The course will take place on Wednesdays from 10 to 12 in room 1.13.
This is a reading & discussion class. We will read selected papers where different kinds of computational models are applied to various forms of empirical data. To cover a range of topics and to illustrate the commonality of applications in various domains, we will look at syntax, semantics, pragmatics and acquisition. The goal of this course is to convey a feeling for what it means to link model predictions with (aspects) of an empirical data set.
We will read one or two papers per session. Everybody must have studied each paper carefully at home. Remaining questions will be answered in class. We will then indulge lavishly in collective reflection and discussion on each paper’s potential merits, weaknesses, implications and open issues.
Each class begins with a very short quiz (2 - 4 questions, possibly multiple choice) about the text that we will discuss in that class. All quizes receive scores. In order to be admitted to the final exam, it is necessary to have at least 70% of the maximally possible total score from all quizes. The grade is based on the score from the final exam.
For a “Proseminar Schein” (3 credit points), participants can use one wildcard: they will not have to do the quiz and the exam questions for one paper of their choice. Which paper to use the wildcard for should be communicated in advance. For a “Hauptseminar Schein”, pariticipants can either stop after the final exam (3 credit points), or engage in an additional course project (literature discussion, programming project etc) for an additional 3 credit points.
| April 13, 2016 |
course overview |
| April 20, 2016 |
what is computational psycholinguistics? Crocker (2009) “Computational psycholinguistics” |
| syntax |
|
| April 27, 2016 |
surprisal theory and experimental data Levy (2008) “Expectation-based syntactic comprehension” [focus: sections 1-3 & the question how theory is related to what kind of data in sections 5-7] |
| May 4, 2016 |
information density in production Jäger (2010) “Redundancy and reduction: Speakers manage syntactic information density” [focus: sections 1, 2.1-2.3, 2.6 & 3; what is the “Uniform Infomation Density” hypothesis? how are which of its predictions tested here?] |
| May 11&18, 2016 |
no class |
| semantics |
|
| May 25, 2016 |
the meaning of vague quantifiers Schöller & Franke (2015) “Surprising few and many” |
| June 1, 2016 |
approximate number sense and processing of most Lidz et al. (2011) “Interface transparency and the psychosemantics of most” |
| June 8, 2016 |
building meaning represenations Brasoveanu & Dotlacil (2015) “Incremental and predictive interpretation” |
| pragmatics |
|
| June 15, 2016 |
processing pragmatic presuppositions Schwarz & Tiemann (2016) “Presupposition Projection in Online Processing” |
| June 22, 2016 |
implicatures of complex sentences (part 1) Potts et al. (2016) “Embedded implicatures as pragmatic inferences” |
| June 29, 2016 |
implicatures of complex sentences (part 2) Franke et al. (2016) “Embedded Scalars, Preferred Readings and Prosody” |
| learning |
|
| July 6, 2016 |
cognitive decline vs. life-long learning Ramscar et al. (2014) “The Myth of Cognitive Decline” [presented by Christian Adam] |
| fini |
|
| July 20, 2016 |
exam |
contact: Michael Franke