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Some recent developments in models of retrieval processes by Prof. Shravan Vasishth

February/12/2018 @ 08:00 - 17:00

Some recent developments in models of retrieval processes by Prof. Shravan Vasishth (University of Potsdam)

In this talk, I will discuss some recent empirical and theoretical developments in cue-based retrieval theory [1-13]. I will begin by talking about what we know so far about the underlying mechanisms driving retrieval processes in sentence comprehension and the evidence for and against the cue-based retrieval account [14]. Then I will present some recent work showing that two alternative models of retrieval are viable candidate theories [15]. 

1. Richard L. Lewis and Shravan Vasishth. An activation-based model of sentence processing as skilled memory retrieval. Cognitive Science, 29:1-45, 2005.

2. Richard L. Lewis, Shravan Vasishth, and Julie Van Dyke. Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences, 10(10):447-454, 2006.

3. Shravan Vasishth, Sven Bruessow, Richard L. Lewis, and Heiner Drenhaus. Processing Polarity: How the ungrammatical intrudes on the grammatical. Cognitive Science, 32(4):685-712, 2008.

4. Shravan Vasishth, Katja Suckow, Richard L. Lewis, and Sabine Kern. Short-term forgetting in sentence comprehension: Crosslinguistic evidence from head-final structures. Language and Cognitive Processes, 25(4):533-567, 2011. 

5. Felix Engelmann, Shravan Vasishth, Ralf Engbert, and Reinhold Kliegl. A framework for modeling the interaction of syntactic processing and eye movement control. Topics in Cognitive Science, 5(3):452-474, 2013. 

6. Lena A. Jäger, Zhong Chen, Qiang Li, Chien-Jer Charles Lin, and Shravan Vasishth. The subject-relative advantage in Chinese: Evidence for expectation-based processing. Journal of Memory and Language, 79-80:97-120, 2015. 

7. Stefan L. Frank, Thijs Trompenaars, and Shravan Vasishth. Cross-linguistic differences in processing double-embedded relative clauses: Working-memory constraints or language statistics? Cognitive Science, page n/a, 2015.

8. Umesh Patil, Sandra Hanne, Frank Burchert, Ria De Bleser, and Shravan Vasishth. A computational evaluation of sentence comprehension deficits in aphasia. Cognitive Science, 40:5–50, 2016.

9. Molood Sadat Safavi, Samar Husain, and Shravan Vasishth. Dependency resolution difficulty increases with distance in Persian separable complex predicates: Implications for expectation and memory-based accounts. Frontiers in Psychology, 7, 2016.

10. Umesh Patil, Shravan Vasishth, and Richard L. Lewis. Retrieval interference in syntactic processing: The case of reflexive binding in English. Frontiers in Psychology, 2016. Special Issue on Encoding and Navigating Linguistic Representations in Memory.

11. Felix Engelmann, Lena A. Jäger, and Shravan Vasishth. The effect of prominence and cue association in retrieval processes: A computational account. Manuscript (under revision), 2017.

12. Fuyun Wu, Elsi Kaiser, and Shravan Vasishth. Effects of early cues on the processing of Chinese relative clauses: Evidence for experience-based theories. 2017. In Press, Cognitive Science.

13. Paul Mätzig, Shravan Vasishth, Felix Engelmann, and David Caplan. A computational investigation of sources of variability in sentence comprehension difficulty in aphasia. In Proceedings of MathPsych/ICCM, Warwick, UK, 2017. 

14. Lena A. Jäger, Felix Engelmann, and Shravan Vasishth. Similarity-based interference in sentence comprehension: Literature review and Bayesian meta-analysis. Journal of Memory and Language, 94:316-339, 2017.
https://github.com/vasishth/MetaAnalysisJaegerEngelmannVasishth2017

15. Bruno Nicenboim and Shravan Vasishth. Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling. 2017. Accepted, Journal of Memory and Language.
http://mc-stan.org/events/stancon2017-notebooks/stancon2017-nicenboim-vasishth-retrieval-models.html

*substitute for the Lunch Talk of October

Details

Date:
February/12/2018
Time:
08:00 - 17:00
Event Category: