five male subjects showed a left-over-right asymmetry for area 44,which is adjacent to area 6 in the mo-tor cortex, only three of the femalesubjects did. Neither group showed asimilar asymmetry for area 45, which

is further forward than area 44 andborders on frontal areas of the cortexsuch as area 10. Their other findingsalso show clear structural differencesbetween areas 44 and 45, as well assignificant differences between sub-

jects. Thus, the size of area 44 on theleft could vary by a factor of 10 be-tween subjects, while the differencesbetween subjects for both areas weregreater than the differences betweenhemispheres in a single subject. Of particular concern to anyone conduct-ing brain imaging studies, they re-

ported the absence of any large-scalelandmarks that definitively identifyBroca’s area or these subdivisions.

Amunts et al. (1999) also describedsome cellular details in their descrip-tions of the layers in areas 44 and 45.Both areas have conspicuous large py-ramidal cells deep in layer III and inlayer V. Layers II and III do not have adistinct boundary, and layer VI has a

very low cell density. Layer IV is al-most nonexistent in area 44 and de-scribed as “dysgranular.” In area 45,

layer IV is much more clearly visible,but less distinct than adjacent corticalareas, such as area 10. Area 44, whichshows considerable left-over-rightasymmetry with respect to its volume,also shows a left-over-right asymme-try in cellular density: the cells in theleft hemisphere had a smaller separa-tion than those on the right. Hereagain, area 45 did not show a similarasymmetry in cellular separations.Amunts and colleagues also confirmearlier observations that areas 44 and45 are further subdivided (Economo

and Koskinas, 1925), but were not

able to provide definitive maps of the

subareas.The tremendous variation in the

brain volume in area 44 is very inter-esting because its absence in area 45suggests some kind of functional dif-

ference between the two areas. It isalso interesting, however, becauseboth portions of Broca’s area are con-served in genetic expression, both forhumans and in primates generally.Highly localized variation of this mag-nitude would probably not be the re-sult of noise in genetic transcription,which one would expect to have amore regular pattern across affectedareas in individuals. If, on the otherhand, it is related to noise in genetictranslation, it might indicate that this

variation depends on persistent activ-ity subsequently reinforced by a learn-ing process and hence rule out a pre-determined structure for area 44.

The lack of major unambiguouslandmarks delineating Broca’s areaand its subdivisions should provide amajor warning when trying to under-stand imaging data. Without resolu-tion sufficient to measure cellulardensities in individual cortical layers,we simply cannot distinguish betweenareas 44 and 45 from image data,much less one of their anterior or pos-

terior subareas, and precise bound-aries between areas 44 and 45 andregions outside Broca’s area areequally indistinct. The only othermethod to calibrate brain images tothis level would be to create thou-sands of microscopic cortical profilesdissected from subjects who had pre-

viously been used in the imaging ex-periments—the ultimate longitudinalstudy. Basically, we can only makegeneral conclusions broadly associ-ated with Broca’s area from images

when using current imaging technol-ogy.Nevertheless, it is possible to learn a

great deal about Broca’s area evenwithin these constraints. For example,there are homologous regions in otherprimates, which clearly providedsome selective advantage other thanhuman language production. An areaequivalent to area 45 is present inmonkeys (Preuss and Goldman-Rakic,1991). Chimpanzees have equivalentsto areas 44 and 45 (Carroll, 2003), anda recent cytoarchitectural and electro-




stimulation study revealed an equiva-

lent to area 44 in macaques as well

(Petrides et al., 2005). Moreover, theBroca homolog in great apes showsthe same kind of left-over-right asym-metry found in area 44 in humans(Cantalupo and Hopkins, 2001).

Consequently, this left-biased asym-metry clearly cannot be related just tohuman language. In fact, there is evi-dence for left-hemispheric dominancein all the early hominins, as well asmodern chimapanzees, bonobos, andgorillas (Hopkins and Leavens, 1998).This dominance corresponds to right-handedness, pointing gestures, and

vocalizations. Similarly, there is a par-allel left-asymmetrical extension of the planum temporale, adjacent toWernicke’s area in humans, found in

Homo habilis, Homo erectus, and Homo neanderthalensis (Holloway,1980). This left-asymmetric extensionis also found in chimpanzees (Gannonet al., 1998; Hopkins et al., 1998).Thus, the asymmetry existed through-out the perisylvian region, includingthe areas homologous to Broca’s,prior to the development of languagecapable of symbolic representation,and correlated with nonlinguistic fac-tors.

However, these left-biased asymme-tries are not identical across primates.

For example, left lateralization in Bro-ca’s area in great apes is evident downto the level of fine structure (Amunts etal., 1999), yet the lateralization in grossstructure of the planum temporale doesnot correspond to a similar asymmetryin minicolumn size and connectivity inchimpanzees, while it is found in hu-mans (Buxhoeveden et al., 2001). Thisproduces an overall pattern that impliesthat the perisylvian area has been im-portant to primates for millions of years, based on gross structure. On the

other hand, fine-structure asymmetry,which probably has an activity-relatedcomponent, appears in great apes forBroca’s area at one end of the perisyl-

vian region, while it does not at theother end, at the planum temporale.This would make the fine-structurechanges near Wernicke’s area, the pla-num temporale, and the auditory cortexin general more likely locations for lan-guage-specific changes than Broca’sarea, where fine-structure changes al-ready took place before the appearanceof language.

What does Broca’s area do in non-

speaking primates that makes it im-

portant for survival? Kohler et al.(2002) shed some important light onthis question by examining the firingpatterns of individual visuomotor“mirror neurons” in area F5 of ma-

caques, the homolog to Broca’s areain humans. These mirror neuronswere already associated with action-related perception that required view-ing both an agent, such as a hand or amouth, and an object manipulated bythe agent (Gallese et al., 1996). Theseneurons are thought to be importantin planning and execution of move-ment. Kohler et al. (2002) showed thatthese neurons are multifunctional andalso react to the sounds produced byobjects on which the monkey per-

formed an action.It is easy to understand how an areathat links all of these functions mightbe important to individual survival.Gestures are important to social ani-mals, and this area is well situated tocontemplate and plan such gestures.Understanding agent-object relation-ships is also important in resolvingobjects of interest in the environment,as well as predicting what might be-come of them if they are manipulated.The auditory connection would makethis more powerful, as such a capacity

provides a multidimensional repre-sentation of sensory data, as well asfeedback in both visual and audiomodes.

