BSc Physiological Sciences (Hons)
Design of Artificial Languages to Study Syntax in Language and
its Evolutionary Origins
Matthew Collison1,*, Chris Petkov1
1
Institute of Neuroscience, Henry Wellcome Building, Newcastle University, NE2 4HH UK.
Submitted 20th March 2009
ABSTRACT
In this study I designed and implemented a paradigm that can be
used to study how human and primate species perceive linguistic
structure from artificial languages. Furthermore, we used the artificial languages to evaluate how the comparison of tonal and nonsense words, as separate sensory channels, affect the perception of
structure in language. Here we looked at ‘how’ subjects were learning to discriminate the rules in artificial languages, whether it’s by
statistics or global rule-based patterns. I gathered behavioral data
on the ability of subjects to discriminate specific grammatical and
ungrammatical structures, showing that human adults were able to
learn both local structures and rule-based structures from tone languages. Furthermore I showed that there are two discrete mechanisms for discriminating different levels of structure, one identified as
being reliant on the statistical processing for local structures and the
other reliant on picking up the rules-based associations of the artificial language. Finally we link this functional processing difference to
evolutionary events which have lead to the current model of structural language perception and propose an optimised tone structure
paradigm for future comparative fMRI studies in humans and monkeys to localize the processing functions across the species and
determine the evolutionary conservation of these capabilities.
Key Words: Language evolution, syntax, local structure, rule-based
structure, transitional probabilities
1
INTRODUCTION
Through evolution humans have developed an advanced communication system. This project investigates how the homo Saipan
species differentiate from their primate ancestors and particularly
what neural systems formed the foundation of our advanced language capability?
I designed a series of artificial tone and non-sense word languages
based on the Saffran and Hauser ’08 (1)grammatical structure,
although using an improved more versatile testing procedure. By
using artificial languages as opposed to natural language allowed
me to closely control structural aspects of the language which
meant I was able to determine what cues to include or exclude
(words or tones and structural complexity) as a means of influencing how subjects learn to discriminate structures. Furthermore
*m.g.collison@ncl.ac.uk.
© Oxford University Press 2005
being aware of the transferability of these studies to be used in
both humans and monkeys will allow me to develop ideal stimuli
to isolate mechanisms across species and identify the unique components of the evolutionary advancement. The main goals of my
project: 1) design structurally identical tone and word languages in
which human subjects can identify structure in both 2) design a
paradigm that is shown to behaviourally isolate structural processes for discriminating statistical against rule-based structures 3) find
a solution the functional processes involved in perceiving statistics
and rule-based structures 4) develop a tonal paradigm that can be
used in future comparative fMRI studies across humans and monkeys that is optimised to isolate specific processes involved in
structural perception.
Recently there has been a lot of detailed literature released on the
three main fundamental processing functions involved in human
language perception(7-9). I have designed the artificial languages
with parameters so I can isolate each fundamental component and
compare them to one another to determine their position in a possible integrated system within language perception and processing.
Firstly, recognition of phonetic acoustical cues is a crucial aspect
to identifying the diverse auditory signals which make up the sub
units of language(9). This is thought to be sub served by auditory
cortex(10) and localized in areas around the superior temporal
sulcus (11), which are capable of identifying complex acoustics
involving speaker identification and speaker vocalization(11). In
humans, this ability is inherent as an uncommitted neural network
susceptible to a wide range of diverse phonetic inputs, until around
9 months when the individual will undergo a neural commitment to
a particular native phonetic language, which advances their ability
to further differentiate intricate phonology of that particular language(9). Monkeys also have a well established acoustic identification ‘voice’ region in the superior temporal lobe(6), although it
has not reached the same level of development as the human system. This may justify their inability to perceive and produce such
complex intricate sound patterns. Although in non-human primate
studies of structural processing in language it is still common place
to use human phonology as sub units of structure(1, 12). Here I
investigate the input of tones as opposed to words, an intermediate
that would remove the bias for human performance in structural
experiments and give monkeys a better chance of understanding
the structure. Furthermore it has been demonstrated that both hu-
1
M. Collison et al.
Figure1. Shows a collection of imaging studies that identify the different functional areas for language perception across A)
human brain(2-5) and B) macaque monkey brain, adapted from Petkov ’08. (2, 6)
mans and monkeys have an ability to learn links between simple
phonetic identities and differentiated sounds tailored to a particular
task(13).
