A Distributed Memory Model of the Associative Boost in Semantic Priming
作者:
H. E. MOSS,
M. L. HARE,
P. DAY,
L. K. TYLER,
期刊:
Connection Science
(Taylor Available online 1994)
卷期:
Volume 6,
issue 4
页码: 413-427
ISSN:0954-0091
年代: 1994
DOI:10.1080/09540099408915732
出版商: Taylor & Francis Group
关键词: Semantic priming;associative priming;co-occurrence;distributed memory;simple recurrent network.
数据来源: Taylor
摘要:
Evidence from priming studies indicates that both semantic and associative relations between pairs of words facilitate word recognition, and that pairs of words related in both ways (e.g. hammer-nail, cat-dog) produce an additional ‘associative boost’. We argue that while semantic priming may result from overlapping patterns of micro-features in a distributed memory model (e.g. Masson, 1991), associative priming is a result of frequent co-occurrence of words in the language. We describe a simple recurrent network, with distributed phonological and semantic representations, which is sensitive to the sequential occurrence of phonological patterns during training, and which produces associative facilitation of word recognition in a simulation of the priming task.
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