Mirror neurons may have an evencloser tie to linguistic performancethan multimodal correlation. Theseneurons in area F5 appear to codegoal-oriented movement of the handand mouth (Rizzolatti and Camardaet al., 1988; Murata et al., 1997; Riz-zolatti et al., 2000). Some of these mir-ror neurons are highly specific, coding

particular types of grasping move-ments, for example, but most of themare active under much broader sets of stimuli and appear to generalizeacross classes of particular instances(Rizzolatti et al., 2001). Moreover, un-derstanding of these gestures may

very well be accomplished by map-ping the visual representations ontotheir motor representations, creatinga type of “motor knowledge.” Impor-tantly, there is very good evidence,both direct and indirect, that humanshave mirror neurons, and that Broca’s

area responds similarly to area F5

when humans undergo experiments

on arm and hand actions (Grafton et

al., 1996; Rizzolatti et al., 1996; De-

cety, 1997; Grezes, 1998). These ex-

periments show the left-over-right

asymmetry associated with primate

anatomy already noted and associate

Broca’s area very clearly with “mean-

ingful” rather than “meaningless” ges-

tures.

Returning briefly to Figure 1 and

jumping ahead to the question of the

computational purpose for Broca’s

area, this evidence helps to localize

the particular functions that might be

performed there in monkeys, great

apes, and humans. First, Broca’s area

is clearly a place where multimodal

information converges: visual, audi-

tory, and motor. In Figure 1, that

makes it a candidate for applying al-

gorithms of some sort to juxtaposed

data sets, both along the verbal and

nonverbal paths. That leads to infer-ential or predictive steps that precedethe acquisition of new knowledge andthe completion of learning steps. Inassessing the evidence for and againstcompeting hypotheses related to theprimate mirror system, Rizzolatti etal. (2001) point out that the mainweakness in the “visual hypothesis”—

whereby actions are understood solelyon the basis of their visual inputs,without reference to motor represen-tations—is that there is no mecha-nism for validation of the meaning of the observed action. By contrast, “mo-tor knowledge” provides the mecha-nism for validating and understand-ing gestures under the “directmatching hypothesis.” Thus, we havea prediction step and a validationstep, which are adjacent in Figure 1,as well as adjacent in the flow of data

in the brain: In the case of Broca’sarea, the inputs to the adjacent Brod-mann’s area 6 in the premotor cortexas well as the subcortical areas thatcoordinate fluent, multicomponentmotor activity in subcortical areas.

These same areas will be prominentwhen we examine the expression of the FOXP2 gene next: another elementin the “hardware” box in Table 1. Inthis case, because the brain employsphysical representations and becausethese are laid down during develop-ment, the patterns of gene expression




will help to outline the kinds of repre-

sentations and algorithms associatedwith Broca’s area.

ALGORITHMS: GENETIC

EVIDENCE

In reviewing evidence from genes, it isoften necessary to distinguish be-tween the gene itself, which consistsof a string of nucleic acids, and theproducts of that gene, expressed asamino acids and peptide chains.Much genetic evidence comes fromanimal studies, so it is also useful to

distinguish between the human andnonhuman forms of a gene and itsproducts. There is a shorthand to con-

vey these distinctions, summarized inTable 2. Italics appear when we aretalking about the gene itself. Humanforms of genes and their products arealways in upper case. Table 2 uses theFOXP2 gene as an example, since it isthe gene that will concern us the most.

The FOXP2 gene was isolatedthanks to a point mutation in the KEfamily in which afflicted members

have problems with fluency and gram-mar. However, just as the parallel an-atomical patterns and behaviors re-lated to Broca’s area in great apes andarea F5 in monkeys rule out an exclu-sive tie between Broca’s area and hu-man language, the evidence also ex-cludes an exclusive tie betweenFOXP2 and language. It is highly con-served, among the 5% most highlyconserved genes in human-rodentpairings, for example, which is consis-tent with brain areas with ancient as-sociations. Moreover, the human

form of the gene differs from the

chimpanzee version at only two

amino acids. The two changes oc-curred in the last 4 to 6 million years,after the branching of the hominidline from its common ancestor withthe chimpanzee. This is twice the ex-

pected mutation rate, providing fur-ther evidence for intense evolutionarypressure on human ancestors at thattime. In fact, these two changes, inassociation with colocated alleles onthe seventh chromosome, show evi-dence of an evolutionary “sweep” nomore than 200,000 years ago (Enardet al., 2002). That is, the evidencepoints to a small, important changerelated to Broca’s area, language, andto the brain.

Just as the differing evidence of left-

biased asymmetry across humans andgreat apes for Broca’s area and theplanum temporale indicates thatmore than one set of changes under-lies the emergence of language, evi-dence of development in homininsand great apes also shows that hu-mans emerged from more than justone change, no matter how impor-tant. Compared to chimpanzees, forexample, humans have an immatureskull shape and size. This accounts forits relatively large size in humans, butit is unlikely that these differences

stem from a single source. For exam-ple, human and chimpanzee growthrates differ substantially through ado-lescence (Gould, 1977). Humans like-wise developed more slowly and hadmore immature features than the ear-lier hominids (Dean et al., 2001; Rice,2001), and differences between hu-mans and Neanderthals also aroseearly in child development (Ponce deLeon and Zollikofer, 2001). Generally,comparative evidence indicates a mo-saic pattern of developmental traits,

and not a simple change of rates orthe acquisition of a single new trait(Moggi-Cecchi, 2001). Similarly, lan-guage is likely the outcome of a mo-saic of changes as well, but those as-sociated with FOXP2 were clearlyimportant.

