Oxford university press how the br ain evolved language

Figure 7.5. 
Propagation of [i] from cochlea to primary (A
1
) and tertiary (A
3
) 
auditory neocortex. 
For example, in figure 7.6, a variant acoustic pattern, [
I
], is input to the 
mature system after it has already learned the normal phoneme /i/ at A
3
. Even 
though this new, variant input has its F
2
 peak at x
2
, feedback is stronger around 
the learned A
1
–A
3
 loop at x
3
 because of the learned and heavily weighted long-
term memory trace at z
3
. Thus, activity at x
3
 becomes greater than at x
2
, even 
though the phonetic input to x
2
 is greater than the input to x
3
. As a result, the 
phone [
I
] is deformed and perceived in the phonemic category /i/. 
Elsewhere in psychology, this deforming-to-match is known as “feature fill­
ing.” By the same kind of on-center off-surround polypole mechanisms, we can 
recognize a partially masked face or read a poor-quality photocopy of a docu­
ment, filling in missing features from long-term memory. In all such cases, 
information that is missing in the sensory input is reconstructed from memory. 
In ART, these “top-down” signals shaped by (learned) long-term memory traces 
are the same expectancies we discussed in chapter 5. 
Perceptual Interference and Learning New Patterns 
The ability to fill in features has obvious survival value. In certain circumstances, 
however, it can give rise to the phenomenon known as interference. For example, 
in Spanish, the [i] of figure 7.5 and the [
I
] of figure 7.6 are allophones—vari-
ants that both match the Spanish phonemic category /i/. The Spanish speaker’s 
long-term memory array at A
3
 in figure 7.6 has learned to deform the lower 
second formant of [I] to match the higher second formant of /i/, so she does 

SPEECH  PERCEPTION 
•  117 
Figure 7.6. 
Phonemic normalization. 
not distinguish between bit and beet.
1
 This learned equivalence works fine in 
Spain, but when a Spanish speaker attempts to use the same circuits to process 
English, interference can arise. Like Spanish, English maps [i] onto the phoneme 
/i/, but unlike Spanish, English maps [
I
] onto a distinct phoneme, /
I
/. Thus, 
beet (/bit/) and bit (/b(t/) are two distinct words in English, but they would 
simply be different ways of pronouncing the same word in Spanish. 
In the 1950s, the contrastive analysis hypothesis proposed that the more 
different two languages were, the more mutually difficult they would be to learn. 
So, for example, Italian speakers should find it easier to learn Spanish than 
Chinese since Italian and Spanish are both Romance languages and totally 
unrelated to Chinese. In general, the contrastive analysis hypothesis held up 
fairly well, but under close examination it was found to break down. Sometimes, 
second-language learners found it most difficult to learn things that were only 
minimally different from their first language. This interference caused a seri­
ous conceptual problem that behaviorism was unable to solve. Why should both 
maximally different and minimally different structures be more difficult to learn 
than structures that were only moderately different? Figure 7.6 explains what 
behaviorism could not: previously learned features only interfere with new 
features within their off-surround. The polypole simply resolves this contradic­
tion between interference theory and contrastive analysis. 
In the early stages of learning a second language, gross and confusing 
miscategorization of speech sounds is a familiar experience, but it can be rela­
tively quickly overcome. To say that our Spanish speaker does not distinguish 
beet and bit is not to say that she cannot learn to do so. But how does she learn? 
If x
3
dominates x
2
 in figure 7.6, how can x
2 
ever activate in response to [
I
]? For 

