Brains, Genes, and Language Evolution Morten H. Christiansen Cornell University Santa 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 from Constraints sensori- thought motor Language socio- cognition pragmatic 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)
Simulating the Role of Sequential Learning in Language Evolution Language + Sequential learning Biological + Linguistic Adaptation Sequential learning Biological Adaptation Time 500 generations
The Learners: SRNs (Simple Recurrent Network – Elman, 1990) next location Output copy-back Hidden Input Context current location previous internal state • Trained on a serial-reaction time (SRT) task (Lee, 1997) Source: Reali & Christiansen, Interaction Studies , 2009
1 2 3 4 5
1 2 3 4 5
1 4 3 2 5
1 3 2 4 5
3 2 4 1 5
Scoring SL Performance Full-conditional Probability vector probability vector for possible next for possible next location location 1 Mean 5 2 3... Cosine 4 Output copy-back Hidden 5 2 3 ... Input Context
Biological Evolution in SRNs Generation n + 1 Generation n
Results after 500 Generations 1.0 0.9 p < .001 Mean Cosine 0.8 0.7 0.6 0.5 Initial Final Source: Reali & Christiansen, Interaction Studies , 2009
Introducing Language Language + Sequential learning Biological + Linguistic Adaptation Sequential learning Biological Adaptation Time 500 generations
Language Learning SRN next grammatical role Output copy-back Hidden Input Context current word previous internal state Source: Reali & Christiansen, Interaction Studies , 2009
Grammar Skeleton ! ! {NP S ! VP} ! (1) ! ! {N (PP)} ! NP ! (2) ! ! {adp NP} ! PP ! (3) ! ! {V (NP) (PP)} ! VP ! (4) ! ! {N PossP} ! NP ! (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 Full-conditional Probability vector probability vector for possible next for possible next grammatical roles grammatical roles S O Mean V Prep ... Cosine Poss EOS Output copy-back Hidden V Prep ... Input Context
Biological Evolution ! ! 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)
Linguistic Evolution Language Language Language Language Language Language 4’ 1’ 2’ 3’ P’ P Language Language 1 3 Language Language 2 4
Evolving Head-Order Consistency Consistency Flexibility 1.00 0.75 0.50 0.25 0 1 20 40 60 80 100 120 Generations Source: Reali & Christiansen, Interaction Studies , 2009
Biological vs. Linguistic Adaptation Initial Final 1.0 ns p < .001 0.9 Mean Cosine 0.8 0.7 0.6 0.5 Linguistic Biological Source: Reali & Evolution Evolution Christiansen, Interaction Studies , (N constant) (L constant) 2009
The Role of Sequential Learning Constraints Initial SRNs Final SRNs 1.0 ns ns 0.9 Mean Cosine 0.8 0.7 0.6 0.5 Original Seq. Learning Source: Reali & Simulations Constraint Christiansen, Interaction Studies , (No L change) 2009
Modeling Recap: Word Order from Sequential Learning Constraints • 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
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
Prediction1Recap: Structure from Iterated Sequential Learning • Language-like distributional regularities emerge, facilitating learning • Sequential learning constraints, amplified by cultural transmission, could have shaped language
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 2, 4, 1, 3, 4, 2, 1, 4, 3, 1 100 trials 100 trials 100 trials 100 trials
Genetics 101 • DNA base difference between individuals: Single Nucleotide Polymorphism (SNP) C C G G SNP T T A A
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
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
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