Volume 5, Issue 1
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
Buy:$35.00 + Taxes


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.


Article metrics loading...

Loading full text...

Full text loading...

  • Article Type: Research Article
Keyword(s): and graph theory; clustering coefficient; complex networks; mental lexicon
This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error