scholarly journals Effects of word frequency, contextual diversity, and semantic distinctiveness on spoken word recognition

2012 ◽  
Vol 132 (2) ◽  
pp. EL74-EL80 ◽  
Author(s):  
Brendan T. Johns ◽  
Thomas M. Gruenenfelder ◽  
David B. Pisoni ◽  
Michael N. Jones
Keyword(s):  
Word Recognition ◽  
Word Frequency ◽  
Spoken Word Recognition ◽  
Spoken Word ◽  
Contextual Diversity

scholarly journals Effects of Age, Word Frequency, and Noise on the Time Course of Spoken Word Recognition

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Kristin J. Van Engen ◽  
Avanti Dey ◽  
Nichole Runge ◽  
Brent Spehar ◽  
Mitchell S. Sommers ◽  
...  
Keyword(s):  
Older Adults ◽  
Young Adults ◽  
Word Recognition ◽  
Target Word ◽  
Word Frequency ◽  
Time Course ◽  
Spoken Word Recognition ◽  
Low Frequency ◽  
Spoken Word ◽  
Lexical Activation

This study assessed the effects of age, word frequency, and background noise on the time course of lexical activation during spoken word recognition. Participants (41 young adults and 39 older adults) performed a visual world word recognition task while we monitored their gaze position. On each trial, four phonologically unrelated pictures appeared on the screen. A target word was presented auditorily following a carrier phrase (“Click on ________”), at which point participants were instructed to use the mouse to click on the picture that corresponded to the target word. High- and low-frequency words were presented in quiet to half of the participants. The other half heard the words in a low level of noise in which the words were still readily identifiable. Results showed that, even in the absence of phonological competitors in the visual array, high-frequency words were fixated more quickly than low-frequency words by both listener groups. Young adults were generally faster to fixate on targets compared to older adults, but the pattern of interactions among noise, word frequency, and listener age showed that older adults’ lexical activation largely matches that of young adults in a modest amount of noise.

scholarly journals LexFindR: A fast, simple, and extensible R package for finding similar words in a lexicon

2021 ◽  
Author(s):  
James Magnuson ◽  
ZHAOBIN LI ◽  
Anne Marie Crinnion
Keyword(s):  
Word Recognition ◽  
Word Frequency ◽  
Word Length ◽  
Spoken Word Recognition ◽  
R Package ◽  
Spoken Word ◽  
Online Tools ◽  
Source Data ◽  
Uniqueness Point ◽  
Theoretical Predictions

Language scientists often need to generate lists of related words, such as potential competitors. They may do this for purposes of experimental control (e.g., selecting items matched on lexical neighborhood but varying in word frequency), or to test theoretical predictions (e.g., hypothesizing that a novel type of competitor may impact word recognition). Several online tools are available, but most are constrained to a fixed lexicon and fixed sets of competitor definitions, and may not give the user full access to or control of source data. We present LexFindR, an open source R package that can be easily modified to include additional, novel competitor types. LexFindR is easy to use. Because it can leverage multiple CPU cores and uses vectorized code when possible, it is also extremely fast. In this article, we present an overview of LexFindR usage, illustrated with examples. We also explain the details of how we implemented several standard lexical competitor types used in spoken word recognition research (e.g., cohorts, neighbors, embeddings, rhymes), and show how “lexical dimensions” (e.g., word frequency, word length, uniqueness point) can be integrated into LexFindR workflows (for example, to calculate “frequency weighted competitor probabilities”), for both spoken and visual word recognition research.

Individual differences in executive function affect spoken word recognition

2009 ◽  
Author(s):  
Julie Mercier ◽  
Irina Pivneva ◽  
Corinne Haigh ◽  
Debra A. Titone
Keyword(s):  
Executive Function ◽  
Individual Differences ◽  
Word Recognition ◽  
Spoken Word Recognition ◽  
Spoken Word
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