Abstract
High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) - a graph theoretic variable measuring the degree to which a word's neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-21 |
| Number of pages | 21 |
| Journal | Mental Lexicon |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jul 5 2010 |
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Keywords
- Clustering coefficient
- Complex networks
- Graph theory
- Mental lexicon
ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language
- Cognitive Neuroscience
Cite this
Clustering coefficients of lexical neighborhoods : Does neighborhood structure matter in spoken word recognition? / Altieri, Nicholas; Gruenenfelder, Thomas; Pisoni, David B.
In: Mental Lexicon, Vol. 5, No. 1, 05.07.2010, p. 1-21.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Clustering coefficients of lexical neighborhoods
T2 - Does neighborhood structure matter in spoken word recognition?
AU - Altieri, Nicholas
AU - Gruenenfelder, Thomas
AU - Pisoni, David B.
PY - 2010/7/5
Y1 - 2010/7/5
N2 - High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) - a graph theoretic variable measuring the degree to which a word's neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.
AB - High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) - a graph theoretic variable measuring the degree to which a word's neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.
KW - Clustering coefficient
KW - Complex networks
KW - Graph theory
KW - Mental lexicon
UR - http://www.scopus.com/inward/record.url?scp=77954110991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954110991&partnerID=8YFLogxK
U2 - 10.1075/ml.5.1.01alt
DO - 10.1075/ml.5.1.01alt
M3 - Article
AN - SCOPUS:77954110991
VL - 5
SP - 1
EP - 21
JO - The Mental Lexicon
JF - The Mental Lexicon
SN - 1871-1340
IS - 1
ER -