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Neural Substrates for String-Context
Mutual Segmentation: A Path
to Human Language

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Kazuo Okanoya and Bjorn Merker

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22.1. Introduction

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Linguistic structures are products of biological prerequisites and historical
processes. Here we consider a number of neural, behavioral, and learning mechanisms that serve necessary or facilitating roles in the initiation of historical
processes. We hypothesize that if mutual segmentation of strings and contexts
is promoted by particular biological adaptations and ecological pressures, this
could initiate a subsequent historical process of linguistic elaboration. To enable
this mutual segmentation, three biological sub-faculties are indispensable: vocal
learning, string segmentation, and contextual segmentation. Vocal learning
enabled intentional control of vocal output via the direct connection between facemotor cortex and medullary vocal nuclei. String segmentation became possible
by bottom-up statistical learning by basal ganglia and top-down rule extraction
by the prefrontal cortex. Contextual segmentation was implemented also by
bottom-up induction of situational correlations at hippocampal and related limbic
structures and top-down segmentation of perceived states by the prefrontal cortex.
Taken together, we propose that string-context mutual segmentation got its
start through the interaction of the prefrontal-hippocampal and prefrontal-striatal
parallel loops.

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22.1.1. String-Context Mutual Segmentation
In the previous chapter we presented a hypothesis (first suggested by Darwin)
for the possible origin of human language. Briefly, we assume our ancestor
was a singing ape. Song display was an honest indicator of the singer’s fitness.
Through the handicap principle, song displays became complex and lengthy(Zahavi & Zahavi, 1996) and included many vocal tokens that were shared withthe group members through imitation. Song was initially used for attracting
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mates and repelling rivals, but gradually different variations of songs were usedin different behavioral context. Since songs were learned and song elements
were shared within society, a fixed sub-part of these songs appeared in several
situation-specific songs. These sub-strings began to be associated with the subcommonality in the situations within which songs were sung. As processes wererepeated and transmitted through generations, linguistic structures would emerge
as historical consequences (Figure 22.1).
We divided the requirements for linguistic structures into biological prerequisites and the historical process and then defined what are required as biologicalpreparations to initiate the historical process of linguistic elaboration. The
historical process may simply be stated as the process of string-context mutual
segmentation: song strings and behavioral contexts are mutually segmented
during social interactions. Over generations, this would lead to the emergenceof linguistic structure. The natural history up until the historical process was
initiated has been described in detail in the previous chapter. The aim of thepresent chapter is thus to provide neural substrates for the biological prerequisites
required to start the process of mutual segmentation.

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22.1.2. Sub-Faculties for Mutual Segmentation
Sub-faculties we consider here are vocal learning, string segmentation, andcontext segmentation (Figure 22.2). Vocal learning is strictly defined as apostnatal acquisition of a novel motor pattern for a novel vocal performance(Jarvis, 2004). A mere modification of innate vocal patterns, which is often
seen in vocal animals, should be referred to as vocal plasticity rather than vocal
learning. Vocal learning should not be confused with auditory learning that refers
to an acquisition of the association between a specific auditory stimulus anda specific behavior: auditory learning is a form of general associative learningwith stimuli being auditory, but vocal learning is a specific imitative learningthat requires dense vocal-auditory interactions.

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String A .acghjkdefkpmtkditmzxw

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Context A

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Context B

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String B .pswtrdef kzcxmpx

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Figure 22.1. The string-context mutual segmentation hypothesis.

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Linguistic Structures

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Historical
Processes

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Vocal Learning

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Figure 22.2. Biological preparations for mutual segmentation.

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String segmentation is an ability to divide a continuous stream of sensory
stimuli into smaller functional units. Strings can be mediated by any sensory
modalities including visual, auditory, olfactory and tactile, but we specificallydeal with auditory segmentation in this chapter, since the primary mode of bothsong and language is auditory, although language could be expressed in otherdomains. Auditory segmentation could possibly be based on several cues suchas pauses between auditory tokens, sudden discontinuities or transitions in pitch
or intensity continua, and probabilistic relations between auditory tokens. In first
language acquisition, infants can use any of these cues to segment words fromthe continuous speech stream (Gomez & Gerken, 2000). This is a first step
towards acquiring the syntax.
Context segmentation is an ability to divide a continuous contextual/cognitive
space into smaller (functional) units. Contextual space here is loosely defined asa multivariate space whose dimensions might include sensory modalities, internal
states including memory, and reinforcement contingencies. Animals explicitly
or implicitly can learn and discriminate complex context and learn to selectadaptive behavior accordingly. Undoubtedly, this form of learning is widespreadamong vertebrates. This is the very first step in acquiring semantics.

