THE BALDWIN EFFECT IN THE EVOLUTIONARY NAMING GAME …logic.amu.edu.pl/images/9/9c/Baldwin.pdf ·...
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THE BALDWIN EFFECT
IN THE EVOLUTIONARY
NAMING GAME MODEL
DOROTA L IPOWSKA
Department of Applied Logic Institute of Linguistics
Adam Mickiewicz University in Poznań
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computer modelling
simulations of the naming game
shared vocabulary
evolutionary naming game model
Baldwin effect
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individual learning
cultural transmission
biological evolution
Language is a complex adaptive system, which
emerges from local interactions between its users
and develops according to principles of evolution
and self-organization.
(KIRBY, 2007)
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individual’s adaptation shall not affect genetic evolution
James Baldwin (1896):
epigenetic factors can shape the congenital endowment
The Baldwin effect:
what must be learned ontogenetically, can become innate
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wrodzone
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George G. Simpson (1953)
– reintroduction of Baldwinian evolution
Conrad Waddington
– canalization
– genetic assimilation
Geoffrey E. Hinton & Steven J. Nowlan (1987)
– computer simulations
– growing interest
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the Baldwin effect as a significant factor in the evolution of language
– Waddington (1975)
– Pinker & Bloom (1990)
– Deacon (1997)
– Newmeyer (2000)
– Briscoe (1998, 2002)
– Turkel (2002)
– Yamauchi (2004)
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1. nature–nurture problem
2. Darwinian account for
language evolution
3. connection of learning and
evolution
(cultural and phylogenetic
aspects of language)
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Conrad H. Waddington
– ability to use language
– gradual evolution
– accumulation
– genetic assimilation
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Steven Pinker & Paul Bloom
– language has evolved gradually
by natural selection
– Baldwin effect may be involved
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Terrence W. Deacon (1997):
No innate rules, no innate general principles, no innate symbolic categories can be built in by evolution.
– LAD – “monolithic innatism”
– coevolution of language and brain
– the Baldwin effect – not directly on the language faculty
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Frederick J. Newmeyer
– cost of learning
– acquisition failure
– Universal Grammar constraints
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unresolved problems
stable environment
Christiansen & Chater (2008)
– language adapted to brain
lack of rigorous theory
reconsideration
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LANGUAGE GAMES NAMING GAME (Steels, 1995)
BUBA
? !?
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local interactions
global vocabulary
(STEELS, 1995; BARONCHELLI et al., 2006; DALL ’ASTA et al., 2006)
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wow
bad
ole
uma
wow
goo
oj
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weights of words (w > 0 )
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learning abilities of agents (0 < l < 1 )
success – agents increase the weights
failure – listener adds the word
– speaker decreases the weight
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communication probability
survival probability
• age
• linguistic performance
mutation probability
• learning ability
• main word
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p=0.15 p=0.30
LANGUAGES
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p=0.15 p=0.30
LEARNING ABILITIES
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s, l
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
success rate
learning ability
0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26
0.28 p
Success rate s and learning ability l
as a function of communication probability p.
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learning get coupled with evolutionary traits
the Baldwin effect
niches directing evolution
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BARONCHELLI, A., FELICI, M., LORETO, V., CAGLIOTI, E., & STEELS, L. 2006. Sharp transition towards shared vocabularies in multi-agent systems. Journal of Statistical Mechanics, P06014.
BALDWIN, J. 1896. A new factor in evolution. American Naturalist, 30, 441–451.
BRISCOE, E. J. 1998. Language as a Complex Adaptive System: Coevolution of Language and of the Language Acquisition Device. In H. van Halteren et al. (Eds.), Proceedings of Eighth Computational Linguistics in the Netherlands Conference, 3-40.
CANGELOSI, A., PARISI, D. (Eds.) 2002. Simulating the Evolution of Language. London: Springer Verlag.
CHRISTIANSEN, M. H., & CHATER, N. 2008. Language as shaped by the brain. Behavioral and Brain Sciences, 31(5), 489--509.
DALL’ASTA, L., BARONCHELLI, A., BARRAT, A., & LORETO, V. 2006. Nonequilibrium dynamics of language games on complex networks. Physical Review E, 74, 036105.
DEACON, T. W. 1997. The Symbolic Species: The Co-evolution of Language and the Brain. New York: W.W. Norton.
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DE BOER, B. 2006. Computer modelling as a tool for understanding language evolution. In: N. Gonthier et al. (Eds.) Evolutionary Epistemology, Language and Culture – A Non-adaptationist, Systems Theoretical Approach. Dordrecht: Springer, 381–406.
HINTON, G.E., NOWLAN, S.J. 1987. How learning can guide evolution. Complex Systems 1, 495–502.
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LIPOWSKA, D. 2011. Naming game and computational modelling of language evolution. Computational Methods in Science and Technology, 17(1-2), 41-51.
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PINKER, S., BLOOM, P. 1990. Natural language and natural selection. Behavioral and Brain Sciences, 13(4), 707–784.
STEELS, L. 1995. A self-organizing spatial vocabulary. Artificial Life, 2(3), 319-332.
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TURKEL W. J. 2002. The Learning Guided Evolution of Natural Language, in E. J. Briscoe (Ed.) Linguistic Evolution through Language Acquisition: Formal and Computational Models. Cambridge: Cambridge University Press, chapter 8.
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YAMAUCHI, H. 2004. Baldwinian Accounts of Language Evolution. PhD thesis, Theoretical and Applied Linguistics, University of Edinburgh, Scotland.
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THANK YOU FOR YOUR ATTENTION
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Acknowledgements
This research was supported with NCN grant 2011/01/B/HS2/01293.
The author wishes to thank Adam Lipowski for his cooperation.