Work

Lexical access in sign language: a computational model

Year: 2014
Type: article
Abstract: 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.
Institution: Tufts University
Language: English
Field: Psychology
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W2111112092
FWCI: 17.21
Cites: 71
Cited by: 79
Related to: 10