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22.2. Vocal Learning

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22.2.1. Species with Vocal Learning
Although auditory learning is widespread among animals possessing an auditory
system, vocal learning is scarce. Among primates, we humans are the only

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species that show vocal learning. There is evidence of vocal plasticity in
non-human primates (Hihara et al., 2003), but these exemplify the degree ofplasticity that cannot be classified as an acquisition of novel vocal patterns(Janik and Slater, 1997). In mammals, only three phylogenic lines of animals
show vocal learning. These are primates (only humans), bats (Boughman, 1998),
and cetaceans (Payne, 2000). Coincidently, also in birds, only three classes ofanimals show vocal learning; passerines (songbirds), psittacines (parrots), andswifts (humming birds) (Jarvis, 2004). It is important to note that these classesof animals are only distantly related when considering evolutionary origins of
vocal learning.
This observation provides three possibilities as to the evolution of vocal
learning. First, vocal learning independently evolved in these animals. Thus,
neural substrates for vocal learning are different among animals with vocal
learning. Second, vocal learning independently disappeared from many classesof animals and vocal learning was actually widespread in the past. In this case,vocal learning was present in a common ancestor of birds and mammals andwe should consider examining some existing reptiles for vocal learning. Also,in this case, neural substrates utilized for vocal learning should be common
among mammals and birds. This is unlikely considering the vast divergence of
telencephalic architecture between mammals and birds.
Third, and most likely, vocal learning evolved independently, but out ofcommon plans of brain circuitry under similar selection pressures. A general planfor intentional motor execution and a general plan for sensory-motor interface
were probably precursors for vocal learning. In fact, Jarvis (2000) observed
similar topography and connectivity as well as the pattern of gene expression in
the forebrain nuclei responsible for vocal learning in three lines of avian vocal
learners. If vocal learning evolved independently out of common sensory-motor
systems, under common selection pressures, we need to consider the neuralsubstrates and pre-adaptations for vocal learning.

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22.2.2. Neural Substrates for Vocal Learning

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Are there any specific anatomical substrates that correlate with the facultyof vocal learning? One candidate substrate would be the direct corticalmedullary pathway for articulation and breathing. In humans, a part of the
motor cortex directly projects to the nucleus retro-ambiguous of the medulla(Kuypers, 1958). This projection is absent in the squirrel monkey and chimpanzee
and Jurgens (2002) thus assumes that this projection exists only in humans
among primates. Similarly, there is the same direct cortical-medullary pathway
for articulation and breathing in the zebra finch, a species of songbirds, but
a similar projection in pigeons does not exist and all of pigeon vocalizations
are innate (Wild, 1993; Wild et al., 1997). Considering this evidence, we canhypothesize that this projection exists in those species that show vocal learning
while it is absent in the species without vocal learning (Okanoya et al., 2004).

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While this projection exists only in a limited number of species, there is still
a possibility that a very faint projection of this kind is nevertheless present
in most species. Deacon (1997) introduced the anecdotal story of an orphaned
harbor seal that learned to mimic the speech of the fisherman who raised theseal in his home. This animal had a brain inflammation when young and Deaconsuggested that during the process of recovery the cortical medullar projection
might have been reinforced in this particular animal. If so, the possibility arisesthat animals trained to perform spontaneous vocalizations while young might
have this pathway reinforced, thus inducing vocal learning in a species thatwould not ordinarily possess this capacity.