FOXP2 belongs to a family of “fork-head box” proteins, which regulatethe expression of their respective DNAsequences by means of a three-wingedhelical structure (Carlsson and Mahl-apouu, 2002). FOX genes invariablyhave arginine at site 553, while mu-

tant FOXP2, as found in the KE fam-

ily, substitutes histidine, which is ad-

jacent to another histidine in the thirdhelix (Lai et al., 2001). The analogousmutation at that site in FOXC1 causesa critical loss of function (Saleem etal., 2003).

As noted earlier, the structure of FOXP2 is highly conserved. There areonly two differences between humanson the one hand and chimpanzees andgorillas on the other (threonine to as-paragine at site 303 and asparagine toserine at site 325), three between hu-mans and orangutans, and five be-tween humans and mice (Zhang et al.,2002). Of particular interest when wetake up comparisons of functions atBroca’s area to similar functions insongbirds later, there are only eight

differences between humans and ze-bra finches, making the protein 98%identical (Haesler et al., 2004). Thehuman-specific change at site 325probably created a substrate for phos-phorylation by protein kinase C(Enard et al., 2002). This particularprediction is based on an artificialneural network method that can esti-mate a protein’s structure with an ac-curacy that exceeds 70% and gener-ally lies between 75% and 82% (Rost,1996; Sun, 1997). This new substratemay well be the small but important

change that enabled Broca’s area andrelated areas, particularly in the lim-bic system and cerebellum, to func-tion as they do in language. On theother hand, a new phosphorylationsubstrate may simply be an exampleof the general upregulation of geneexpression in the brain that character-izes the differences between humansand chimpanzees (Preuss et al., 2004).

FOXP2 is one of a subfamily of FOXP proteins that has at least threeother members. Proteins in the sub-

family contain four signature do-mains: a DNA-binding winged helix, aleucine zipper, a zinc finger, and apolyglutamine tract (Wang et al.,2003). All of them appear to be highlyconserved, with the winged-helix themost divergent in structure. Gener-ally, all seem to function by repressinggenetic transcription, especially dur-ing development. The leucine zipperappears to foster dimerization of Foxpproteins, and both DNA binding anddimerization may be required forthese proteins to function. Possibly as

TABLE 2. Shorthand expressions for

genes and gene products

Gene

Gene

Expression

(Protein)

Human FOXP2 FOXP2Non-Human FoxP2 FoxP2

Genes are in italics and human forms

for genes and gene products are in

upper case. Here FOXP2, a gene

product, would have an extra site for

protein kinase C phosphorylation,

while FoxP2 would not. FOXP2 would

code for that site, while FoxP2 would

not.




a result, Foxp1 and Foxp2 are ex-

pressed in mice in different ways in

the epithelial tissue of airwaybranches in lungs, in motor pathwaysin the brain, in the outer mesoderm of the intestines, and in the outflow tractof the atria of the heart (Shu et al.,

2001). Foxp1 and Foxp3 are coex-pressed in lymphoid cells. Foxp4 over-laps the Foxp1/Foxp2 pattern in lung,intestine, and neural tissues as well,which further supports a complex pat-tern for transcription control (Lu etal., 2002).

The zinc finger domain that appearsin Fox proteins in all species with Fox

genes can modify repression in eitherdirection. For example, while a zincfinger domain normally provides oneof the mechanisms for repression,

when tested in yeast cells, this domainseems to do the opposite: Foxp2 tran-scription activity triples when fused tothe GAL4 binding domain (Li et al.,2004). The polyglutamine region,larger in Foxp2 than Foxp1, seems tomodulate repression activity as well.The phosphorylation site in humanFOXP2 provides yet more capacity for

varying the activity of the forkheaddomain. In general, this set of com-plex interactions, whether by variouscombinations of dimerization, or byother means of cooperative or antag-

onistic control, is representative of theforkhead box family of genes. In fact,the number of types of Fox genes in anorganism is directly correlated withthat organism’s complexity: humanshave more than 40 kinds of FOX genesoverall. They are related to a wide va-riety of developmental disorders, in-cluding the linguistic difficulties of the KE family stemming from a mu-tation in FOXP2 (Carlsson and Mahl-apuu, 2002).

Keeping this evidence in mind, we

can turn to the representational levelin Table 1. During development, genescause the basic structures in the brainto arise, where specific connectionsbetween inputs and outputs are laiddown, and where adjacent regionsprovide the probability for lateral con-nectivity as the organism matures.Since sense organs measure specifictypes of input data, and connected oradjacent areas are primarily affectedby these specific information types,this essentially organizes inputs intodata sets, thereby providing the phys-

ical framework for information pro-

cessing—the basis of implicit compu-

tation of sensory data.Gene transcription is subject to two

principal sources of noise that can af-fect the gene regulation function:noise internal to the cell and external

noise. Quantitative assessment of these two sources indicates that theinternal noise is subject to very rapiddecay, so that the principal influenceson single gene regulation are the bio-chemical factors that trigger genetranscription, together with externalnoise and only those internal factorsthat change slowly relative to an en-tire cell cycle (Rosenfeld et al., 2005).In gene networks, including the genecascades that proceed as a cell devel-ops, even the small perturbations at

the local noise level, however, canhave major effects downstream(Elowitz et al., 2002; Pedraza and vanOudenaarden, 2005). Translationalnoise is also a source of variation inphenotypes, so the combination of these factors probably led to selectionpressure in favor of inefficient trans-lation of genes even when efficienttranslation would consume less en-ergy, since inefficient translationwould lead to lower fluctuations(noise) in protein concentrations inthe cell. One well-known example of

this inefficient translation is cyclicAMP (Ozbudak et al., 2002).

At the same time, cell-to-cell varia-tions can depend on noise at the tran-scription level, so that cell populationsdemonstrate extended bistable statesin gene expression. Thus, noise in ge-netic cascades may very well play asignificant role in cell phenotype vari-ation, or cell differentiation as well(Blake et al., 2003). Taken further, thismeans that incorporation of noise inregulation is an evolvable trait that

can help maintain a balance betweenthe fidelity of gene expression and increating cellular diversity (Raser andO’Shea, 2004). Autocatalysis, or posi-tive feedback, demonstrably contrib-utes to control of cellular function inthe context of these bistable states(Becksei et al., 2001), while negativefeedback tends to function in homeo-static adjustments (Becksei and Ser-rano, 2000) and can produce effectsfive times faster than when negativeautoregulation is absent (Rosenfeld etal., 2002). Bistability of this sort es-

sentially creates an all-or-nothing

switching mechanism, illustrated in

another well-known example by theactivity of MAP kinase in the cell cycle(Ferrell and Machleder, 1998).

