Brains, Genes, and Language Evolution

Morten H. ChristiansenCornell UniversitySanta Fe Institute

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

“It’s not a question of Nature vs. Nurture;

the question is about the Nature of

Nature.” Liz Bates

• 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 Shaped by the Brain

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

Language Shaped by the Brain

Language Language

Case Study:Sequential Learning

and Language

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

copy-back

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

1 2 3 4 5

3

1

4

5

2

3 2 4

5

1

43 2

5

1

43 2 1 5

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

copy-back

Output

Hidden

Input

Biological Evolution in SRNs

Generation n

Generation n + 1

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

copy-back

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

copy-back

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

Prediction 2:There should be a genetic link between sequential learning

and language

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

Random Pattern Random

100 trials 100 trials 100 trials 100 trials

2, 4, 1, 3, 4, 2, 1, 4, 3, 1

• 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

Acknowledgments

Nick Chater Florencia Reali Bruce Tomblin

Hannah Cornish Simon Kirby

Thanks!