Language evolution by iterated learning with bayesian agents.
about
Greater learnability is not sufficient to produce cultural universalsCultural evolution: implications for understanding the human language faculty and its evolutionBrain potentials predict learning, transmission and modification of an artificial symbolic systemCulture and biology in the origins of linguistic structureInfluence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment.Individual biases, cultural evolution, and the statistical nature of language universals: the case of colour naming systems.Cumulative cultural evolution in the laboratory: an experimental approach to the origins of structure in human languageCulture shapes the evolution of cognition.Introduction. Cultural transmission and the evolution of human behaviour.Review. Theoretical and empirical evidence for the impact of inductive biases on cultural evolutionThe proper treatment of language acquisition and change in a population setting.Kinship categories across languages reflect general communicative principles.Language evolution can be shaped by the structure of the world.The Wisdom of Individuals: Exploring People's Knowledge About Everyday Events Using Iterated Learning.When learners surpass their models: mathematical modeling of learning from an inconsistent source.The effects of cultural transmission are modulated by the amount of information transmitted.Analyzing the rate at which languages lose the influence of a common ancestor.Revealing human inductive biases for category learning by simulating cultural transmission.Modeling Co-evolution of Speech and Biology.Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.Biology-Culture Co-evolution in Finite Populations.Frequency patterns of semantic change: corpus-based evidence of a near-critical dynamics in language change.Phonological Concept Learning.Models of language evolution and change.Words as alleles: connecting language evolution with Bayesian learners to models of genetic drift.The interactive evolution of human communication systems.Self domestication and the evolution of language.Cultural transmission results in convergence towards colour term universals.Modelling the coevolution of joint attention and language.Compression in cultural evolution: Homogeneity and structure in the emergence and evolution of a large-scale online collaborative art project
P2860
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P2860
Language evolution by iterated learning with bayesian agents.
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
2007年學術文章
@zh-hant
name
Language evolution by iterated learning with bayesian agents.
@en
Language evolution by iterated learning with bayesian agents.
@nl
type
label
Language evolution by iterated learning with bayesian agents.
@en
Language evolution by iterated learning with bayesian agents.
@nl
prefLabel
Language evolution by iterated learning with bayesian agents.
@en
Language evolution by iterated learning with bayesian agents.
@nl
P1433
P1476
Language evolution by iterated learning with bayesian agents.
@en
P2093
Michael L Kalish
P2860
P304
P356
10.1080/15326900701326576
P577
2007-05-01T00:00:00Z