Dissociable Perceptual Learning Mechanisms Revealed by Diffusion-Model Analysis

Abstract:

Performance on perceptual tasks improves with practice. Most theories address only accuracy data and tacitly assume that perceptual learning is a monolithic phenomenon. The current study pioneers the use of response-time distributions in perceptual learning research. The 27 observers practiced a visual motion-direction discrimination task with filtered-noise textures for 4 sessions with feedback. Session 5 tested whether the learning effects transferred to the orthogonal direction. The diffusion model (Ratcliff, Psychological Review, 85, 59-108, 1978) achieved good fits to the individual response-time distributions from each session and identified two distinct learning mechanisms with markedly different specificities. A stimulus-specific increase in the drift-rate parameter indicates improved sensory input to the decision process. A stimulus-general decrease in the nondecision time variability suggests improved timing of the decision-process onset relative to stimulus onset (which was preceded by a beep). The traditional d' analysis misses the latter effect but the diffusion-model analysis identifies it in the response-time data.

Preprint (pdf) Supplement (pdf) Matlab report (html) Companion paper
Perceptual Learning Project

Matlab Report

In the process of analyzing the data, developing the models, and fitting them to the data, we wrote various Matlab scripts. These scripts generated HTML reports via Matlab's "publish" feature. The most recent report is made available here. Use the Back button in your browser to return to this page.

• MAESpec_diffusion_fits.html -- All diffusion-model analyses performed on this data set