Proceedings of the Annual Meeting of the Cognitive Science Society

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.