Statistical learning of conjoining phonetics gives the human language its first level of complexity and an exponential level of diversity. Through reinforcement, humans learn the probabilities that
discrete phonetic identities will follow one another to form recognizable words or syllables, transitional probabilities (TPs). By
identifying low transitional probabilities it has been shown that
infants are capable of learning word boundaries, and therefore
segregating continuous speech into learnable phonetic sub units(8,
14). In human adults, this statistical ability has been neurally localized to the inferior frontal cortex(2, 15), with increased activation
during active learning of tri-tone word languages.
Recently both human and cotton-top tamarin monkeys and humans
have been shown to discriminate local transitions in linguistic
structures representing finite state grammar (FSG)(1, 12). Discriminating violations in structures by local transitions involves learning probability links between words of a structure. This process
has been passed off as transitional probabilities before but later in
this study we will test effect of lower level transitional probabilities as a supporting mechanism for local transitions as a grouped
TP mechanism.
The human language faculty is a complex function of structural
processing, spanning many levels of complexity(16-18). Bound in
recursive rule based hierarchical structures language incorporates
interplay of phonetic and semantic word associations to achieve an
infinite capacity of expressive power, syntax(19, 20). The rulebased associative function of language can be modelled in its simplest form as phrase state grammar (PSG)(3, 12). This structural
function is thought to be served by an elaborate network in a higher order processing region of the frontal brain possibly incorporat2
ing Broca’s area(3, 4, 7). Also this function of language is thought
to be evolutionarily unique to the human species as studies have
shown that monkeys are oblivious to this structural aspect (1,
12)whereas human infants and adults have shown a significant
ability to discriminate phrase structure grammar (PSG)(1, 3). We
have identified this aspect in the artificial languages by adding
multiple exemplars to each subclass within structures therefore
reducing the recognisable local transitions promoting learning of
rule-based structure.
Recent literature has shown humans interpreting statistical and
rule-based language structure(3, 19). Also studies have shown
cotton-top tamarin monkeys can learn statistical language structures but not rule-based language structures(1). Although these
studies are confounded because they use human vocalization which
the monkeys cannot easily distinguish due to their alternative voice
region.
I have consolidated the main localization studies, figure 1, corresponding to functional areas in language processing for humans
and monkeys. In this study I aim to design a paradigm that can be
used to isolate these functional areas through fMRI and make an
unbiased direct comparison between humans and monkeys processing ability.
By using the Saffran and Hauser ’08 structure I include recursive
and optional elements(1). I can represent finite state grammar
(FSG) using single exemplars in the structural subclasses, as this
allows local transitions to be easily computed. To represent phrase
state grammar (PSG) I used multiple exemplars per structural subclass, giving a wide range of variation. In this study I used human
adults, as I require well established phonetic and structural processing networks, also educated responses during the testing procedure when attention and multisensory input have been controlled
assuming the subject maintains concentration.
Design of Artificial Languages to Study Syntax in Language and its Evolutionary Origins
2
METHODS
Method for Experiment 1: Artificial Word Language
2.
3.
6 specific predictive and less precise language sentence structures
were designated for exposure during training. 4 specific sentence
structures were designated for testing, these were grammatical for
all languages. 4 ungrammatical testing sentences were also chosen
from preliminary data for testing. We included a 100 millisecond
break between words within each sentence and a 1 second pause
between sentences shown during the training programme.
In this experiment we expose subjects to local structural (FSG)
non-sense word languages. We are testing to see if the subjects can
learn precise structure over less precise structure. This will show
whether human adults can discriminate local phonetic based structure as shown in Saffran and Hauser ’08 after we have controlled
for weaknesses in their paradigm.
Participants
Six healthy undergraduate participants (aged 20-23) were divided
into three groups. Group1a was assigned to learn precise FSG nonsense word language 1, group1b were assigned to learn precise
FSG non-sense word language2 and group 2 were assigned to learn
the less precise FSG non-sense word language. All participants
were right handed, had normal hearing and no history of neurological disease. Written consent was given prior to every experiment
by each participant.