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22.2.3. Pre-Adaptations for Vocal Learning
Among vocal learners, birds, bats, and whales seem to have reasonable preadaptation for vocal learning. Birds and bats have to control their breathing
while they are flying and this requirement was probably the pre-adaptation for
intentional vocal control. Similarly, whales need to control breathing while they
are submerged and this led to intentional vocal control (Janik & Slater, 2000).
The issue of the origin of vocal learning in humans is a major unsolved issue,
as noted in our previous chapter (this volume). Here too, intentional control ofrespiration may have played a significant role, and that in the context of thespecial circumstances surrounding the immaturity of the human neonate and its
need for parental care (Okanoya et al., 2002).
Human babies are conspicuous among primates in that they emit highintensity, long-lasting cries right after birth. Such cries are obviously maladaptive
in wild animals in that it could easily attract predators. We suggest therefore
that the infant cry must be a behavior obtained after ancestral humans acquiredsocial and cultural skills to protect themselves from predation pressure. The firstcry after the birth has a function to eliminate amniotic fluid, but crying continues
long after that. Likewise, nidicolous species of birds emit very loud begging calls
and isolation calls that recruit parental behavior including feeding and protectionof the hatchlings. Begging calls are so loud that they easily can attract predators.
Therefore, parents have to engage in strong parental behavior to avoid predationof hatchlings and themselves (cf. Trivers, 1974). Chicks can thus manipulate
parents because they acquire cortical control of breathing to adapt for flying. In
this regard, the fact that humans and nidicolous songbirds share helplessness ininfancy may also be related to this pre-adaptation (Alexander, 1990).
We analyzed syntactical and phonological developmental changes in the baby
cry to show how the pattern becomes complex as the baby grows (Okanoya
et al., 2002). At least three stages of cry development could be identified; eachmay be associated with respective anatomical changes. Baby cries begin as aregular repetition of a stereotyped vocal unit. At this stage, the cry probably iscontrolled by the midbrain vocal center only. This type of vocalization continues
for two post-natal weeks after which cries begin to be more irregular, showing
variable patterns of phonology and rhythmic patterns. At this stage, limbic

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influences are presumably gradually growing stronger. Mothers often can identify
what babies want based on the pattern of their cries. During the interaction withmothers, the cry probably became more adaptive by allowing cortical control
so that mothers can be more precisely governed by the baby’s crying. Infantcries thus may function to train cortical-midbrain connections necessary for more
intentional vocal output that eventually results in speech competence.

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22.3. String Segmentation

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22.3.1. Behavioral Evidence for String Segmentation
It is essential for our sensory systems to segment continuous streams of stimuli
into functional units. Especially, the very beginning of language acquisition
involves segmentation of words out of a continuous speech stream. Among
several cues available for string segmentation, let us examine the statistical cue.
Learning of sequence statistics or “statistical learning” has attracted a great dealof attention in recent years (Gomez & Gerken, 2000). When a nonsense auditory
stream consisting of 6 tri-syllable “words” were continuously presented, thetransitional probability within a “word” was always 1 but that between “words”
was 1/6. Without knowing each word and without knowing the structure of
the stimulus, we only hear a nonsense stream of syllables but gradually begin
to appreciate word boundaries because of statistical learning. When asked thefamiliarity of the stimulus “word” that was presented in the task versus a “pseudoword” that was never presented, we can usually identify which was more familiar.
As early as at eight months after birth, babies can detect transitional probabilities between on-going speech syllables and use those statistics to find wordboundaries, as shown by a habituation paradigm (Saffran et al., 1996). The
same “word boundary” detection was possible with non-linguistic stimuli of puretones (Saffran et al., 1999). Similar procedures were used to examine statisticallearning in a New World monkey, the cotton-top Tamarin (Hauser et al., 2001)
showing that this ability is not specific to humans. These were taken as evidence
that word segmentation learning may not require any special language device
but rather is made possible by a domain general ability of statistical learning(Bates & Elman, 1996).

