1887
Methodological and Analytic Frontiers in Lexical Research (Part II)
  • ISSN 1871-1340
  • E-ISSN: 1871-1375
GBP
Buy:£15.00 + Taxes

Abstract

A basic assumption of the lexical decision task is that a correct response to a word requires access to a corresponding mental representation of that word. However, systematic patterns of similarities and differences between words and nonwords can lead to an inherent bias for a particular response to a given stimulus. In this paper we introduce LD1NN, a simple algorithm based on one-nearest-neighbor classification that predicts the probability of a word response for each stimulus in an experiment by looking at the word/nonword probabilities of the most similar previously presented stimuli. Then, we apply LD1NN to the task of detecting differences between a set of words and different sets of matched nonwords. Finally, we show that the LD1NN word response probabilities are predictive of response times in three large lexical decision studies and that predicted biases for and against word responses corresponds with respectively faster and slower responses to words in the three studies.

Loading

Article metrics loading...

/content/journals/10.1075/ml.6.1.02keu
2011-01-01
2018-11-14
Loading full text...

Full text loading...

References

http://instance.metastore.ingenta.com/content/journals/10.1075/ml.6.1.02keu
Loading
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