Modelling of Learning Unnatural Language Quantifiers

2022-2-2
Aktepe, Semih Can
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The fact that all natural language quantifiers are conservative brings out the question of whether people have a predisposition to learn the conservative quantifiers over the non-conservative quantifiers. Many developmental and computational studies attempt to find an answer to this question, yet they fail to reach a consensus. This study tries to bring new insight into this debate by investigating the acquisition of four unnatural language quantifiers with a computational model and an eye-tracking experiment with a novel paradigm. The computational model makes some predictions about the effects of conservativity and complexity on quantifier learning. The predictions of the model are evaluated with a human subject eye- tracking experiment. This experiment employing the occluded referent paradigm provides us with what referents the participants utilize when discovering the meanings of the quantifiers. The results show that people have a bias for the most lexically-related referent rather than the conservativity bias, although that referent is not related when figuring out the meaning of the quantifiers. These findings open a new discussion about how people learn the meanings of the natural language quantifiers over the unnatural language quantifiers.
Conservativity, Quantifier Learning, Probabilistic Modelling, Language of Thought, Eye-Tracking
https://hdl.handle.net/11511/95959
Graduate School of Informatics, Term Project

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Citation Formats
S. C. Aktepe, “Modelling of Learning Unnatural Language Quantifiers,” M.S. - Master Of Science Without Thesis, Middle East Technical University, 2022.
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