What are some potential implica-tions for FOXP2, which is active dur-

ing the development of brain struc-tures, and which normally functionsby repressing genetic transcription?In the cortex, the first layers to formfrom the neural preplate are the lowerlayers, then the upper ones (Sidmanand Rakic, 1973; Marin-Padilla,1999). FOXP2 appears only in layersIV to VI (Maviel et al., 2004), so it isexpressed only during the first half of that process and thus affects the lay-ers that communicate outside the cor-tex. In Broca’s area, layer V contains

numerous and noticeably large pyra-midal cells (Amunts et al., 1999). If mutant FOXP2 fails to trigger theproper conditions for layer V, thiswould have implications for the estab-lishment of the requisite connectionsto other brain areas, as well as thecomposition of the layer itself. Thismay explain the relatively low levels of gray matter to the limbic system inmembers of the KE family affected bythe gene mutation. The areas of thelimbic system and the cerebellum af-fected by the mutation may also be

subject to similar failures in switchingfrom one stable state to another dur-ing development.

Genes also control the migration of cell types during development,thereby contributing to the fine struc-tural detail of the brain’s implicitcomputational framework. In the for-mation of the six-layered cortex, forexample, the DCX protein appears toplay a key role. It is located preferen-tially in the growth cones of develop-ing neurons and activated by c-Jun

N-terminal kinase (JNK) phosphory-lation (Gdalyahu et al., 2004). Differ-ent mechanisms are involved for themigration of neurons when guided byglial cells. In this case, the controlmechanism appears to involve thePar6 protein, as well as protein ki-nase C (PKC, in this case) and -tu-bulin as a cytoskeletal component(Solecki et al., 2004). This is the kindof mechanism that is likely to revealwhat is happening with FOXP2, be-cause the normal human form of thegene adds a phosphorylation site for




protein kinase C. The involvement of protein kinase C is potentially quitesignificant, as its presence would shiftthe noise parameters associated withthe gene and possibly create new pos-sibilities for migration as well as celldifferentiation. Protein kinase C

(PKC) is critical in a developmentalcascade in cerebellum when climbingfiber cells are culled to leave oneclimbing fiber synapse to one Purkinjecell in circuits that control fine motormovement (Kano et al., 1995).

While genes, by virtue of their con-trol of cell differentiation and migra-tion, set down a framework for dataprocessing that specifies what datatypes interact with each other, theneural constituents of the brain musttake that input, use it, and learn from

it. Development sets the conditions,while the components of the activebrain must then follow through to cre-ate an individual’s actual competenceat any given task. This is where Bro-ca’s area and possibly its subregionsand even individual neurons wouldplay a computational role.

ALGORITHMS: BISTABILITY AND

NETWORK DYNAMICS

The timescale for this activity alsoshifts to both faster and longer-term

processes. Migrating cerebellar neu-rons have average velocities of 3–4m/min (Maviel et al., 2004), whilemigrating cells in rat neocortex couldachieve their final position in severaldays (Bai et al., 2003). By contrast, theactual processing based on the resultsof all this movement and positioningtakes place in tens and hundreds of milliseconds, while learning processesset in over different time courses fromminutes to days, months, and years.

The processes at this level are very

different from genetic regulation, of course, but they also are essentiallystochastic and lead to very similar pat-terns of stability. For example, sen-sory changes that affect potassiumconductances can shift Purkinje cellsfrom a bistable spiking mode to eitherof two single stable spiking states(Loewenstein et al., 2005). Bistablebehavior is also easy to find in assem-blies of neurons. Networks with N-methyl-D-aspartate (NMDA)-medi-ated recurrent synapses, consisting of pyramidal cells and interneurons that

provide the feedback, show bistable

behavior that shifts to a single stable

state either when -aminobutyric acid(GABA) conductance prompted by theinterneurons shifts above a criticalthreshold, or when -amino-3-hy-droxy-5-methyl-4-isoxazole propionic

acid (AMPA)-mediated conductanceshifts the state of the network in theother direction (Lisman et al., 1998).

Models of working memory consist-ing of recurrent excitatory networkswith simpler leaky integration neu-rons also produce this behavior(Durstewitz et al., 2000). Other stud-ies of bistability in networks showthat the hysteresis loops that createthese bistable-reactive regions also al-low sensitivity by the network to itsrecent history on input patterns

(Pouget and Latham, 2002). In termsthat would match a Turing-like frame-work for network computation, maxi-mization of Gaussian mutual infor-mation in the presence of noise turnsout to provide the stable computa-tional behavior and sparse codingfound in biological neural networks aswell (Linsker, 1993). In this case, thenetwork’s problem is simply to cap-ture the characteristics of an input ar-ray using local processes, as we wouldrequire within the implicit Turingframework.

Activity-dependent processes in ex-citatory neurons show just this kind of bistable behavior in experimentalstudies of CA1 hippocampal neurons(Lisman et al., 2002). While the AMPAand NMDA receptors in these neuronsfunction at timescales of far less thana second, NMDA receptors, anchoredby PSD95 connected to AMPA an-chored by assemblies containing acti-nin, actin, and SAP97, all phosphory-lated by calcium/calmodulin-relatedkinase II (CaMKII), provide an ener-

gy-efficient, bistable “switch” that per-sists on the order of days. At normallocal concentrations of Ca2, this con-figuration, which relies on an auto-phosphorylated state of CaMKII,shows only 10% dephosphorylation of the kinase after 45 hr (Lisman andZhabotinsky, 2001).

