Brains, Genes, and Language Evolution · •The role of language evolution modeling: ... copy-back...
Transcript of Brains, Genes, and Language Evolution · •The role of language evolution modeling: ... copy-back...
Brains, Genes, and Language
• We need genetic constraints to explain
• the close match between language and underlying neural mechanisms
• the complex and intricate structure of language
• the existence of cross-linguistic patterns of similarity
• the uniqueness of human language
• The role of language evolution modeling:
• Evaluation of existing theories
• Exploration of theoretical constructs
• Exemplification of how a new theory may work
• Predictions for new experimental research
Outline
• Language shaped by the brain
• Case study: Sequential learning and language
• Modeling the emergence of word order
• Prediction: Structure from iterated sequential learning
• Prediction: Genetic link between sequential learning and language
Language Learning and Evolution
• Why is the brain so well-suited for learning language?
• Why is language so well-suited to being learned by the brain?
• Cultural transmission has shaped language to be as learnable as possible by human learning mechanisms
E.g., Christiansen (1994), Deacon (1997), Kirby (2000)
“The formation of different languages and of distinct species, and the proofs that both have been developed through a gradual process, are curiously parallel . . . A struggle for life is constantly going on among the words and grammatical forms in each language. The better, the shorter, the easier forms are constantly gaining the upper hand . . . The survival and preservation of certain favored words in the struggle for existence is natural selection.”
Darwin (1874: 106)
Language
thought
cognition
sensori-motor
socio-pragmatic
Language from Constraints
Source: Christiansen & Chater, BBS, 2008
Might word order derive from constraints on sequential learning amplified through
cultural evolution?
Modeling Goals
• Explore the role of pre-adaptations for complex sequential learning
• Evaluate the effect of retention of pre-language sequential learning abilities
• Exemplify interactions between cultural and biological evolution
• Make predictions regarding the relationship between sequential learning and language
Constraints on Sequential Learning
• Sequential Learning: The ability to encode and represent the order of discrete elements occurring in a sequence
• Non-human primates not good at learning hierarchically ordered sequences (Conway &
Christiansen, 2001)
Sequential learning Biological Adaptation
500 generations
Simulating the Role of Sequential Learning in Language Evolution
Time
Language + Sequential learning Biological + Linguistic
Adaptation
The Learners: SRNs(Simple Recurrent Network – Elman, 1990)
Context
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Output
Hidden
Input
• Trained on a serial-reaction time (SRT) task (Lee, 1997)
current location
next location
Source: Reali & Christiansen, Interaction Studies, 2009
previous internal state
Scoring SL Performance
5 2 3...
4
1
Full-conditionalprobability vector for possible next
location
Probability vectorfor possible next
location
5 2 3 ...
Mean Cosine
Context
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Output
Hidden
Input
p < .001
Results after 500 GenerationsM
ean C
osin
e
0.5
0.6
0.7
0.8
0.9
1.0
Initial Final
Source: Reali & Christiansen, Interaction Studies, 2009
Introducing Language
Time
Sequential learning Biological Adaptation
500 generations
Language + Sequential learning Biological + Linguistic
Adaptation
Language Learning SRN
Context
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current word previous internal state
next grammatical role
Output
Hidden
Input
Source: Reali & Christiansen, Interaction Studies, 2009
Grammar Skeleton
S! ! !{NP VP}! (1)
NP! ! !{N (PP)}! (2)
PP! ! !{adp NP}! (3)
VP! ! !{V (NP) (PP)}! (4)
NP! ! !{N PossP}! (5)
PossP!! !{Poss NP}! (6)
Grammar Example
S! ! ! VP NP! ! (Head Final)
NP! ! ! N (PP)! ! (Head First)
PP! ! ! adp NP | NP adp! (Flexible)
VP! ! ! V (NP) (PP)! ! (Head First)
NP! ! ! PossP N ! (Head Final)
PossP!! ! Poss NP | NP Poss ! (Flexible)
Scoring Language Performance
V Prep ...Mean
Cosine
EOS
Poss
O
S
Full-conditionalprobability vector for possible nextgrammatical roles
Probability vectorfor possible next grammatical roles
