{"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/"}]}