Processes that persist longer thanthis would presumably require the re-arrangement or reconfiguration of synapses, rather than adjustments atthe channel level. However, thisNMDA-AMPA/CaMKII mechanism

would be a reasonable one to imputeto the numerous and prominentlylarge pyramidal cells in layer III inBroca’s area. Layer III is also predom-inantly involved in remote, ratherthan recent, memories (Franklandand Bontempi, 2005), so activation of

these neurons in language taskswould probably account for the fMRIpatterns that can distinguish nativefrom learned second languages.

Whether the other structuralchanges to synapse formation ortransformation are needed for long-term memories is still an open ques-tion. If they are needed, some form of genetic translation would probablyalso be required, if not transcriptionas well. Incidentally, FOXP2 is not im-plicated in any of these functions, as

FOXP2 is not expressed in layer III.Perhaps this is the reason that the KEfamily members with the FOXP2 mu-tation show fewer difficulties withcomprehension than with speech pro-duction (Belton et al., 2003b).

ALGORITHMS: COMPARATIVE

GENETICS AND NETWORK

STUDIES

Much like humans, many songbirdslearn their vocal patterns by copyingthe vocal cues they hear. Unlike hu-

mans, birds do not have a six-layeredcortex, so there is no question of anexact homolog to Broca’s area, butthey do express Foxp1 and Foxp2 in amanner strikingly similar to humanfetuses (Teramitsu et al., 2004). In-stead of a cortex, birds have a pallium,from the Latin for a cloak, which de-scribes how it forms a cover over thesubpallial areas that bear somewhatcloser correspondences to mamma-lian subcortical areas. The parallelstructures for bird song are located in

avian pallial and subpallial areas, aswell as the homologous sets of nucleiwithin the dorsal thalamus. Theseavian brain areas help provide senso-rimotor integration, as well as skilled,coordinated movement, strikinglysimilar to the cognitive and motor ca-pacities related to Broca’s area. Sincethe eight differences between Foxp2 inzebra finches and FOXP2 in humansinclude sites 303 and 325 (zebrafinches have the same amino acids aschimpanzees), there is no questionthat the human phosphorylation sub-




strate for protein kinase C provided

the mechanism for parallel develop-

mental and behavioral traits, but theother changes in these birds may haveresulted in similar results by alternatemeans.

In humans, there are both cognitive

and motor pathways associating thecortex with the basal ganglia and thecerebellum (Middleton and Strick,2000). Both are implicated in KE fam-ily sufferers from the FOXP2 mutation(Vargha-Khadem et al., 2005). Theprincipal subcortical areas implicatedin human cognitive and motor cir-cuits by FOXP2 expression are thecaudate nucleus and putamen, thesubstantia nigra pars reticulata andglobus pallidus internal segment, aswell as the medial dorsal, ventral an-

terior, and other nuclei of the thala-mus on the cognitive loop, while thecerebellum (lobules VIIB, VIIIB, aswell as the inferior olivary complexand red nucleus), the dentate nucleus,and the medial dorsal, ventral lateral,and other nuclei of the thalamus areon the motor loop. It is exceedinglyinteresting to note here that knockoutmice lacking a form of protein kinaseC (PKC) that occurs in climbing fibercells are capable of learning simplemotor skills, but are impaired in thesmooth coordination of those same

skills, such as in walking or balancingon a narrow object (Chen et al., 1995).With these mice, the usual paringback of synapses between climbing fi-ber cells and Purkinje cells does nottake place during development, sothat more than one climbing fiber cellwill have synpases with the same Pur-kinje cell in adults. In normal maturemice, there is a one-to-one correla-tion. This is an intriguing correlationof the natural repression of multipleneural pathways during development,

protein kinase C (implicated by site325 in FOXP2, possibly to added ef-fect), and the smooth coordination of compound motor activity, such asthat required for fluent speech.

As for songbirds, there are evidentcorrespondences between these sub-cortical areas implicated by FOXP2and the subpallial areas involved inthe avian song cycle (Jarvis et al.,2005). For example, the anterior (cog-nitive) loop in both involves the stria-tum and thalamus. In birds, it con-tains the lateral area X (LAreaX) in

the striatum, which passes signals to

the dorsal lateral nucleus of the me-

dial thalamus (DLM). In zebrafinches, Foxp2 is expressed in area Xduring the critical period for songlearning. In adult canaries, it is ex-pressed in area X seasonally, when

song production is unstable. Its ex-pression in other birds varies simi-larly, indicating its association with

vocal plasticity (Haesler et al., 2004).Similarly, the motor loop also con-tains the thalamus, in this case thenucleus uvaeformis.

Other correspondences are morenotional, but the subcortical analogiesto avian subpallial areas seem verystrong. To extend the analogies intothe equivalent of the cortex for song-birds, there are also areas in the avian

pallium that participate in both theauditory and motor pathways—thehigher vocal center (HVC), and therobust nucleus of the arcopallium(RA). Another key area in the cere-brum on the cognitive loop is the lat-eral magnocellular nucleus of the an-terior nidopallium (LMAN). Thesemay play the computational role of Broca’s area. They are involved in themoment-to-moment modulation of syllables in the songs of zebra finches(Kao et al., 2005).

Birds are also capable of acquiring

“syntax.” White-crowned sparrows,when exposed to their native song intwo syllable phrases, were able tolearn the entire song sequence despitenever hearing the entire song fromend to end. Exposed to the song whenthe syllable pairs were in reverse or-der, the sparrows learned the songbackward (Rose et al., 2004).

To summarize briefly, Broca’s areawas first identified with language def-icits related to sequences (syntax) andmotor control. It has structural ho-

mologs in primates, where the area isassociated with execution of motorfunctions, especially gestures, andcross-modal sensorimotor perception.It has a functional homolog withsongbirds, which learn and executecomplex acoustic sequences. A pointmutation in the FOXP2 gene also tiesthe area to a gene that is expressed ina highly conserved form in all theseanimals. That gene is normally a de-

velopmental repressor intimately involved in the development of complexpathways in the brain, intestines,

lungs, and heart. In the brain, it shows

a consistent pattern of expression in

the cognitive and motor-related areain the basal ganglia or their equiva-lent.