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22.3.2. String Segmentation in Birdsong
Songbirds learn courtship songs from adult males (reviewed in Catchpole &Slater, 1995; Zeigler & Marler, 2004). Their songs consist of temporally isolatedsong elements and their elements are arranged in a fixed order or in accordancewith a specific syntactical rule. Most of these song elements are learned from theirfathers and conspecific neighbors. When song learning occurs in a multi-tutor
environment, learners often splice parts of songs that are sung by different males
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element types, or at the point of interruption (Williams & Staples, 1992). In a
species with more complex song syntax, birds also use statistical informationin conspecific songs: chunking of song notes often occurs at junctions of lower
transition probability (Takahasi & Okanoya, unpublished data). Thus, string
segmentation occurs in songbirds during song learning and song production.
When perceiving speech streams, a phrase structure is processed as aperceptual unit in humans. When a sentence like “I love you” is presented into
one ear and a click is presented into the other in a temporal position that overlaps
with the word “love”, the perceptual position of the click is likely to shift in
between “I” and “love.” That is, the perceptual position of an embedded click
moves outside of the phrase structure (Fodor & Bever, 1965). We examined
whether a similar phenomenon could be observed in songbirds. We trained
Bengalese finches, a species of songbirds with complex song syntax, in a clickdetection experiment. Birds were trained to peck a key when they heard a click
in an operant conditioning task with food reinforcement. When a backgroundof his own song was played in the test box, the reaction time of the subject
bird to detect the click was longer than without the background song or withthe background song played in reverse: detection of clicks is postponed until
a chunk of song notes is processed. Thus, chunks of birdsong, like chunks oflinguistic elements, are processed as a cognitive unit (Suge & Okanoya, underrevision).
These data suggest that one essential prerequisite for the type of historicalstructural transformation dealt with in our previous chapter – namely the capacity
for segmentation (cf. Hurford, 2000) – is already present in the vocal learningof some singing species. The capacity for segmentation might also lead to thehierarchical nature of songs in some species (Okanoya, 2004).

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22.3.3. Neural Substrates for String Segmentation
If statistical learning is possible with domain-general brain mechanisms, language
areas in the brain may not be required for such processes. However, brain
imaging studies on learning of sequential rules in music have shown the contrary:
Broca’s area was active during such tasks (Maess et al., 2001; Patel, 2003).
More directly, we have used a similar statistical stimulus and the stimulus withthe same token but without statistical rules to compare brain activation by nearinfrared optic spectroscopy and found that Broca’s area was active only during
statistical stimulation (Abla & Okanoya, in preparation). Thus, statistical learning
probably utilizes Broca’s areas but it is also possible without such specialized
areas as shown in the new world monkey, tamarins. Perhaps, results of statisticallearning is expressed in Broca’s area in humans, but computation of statisticalinformation could be processed by some other systems as well.
Here again, results from birdsongs may be suggestive (Okanoya, 2004).
Most neural substrates for song learning, song maintenance, and song performance have been identified in the bird brain (Zeigler & Marler, 2004). Briefly,
birdsong is controlled by two major forebrain pathways: anterior pathway andBook_Lyon et al_1846284910_Proof2_October 6, 2006

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posterior pathway. Auditory information is conveyed up to a part of the forebrain
equivalent to the primary auditory cortex, Field L (a part of the primary
auditory cortex). From there, processing for more specific information occurs in
NCM (nidopallium code-laterale, a part of the secondary auditory cortex) andcHV (caudal Hyperpallium ventrale, a part of the secondary auditory cortex).
Programming for song motor patterns occurs in a region of the face motor cortex
equivalent, the RA (robustus archipallium, a part of the primary motor cortex),
which directly projects to the medullary respiratory and motor centers, or via the
midbrain vocal area, the DM (dorsal medial nucleus of the midbrain), which isequivalent to the periaquadactal gray in mammals. The auditory information is
fed into NIf (nucleus interfacialis, a part of the association cortex) and then to
HVC (hyperpallium ventrale pars caudalis, a part of the association cortex). HVC
has direct projection to RA and also to Area X, which is a part of basal ganglia.
Area X then projects to a thalamic nucleus DLM and from there, projection
comes back to a telencephalic nucleus LMAN (lateral magnocellular nucleus of
the nidopallium, a part of the sensory/motor cortex). LMAN then projects toRA, completing the indirect connection between HVC and RA. The HVC-RAmedullar connection is called the posterior motor pathway and the HVC-Area
X-DLM-LMAN-RA connection is called the anterior pathway.
Thus, when an analogy between human language and birdsong is considered,
we can propose the followings: NIf may be comparable with Wernicke’s areaand HVC may be comparable with Broca’s area, because NIf receives higher
auditory information and sends to HVC that controls the face-motor cortex
equivalent, the RA. Also, LMAN may be comparable with the anterior cingulate
cortex, because LMAN receives projection from the basal ganglia and also sends
projection to RA, the motor cortex equivalent (Figure 22.3).

