This paper combines two different approaches to modeling reaction time
data from lexical decision experiments, viz. a data-oriented statistical
analysis by means of a linear mixed effects model, and a process-oriented
computational model of human speech comprehension.
The linear mixed effect
model is implemented by lmer in R. As computational model we apply
DIANA, an end-to-end computational model which aims at modeling the
cognitive processes underlying speech comprehension. DIANA takes as
input the speech signal, and provides as output the orthographic transcription
of the stimulus, a word/non-word judgment and the associated reaction
time. Previous studies have shown that DIANA shows good results for
large-scale lexical decision experiments in Dutch and North-American
English.
We investigate whether predictors that appear significant in an
lmer analysis and processes implemented in DIANA can be related and
inform both approaches. Predictors such as ‘previous reaction
time’ can be related to a process description; other predictors,
such as ‘lexical neighborhood’ are hard-coded in lmer and
emergent in DIANA. The analysis focuses on the interaction between
subject variables and task variables in lmer, and the ways in which
these interactions can be implemented in DIANA.