Combining data-oriented and process-oriented approaches to modeling
reaction time data
Ten Bosch, L., Boves, L., & Ernestus, M.
(2016). Combining data-oriented and process-oriented approaches to modeling reaction time data. In
Proceedings of Interspeech 2016: The 17th Annual Conference of the International Speech Communication Association (pp. 2801-2805). doi:10.21437/Interspeech.2016-1072.
This paper combines two different approaches to modeling reaction
time data from lexical decision experiments, viz. a dataoriented
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.
Publication type
Proceedings paper
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