Thus, Broca’s area is not just forlanguage, any more than the FOXP2

gene is, although the human form of the gene may well contain a small butimportant shift that was necessary forlanguage. Mirror neuron functions inprimates and Foxp2 expression in ver-tebrates help to shed some light on thenext representational question in Ta-ble 1, as well as the ultimate questionon what Broca’s area is “for.” In otherwords, this evidence, despite the se-ries of questions that still remain un-resolved, helps us understand whatroles Broca’s area plays in brain func-

tions, how these are accomplished,and how the change unique to hu-mans affected them.

The abnormalities associated withthe KE family help to clarify some of these issues even more. FOXP2 is ex-pressed in Broca’s area and Brod-mann area 6 in the motor cortex, aswell as the subcortical areas listedabove. In addition, the KE point mu-tation results in reduced gray matterconnecting these areas to other areasin the brain, especially in the caudatenucleus, the cerebellum, and the left

and right inferior frontal gyrus. Inter-estingly, it is also associated with in-creased gray matter in the planumtemporale (Belton et al., 2003a).These differences would indicate nodysfunction related to the communi-cation of auditory information to Bro-ca’s area, but decreased informationflowing from it to motor control areas,consistent with the KE family prob-lems with fluency and fine motormovement in the face and mouth.

This pattern is further consistent

with expression of Foxp2 in mammalfetuses, where the mRNA signal ap-pears on the inner cortical plate and islimited to the tissue below the granulecells in layer IV and especially in layerVI (Ferland et al., 2003; Lai et al.,2003; Takahashi et al., 2003). Thus,the gene appears in the layers thatconnect to subcortical areas, so thatdamage from mutations would alsooccur there. In fact, KE family mem-bers with the point mutation showsignificant lack of activity in Broca’sarea and the putamen between Bro-




ca’s area and the limbic system in verbal generation tasks. Possibly inpartial compensation, they showheightened activity in verbal tasks inareas further to the rear of the cortexand in both hemispheres (Liegeois etal., 2003). This provides yet more evi-

dence for a role for Broca’s area inmotor control related to language inhumans.

ALGORITHMS: NONLINGUISTIC

FUNCTIONS AND MEMORY IN

HUMANS

Besides language, meaningful pat-terns and sequences related to musicalso trigger responses to areas in hu-man brains that are generally associ-ated with language. In an fMRI study,

which provides good spatial resolu-tion, but not very good temporal res-olution, subjects exposed to musicalsequences ending in discordant notesreacted significantly more throughoutthe perisylvian region than when ex-posed to note sequences that behavedaccording to rules of tonality withwhich they were familiar (Koelsch etal., 2002). Magnetic encephalography(MEG), which has excellent temporalresolution, provides good evidencethat Broca’s area is involved in pro-cessing this “musical syntax” at differ-

ent timescales. For example, “in key”and discordant tones produce dis-tinctly different levels of activity, andBroca’s area is particularly active inthe case of discordant tones, with thereaction occurring approximately 200msec after the tones are heard (Maesset al., 2001). These are consistent withdata on linguistic perception, wheregaps in a “tone group” are perceivedfrom 80 to about 240 msec and ig-nored otherwise (Butcher, 1981). Insequential processing, when con-

fronted with a key phrase that wasgrammatical in a simple sentence,grammatical in an embedded sen-tence, and inserted ungrammaticallyinto a third sentence, subjects showeda similar electrical activity (a P600event-related potential/ERP) begin-ning at 200–300 msec, and reaching amaximum amplitude at 800–900msec for the ungrammatical struc-ture. Incongruous tones in musical se-quences showed a virtually identicalpattern (Patel et al., 1998). Reactiontimes and amplitudes tend to be pro-

portional to the “distance” from antic-

ipated musical values, similar to the

hierarchy of reaction times in lan-guage as phrases become difficult orimpossible to interpret (Koelsch et al.,2000; Patel, 2003).

Broca’s area also appears to have an

important role in memory. For lan-guage, bilingual subjects show dis-tinctly different fMRI activation pat-terns for a given language dependingon whether their second language wasacquired simultaneously with theirfirst one, or later when they wereadults. In the case of subjects wholearned two languages at the sametime, activation patterns in Broca’sarea overlap considerably. When thesecond language was acquired inadulthood, the two areas are distinct

(Kim et al., 1997).Broca’s area thus makes associa-tions over times less than a secondand stores patterns acquired over alifetime. This implies a function re-quiring both short- and (very) long-term memory. Recent and remotememories have different processesassociated with them. In the case of episodic memories, these are relatedto cortical-hippocampal networks(Frankland and Bontempi, 2005). Theexpression of c-fos, which is activity-dependent, in cortical layers demon-

strates these different processes andshows distinct differences in storingspatial memories in the parietal cor-tex of mice (Maviel at el., 2004). Thatpattern is essentially identical to theexpression of Foxp2 with respect tocortical layers: Foxp2 is expressed pre-dominantly in layers V and VI, whichare also the layers showing the great-est c-fos activation and implying a tieto recent memories; Foxp2 is not ex-pressed in layers II and III and hardlyexpressed in layer IV, where the lack

of c-fos activity implied a significantrelationship to remote memories.Broca’s area is also involved with a

number of memory tasks apart fromlanguage. This returns to the multi-modal associations with the areanoted earlier in monkeys. In humans,Broca’s area is involved in both spatialand object memory, in storage tasksand in executive tasks (Smith andJonides, 1999).

Considering the conceptual sketchat Figure 1, the simplest computa-tional association we can make with

Broca’s area that is consistent with itsconnections to sequences, multimo-dal inputs, and motor executive func-tions would seem to be the transfor-mation of multimodal input data setsinto motor or conceptual inferences.These transformed data sets would be

validated (“understood”) in adjacentpremotor areas or used to performmotor commands. For humans, theconceptual inferences would essen-tially be parsed constructions, whichmay also be validated by subsequentinputs.