118  • 
HOW  THE  BRAIN  EVOLVED  LANGUAGE 
the answer, recall how a flash of white light rebounded a green percept to red 
in figure 5.9. White light accomplished this rebound because it contains all 
colors, including green and red, and so it stimulated both poles of the retinal 
dipole nonspecifically. The flash of white light was an example of nonspecific 
arousal (NSA). 
Like the flash of white light in the red-green dipole of chapter 5, any sur­
prising, arousing event tends to elicit NSA and so has the capacity to rebound 
cortical activity and initiate the learning of new information at contextually 
inactive sites. To understand how this learning begins, let us continue the pre­
ceding example by imagining that our Spanish speaker has been wondering at 
the force with which English speakers use the word sheet. Suddenly she realizes 
that they are not saying /
∫it/ at all; they are saying /∫
I
t/. This shocking devel­
opment unleashes a neocortical wave of NSA, which rebounds the polypole of 
figure 7.6 as depicted in figure 7.7. 
In figure 7.7, as in figure 7.6, the phone [
I
] is being presented to A
1
. In 
figure 7.7, however, a burst of NSA has rebounded the A
1
 polypole. In A
1
, x
2 
and x
4
, which had previously been dominated by x
3
 and its strong long-term 
memory trace, have been nonspecifically aroused and have wrested control 
from x
3
. Now x
2
 is active and its long-term memory trace at z
2 
can grow in re­
sponse to the bottom-up input of [
I
]. 
Bilingualism 
In a polypole like A
3
 of figure 7.7, NSA turns off the sites that were on and 
turns on the sites that were off. But now what is to prevent z
2
 and z
3
 in A
3
 from 
equilibrating? In figure 7.8, the A
3
 long-term memory traces encoding [
I
] as 
Figure 7.7. 
Rebound across the vowel polypole of /i/. 

SPEECH  PERCEPTION 
•  119 
Figure 7.8. 
A dipole enables bilingual code switching. 
/i/ (Spanish) and /
I
/ (English) have reached equilibrium. In this state, how 
could such a “balanced” bilingual ever know which language is being spoken? 
Why don’t balanced bilinguals randomly perceive [
I
] as either /i/ or /
I
/ (or, 
mutatis mutandis, produce /
I
/ as either [i] or /
I
/)? Worse, how can the bal­
ance be maintained? If such a bilingual moves to a community where Span­
ish is never spoken, why doesn’t he or she forget Spanish promptly and utterly? 
Why are learning and unlearning not strictly governed by overall input 
frequency?
2 
One answer, which we discovered first in chapter 5, is that a rebound 
complements memory into active and inactive sites, so that the new input be­
comes remembered at sites which have been inactive in the current context. 
No brain cell is ever activated without activating other neurons, so these inac­
tive sites encode the new memory in a new, contextually modulated subnet­
work. So our answer to the long-term invariance of language learning lies in a 
contextual Spanish-English dipole like that of figure 7.8. When the balanced 
bilingual is in a Spanish context, the Spanish pole of the contextual subnet­
work is active. This biases the A
3
 polypole toward interpreting [
I
] as /i/. But 
when the balanced bilingual is in an English context, the English pole of the 
dipole is active, and the phonemicization network is biased toward the English 
phoneme /
I
/. In mixed contexts, the dipole can oscillate, and the balanced 
bilingual can “code-switch” in centiseconds between Spanish and English and 
between /
I
/ and /i/. 
This code-switching dipole was predicted in Loritz 1990. In 1994, Klein 
et al. reported a PET study of bilinguals that appears to have located part of 
just such a dipole in the left putamen. They analyzed cerebral blood flow when 

