Large-Scale Modeling of Wordform Learning and Representation.

Cognitive Science
Daragh E SibleyJeffrey L Elman

Abstract

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed the sequence encoder is used to learn nearly 75,000 wordform representations through exposure to strings of stress-marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional slot-based codes. Then, two large-scale simulations are reported that learned to represent lexicons of either phonological or orthographic word-forms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well-formed pseudowords as opposed to ill-formed (scrambled) pseudowords, and by accounting for variance in well-formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing.

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Citations

Mar 26, 2013·Cognitive Neuropsychology·Tania BuiattiTim Shallice
Feb 14, 2012·Linguistic Inquiry·Iris BerentDoug Bemis
Sep 10, 2013·Trends in Cognitive Sciences·Dennis Norris
Jan 5, 2010·Cognitive Science·Daragh E SibleyJeffrey L Elman
Jan 1, 2009·Topics in Cognitive Science·James L McClelland
Mar 22, 2012·Cognitive Science·Thomas Hannagan, Jonathan Grainger
Aug 5, 2014·Cognitive Science·Timothy T Rogers, James L McClelland
Jun 16, 2015·Cognitive Science·Alberto TestolinMarco Zorzi
Apr 22, 2015·Language, Cognition and Neuroscience·Anna M Woollams
Mar 7, 2014·Language and Speech·Michael S Vitevitch, Holly L Storkel
Jun 11, 2016·Psychonomic Bulletin & Review·Stephen D GoldingerMichael C Hout
Aug 9, 2020·Genes·Mobeen Ur Rehman, Kil To Chong

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