V Prep ...Context
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Output
Hidden
Input
S! ! !{NP VP}! (1)
NP! ! !{N (PP)}! (2)
PP! ! !{adp NP}! (3)
VP! ! !{V (NP) (PP)}! (4)
NP! ! !{N PossP}! (5)
PossP!! !{Poss NP}! (6)
Biological Evolution
Language 3’
Language 2’
Language 4’
Language 1’
Language P
Language 2
Language 1
Language 3
Language 4
Language P’
Linguistic Evolution
0
0.25
0.50
0.75
1.00
1 20 40 60 80 100 120
Consistency Flexibility
GenerationsSource: Reali & Christiansen, Interaction Studies, 2009
Evolving Head-Order Consistency
Biological vs. Linguistic Adaptation
p < .001ns
Biological Evolution
(L constant)
Linguistic Evolution
(N constant)
Initial Final
Source: Reali & Christiansen,
Interaction Studies, 2009
Mean C
osin
e
0.5
0.6
0.7
0.8
0.9
1.0
The Role of Sequential Learning Constraints
ns ns
Original Simulations
Seq. LearningConstraint
(No L change)
Mean C
osin
e
0.5
0.6
0.7
0.8
0.9
1.0
Initial SRNs Final SRNs
Source: Reali & Christiansen,
Interaction Studies, 2009
• If language and learners evolve simultaneously, cultural evolution constrained by sequential learning overpowers biological adaptation
• Sequential learning constraints become embedded in the structure of language
• Linguistic forms that fit these biases are more readily learned, and hence propagated more effectively from speaker to speaker
Modeling Recap:Word Order from
Sequential Learning Constraints
Prediction 1:Sequential learning constraints
should drive language-like cultural evolution in humans
Iterated Artificial Language Learning
• Can sequential learning biases lead to the cultural evolution of structure, independent of any language-like task?
Iterated Sequential Learning
• Diffusion chains
• Training on 15 consonant strings
• Recall of all 15 strings
• Output recoded and used as input for the next participant
• 10 participants in each chain
• Language-like distributional regularities emerge, facilitating learning
• Sequential learning constraints, amplified by cultural transmission, could have shaped language
Prediction1Recap:Structure from Iterated
Sequential Learning
FOXP2 and Sequential Learning
• Recent selection for FOXP2 in humans (Enard et al., 2002)
• FOXP2 important for the development of cortico-striatal system (Watkins et al., 2002)
• Cortico-striatal system implicated in sequential learning (Packard & Knowlton, 2002)
• Could sequential learning be an intermediate phenotype (endophenotype) for FOXP2 and language?
Molecular Genetic Study
• Participants: 159 8th-graders
• 100 typical language learners
• 59 children with language impairment (LI)
• Both groups have equivalent non-verbal IQ
• Blood or saliva samples obtained for recovery of DNA
• Visual serial-reaction time (SRT) task
• DNA base difference between individuals: Single Nucleotide Polymorphism (SNP)
T
A
C
G
C
G
T
A
SNP
Genetics 101
• DNA base difference between individuals: Single Nucleotide Polymorphism (SNP)
• Sets of nearby SNPs inherited in blocks
• Pattern of adjacent SNPs in a block form a Haplotype
• Tag SNP: An indicator SNP for the composition of a haplotype block
Genetics 101
Prediction 2 Recap:FOXP2 Links Sequential Learning and Language
• FOXP2 genotypic variance is associated with individual differences in SRT learning and language status
• Fits recent molecular genetic results:
• Humanized Foxp2 affects the striatum in mice (Enard et al., 2009)
Case Study Summary
• Constraints on sequential learning, amplified by cultural transmission, may help explain word order patterns
• Similar neural and genetic bases for sequential learning and language
• Sequential learning provides an important constraint on the cultural evolution of language
Lessons from Language Evolution
• The cultural evolution of language simplifies the problem of acquisition
• Language acquisition involves learning how to coordinate linguistic behavior with others, not grammar induction
• The learner’s biases will be the right biases because language has been optimized by past generations of learners
Source: Chater & Christiansen, Cognitive Science, in press
Conclusion
• The fit between language and the brain arises because language has been shaped to fit pre-existing domain-general constraints
• Languages have evolved to rely on multiple-cue integration for their acquisition
• We need to uncover the constraints that shape the cultural evolution of language