Computational Psycholinguistics (ESSLLI 2014 Advanced Course in Language and Computation)

1 Course information

Lecture Dates August 18 through August 22, daily
Lecture Times 5:00pm-6:30pm
Lecture Location N1
Class webpage

2 Instructor information

Instructors Klinton Bicknell ( and Roger Levy (
Instructor Title Assistant Professor, Department of Linguistics, Northwestern University
  Associate Professor, Department of Linguistics, University of California at San Diego

3 Course Description

Over the last two and a half decades, computational linguistics has been revolutionized as a result of three closely related developments: increases in computing power, the advent of large linguistic datasets, and a paradigm shift toward probabilistic modeling. At the same time, similar theoretical developments in cognitive science have led to a view major aspects of human cognition as instances of rational statistical inference. These developments have set the stage for renewed interest in computational approaches to human language use. Correspondingly, this course covers some of the most exciting developments in computational psycholinguistics over the past decade. The course spans human language comprehension, production, and acquisition, and covers key phenomena from both phonetics and syntax. Students will learn key technical tools including probabilistic models, formal grammars, and decision theory, and how theory, computational modeling, and data can be combined to advance our fundamental understanding of human language use.

4 Course organization

Each day of the 5-day course will be primarily lectures with some boardwork, but with ample opportunity for discussion. Don't hesitate to ask questions during the lectures!

Every day of the course has a core reading – a short conference paper describing one of the models receiving primary focus for that day. We encourage you to read the core reading before class each day, as it will prepare you better to absorb the material in the day's class. We've also included a list of other readings to give you a broader range of pointers to key related literature for each day's lecture.

5 Intended Audience

Graduate students and researchers in linguistics, cognitive science, logic, psychology, computer science, and any other discipline who are interested in using computational modeling techniques, especially probabilistic modeling, to study human language processing and acquisition.

6 Syllabus (subject to modification)

Day Topic Slides Core reading Other readings materials
Mon 6 Aug Introductory probability theory; Bayes nets; probabilistic models of human speech perception; the perceptual magnet Lecture 1 Feldman & Griffiths, 2007 Feldman et al., 2009a; Clayards et al., 2008; Sonderegger & Yu, 2010
Tue 7 Aug Probabilistic grammars, incremental syntactic comprehension, garden-pathing, surprisal Lecture 2 (surprisal slides, without builds) Hale, 2001 Jurafsky, 1996; Levy, 2008a; Narayanan & Jurafsky, 1998; Narayanan & Jurafsky, 2002; Demberg & Keller, 2008; Smith & Levy, 2013
Wed 8 Aug Noisy-channel models Lecture 3 (without builds) Levy, 2008 Norris, 2006; Norris & McQueen, 2008; Bicknell & Levy, 2010; Levy, 2011; Gibson et al., 2013; Lewis et al., 2013
Thu 9 Aug Optimality in sentence production; phonetic reduction; Uniform Information Density Lecture 4 (without builds) Levy & Jaeger, 2007 Jurafsky et al., 2001; Bell et al., 2009; Genzel & Charniak, 2002; Genzel & Charniak, 2003; Keller, 2004; Jaeger, 2010; Piantadosi et al., 2011; Seyfarth, 2014
Fri 10 Aug Probabilistic models of language acquisition: sound category acquisition, syntactic grammar learning Lecture 5 (without builds) Pajak, Bicknell, & Levy, 2013 Feldman et al., 2009b; Perfors et al., 2011

Author: Klinton Bicknell and Roger Levy

Created: 2014-08-24 Sun 09:32

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