Using neural network models to model cerebral hemispheric differences in processing ambiguous words

Orna Peleg, Zohar Eviatar, Larry Manevitz, Hananel Hazan

Research output: Contribution to journalConference articlepeer-review

Abstract

Neuropsychological studies have shown that both cerebral hemispheres process orthographic, phonological and semantic aspects of written words, albeit in different ways. The Left Hemisphere (LH) is more influenced by the phonological aspect of written words whereas lexical processing in the Right Hemisphere (RH) is more sensitive to visual form. We explain this phenomenon by postulating that in the Left Hemisphere (LH) orthography, phonology and semantics are interconnected while in the Right Hemisphere (RH), phonology is not connected directly to orthography and hence its influence must be mitigated by semantical processing. We test this hypothesis by complementary human psychophysical experiments and by dual (one RH and one LH) computational neural network model architecturally modified from Kowamoto's [1993] model to follow our hypothesis. In this paper we present the results of the computational model and show that the results obtained are analogous to the human experiments.

Original languageEnglish
Pages (from-to)24-31
Number of pages8
JournalCEUR Workshop Proceedings
Volume230
StatePublished - 2007
Event3rd International Workshop on Neural-Symbolic Learning and Reasoning, NeSy 2007, Held at IJCAI 2007 - Hyderabad, India
Duration: 8 Jan 20078 Jan 2007

ASJC Scopus subject areas

  • Computer Science (all)

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