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HOW  THE  BRAIN  EVOLVED  LANGUAGE 
sequential bilinguals, who had learned a second language after age five, re­
peated words in both languages. There was a significant increase in blood flow 
in the left putamen when the second language was spoken. Considering that 
the putamen and the other basal ganglia are also implicated in parkinsonism, 
a disorder of tonic muscular control, a plausible hypothesis is that the left 
putamen is implicated in maintaining articulatory posture.
3 
Vowel normalization 
[i
In the last chapter we observed that, because vocal tracts are all of different 
lengths, the infant language learner faces the daunting task of phonemic nor­
malization. That is, in the clear case of vowels, how is a child to learn that [i
mommy
], 
daddy
], and [i
baby
] are all allophones of /i/ when mommy, daddy, and baby all 
have vocal tracts of different lengths and therefore vowel formants at different 
frequencies? Yet Kuhl (1983) established that infants learn that mommy’s and 
daddy’s vowel sounds are equivalent in the first year of life! 
The first part of the answer is to be found in a classic study by Peterson 
and Barney (1952). They asked seventy-six men, women, and children to record 
the vowels [hid], [h
I
d], [h
I
d], etc., and spectrographically measured their 
formant values. The results are presented in figure 7.9. 
The various English vowels clustered along axes in a 2–space defined by F
1 
and F
2
, joined at the origin. Within each phoneme, male vowels were located 
toward the low-frequency pole of the cluster while children’s vowels were located 
toward the high-frequency pole, with female vowels in between. Rauschecker 
et al. (1995) found just such an array in A
2
 of rhesus monkey cortex: two tonotopic 
maps, joined at the origin. Many monkeys have two types of calls which can be 
broadly classed as /i/- and /u/-calls, and these calls raise the same basic prob­
lem as human vowels. To determine if the call is the call of mommy, daddy, or 
child, the calls must somehow be perceptually normalized. The hypothesis that 
these two rhesus maps project to an A
3
 like the Peterson and Barney vowel chart 
has not been tested, but logically such a process must intervene between audi­
tion and final phoneme perception in the human case. Positing such a normal­
ization mechanism in A
2
, I omitted A
2
 from figures 7.5–7.8. 
Tonotopic Organization 
Having now discussed the dynamics of polypoles at some length, we can re­
turn to the question first raised in chapter 5 in connection with the topographic 
organization of striate cortex. The existence of retinotopic organization in 
vision and tonotopic organization in audition lends itself naturally to the theory 
that the brain is genetically preprogrammed in exquisite detail. But as noted 
in chapter 5, with some 10
8
 rod cells in the retina alone and only 10
5
 genes in 
the entire genome, this interpretation had to be less than half the answer. Most 
of the answer apparently has to do with the on-center off-surround anatomy 
of the afferent visual and auditory pathways. To illustrate how polypoles en­

SPEECH  PERCEPTION 
•  121 
Figure 7.9. 
English vowels of male, female, and child speakers. (Peterson and 
Barney 1953. Reprinted by permission of the American Institute of Physics.) 
force tonotopic organization, figure 7.10 follows a “F#” afferent from the coch­
lear keyboard to the cochlear nucleus. 
In figure 7.10, four axon collaterals leave the cochlea, C, encoding the 
frequency F #. At the cochlear nucleus (CN), three arrive at a common site 
(F #
CN
), but one goes astray to G. As F# is experienced repeatedly, long-term 
memory traces in the F#-F# pathway will develop, and at CN, F # will inhibit G. 
By equation 5.2, the long-term memory trace from F #
C
 to G
CN
 will not develop. 
With experience, the tonotopic resolution of C-CN pathways will become con-
trast-enhanced and sharpened. 
In this chapter, we have seen how dipoles and polypoles can account for the 
phonemic perception of voice onset time, the phonemic categorization of 
vowels, feature completion, phonemic interference, tonotopic organization, 
and vowel normalization. These are all low-level features of speech and audi­

122  • 
HOW  THE  BRAIN  EVOLVED  LANGUAGE 
Figure 7.10. 
Tonotopic organization enforced by polypoles. 
tion, and for the most part, they find analogues in the more widely studied 
visual system. The fact that these auditory cases are rather simpler than com­
parable cases in the visual system makes them a better starting point for under­
standing the essentials of cognitive organization. In the next chapter, however, 
audition finds its own complexity in the fact that speech is a serial behavior— 
indeed, the most complex serial behavior known. 

• 
E 
I 
G 
H 
T
• 
One, Two, Three
Pooh and Piglet were lost. “How many pebbles are in the sock?” 
Pooh asked. 
“One,” Piglet said. 
“Are you sure?” Pooh said. “You’d better count again, 
carefully.” 
Piglet counted very slowly. 
“One.” 
A. A. Milne
One, two, three, four, five, six, seven, eight, nine, ten. This seems to form a 
simple and perfectly natural sequence. And since the microscope had revealed 
that one neuron connected to the next, behaviorists were quick to fasten on 
the notion that these neural connections formed “stimulus-response chains.” 
In such a chained sequence, the neuron for one could be thought to stimulate 
the neuron for two, which stimulated the neuron for three, and so on, like the 
crayfish tail in figure 2.6. 
Although generative philosophy seemed to reject behaviorism after Chomsky’s 
review of Skinner’s Verbal Behavior, it did not reject behaviorism’s belief that 
the brain is a serial processor. In a serial computer program, one machine in­
struction follows another. Generative philosophy and artificial-intelligence 
theory merely replaced the notion that one mental stimulus follows another 
with the notion that one mental instruction follows another. Like behaviorism, 
this serial theory yielded superficially satisfying initial results, but the effort 
ultimately failed to solve many of the same cognitive and linguistic problems 
that behaviorism had failed to solve. 
Bowed Serial Learning 
In the first place, serial theories could not account for the fact that children, 
when learning to count to ten, go through a stage in which they count one, two, 
123 

