Cogprints

A broad-coverage distributed connectionist model of visual word recognition

Moscoso del Prado Martin, Dr Fermin and Baayen, Prof R. Harald (2005) A broad-coverage distributed connectionist model of visual word recognition. (Unpublished)

Full text available as:

[img]
Preview
PDF
507Kb

Abstract

In this study we describe a distributed connectionist model of morphological processing, covering a realistically sized sample of the English language. The purpose of this model is to explore how effects of discrete, hierarchically structured morphological paradigms, can arise as a result of the statistical sub-regularities in the mapping between word forms and word meanings. We present a model that learns to produce at its output a realistic semantic representation of a word, on presentation of a distributed representation of its orthography. After training, in three experiments, we compare the outputs of the model with the lexical decision latencies for large sets of English nouns and verbs. We show that the model has developed detailed representations of morphological structure, giving rise to effects analogous to those observed in visual lexical decision experiments. In addition, we show how the association between word form and word meaning also give rise to recently reported differences between regular and irregular verbs, even in their completely regular present-tense forms. We interpret these results as underlining the key importance for lexical processing of the statistical regularities in the mappings between form and meaning.

Item Type:Other
Keywords:Distributed connectionist, family size, inflectional entropy, co-occurrence vectors
Subjects:Neuroscience > Computational Neuroscience
Linguistics > Morphology
Psychology > Psycholinguistics
Psychology > Cognitive Psychology
Computer Science > Neural Nets
ID Code:4739
Deposited By:Moscoso del Prado Martin, Dr Fermin
Deposited On:26 Feb 2006
Last Modified:11 Mar 2011 08:56

Metadata

Repository Staff Only: item control page