Decomposing encoding and decisional components in visual-word recognition: a diffusion model analysis.

Abstract

In a diffusion model, performance as measured by latency and accuracy in two-choice tasks is decomposed into different parameters that can be linked to underlying cognitive processes. Although the diffusion model has been utilized to account for lexical decision data, the effects of stimulus manipulations in previous experiments originated from just one parameter: the quality of the evidence. Here we examined whether the diffusion model can be used to effectively decompose the underlying processes during visual-word recognition. We explore this issue in an experiment that features a lexical manipulation (word frequency) that we expected to affect mostly the quality of the evidence (the drift rate parameter), and a perceptual manipulation (stimulus orientation) that presumably affects the nondecisional time (the Ter parameter, time of encoding and response) more than it affects the drift rate. Results showed that although the manipulations do not affect only one parameter, word frequency and stimulus orientation had differential effects on the model's parameters. Thus, the diffusion model is a useful tool to decompose the effects of stimulus manipulations in visual-word recognition.