Language Evolution and Computation Bibliography

Our site (www.isrl.uiuc.edu/amag/langev) retired, please use https://langev.com instead.
Proceedings :: Proceedings of the 28th Annual Conference of the Cognitive Science Society
2006
From Syllables to Syntax: Investigating Staged Linguistic Development through Computational ModellingPDF
Proceedings of the 28th Annual Conference of the Cognitive Science Society, 2006
A new model of early language acquisition is introduced. The model demonstrates the staged emergence of lexical and syntactic acquisition. For a period, no linguistic activity is present. The emergence of first words signals the onset of the holophrastic stage that continues to ...MORE ⇓
A new model of early language acquisition is introduced. The model demonstrates the staged emergence of lexical and syntactic acquisition. For a period, no linguistic activity is present. The emergence of first words signals the onset of the holophrastic stage that continues to mature without syntactic activity. Syntactic awareness eventually emerges as the result of multiple lexically-based insights. No mechanistic triggers are employed throughout development.
Revealing priors on category structures through iterated learningPDF
Proceedings of the 28th Annual Conference of the Cognitive Science Society, 2006
We present a novel experimental method for identifying the inductive biases of human learners. The key idea behind this method is simple: we use participants' re- sponses on one trial to generate the stimuli they see on the next. A theoretical analysis of this ``iterated learn- ...MORE ⇓
We present a novel experimental method for identifying the inductive biases of human learners. The key idea behind this method is simple: we use participants' re- sponses on one trial to generate the stimuli they see on the next. A theoretical analysis of this ``iterated learn- ing'' procedure, based on the assumption that learners are Bayesian agents, predicts that it should reveal the inductive biases of the learners, as expressed in a prior probability distribution. We test this prediction through two experiments in iterated category learning.