124  • 
HOW  THE  BRAIN  EVOLVED  LANGUAGE 
three, eight, nine, ten. Explanations invoking the child’s “limited attention span” 
or “limited memory span” do not come to the crux of the matter. Such expla­
nations just mask the behaviorist assumption that serial processing must un­
derlie serial performance. The middle of the list gets lost. If stimulus-response 
chains were really responsible for such serial learning, one would expect the 
end to be forgotten. Why is it that the end is remembered? 
Nor is learning to count an isolated case. Difficulty with the middles of lists 
appears ubiquitously in the experimental psychology literature under the 
rubric of the “bowed learning curve” (figure 8.1, see Crowder 1970 for a para­
digmatic example). The bowed learning curve describes a pattern of results in 
which items at the beginning of a list and at the end of a list are remembered 
better (or learned faster) than items in the middle. But why? To understand 
the bowed learning curve, consider figure 8.2, which illustrates how a competi­
tive, parallel anatomy learns to count to three. 
z
In figure 8.2, we look more closely at how x
j
, a node in a parallel, on-center 
off-surround cerebral anatomy, can learn to count to three. That is, x
j
 must 
somehow faithfully remember the order of the three x
i
 motor patterns x
1
, x
2
, 
and x
3
, which correspond to the English words one, two, and three. In an ART 
anatomy, x
j
 must remember this at its three long-term memory (LTM) sites, 
j1
, z
j2
, and z
j3
. 
Recall now that any z
ji
 can grow only when both sites x
i
 and x
j
 are “on” (see 
table 5.1 and equation 5.2). Then, at time t = 1, x
1
 is active, and z
j1
 grows.
1
 At t = 
2, x
2
 will be activated, but it will be inhibited by the persistent, lateral inhibitory 
surround of x
1
. At t = 3, x
3
 will be activated, but it will be inhibited by both x
1
 and 
x
2
, so the trace z
j3
 cannot grow as rapidly as z
j2
, much less z
j1
. In time, with re­
peated rehearsal, the gradient of LTM strengths in figure 8.2 will become z
j1
 > z
j2 
> z
j3
, and x
j
 will remember the serial order one, two, three. Thereafter, activation of 
x
j
 will cause the remembered serial pattern to be “read out” across x
1–3
: x
1
 will be 
gated by the largest LTM trace, z
j1
, so it will receive the largest signal from x
j
. 
The first motor control site to reach threshold will therefore be x
1
 and the sys-
Figure 8.1. 
Bowed learning curve. 

ONE
, 
TWO
, 
THREE 
•  125 
Figure 8.2. 
Learning to count to three. 
tem will perform the word one. After x
1
 is produced, x
2
, gated by the next-largest 
LTM trace, z
j2
, will be the next site to reach threshold, and it will perform two. 
Finally, x
3
 will perform three. In this manner, the serial behavior one, two, three is 
learned and performed by a parallel, cerebral architecture. 
x
There are, however, problems and limits to this simple parallel architec­
ture. Figure 8.3 depicts the first such problem, which is encountered when 
learning the end of a list. In figure 8.3, when nine is learned, x
9
 inhibits the 
next item at x
10
. But inhibition from x
9
 also works backward, inhibiting x
8
! When 
10
 is learned, it will likewise inhibit x
9
 and x
8
. But if x
10
 is the last element of 
the list, there will be no x
11
 or x
12
 to inhibit it! Accordingly, an x
8
 < x
9
 < x
10
 short-
term memory (STM) activity gradient will develop. With time, this will trans­
late into a z
j8
 < z
j 9
 < z
j10
 LTM gradient. 
This kind of backward learning defied explanation under serial theories, 
but ART still has some explaining to do, too. Otherwise, it would imply that 
children learn to count backward when they learn to count forward! Before 
addressing this problem, let us see how the LTM gradients solve the problem 
of the lost middle. 
z
If we combine figures 8.2 and 8.3 in figure 8.4, the LTM gradient z
j1
 > z
j2
 > 
j3
 creates a “primacy effect” whereby earlier elements of a list are learned bet­
ter and faster. At the same time, the LTM gradient z
j10
 > z
j9
 > z
j 8
 creates a “recency 
effect” whereby later elements of a list are learned better and faster. The middle 
of the list is inhibited by both of these effects. That is why it is learned worst 
and last. 
So why don’t children learn to count to ten in the fashion of one, two, three, 
ten, nine, eight? In order to completely account for serial learning, we must first 