COMPUTATION: WHAT BROCA’S

AREA IS “FOR”

Transforming sequential data intopredictive inferences requires choices,

which require the presence of the ap-propriate bi- or multistable switchingmechanisms to make them. Both neu-ral models and experiments on similarcells to those that are prominent inBroca’s area show that such bimodalbehavior is to be expected there. Thisimplies that the area may very wellfunction as the place where such pre-dictive constructs are stored, andwhere already stored constructs areevoked by current inputs or for plan-ning future activity. For language, lay-ing down the long-term storage infra-

structure is part of languageacquisition; the moment-to-momentuse of that structure to understand ormake utterances is part of real-timelanguage processing.

Figure 2 helps to show preciselywhat this might mean. It is adaptedfrom Croft’s causal-order hypothesislinked to his analysis of causationtypes. This may help to understandthe different patterns of case expres-sion across numerous languages(Croft, 1991). He is one of the “other”

cognitive linguists mentioned earlierin connection with Langacker, andthe creator of radical constructiongrammar, which dispenses with theneed for a formalist set of syntacticrules for explaining linguistic phe-nomena (Croft, 2001). Tomasello’spsychological theories for languageacquisition rely on these types of “construction” theories (Marin-Pa-dilla, 1999), so together this body of work provides the necessary theoreti-cal framework to explain language ac-quisition and processing in humans.




Figure 2 attempts to provide a pre-dictive framework for how differentlanguages might go about signalingreal-world causal relationships andtheir related descriptions in terms of case marking, such as the use of accu-sative case for direct objects in manyEuropean languages. For example, inthe English sentence “George hit

him,” “him” is descended from an ac-cusative form. To explain some of theless obvious annotations as well, the“X’s” in the diagram refer to two dif-ferent candidates as points of focusfor linguistic signaling. In English, thepattern follows the accusative format.In ergative languages such as Dyirbalin Australia, the pattern is absolutive.Logical subjects and objects are thesame, but the way they are signaled isdifferent. However, the diagramshows that the choice made by therespective speech community falls at

one end or the other of the segmentthat is encoded by verbs.

For computational purposes, Figure2 shows that this complex array of interrelated signals can be mappedback to a simple linear pattern of cause and effect that can be observedby any intelligent organism in its en-

vironment: monkeys can see these

patterns even if they cannot describethem. This pattern lies beneath themultimodal perceptions monkeysmake when looking at and listening totheir environment and comprehend-ing the gestures of their fellow mon-keys (Gallese et al., 1996; Kohler et al.,2002), and these aspects are associ-ated with F5, the very early equivalentof Broca’s area in primate evolution.

Tomasello (1999) posits a simpleperceptual framework like this forlanguage acquisition. He further re-quires the recognition in growing chil-

dren that they and the adults they are

listening to have similar mental states

and goals, and that the adults makefocused, identifiable speech sequences(constructions) within that frame-work. Gestures figure prominently inthis framework as well, along with

joint attention that is certainly moredifficult for other primates. It is alsointeresting that the difficulty of Bro-ca’s aphasia patients and KE familymembers with the English passiveconstruction corresponds to a reversalof the underlying causal arrow in Fig-ure 2 in the arguments of the con-struction. These patients will often saythat “George hit Bill” when they hear“George was hit by Bill,” which is con-sistent with the “primate core” inter-pretation of the order of entities but a

wrong interpretation of the construc-tion semantically. In any event, asso-ciating Broca’s area within this multi-modal framework, as well as callingon it to organize speech inputs alongparallel lines, is not a very large leap.

Do these associations account forlanguage in general? Reference to theconceptual sketch in Figure 1 wouldrule out such a broad role for Broca’sarea, in addition to the extensive evi-dence tying the entire perisylvian areato language processing. The symbolicrepresentations corresponding to ex-

ternal entities would be far morelikely in the parietal and temporallobe structures linked to Wernicke’sarea, for example.

Downstream from the data organi-zation step in Figure 1 is the inferen-tial computation that enters the feed-back loop for learning, which wouldhave a motor variant related to predic-tive estimates for motor actions. Areall the functions on the feedback loopperformed in Broca’s area, or in otherareas to which it is connected? Con-

nections to the premotor cortex wouldindicate that the validation function isperformed downstream. For motorsequences, the more likely candidatefor validation would be the cerebel-lum, where error estimates in motorfeedback loops are a very likely output(Marr, 1969; Albus, 1971; Ito, 1989).For the cortex, the anterior cingulatecortex seems to function as an errordetector (Paus et al., 1998; Koski andPaus, 2000; Brown and Braver, 2005).It has connections in cognitive, motor,and arousal pathways (Paus, 2001), as

Figure 2. Causal Chains and Linguistic Phenomena. Perception of causation requires aninitiator and an endpoint, with no presumption of telekinesis. For mental events, whichrequire a “theory of mind,” it is possible for a mental stimulus to affect another mental state.A mental initiator can also affect a physical endpoint, which may initiate other physicaleffects. Similarly, physical causes can have an influence on mental states, or, sometimes byan intermediate physical mechanisms, they also affect a physical endpoint. These casualrelationships are reflected directly in how human language describes the equivalentevents. Thematic roles are explicitly encoded in various human languages. The causal roleimmediately precedes the event described by the verbal segment. Passive agents are alsoexpressed external to the verb segment when there is a passive construction. “Subjects” arelogical initiators of verbal actions. Their expression may vary by language. For example, inaccusative languages such as English, or other European languages, this is uniquely codedfor the verb “X.” The comitative role is performed by the entity that participates in this causalchain as the initiator. Means, manner, and instrumental roles are informative about how theverbal action is implemented. The “object” is the logical recipient of the verbal action. Inergative languages, this role receives the same marker as the “subject” in intransitivesentences, coded as an absolutive “X.” Thus, across languages, either end of the verbal

segment can receive the focus of the coding system. Results, benefactive (good result),malefactive (bad result), and recipient roles follow the span of the verbal segment. Theobservation of physical causation, which also corresponds to basic verbal descriptions, iscommon to the primate goal-oriented perception in mirror neurons.




well as to the prefrontal cortex (Bar-bas, 1997) that overlap many of thelanguage connections we have already

reviewed. These connections includethe motor cortex (Dum and Strick,1991; Morecraft and Van Hoesen,

1992), vocal regions (Barbas et al.,1999), and the limbic region as well(Montaron and Buser, 1988; Barbas

Figure 3. Projections of Basic Clausal Constructions onto Grammatical Modern English, Old English, and Modern German Variants of theSame Sentence. a) Modern English, b) Old English, c) Modern German. The upper sets of blocks chart basic constructions that are mergedinto the final sentence. The semantic content is identical for all three languages.




and De Olmos, 1990; Barbas et al.,

1991; Kunishio and Haber, 1994).

