Computing Dynamic Meanings: Day 3 [ESSLLI 2018 Course]
Sun 05 August 2018 by Jakub Dotlačil, Adrian Brasoveanu-
Introduction to Bayesian statistical modeling for linguists
introduction to Bayesian methods for data analysis / cognitive modeling
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Modeling linguistic performance and Bayesian estimation of ACT-R model parameters
we introduce subsymbolic declarative memory components of ACT-R that are essential for modeling linguistic performance — i.e., actual human behavior in experimental tasks
we build end-to-end models for a self-paced reading experiment -> quantitative comparison for qualitative theories
end-to-end models include: (i) explicit linguistic analyses primarily encoded in the production rules, i.e., in procedural memory; (ii) realistic model of declarative memory (includes subsymbolic components); (iii) simple, but reasonably realistic vision and motor modules
code and related materials: parser_rules.py, run_parser.py, estimation_subj_obj_extraction.py, sentences.csv