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Human Brain

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Auditory system

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Figure 22.3. Analogy for string segmentation by human brain and by bird brain.

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In an ERP study with human subjects, we found that string segmentation
was associated with a negative brain potential that was strongest at an electrodeplaced near the anterior cingulate cortex (Abla et al., in preparation). When asource estimation algorithm was applied on the same data recorded from 32
channels, the basal ganglia was the source of the signal. With the data from thenear infrared spectroscopy stated earlier, these data suggest that the prefrontal
cortex – basal ganglia loop maybe responsible for statistical segmentation of thecontinuous auditory stream. The same loop may be responsible for controllingcomplex serial behavior involving string segmentation of a continuous stream,such as piano playing or type writing (Parsons et al., 2005).
The same loop is also known to be used in birdsong learning and maintenance.
When LMAN was lesioned in juvenile zebra finches, their songs became stereotyped without further elaboration (Bottjer et al., 1984). On the other hand, when
Area X was lesioned in adult Bengalese finches, segmental structure of the song
was impaired by causing stuttering (Kobayashi et al., 2001). Furthermore, whenpharmacological manipulation was made to suppress neural activities in LMAN,
variations in juvenile song disappeared but it recovered as the drug effect waned(Ölveczky et al., 2005). Thus, segmentation of birdsong strings may also be
governed by the cortex-basal ganglia pathway.
Both HVC in songbirds and Broca’s area in humans are song- or languagespecific organs in the sense that these could not be identified in the brainsof related species (i.e., non-songbirds or non-human primates) without vocal
learning. In both humans and birds, these structures appeared on the motorside of the sensory-motor junction in the forebrain. These structures may be
a specialized form of prefrontal structures that promote statistical learning andrule learning. In this regard, it may be interesting to examine the efficiencyof statistical learning in animals with and without these specialized structures.
The controversy between the nativist and the empiricist could partially reside
in how the modularity and domain specificity of these structures are seen. Are
they structures specially evolved for that specific purpose or are they simply a
specialization of existing structures? This is an essential question to be solved
by comparative neuroethological investigations in birds and humans.

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22.4. Context Segmentation

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We routinely segment behavioral context based on multiple environmental andinternal cues and this would be a rudimentary form of semantics. A somewhat
simpler example is spatial navigation. The idea that the hippocampus may
function as a spatial map was first presented by O’Keefe and Dostrovsky in 1971
with some experimental evidence. When a rat navigates a novel environment
freely, a particular neuron in the hippocampus fires when the rat is exploiting aparticular part of the environment. This finding was later expanded to include

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modeling research. In general, it was shown that with Hebbian learning and lateral
inhibition, a network of suitably interconnected neurons begins to fire when aspecific class of inputs is present. Such a network can establish attractor dynamics
and several attractors can specify specific locations of the environment. Not
only that, since the hippocampus receives sensory, emotional, and reinforcement
information from different brain areas, this structure is suitable for segmenting
a multi-dimensional behavioral context (Gluck & Myers, 1993).
In fact, a recent experiment by O’Keefe’s group showed that these cells not
only respond to specific locations, but also begin to behave as attractors for
specific environmental shapes (Wills et al., 2005). This is in good agreementwith our current hypothesis that the hippocampus could function as a bottomup categorizer for behavioral context in general. In common with many otherlearning networks, such a categorizing network will show the capacity for generalization (Ghirlanda & Enquist, 2003), supplying the other essential prerequisite
for the type of historical structural transformation dealt with in our previous
chapter (cf. Hurford, 2000).
While the hippocampus functions as a bottom-up categorizer, top-down effects
are probably governed by the prefrontal cortex. While the learning rate of
the neocortex is slow, the hippocampus learns rapidly based on concurrent,
local, and time-limited information (O’Reilly & Rudy, 2000; O’Reilly &Norman, 2002). Thus, the hippocampus can segment contextual parameters
quickly, and the result of segmentation is tested by statistical or rule-based
prediction by the prefrontal cortex. This is similar to the case of string segmentation in which basal ganglia give rise to short term statistics of on-going stimuli,
the prefrontal cortex is more likely to function at a slower rate with a longer
time constant.

