{"pk":27834,"title":"Feedback in the Time-Invariant String Kernel model of spoken word recognition","subtitle":null,"abstract":"The Time-Invariant String Kernel (TISK) model of spokenword recognition (Hanngan et al., 2013) is an interactiveactivation model like TRACE (McClelland & Elman, 1986).However, it uses orders of magnitude fewer nodes andconnections because it replaces TRACE's time-specificduplicates of phoneme and word nodes with time-invariantnodes based on a string kernel representation (essentially aphoneme-by-phoneme matrix, where a word is encoded as byall ordered open diphones it contains; e.g., cat has /kæ/, /æt/,and /kt/). Hannagan et al. (2013) showed that TISK behavessimilarly to TRACE in the time course of phonologicalcompetition and even word-specific recognition times.However, the original implementation did not includefeedback from words to diphone nodes, precluding simulationof top-down effects. Here, we demonstrate that TISK can beeasily adapted to lexical feedback, affording simulation oftop-down effects as well as allowing the model todemonstrate graceful degradation given noisy inputs.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"computational models"},{"word":"Neural Networks"},{"word":"Spoken word recognition"},{"word":"interaction"},{"word":"Feedback"}],"section":"Publication-based-Talks","is_remote":true,"remote_url":"https://escholarship.org/uc/item/7fr0x16z","frozenauthors":[{"first_name":"James","middle_name":"S","last_name":"Magnuson","name_suffix":"","institution":"U of Conneticut","department":""},{"first_name":"Heejo","middle_name":"","last_name":"You","name_suffix":"","institution":"U of Conneticut","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2018-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27834/galley/17473/download/"}]}