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Detecting inherent bias in lexical decision experiments with the LD1NN algorithm

Emmanuel Keuleers (UGent) and Marc Brysbaert (UGent)
(2011) THE MENTAL LEXICON. 6(1). p.34-52
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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 pre- dicted biases for and against word responses corresponds with respectively faster and slower responses to words in the three studies.
Keywords
visual word recognition, lexical decision, pseudowords, nonwords, machine learning, nearest-neighbor, decision bias, levenshtein distance

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Please use this url to cite or link to this publication:

MLA
Keuleers, Emmanuel, and Marc Brysbaert. “Detecting Inherent Bias in Lexical Decision Experiments with the LD1NN Algorithm.” THE MENTAL LEXICON, edited by Jarema Gonia et al., vol. 6, no. 1, 2011, pp. 34–52.
APA
Keuleers, E., & Brysbaert, M. (2011). Detecting inherent bias in lexical decision experiments with the LD1NN algorithm. THE MENTAL LEXICON, 6(1), 34–52.
Chicago author-date
Keuleers, Emmanuel, and Marc Brysbaert. 2011. “Detecting Inherent Bias in Lexical Decision Experiments with the LD1NN Algorithm.” Edited by Jarema Gonia, Libben Westbury, and Chris Westbury. THE MENTAL LEXICON 6 (1): 34–52.
Chicago author-date (all authors)
Keuleers, Emmanuel, and Marc Brysbaert. 2011. “Detecting Inherent Bias in Lexical Decision Experiments with the LD1NN Algorithm.” Ed by. Jarema Gonia, Libben Westbury, and Chris Westbury. THE MENTAL LEXICON 6 (1): 34–52.
Vancouver
1.
Keuleers E, Brysbaert M. Detecting inherent bias in lexical decision experiments with the LD1NN algorithm. Gonia J, Westbury L, Westbury C, editors. THE MENTAL LEXICON. 2011;6(1):34–52.
IEEE
[1]
E. Keuleers and M. Brysbaert, “Detecting inherent bias in lexical decision experiments with the LD1NN algorithm,” THE MENTAL LEXICON, vol. 6, no. 1, pp. 34–52, 2011.
@article{1852097,
  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 pre- dicted biases for and against word responses corresponds with respectively faster and slower responses to words in the three studies.},
  author       = {Keuleers, Emmanuel and Brysbaert, Marc},
  editor       = {Gonia, Jarema and Westbury, Libben and Westbury, Chris},
  issn         = {1871-1340},
  journal      = {THE MENTAL LEXICON},
  keywords     = {visual word recognition,lexical decision,pseudowords,nonwords,machine learning,nearest-neighbor,decision bias,levenshtein distance},
  language     = {eng},
  number       = {1},
  pages        = {34--52},
  title        = {Detecting inherent bias in lexical decision experiments with the LD1NN algorithm},
  url          = {http://dx.doi.org/10.1075/ml.6.1.02keu},
  volume       = {6},
  year         = {2011},
}

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