Computational simulations of natural selection for communicative success show that natural selection alone is capable of evolving optimal communication systems. Simulations of the interactions between natural selection and learning show that the biases of learners, when placed in the framework of iterated cultural transmission of communication systems, result in cultural selection of communication systems. Cultural selection may be in opposition to natural selection as communication systems which are optimal in terms of cultural selection may not be optimal in terms of natural selection. Cultural selection is the determining factor in the development of communication systems in the simulated populations, with natural selection being relegated to a secondary role. This paper suggests that the role of cultural selection in the evolution of language should not be underestimated. 1 1 Introduction Language is transmitted from generation to generation within a speech community...
|
919
|
M: An introduction to genetic algorithms The
– Mitchell
- 1997
|
|
806
|
Aspects of the Theory of Syntax
– Chomsky
- 1965
|
|
264
|
How learning can guide evolution
– E, Nowlan
- 1987
|
|
224
|
Knowledge of Language. Its Nature, Origin, and Use
– Chomsky
- 1986
|
|
185
|
A new factor in evolution
– Baldwin
|
|
182
|
Rules and representations
– Chomsky
- 1990
|
|
178
|
Interactions between learning and evolution
– Ackley, Littman
- 1991
|
|
168
|
Evolving networks: using genetic algorithms with connectionist learning
– Belew, McInerney, et al.
- 1991
|
|
146
|
How Children Learn the Meanings of Words
– Bloom
- 2000
|
|
130
|
Natural language and natural selection
– Pinker, Bloom
- 1990
|
|
120
|
Grounding adaptive language games in robotic agents
– Steels, Vogt
- 1997
|
|
113
|
The Evolution of Communication
– Hauser
- 1996
|
|
113
|
Learning and Evolution in Neural Networks
– Nolfi, Elman, et al.
- 1994
|
|
109
|
Evolution of communication in artificial organisms
– Werner, Dyer
- 1991
|
|
95
|
Adaptation in Natural and Arti Systems. The
– Holland
- 1975
|
|
79
|
How to invent a lexicon: the development of shared symbols in interaction
– Hutchins, Hazlehurst
- 1995
|
|
70
|
Syntax without natural selection: how compositionality emerges from vocabulary in a population of learners
– Kirby
- 2000
|
|
68
|
Biological evolution of the saussurean sign as a component of the language acquisition device
– Hurford
- 1989
|
|
59
|
Spontaneous evolution of linguistic structure: an iterated learning model of the emergence of regularity and irregularity
– Kirby
- 2001
|
|
58
|
The emergence of a ‘language’ in an evolving population of neural networks
– Cangelosi, Parisi
- 1998
|
|
58
|
Synthetic ethology and the evolution of cooperative communication
– MacLennan, Burghardt
- 1993
|
|
55
|
Computational simulations of the emergence of grammar
– Batali
- 1998
|
|
55
|
Derivational Complexity and the Order of Acquisition of Child Speech
– Brown, Hanlon
- 1970
|
|
53
|
Coevolution, Genes, Culture and Human Diversity
– Durham
- 1991
|
|
49
|
Innate biases and critical periods: combining evolution and learning in the acquisition of syntax
– Batali
- 1994
|
|
48
|
Learning, culture and evolution in the origin of linguistic constraints
– Kirby, Hurford
- 1997
|
|
45
|
Biological signals as handicaps
– Grafen
- 1990
|
|
43
|
Altruism in the evolution of communication
– Ackley, Littman
- 1994
|
|
40
|
Animal signals: Mind reading and manipulation
– Krebs, Dawkins
- 1984
|
|
39
|
The emergence of linguistic structure: an overview of the iterated learning model
– Kirby, Hurford
- 2002
|
|
38
|
How Monkeys See the World: Inside the Mind of Another Species
– Cheney, Seyfarth
- 1990
|
|
37
|
The dilemma of Saussurean communication
– Oliphant
- 1996
|
|
32
|
Mate selection - a Selection for a Handicap
– Zahavi
- 1975
|
|
28
|
Learning and the emergence of coordinated communication
– Oliphant, Batali
- 1996
|
|
28
|
Fitness and the selective adaptation of language
– Kirby
- 1998
|
|
27
|
An exploration of signalling behaviour by both analytic and simulation mea for both discrete and continuous models
– Bullock
- 1997
|
|
27
|
Training feedforward networks using genetic algorithms
– Montana
- 1989
|
|
27
|
The learning barrier: Moving from innate to learned systems of communication
– Oliphant
- 1999
|
|
25
|
Social transmission favours linguistic generalization, in
– Hurford
- 2000
|
|
21
|
An investigation into the evolution of communication
– Paolo
- 1997
|
|
21
|
The survival of the smallest: Stability conditions for the cultural evolution of compositional language
– Brighton, Kirby
- 2001
|
|
20
|
in press) ‘Expression/induction models of language evolution: dimensions and issues
– Hurford
- 2000
|
|
19
|
Modelling the Evolution of Linguistic Diversity
– Livingstone, Fyfe
- 1999
|
|
18
|
Recursive inconsistencies are hard to learn: A connectionist perspective on universal word order correlations
– Christiansen, Devlin
- 1997
|
|
18
|
On the logic of contrast
– Clark
- 1988
|
|
18
|
The cultural evolution of communication in a population of neural networks
– Smith
|
|
16
|
The Dilemma of Saussurean Communication. BioSystems
– Oliphant
- 1996
|
|
14
|
The evolution of understanding: a genetic algorithm model of the evolution of communication
– Levin
- 1995
|
|
14
|
On the design of neural networks in the brain by genetic evolution
– Rolls, Stringer
- 2000
|
|
14
|
Too many love songs: Sexual selection and the evolution of communication
– Werner, Todd
- 1997
|