Table 1 A table to show the sentence structures during training
and testing.
Training
Predictive
ADCGFC
ACGFCG
ADCFC
ACFCG
ACFC
ADCF
Non-predictive
ACGFCG
ADCGFC
DCGFG
DCFCG
AGFC
ADGF
Material
5 non-sense words (from Saffran & Hauser ’08)(1), lasting 500ms
were classified, table 1. Each word was randomly assigned to a
word subclass. Language 1 was randomly assigned different words
to language 2, to later control for biases towards either language.
These subclasses formed the basis to the precise and non precise
grammatical rule structured sentences. The sentences were formed
from the structures, figure 1& figure 3, and therefore followed
three grammatical rules, figure 2 & figure4.
Figure 3 Rules of precise grammatical sentences;
1.
2.
3.
The basic structure must include A –C – F –
Subclass D must follow subclass A.
Subclass G must follow subclass C.
Figure 4.Structure of less precise grammatical sentences,
adapted from Saffran and Hauser ’08 {Saffran, 2008};
S
AP + BP + CP
AP
{(A) + ( D)}
BP
CP + F
CP
{(C) + (G)}
* (X) letters in brackets are optional elements
Testing
Grammatical
ADCFCG
ACGFC
ADCGF
ACGF
Ungrammatical
ADGCFC
ADFCG
AGFCD
AFCD
Table 2 A table to show the subclass word allocations within the
strucure used in language 1
Figure2. Structure of precise grammatical sentences, taken
from Saffran and Hauser ’08;
S
AP + BP + (CP)
AP
A + (D)
BP
CP + F
CP
C + (G)
* (X) letters in brackets are optional elements
If subclasses A and D are present D must follow A.
If subclasses C and G are present G must follow C.
Word classes used in Language 1
Subclass words used
A
biff
C
cav
D
klor
F
dupp
G
jux
Table 3 A table to show the subclass word allocations within the
structure used in language 2
Word classes used in Language 2
Subclass words used
A
klor
C
hep
D
pell
F
biff
G
pilk
Figure 5 Rules of the less precise grammatical structure;
1.
Basic structure must include subclasses A or D – C or G – F – .
3
M. Collison et al.
Participants were subjected to a familiarisation phase where they
were played the structural language specific training programme
in a sound attenuated room through headphones, whilst concentrating on a red dot in the centre of a computer screen. They were
told they will hear a series of sentences of non-sense words that
follow a simple grammatical structure and that they will be tested
on identifying patterns from this structure later but not to over
analyse the sentences. For 4 minutes 48 seconds subjects heard
12 randomized sets of the six training, language specific, grammatical sentences.
Testing procedure
Directly after the training programme subjects were given testing
information, telling them they will hear a series of non-sense
word sentences. Some of the sentences will be structured and
others will violate from the structure. After hearing each sentence
the subject had to make an educated decision as to whether the
sentence was structured or unstructured by selecting c or m on
the keyboard. During testing, in a sound attenuated room through
headphones, we played 10 randomisations of the 4 novel grammatical and 4 novel ungrammatical sentences to the subjects.
After hearing each stimulus the subject gave a response, if correct
we recorded a ‘hit’, if the subject was incorrect a ‘miss’. We varied
the associations of c and m to avoid tendencies towards particular
hands. It was important the testing stimuli were novel to remove
the familiarisation and memory confound, this forced the subject
to make decisions based on rule based grammatical identity not
memory or familiarisation to exact word sequences.
Method for Experiment 2: Artificial Tone Language
In this experiment we expose subjects to local structured tone
languages. We are testing to see if subjects can learn the precise
structure over the less precise structure. This will show whether
human adults are capable of learning local structure as a function
of tones compared to words shown in experiment 1.
Participants
Six healthy undergraduate participants (aged 20-23) were divided
into three groups. Groups 1a was assigned to learn predictive FSG
tonal language 1, group 1b were assigned to learn predictive FSG
tonal language2 and group 2 were assigned to learn the nonpredictive FSG tonal language. All participants were right handed,
had normal hearing and no history of neurological disease. Written consent was given prior to every experiment by each participant.