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22.5. Putting them Together

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22.5.1. Mutual Categorization of Vocalizations
and Behavioral Contexts: A Rudimentary Step

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It is not easy to show the process of string-context mutual segmentation experimentally since it is a historical process. But even in the short run, similar
processes might occur. Precedents for a rudimentary form of acquired “naming”
achieved through association between behavioral contexts and particular behavioral tokens exists in several animals. When this process is enhanced by theability to learn new behavioral tokens and combine them freely, that is, forexample, by vocal learning, we are almost at the stage to begin the string-context
mutual segmentation.
We observed that when trained to use a rake to retrieve distant food, monkeys
spontaneously began to vocalize “coo” calls. They did so especially when the
preparation of the rake tool by the experimenter was delayed (Hihara et al., 2003).

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To further investigate this phenomenon, we systematically manipulated behavioral contexts by presenting the tool or food whenever the monkey made a
vocalization irrespective of the type of call emitted. In one experimental situation,
the experimenter placed a food morsel at a distance when the monkey produceda coo call (Call occasion A). By the second coo call (Call occasion B) the experimenter gave a rake tool to the monkey. The monkey could retrieve the food by
the rake. In another, the experimenter gave the rake to the monkey beforehand.
A piece of food was placed at a distance when the monkey vocalized a coo call
(Call occasion C). Again, no attempt was made to differentiate the calls by typein these situations (A, B, and C). After 5 sessions of trainings, the monkeys
eventually used acoustically distinct types of calls when they asked for the tool
(Call B) or food (Call A and C). The calls used to ask for the tool was longer
and higher pitched than the ones used to ask for the food. Calls had becomecorrelated with context without being differentially reinforced to this end. We
note the relevance of this finding to the process of segregation of song-stringsby context discussed in our previous chapter.
As a possible explanation for this finding we suggest that the different rewardconditions (food or tool) set up different emotional contexts for the monkeys.
Different emotional contexts, in turn, affected the production of coo calls differently for the tool or food situations. Since the tool training can be assumed
to activate the neocortex very highly, the calls were associated with different
behavioral contexts. Thus, the calls became categorized and emotionally differentiated calls gradually became categorical vocalizations. Through this process,we suspect the emotional coo calls changed into categorical labels denoting the
behavioral situation. We speculate that this categorization of vocal tokens may
be related to highly specified behavioral situations. Such specified behavioral
situations would evoke specific emotional content in the limbic system including
amygdala and hippocampus. States of excitation in the limbic system may thus
be labeled by the emotional vocalizations associated with the situations.
Naturally, our macaque example is limited to a contextual association between
call types and context, but the principle is no less applicable to the associationof contexts with full songstrings produced by a singing animal, as discussed in
the previous chapter.

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22.6. Conclusion

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Taking these speculations together, we propose that longer strings are segmented
by the action of the cortex-basal ganglia pathway and each part-string is represented by a mnemonic. In either case, the parallel, complex behavioral contexts
are segmented by the prefrontal- hippocampal loop and each part-context may
also be represented by a different mnemonic. These two sets of mnemonics caninteract together to represent both part-string and part-context. Alternatively, if
the process advanced slowly, the two sets of mnemonics may not be necessary
and string and context segmentation would proceed with one common mnemonic.

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Kazuo Okanoya and Bjorn Merker

01

Prefrontal Cortex

02
03
04
05

StringSegmentation

06

Context
Segmentation

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08
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10

Basal Ganglia

Hippocampus

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Local, short range prediction

14

Statistical, or rule-based prediction

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Figure 22.4. Neural substrates for string-context mutual segmentation

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In any case, the parallel operation of these two systems would then enable mutual
segmentation and matching of behavioral context and song strings, a processtantamount to an incipient language system (Merker & Okanoya, 2005).
In this regard, language may be possible without assuming a special“recursion” device suggested by Hauser, Chomsky and Fitch (2002). The
recursive function might arise secondarily from the interaction between theprefrontal-basal ganglia loop and the prefrontal-hippocampus loop (Figure 22.4);
that is, although the recursive function reflects the formal aspect of language, it
may actually be the outcome of syntax-semantics interaction. Once a system ofmutual segmentation is in place, the historical process itself may generate theremaining formal linguistic devices and structures. As shown by Kirby (2002)
even recursion can emerge from the historical process, given “predicates that
can take other predicates as arguments.”

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