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{ "pk": 26314, "title": "Bifurcation analysis of a Gradient Symbolic Computation model of incremental processing", "subtitle": null, "abstract": "Language is ordered in time and an incremental processingsystem encounters temporary ambiguity in the middle of sen-tence comprehension. An optimal incremental processing sys-tem must solve two computational problems: On the one hand,it has to keep multiple possible interpretations without choos-ing one over the others. On the other hand, it must rejectinterpretations inconsistent with context. We propose a re-current neural network model of incremental processing thatdoes stochastic optimization of a set of soft, local constraintsto build a globally coherent structure successfully. Bifurcationanalysis of the model makes clear when and why the modelparses a sentence successfully and when and why it does not—the garden path and local coherence effects are discussed. Ourmodel provides neurally plausible solutions of the computa-tional problems arising in incremental processing", "language": "eng", "license": { "name": "", "short_name": "", "text": null, "url": "" }, "keywords": [ { "word": "Harmonic Grammar; Gradient Symbolic Compu-tation; neural networks; incremental processing; parsing; dy-namical systems theory; bifurcations" } ], "section": "Papers", "is_remote": true, "remote_url": "https://escholarship.org/uc/item/7z18n24c", "frozenauthors": [ { "first_name": "Pyeong", "middle_name": "Whan", "last_name": "Cho", "name_suffix": "", "institution": "Johns Hopkins University", "department": "" }, { "first_name": "Paul", "middle_name": "", "last_name": "Smolensky", "name_suffix": "", "institution": "Johns Hopkins University", "department": "" } ], "date_submitted": null, "date_accepted": null, "date_published": "2016-01-01T18:00:00Z", "render_galley": null, "galleys": [ { "label": "PDF", "type": "pdf", "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/26314/galley/15950/download/" } ] }