Material
5 pure tones (musical notes of the 4th Octave), lasting 500ms were
classified, table 1. Each tone was randomly assigned to a word
subclass. Language 1 was randomly assigned different notes to
language 2, to later control for biases towards either language.
4
As in experiment 1 the same structures were used apart from
non-sense words were substituted for tones as shown in the subclasses allocations below, table 4 and table 5.
Table 4 A table to show the tone allocations used in FSG language1
Word classes: tone Language 1
Subclass notes used in FSG
A
A#
C
E
D
D
F
C
G
F#
Table 5. A table to show the tone allocations used in FSG language2
Word classes: tone Language 2
Subclass notes used in FSG
A
C
C
G
D
F
F
A#
G
B
Training
The same training procedure as experiment 1 was
used, but the tonal sequences programme was substituted for non sense word language programme.
Testing procedure
The same test procedure as experiment 1 except we
generated FSG tone sequence structure programmes.
Experiment 3: Complex Grammatical Structure
(Phrase-Structure Grammar)
In this experiment we expose subjects to complex
(PSG) structural non-sense word languages. We are
testing to see if the subjects can learn to discriminate
the complex structure better than chance level after
exposure. This will show whether human adults can
discriminate complex rule based structure as a function of phonetical words, as shown in Saffran and
Hauser ’08(1).
Design of Artificial Languages to Study Syntax in Language and its Evolutionary Origins
Participants
Two healthy undergraduate participants (aged 20-23)
were assigned to learn the predictive phase state
grammar (PSG) non-sense language. Both participants
were right handed, had normal hearing and no history
of neurological disease. Written consent was given
prior to every experiment by each participant.
Material
10 non-sense words (from Saffran & Hauser ’08 {Saffran, 2008 #23}), lasting 500ms were classified, table 6.
Each word was randomly assigned to a word subclass.
The same grammatical structure and rules from experiment one were used, but instead of single word
exemplars for each subclass, we used 2 word exemplars per subclass, table 6. We then created training
and testing programmes where each subclass exemplar was chosen at random for every individual sentence. Furthermore in the testing programme we
used a wider range of ungrammatical sentences as
the memorisation confound (due to the limited
grammatical test sentences) was reduced to insignificance. There were 25 possible grammatical sentences
from the 4 grammatical test structures therefore the
Word classes used in Language 1
Subclass words used
A
A#
G
C
E
A
D
D
F
F
C
G#
G
F#
B
Word classes used in Language 1
Subclass words used
A
Biff
hep
C
Cav
lum
D
Klor
pell
F
Dupp
loke
G
Jux
pilk
possibility of repetition, and the likelihood of a subject memorising their previous response was reduced and we can test the
range 12 ungrammatical structures.
Table 6 A table to show the word allocations used in the PSG
non-sense word language.
Training
The same procedure from experiment 1 except we used PSG nonsense word training programmes which were extended and played
in four sessions. During each session of 2 minutes 56 seconds,
subjects heard 8 randomisations of the six training grammatical
structures with randomised exemplars.
Testing procedure
The same test procedure as experiment 1 except we generated PSG
word testing programmes containing multiple exemplar randomisations and a wider range of ungrammatical structures.
Experiment 4: PSG with a Tonal Language
In this experiment we expose subjects to complex (PSG) structural
tonal languages. We are testing to see if the subjects can show an
ability above chance to learn the complex structure. This will show
whether human adults can learn complex rule based structures as a
function of tones as opposed to words shown in experiment 3.
Participants
Four healthy undergraduate participants (aged 20-23) were assigned to learn the predictive PSG tonal language. All participants
were right handed, had normal hearing and no history of neurological disease. Written consent was given prior to every experiment
by each participant.
Material
10 pure tones (musical notes of the 4th Octave), lasting 500ms were
classified, table 1. Each tone was randomly assigned to a word
subclass.
The same structure from experiment 3 except the subclass exemplars are represented by tones as allocated below.
Table 7 A table to show the tone allocations used in the PSG
tonal language.
Training
The same procedure from experiment 1 and the same exposure
format as experiment 3 except we used multiple exemplar PSG
tonal training programmes.
