Keuleers, E. & Brysbaert, M.
Department of Experimental Psychology, Ghent University, Ghent, Belgium
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 dissimilarities between words and nonwords can introduce inherent biases for a particular response to a given stimulus (e.g., word-stimuli can contain more vowels, nonword stimuli can end frequently with a certain letter). We introduce LD1NN, a simple algorithm based on the Levenshtein Distance (LD) and one-nearest-neighbor classification (1NN), which derives the inherent response bias for each stimulus in an experiment from the distribution of word and nonwords among the most similar previously presented stimuli. We first show that LD1NN is very sensitive to differences between words and matched nonwords generated according to different principles (i.e., random nonword generation, letter replacement, linguistically informed pseudoword generators). Finally, we examine participant data from three lexical decision megastudies and show that the algorithm's predicted biases for and against responses correspond to respectively faster and slower responses to stimuli. The algorithm can be used to examine and limit the degree of inherent bias when designing an experiment and to control for existing bias in statistical analysis of experimental data.