Lexical access in sign language: A computational model
Psycholinguistic theories have predominantly been built upon data from spoken language, which leaves open the question: How many of the conclusions truly reflect language-general principles as opposed to modality-specific ones? We take a step toward answering this question in the domain of lexical a...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2014-05-01
|
Series: | Frontiers in Psychology |
Subjects: | |
Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00428/full |
id |
doaj-062c119568044f8b96f9e2e3f533db64 |
---|---|
record_format |
Article |
spelling |
doaj-062c119568044f8b96f9e2e3f533db642020-11-25T00:00:36ZengFrontiers Media S.A.Frontiers in Psychology1664-10782014-05-01510.3389/fpsyg.2014.0042881594Lexical access in sign language: A computational modelNaomi Kenney Caselli0Ariel M Cohen-Goldberg1Tufts UniversityTufts UniversityPsycholinguistic theories have predominantly been built upon data from spoken language, which leaves open the question: How many of the conclusions truly reflect language-general principles as opposed to modality-specific ones? We take a step toward answering this question in the domain of lexical access in recognition by asking whether a single cognitive architecture might explain diverse behavioral patterns in signed and spoken language. Chen and Mirman (2012) presented a computational model of word processing that unified opposite effects of neighborhood density in speech production, perception, and written word recognition. Neighborhood density effects in sign language also vary depending on whether the neighbors share the same handshape or location. We present a spreading activation architecture that borrows the principles proposed by Chen and Mirman (2012), and show that if this architecture is elaborated to incorporate relatively minor facts about either 1) the time course of sign perception or 2) the frequency of sub-lexical units in sign languages, it produces data that match the experimental findings from sign languages. This work serves as a proof of concept that a single cognitive architecture could underlie both sign and word recognition.http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00428/fullSpeech Perceptionlexical accesssign languageneighborhood densityspreading activationsub-lexical processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Naomi Kenney Caselli Ariel M Cohen-Goldberg |
spellingShingle |
Naomi Kenney Caselli Ariel M Cohen-Goldberg Lexical access in sign language: A computational model Frontiers in Psychology Speech Perception lexical access sign language neighborhood density spreading activation sub-lexical processing |
author_facet |
Naomi Kenney Caselli Ariel M Cohen-Goldberg |
author_sort |
Naomi Kenney Caselli |
title |
Lexical access in sign language: A computational model |
title_short |
Lexical access in sign language: A computational model |
title_full |
Lexical access in sign language: A computational model |
title_fullStr |
Lexical access in sign language: A computational model |
title_full_unstemmed |
Lexical access in sign language: A computational model |
title_sort |
lexical access in sign language: a computational model |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Psychology |
issn |
1664-1078 |
publishDate |
2014-05-01 |
description |
Psycholinguistic theories have predominantly been built upon data from spoken language, which leaves open the question: How many of the conclusions truly reflect language-general principles as opposed to modality-specific ones? We take a step toward answering this question in the domain of lexical access in recognition by asking whether a single cognitive architecture might explain diverse behavioral patterns in signed and spoken language. Chen and Mirman (2012) presented a computational model of word processing that unified opposite effects of neighborhood density in speech production, perception, and written word recognition. Neighborhood density effects in sign language also vary depending on whether the neighbors share the same handshape or location. We present a spreading activation architecture that borrows the principles proposed by Chen and Mirman (2012), and show that if this architecture is elaborated to incorporate relatively minor facts about either 1) the time course of sign perception or 2) the frequency of sub-lexical units in sign languages, it produces data that match the experimental findings from sign languages. This work serves as a proof of concept that a single cognitive architecture could underlie both sign and word recognition. |
topic |
Speech Perception lexical access sign language neighborhood density spreading activation sub-lexical processing |
url |
http://journal.frontiersin.org/Journal/10.3389/fpsyg.2014.00428/full |
work_keys_str_mv |
AT naomikenneycaselli lexicalaccessinsignlanguageacomputationalmodel AT arielmcohengoldberg lexicalaccessinsignlanguageacomputationalmodel |
_version_ |
1725444378612203520 |