Testing procedure
The same test procedure as experiment 1 except we generated PSG
tonal testing programmes containing multiple exemplar randomisations and wider range of ungrammatical structures.
Pilot data:
Before we started final testing we wanted to develop the study to
suit our need and adapt for a few weaknesses we saw in the Saffran
and Hauser ’08 study(1).
5
M. Collison et al.
Firstly to develop the ideal paradigm we had to decide whether to
give the subjects concurrent feedback during testing. We suspected
it would either, aid the learning of the structure and increase concentration, or complicate the learning process by extending learning into the testing phase, causing confusion. We tested 8 subjects
after exposure to local tonal structured languages, 4 subjects with
feedback and 4 without feedback to see the overall effect, 2 of the
subjects were ran both without feedback and then with feedback
straight after to view a direct effect.
could have easily been discriminating the ungrammatical sequences as novel. Furthermore this could have been worse in the monkeys as they were exposed for 2 hours the night before so it is likely they were relying on memory of exact sequences rather than the
grammatical identity. To compensate for this we generated 10
stimuli from each FSG structure to allocate 6 structures to exposure and 4 structures to testing so that both the grammatical and
ungrammatical testing sequences were novel and matched the exposure stimuli for length.
Finally we decided to test a range of ungrammatical sequences
with different characteristics to determine which would be best to
incorporate into the study. After exposure to FSG tonal languages,
we tested 6 subjects on 80 stimuli with the single variable forced
choice test, 10 runs of 8 randomised stimuli, 4 grammatical and 4
(of 12, table 1) ungrammatical.
Table 8 A table to show the range of 12 ungrammatical stimuli and their characteristics performance results.
Figure 4. A
Stimulus
graph to
Stimulus
number
show A)the
ADGCFC
1
overall effect
ADFCG
2
of feedback
on
ACGFD
3
participants
AGCF
4
perforACDFGC
5
mance, %
ACGDF
6
correct in a
forced choice
AGFCD
7
2 alternative
AFCD
8
test (bar
GCDFGA
9
chart) and
B)the
AFDGC
10
reduction in
AFGCD
11
performance
DAGC
12
in
.
individuals after giving feedback (line graph).
rules
violated
3
1
2
3
2,3
1,2
2,3
1,2
1,2,3
1,2,3
1,2,3
1,2,3
The results show conclusively (P=0.0692) that feedback is detrimental to performance and may complicate the study therefore
we will not use feedback in subsequent experiments.
No of rule vioNo of low
lations
TPs
1
2
1
1
1
1
1
2
2
4
2
2
2
2
2
2
3
5
3
4
3
4
rules
3 Stimulus
3
Stimulus
6
violated
violations
% correct
58%
58%
67%
82%
72%
83%
81%
96%
100%
83%
97%
low100%
TPs
Length
ADCFCG
Gram1
0
0
0
6
ACGFC
Gram 2
0
0
0
5
ADCGF
Gram 3
0
0
0
5
ACGF
On review of the Saffran and Hauser ’08 paper we noticed a serious familiarisation and memory confound; the testing structures
were present in the exposure structures. Therefore the subjects
number
length
6
5
5
4
6
5
5
4
6
5
5
No of4 rule
Gram 4
0
0
0
4
Ungram
ADGCFC
1
3,1*
1
2
6
Ungram
ADFCG
2
1
1
1
5
Ungram
AGFCD
3
1,2,3
3
2
5
Ungram
AFCD
4
1,2
2
2
4
Table 1. A table to show the characteristics of the initial selected grammatical and ungrammatical test structures.
Design of Artificial Languages to Study Syntax in Language and its Evolutionary Origins
sense word languages?
After a short period of exposure to FSG structured precise nonsense word sentences, human adults showed an increased ability to
discriminate between grammatical and ungrammatical structured
sentences. Furthermore after a short period of exposure to finite
state grammatically structured less precise non-sense word sentences, human adults showed no improvement above chance to
discriminate between grammatical and ungrammatical sentence
structures on the same test stimuli.
Figure 5. A graph to show the stimulus specific performance
on a range of ungrammatical structures with different characteristics
The strong overall results suggested that the subjects grasped the
experiment easily and allowed us to select specific ungrammatical
stimuli for testing which are harder to discriminate (single and
double rule violation sequences), but will reveal more about the
basis to which they are differentiated as ungrammatical in further
experiments.
