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Does segmental overlap help or hurt? Evidence from blocked cyclic naming in spoken and written production

Overview of attention for article published in Psychonomic Bulletin & Review, July 2015
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Title
Does segmental overlap help or hurt? Evidence from blocked cyclic naming in spoken and written production
Published in
Psychonomic Bulletin & Review, July 2015
DOI 10.3758/s13423-015-0900-x
Pubmed ID
Authors

Bonnie Breining, Nazbanou Nozari, Brenda Rapp

Abstract

Past research has demonstrated interference effects when words are named in the context of multiple items that share a meaning. This interference has been explained within various incremental learning accounts of word production, which propose that each attempt at mapping semantic features to lexical items induces slight but persistent changes that result in cumulative interference. We examined whether similar interference-generating mechanisms operate during the mapping of lexical items to segments by examining the production of words in the context of others that share segments. Previous research has shown that initial-segment overlap amongst a set of target words produces facilitation, not interference. However, this initial-segment facilitation is likely due to strategic preparation, an external factor that may mask underlying interference. In the present study, we applied a novel manipulation in which the segmental overlap across target items was distributed unpredictably across word positions, in order to reduce strategic response preparation. This manipulation led to interference in both spoken (Exp. 1) and written (Exp. 2) production. We suggest that these findings are consistent with a competitive learning mechanism that applies across stages and modalities of word production.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 34%
Researcher 4 11%
Student > Master 4 11%
Professor 3 9%
Professor > Associate Professor 2 6%
Other 5 14%
Unknown 5 14%
Readers by discipline Count As %
Psychology 20 57%
Linguistics 5 14%
Computer Science 2 6%
Neuroscience 2 6%
Economics, Econometrics and Finance 1 3%
Other 2 6%
Unknown 3 9%