Main content
Controlling for human response latency with continuous-time deconvolutional regression
- Cory Shain
- William Schuler
Date created: | Last Updated:
: DOI | ARK
Creating DOI. Please wait...
Category: Uncategorized
Description: Continuous-time deconvolutional regression (CDR) is a recent machine learning method that directly controls for effects that diffuse over time, providing high-resolution estimates of effect timecourses without the use of spillover regressors. In this study, we provide empirical support for the use of CDR in psycholinguistics through both synthetic and psycholinguistic data. Synthetic experiments show that CDR is robust to common challenges like noise, multicollinearity, and model misspecification. Psycholinguistic experiments show plausible estimates of effect timecourses that are consistent across model designs and response types, including self-paced reading, eye-tracking, and fMRI. URL for Zoom meeting where you can talk to me (Cory) live: https://osu.zoom.us/j/825693623
Loading...
Start managing your projects on the OSF today.
Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.