Initially we chose ungrammatical sequences 1,2,7 and 8, which had
a good compromise in number of rule violations and number of
low transitional probabilities as well as specific rule violations and
specific low transitional probabilities (low TPs), which also
matched the grammatical test sequences for length.
This performance shows that the human adults learned the predictive FSG non-sense word based language structure, whereas they
failed to learn the non predictive FSG non-sense word language
structure. Therefore they are capable of learning to discriminate
local structures as a function of phonetic words, which supports
Saffran and Hauser ’08 findings.
As a control, when creating the sentences from the initial precise
grammatical structure, I randomised the word allocations to subclasses twice to create two languages with the same structure but
different inter word associations. By showing performance across
languages, group 1a and group 1b, there is no bias towards either
language therefore I can assume that neither language has specific
recognisable associations which mimic the grammatical structure
or give any unanticipated clues to the structural identity.
Experiment 2:
Can human adults learn to discriminate local structure in tone
languages?
3
RESULTS
The Experiment 1;
Can human adults learn to discriminate local structure in non-
B)
A)
B)
90%
65%
85%
Performance
60%
55%
50%
0.90
0.70
0.85
80%
0.60
0.80
75%
0.50
0.75
70%
65%
60%
Performance
A)
After a short period of exposure to FSG structured predictive tone
sequences, human adults showed an improved ability to discriminate between grammatical and ungrammatical tone sequences.
Furthermore after a short period of exposure to FSG less precise
0.40
Chance
55%
0.30
0.20
Chance
50%
45%
45%
40%
Predictive
40%
Predictive
Non-predictive
0.70
0.65
0.60
0.55
0.50
0.10
0.00
Language 1
Non-predictive
0.45
0.40
Language
Language 21
Language 2
7
Figure 6. A) A chart to show
the comparison
performance
predictive
FSG non sense
word language
learners
and learners
non
Figure
7. A chart tobetween
show the
comparisonin
between
performance
in predictive
FSG tonal
language
and non predictive
predictive FSG non-sense word
language
learners. B) A chart to show the insignificant difference in performance across the 2
FSG tonal
language
differently assigned precise grammar languages.
M. Collison et al.
This increased performance across the two structures, shows subjects understood the local structure in both tone languages and
word languages. This could be the first indication for processing
local structural language from various acoustical inputs. Furthermore as humans can understand structural linguistics in tone languages this paradigm could be used in monkeys to directly compare structural processing without disadvantaging the monkeys due
to their non human phonetic system.
Again showing difference in performance across languages within
the structure has little has little effect. Therefore I have shown
there is no bias towards either particular language and I can assume neither language has specific musical intervals which mimic
the grammatical structure or give any unanticipated clues to the
structural identity.
Experiment 3:
Can human adults learn to discriminate complex long distance
associative structures in non-sense word languages?
Performance
After exposure to PSG structured predictive non-sense word sentences, human adults show an ability to discriminate grammatical
and ungrammatical structures.
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Chance
Chance
tone predictive PSG
Figure9. A graph to show the significant ability above chance
of subjects to discriminate tonal PSG structures.
This shows that human adults are able to learn artificial tonal PSG
structures and therefore able to process structural rule based associations as a function of pure tones. Subjects also understood the
long distance dependant structure in both tone languages and word
languages indicating that PSG structure can also be processed from
various acoustical complex inputs.
As shown in experiment 1 and 2, human adults are capable of discriminating local structure in both words and tones. This mutual
ability may be the early indications of a shared processing mechanism for low level statistical structure from various linguistic inputs.
Although when directly compared, human adults are perform better at learning local tone structures than local word structures.
Figure 8. A chart to show the ability above chance of subjects
discriminating non-sense word PSG structures.
This shows that human adults are able to learn artificial non-sense
PSG structures and therefore able to process structural rule based
associations as a function of phonetic words, which supports Saffran and Hauser ’08 findings(1).
8
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Tones Vs Words
word predictive PSG
Experiment 4:
After exposure to PSG structured tone sequences, human adults
show an ability to discriminate grammatical and ungrammatical
structures.
