Sentence processing: Difference between revisions

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===Computational modeling===

Computational modeling is another means by which to explore language comprehension. Models, such as those instantiated in [[neural networks]], are particularly useful because they requires theorists to be explicit in their hypotheses and because they can be used to generate accurate predictions for theoretical models that are so complex that they render [[discursive psychology|discursive analysis]] unreliable. A classic example of computational modeling in language research is [[James McClelland (psychologist)|McClelland]] and [[Jeff Elman|Elman's]] [[Trace (psycholinguistics)|TRACE]] model of speech perception.<ref>{{cite journal | last1 = McClelland | first1 = J.L. | last2 = Elman | first2 = J.L. | year = 1986 | title = The TRACE model of speech perception | url = | journal = Cognitive Psychology | volume = 18 | issue = 1| pages = 1–86 | doi = 10.1016/0010-0285(86)90015-0 | pmid = 3753912 }}</ref> A model of sentence processing can be found in Hale (2011)'s 'rational' Generalized Left Corner parser.<ref>{{Cite journal | doi=10.1111/j.1551-6709.2010.01145.x| title=What a Rational Parser Would do| journal=Cognitive Science| volume=35| issue=3| pages=399–443| year=2011| last1=Hale| first1=John T.}}</ref> This model derives garden path effects as well as local coherence phenomena. Computational modeling can also help to relate sentence processing to other functions of language. For example, one model of ERP effects in sentence processing (e.g., N400 and P600) argues that these phenomena arise out learning processes that support language acquisition and linguistic adaptation.<ref>{{Cite journal|last1=Fitz|first1=Hartmut|last2=Chang|first2=Franklin|date=2019-06-01|title=Language ERPs reflect learning through prediction error propagation|journal=Cognitive Psychology|volume=111|pages=15–52|doi=10.1016/j.cogpsych.2019.03.002|pmid=30921626|hdl=21.11116/0000-0003-474D-8|issn=0010-0285|hdl-access=free}}</ref>

==See also==