126  • 
HOW  THE  BRAIN  EVOLVED  LANGUAGE 
Figure 8.3. 
Learning eight, nine, ten. 
differentiate between short and long lists. Short lists like one, two, three can hardly 
be said to have a middle. They tend to exhibit primacy effects and are not prone 
to bowing and recency effects. These latter effects only begin to appear in 
longer lists.
2
 To achieve a reliable performance, the child must “chunk” this 
long list into several shorter sublists, each organized by the primacy effect, for 
example, (one two three four) (five six seven) (eight nine ten). 
Figure 8.4. 
Primacy and recency effects produce bowing. 

ONE
, 
TWO
, 
THREE 
•  127 
Unitization 
In a famous paper, “The Magic Number Seven,” George Miller (1956) reviewed 
the serial-learning literature and concluded that seven, “plus or minus two,” 
was an apparent limit on the length of lists which could be learned. Miller ar­
gued that any longer list would normally be “chunked” and memorized as a 
list of several smaller sublists. Note, however, that even lists of length seven, 
like U.S. phone numbers, tend to be broken into smaller chunks of three or 
four items. 
From figures 8.2–8.4, we can observe that serial bowing depends largely 
upon the extent of the inhibitory surround. For example, if the radius of inhi­
bition in figure 8.4 were only two nodes, then the absence of x
11
 would cause a 
recency effect to appear at x
9
. If, however, the inhibitory surround extended 
three nodes left and right, the absence of x
11
 would create a recency effect at 
x
8
. Accordingly, we may take the extent of inhibitory axons to provide a physi­
ological basis for the “magic number.” A typical, inhibitory, cortical basket cell 
axon collateral might have a radial extent of 0.5 mm and synapse with some 
300 target neurons (Douglas and Martin 1990).
3
 Along a single polypole ra­
dius within 5 degrees of arc, a single collateral would therefore synapse with 
about 4 target neurons, making four items a reasonable biological upper limit 
on the transient memory span. We therefore take the magic number to be more 
on the order of “four, plus or minus two.” Following Grossberg, we will call this 
the transient, or immediate, memory span, and we will refer to the “chunking” 
process as unitization. 
Perseveration 
The preceding discussion explains how serial behavior like one, two, three can 
be learned by a parallel brain, but it raises yet another critical question. Since 
one is performed first because it dominates two and succeeding items, why 
doesn’t one tyrannically maintain that domination? Why doesn’t the anatomy 
perseverate and count one one one one one . . . ? In fact, this is very nearly what 
happens when one stutters, but why don’t we stutter all the time? 
Following Cohen and Grossberg (1986), we can solve this problem by sim­
ply attaching an inhibitory feedback loop to each node in figure 8.4, as in fig­
ure 8.5. Now, when one completes its performance, it inhibits itself, thereby 
allowing two to take the stage. 
This is a simple solution to the stuttering problem, but it is not without its 
own complications. The inhibitory feedback loops in figure 8.5 are “suicide 
loops.” If they inhibit the x
i
 sites as soon as the x
i
 are stimulated, then no learn­
ing could ever occur! Each x
i
 would also immediately cease inhibiting its neigh­
bors, and no serial order gradient could be learned either! 
Grossberg (1986) suggested that an (inhibitory) “rehearsal wave” could 
turn these suicide loops off while the system was learning. One would thereby 
be allowed to perseverate during learning and so inhibit two, three, etc., long 

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HOW  THE  BRAIN  EVOLVED  LANGUAGE