Performance
grammatically structured tone sequences, human adults also
showed a slight improvement to discriminate between grammatical
and ungrammatical sequences on the same test stimuli, although
performances were evidently higher in precise language.
Design of Artificial Languages to Study Syntax in Language and its Evolutionary Origins
Figure 11. A graph to show the relationship between number
of rule violations and performance in local structure.
Figure 10. A chart to show the difference in performance between word local structures and tone based local structures.
This result indicates that local transitions structural discrimination
is directly affected by low level acoustic complexities. Therefore
the complex phonetic recognition as opposed to the simple tone
recognition had a detrimental effect on higher processing and reduces the probability the subject would identify the structure. This
indicates a statistical process is involved as the simple tonal cue
would favour this approach.
Furthermore as we have clearly shown that humans can readily
learn structural tone languages. This would be useful in comparative cross species studies as you can give the monkeys the best
chance at understanding the structure in simple tonal cues without
confusion by human phonology.
Transitional Probabilities Vs Rule-Based Structure
By stimulus specific analysis I investigated the basis to which subjects were discriminating structures as ungrammatical in local FSG
structure. 6 subjects were run with 12 ungrammatical sequences on
tonal FSG. I found that performance in local structure follows a
strong correlation with both number of rule violations and number
of low transitional probabilities, although by looking at the initial
gradient of the slopes it seemed obvious that the dominating mechanism in local structure discrimination is identifying low transitional probabilities.
Performance
100%
90%
80%
70%
60%
50%
1
2
3
4
5
Number of low transitional probabilities
Figure 12. A graph to show the relationship between number
of low transitional probabilities and performance in local
structure.
Furthermore by stimulus specific analysis I investigated the basis
to which subjects were discriminating complex PSG structure. I
ran 4 subjects on the complex PSG tone language, experiment 4.
We saw no correlation between performance and number of low
TPs. This could be explained because the number of exposure
sequences was greatly increased therefore reinforcement of local
transitions were reduced to insignificance due to the diversity within the multiple exemplar structures. The graph below shows that
the relationship between performance and TPs for PSG is insignificant.
9
M. Collison et al.
100%
120%
90%
80%
80%
Performance
Performance
100%
60%
40%
20%
70%
60%
50%
40%
30%
20%
0%
1
2
3
4
5
Number of low transotional probabilitie
Figure 13 A graph to show the relationship between transitional probabilities and performance in complex PSG structure.
On the other hand we plotted number of rule violation against performance.
10%
0%
Words PSG
Tones PSG
Figure 15. A graph to show the difference in performance
across words and tones in complex rule based associative structure.
This performance variation under different stimuli inputs suggests
that neither complex acoustics nor statistical tones in the artificial
languages had a preferred input into the higher order rule based
structural processing. The fact they generate similar performances
implies they are being served by the same processing function that
is detached from the lower level statistical processing of the local
transitions, therefore a different rule based association mechanism.
General Discussion
Figure14. A graph to show the relationship between
number of rule violations and performance.
This shows a strong relationship between performance and number
of rule violations. This strongly change in dominant discriminatory
characteristic suggests there are two separate mechanisms for
structural processing at different levels of complexity.
By plotting words against tones we can show whether this structural process is a function of low level statistics or linguistic phonology system.
From the beginning I decided to adapt the Saffran and Hauser ‘08
grammatical structure(1) as it allowed for the simple substitution
of tones and words with level of grammatical complexity. Furthermore the recursive and optional structural approach also allowed me to form strategic stimuli to test for more specific underlying processing. Overall this structural versatility proved useful in
specific focused analysis of language processing.
Initially I showed the ability of human adults to discriminate local
structure in phonetic word languages, supporting the findings in
Saffran and Hauser ’08(1). By direct comparison I showed a clear
performance increase for local structural language discrimination
with tone languages, implying that the acoustical complexity of
acoustical words has a detrimental effect on structural processing
whereas the pure tones gave efficient cues as to the statistical comprehension for local structural processing. Overall this has given a
strong indication of a statistical processing domain for local structure language perception.
