This thesis develops the hypothesis that the systematic patterns of children’s word use over the course of development are the natural consequence of a sophisticated inductive learning mechanism operating with insufficient data. In this thesis, we sketch out a first-principles account of lexical-conceptual development and implement this model framework for the case of children learning kinship. Kinship is a valuable semantic domain to investigate because children show the same developmental trajectory for early word (mis)use, as in their first year of life, spread out over nine years. A major limitation of evaluating this model and all models of conceptual development is that we have poor intuitions about how children make use of data. To remedy this, we build a data analysis model to investigate the profile of data usage in word learning; although this technique will be broadly applicable to developmental science. We then illustrate how this technique can be used to check the first principles model of inductive learning and investigate the learning process by compiling a large cross-cultural dataset assessing children’s knowledge of exact number words. We then take a step back from the learning mechanism and use Fermi-estimation and information theoretic techniques to quantify the scale of language learning tasks and highlight the likelihood of sophisticated learning mechanisms for word meanings.
|Commitee:||Tanenhaus, Michael, Feldman, Naomi, White, Aaron|
|School:||University of Rochester|
|Department:||School of Arts and Sciences|
|School Location:||United States -- New York|
|Source:||DAI-A 81/2(E), Dissertation Abstracts International|
|Subjects:||Cognitive psychology, Developmental psychology, Linguistics|
|Keywords:||Human machine learning, Rational constructivist models, Conceptual development|
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