Figure 1 also has an interpretationstep associated with language down-stream from data ordering, but priorto full description and conceptual in-ference. Given the apparent role of

other regions for symbolic representa-tion, this function also is likely per-formed in other brain regions, partic-ularly in the temporal cortex. In fact,the interpretation function is likely tobe so complex that to trace all its fac-ets would be a search for the neuralcorrelates of semantic interpretation,which would be on a par with theattempt to establish the neural corre-lates of consciousness (Koch, 2004).

Returning to Marr’s “hardware” cat-egory (Marr, 1982), there is also a net-

work level where functions can be an-alyzed in terms of noise (once again),priming, resonance, and the struc-tural changes that adjust the connec-tions among various components.Pulvermuller (2002) has provided a

very complete description of linguisticstructures at this level. His analysis,not surprisingly, comes back repeat-edly to the perisylvian region in thebrain. He also presents extensive sup-port for the idea of “word webs,”which incorporate widely distributedactivations across relevant portions of

the cortex that would provide a verygood first approximation of symbolicrepresentation or the neural corre-lates of semantic interpretation. All of the word web activations would occurwell into the time frame necessary forfMRI imaging, so there is extensiveimaging data available for this analy-sis, which he reviews very thoroughly.

That predictive computationalniche, between the formulation of or-ganized data and the validation of possible inferences, is the equivalent

to the center of the cyber-spider’s webin Figure 1 and what remains for Bro-ca’s area. In cognitive linguistic terms,this is probably sufficient to accountfor syntactic phenomena, illustratedin Figure 3. The original sentence isfrom an Old English homily by Ælfricin the 11th century, quoted becausehe predeceased any controversy aboutlinguistic theory. It means, “Hethought, if he killed them all, that theone he sought could not escape.” Thefirst version of this sentence in Figure3 is from Modern English. The second

and third variants are the original Old

English version, and a translation into

modern German, which project theequivalent basic clauses into the finalfinished sentence.

This sentence, which contains someof the case data explained in Figure 2,

can be accounted for in terms of su-perposition and a minimalizationcomputation step for parallel pro-cesses in connection with “bracket”signals used in Old English (Cooper,1999) and modern German. ModernEnglish clusters its verbs but stillshows the crossover “movement” of elements that are not easily accountedfor by derivational trees. Each showsa decomposition into discrete con-structions, as in Croft (2001) and To-masello (2003). The Old English

pieces are outlined below:Ðohte þæt Y: (He) thought that . . .(This requires recognition of other

people as thinking beings in additionto being a highly frequent construc-tion, as Tomasello would probablypoint out.)

he ofslo ge hı ealle: he killed (slew)them all

he ne ætburste: he (could) not es-cape

he so hte þone a nne: he sought theone

( þone a nne: “the one” is accusative,

a direct object; se a n is nominative, asubject, like he .)

gif . . . þanne: if . . . thenThe arrows show that there are sev-

eral superpositions, and that addi-tional signals are built into the sen-tence, presumably to make it easilycomprehensible (Ælfric was a famouspreacher and educator in his day):“bracket” constructions, which putforms of the verb toward the rear of some Old English clauses to signalsubordination, would reflect require-

ments of the minimalization step forlarge aggregations like this one, aswould the change in case for “theone.” The position of “the one” alsoindicates a minimalization step inmodern English. Modern Germanmarks the end of subordinate clausesby placing the finite portion of the

verb there, very similar to Old En-glish. Generally, the minimalizationstep uses a higher-order constructionto incorporate inputs from basic con-structions.

It is easy to see that any disruption

of sequencing would make these sen-tences impossible to produce. As withPKC-knockout mouse models withcerebellar dysfunctions that preventthe combination of individual move-ments into fluent sequences, here wewould have similar disruptions that

would first cause the failure to inte-grate basic constructions into largerones, and if taken further, possiblyeven the inability to put together thebasic constructions themselves.

That would describe many symp-toms of Broca’s aphasia, as well as theinability of KE family members withthe FOXP2 mutation to produce thiskind of utterance. Perhaps this illus-trates the effect of that single pointmutation and points to the specificfunction for Broca’s area.

So what is Broca’s area “for”? It isclearly linked to language, althoughits ancient connections in primatesprove that it had other functions wellbefore human language was even pos-sible. It has genetic associations thatimplicate it in fluent combinations of motor movements, and these geneticassociations are even more ancientthan an equivalent to Broca’s area. Incomputational terms, it appears tooccupy a critical niche between orga-nized sensory observations and pre-dictive knowledge about the environ-

ment, and this becomes especiallyimportant in primates with respect togestures and in humans with respectto language.

ACKNOWLEDGMENTS

Special thanks to Ann Butler and JimOlds of the Krasnow Institute for Ad-

vanced Study for their helpful sugges-tions and corrections, and particu-larly to the latter for allowing theauthor to follow his nose. The author

also acknowledges in memoriam Wil-liam G. Moulton, from Princeton, whohelped him put together his originalinquiries into computation, informa-tion theory, and language, whicheventually led from dialectology andneostructural linguistics to cognitiveneuroscience.

NOTE ADDED IN PROOF

The importance of Broca’s Areawithin the primate mirror system hasrecently been confirmed and ex-




panded in Nelissen, et al., 2005, whichprovides fMRI evidence for the ma-caque homologs for BA 44 and BA 45.These areas favor gestural informa-tion over object identification, butalso distinguish their focus betweenhad gestures in the homolog to BA 45

and actions taken by an acting personin the homolog to BA 44, representingthe action and its context, respec-tively. This may make BA 45 a loca-tion where action and object identifi-cation information coverage.

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