Speaker: Dr Shohei Hidaka, Japan Advanced Institute of Science and Technology
Abstract
Understanding human natural languages is one of primary goals in cognitive science.
Toward the goal, artificial grammar learning (AGL) has been studied to test human decision making and inference in learning an unfamiliar set of sequential patterns, which may or may not follow some underlying grammar.
Such empirical investigations may have faced a difficulty in deepening understanding human learning of language-like patterns, due to a lack of theoretical understanding of language structure.
In this talk, I would like to give a theoretical exploration of intrinsic nature of n-grams, or sequences of n letters, and show its geometric nature is characterized with n-simplex.
This theoretical insight leads us a natural way to measure a sort of distance between a set of sequences to a grammar or distance between two grammars.
I would discuss a potential use of such theory to systematically design AGL experiments.
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