TY - JOUR
T1 - Saying the right word at the right time
T2 - Syntagmatic and paradigmatic interference in sentence production
AU - Dell, Gary S.
AU - Oppenheim, Gary M.
AU - Kittredge, Audrey K.
N1 - Funding Information:
Correspondence should be addressed to Gary S. Dell, Beckman Institute, University of Illinois, 405 N. Mathews Ave., Urbana IL 61820, USA. E-mail: gdell@uiuc.edu The authors thank the organisers of the Language Production Workshop (Chicago, 2006) for suggesting the topic that led to this work, Matt Goldrick and two anonymous reviewers for comments on the manuscript, and Myrna Schwartz and Jean Gordon for inspiration on many aspects of the models presented here. The research was supported by the National Institutes of Health (DC000191, HD44458, and MH1819990).
PY - 2008/6
Y1 - 2008/6
N2 - Retrieving a word in a sentence requires speakers to overcome syntagmatic, as well as paradigmatic interference. When accessing cat in 'The cat chased the string', not only are similar competitors such as dog and cap activated, but also other words in the planned sentence, such as chase and string. We hypothesise that both types of interference impact the same stage of lexical access, and review connectionist models of production that use an error-driven learning algorithm to overcome that interference. This learning algorithm creates a mechanism that limits syntagmatic interference, the syntactic 'traffic cop', a configuration of excitatory and inhibitory connections from syntactic-sequential states to lexical units. We relate the models to word and sentence production data, from both normal and aphasic speakers.
AB - Retrieving a word in a sentence requires speakers to overcome syntagmatic, as well as paradigmatic interference. When accessing cat in 'The cat chased the string', not only are similar competitors such as dog and cap activated, but also other words in the planned sentence, such as chase and string. We hypothesise that both types of interference impact the same stage of lexical access, and review connectionist models of production that use an error-driven learning algorithm to overcome that interference. This learning algorithm creates a mechanism that limits syntagmatic interference, the syntactic 'traffic cop', a configuration of excitatory and inhibitory connections from syntactic-sequential states to lexical units. We relate the models to word and sentence production data, from both normal and aphasic speakers.
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U2 - 10.1080/01690960801920735
DO - 10.1080/01690960801920735
M3 - Article
C2 - 20622975
AN - SCOPUS:42649095973
SN - 0169-0965
VL - 23
SP - 583
EP - 608
JO - Language and Cognitive Processes
JF - Language and Cognitive Processes
IS - 4
ER -