I then showed an ability in human adults to discriminate long distance associative structures in word languages, supporting the findings in Saffran and Hauser ’08. By direct comparison a similar
level of ability to discriminate long distance associations in tone
languages. This indicates that there is a higher level structural processing domain that does not prefer statistical or acoustical input,
10
Design of Artificial Languages to Study Syntax in Language and its Evolutionary Origins
therefore there a separate process to the local statistical one above
more reliant on rule based associations.
By stimulus specific analysis I was able to further isolate two possible mechanisms for local structure processing and rule based
structure processing.
Direct analysis of performance on specific ungrammatical stimuli I
showed that in local structures the number of low transitional
probabilities is the dominant characteristic on which ungrammatical structures were being discriminated
Currently transitional probabilities are only assumed active on
single level complexity; used to identify single low TPs at word
boundaries and segregate word streams. I would suggest there are
group statistics analysed at a higher level as a function of many
lower level TPs that can isolate inconsistencies which are low
probability local transitions. To prove this theory would take far
more research but t is clear local structure is based on statistics of
some level.
Furthermore stimulus specific analysis in phrase state grammar
(PSG) indicated a second mechanism, with a strong relationship
between performance and number of rules violated. This mechanism was mostly dormant in local structures as the statistical
mechanism was dominant. However at PSG I found that the determining factor for recognising violations in the higher structures
was the number of rule violations. Therefore I think I have identified a rule based structural mechanism perceiving language.
Evolution Vs Revolution
Through evolution humans have developed an advanced communication system. This advanced social and cognitive process has
been arguably traced back in evolution to find the point from
where the homo Saipan race differentiated away from our primate
ancestors and there are many arguments as too the developments of
different components of our advanced linguistic capability.
Some suggest the human advancement stems from a mutation in
the FOXP2 part gene of the 7th chromosome(21-23), involved in
neural growth and localization of frontal cortex during development. It is thought that this mutation lead to the development of a
phylogenically young area(22, 24) in layer IV on the inferior
frontal cortex, identified in early studies as Broca’s area or
BA44/45 (25) on the basis of granular and cytoarchtechtonic profile(26). Further hypotheses link the language faculty to the gradual elongation of the linguistic area of the superior temporal lobe
and the increased efficiency and adaptability of the human brain
associated with increased expression of the mirror neuron system(27, 28).
As Saffran and Hauser ’08 showed there are discrepancy in the
tamarin monkeys processing ability compared to human infants in
that monkeys cannot learn higher order rule based structure PSG
but they can learn local transitions FSG. I would suggest this supports my finding of the separate mechanisms for statistical structural language perception and rule based language perception as a
functional evolutionary processing mechanism that’s could be the
determining factor in our advanced language faculty.
I would further suggest this links to comparative neuroimaging
studies suggest that the rule based human advancement is services
by the phylogenically younger broca’s area whereas the transitional probability mechanism is served by frontal operculum and inferior frontal cortex. Therefore the neural evolutionary juncture in
the human structural language processing ability will be localised
in broca’s area (BA44/45).
Further study
As suggested in the early stages of this study we were ultimately
aiming to design and optimise a paradigm for localising structural
language processing for an fMRI comparative study across humans
and monkeys. This study has shown the relevance and efficiency
of tone languages in modelling structural perception. Furthermore I
have identified the strengths of this paradigm to isolate the mechanisms that determine local finite state grammar and complex
phrase state grammar. In conclusion through comparative fMRI
with human and monkeys this paradigm has shown potential to
bridge the evolutionary processing ability, localise the functional
processes separating the differences in capability and determine the
mechanisms involved in the cross species difference.
Finally further testing on this paradigm including the same behavioural experiments with more human subjects would be useful to
draw more conclusive results. As I only had limited numbers of
participants across my experiments it was not possible to draw any
statistically significant conclusions in any of the data except pilot
data.
acknowledgements
The quick brown fox jumps over the lazy dog. The quick brown
fox jumps over the lazy dog. The quick brown fox jumps over the
lazy dog. The quick brown fox jumps over the lazy dog. The quick
brown fox jumps over the lazy dog. The quick brown fox jumps
over the lazy dog.
Funding: The quick brown fox jumps over the lazy dog. The quick
brown over the lazy dog.
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