The study was supported by Grants MH39349 from the National Institute of Mental Health and DC01947 from the National Institute for Deafness and Other Communication Disorders. We are grateful to Albert Kim and Judith McLaughlin for their assistance in data collection and analyses, and to Melissa Ferguson, Walter Mischel, and Yuichi Shoda for their thoughtful comments and suggestions on earlier drafts of the manuscript.
Unintentional covert motor activations predict behavioral effects: Multilevel modeling of trial-level electrophysiological motor activations
Article first published online: 22 JUN 2010
Copyright © 2010 Society for Psychophysiological Research
Volume 48, Issue 2, pages 208–217, February 2011
How to Cite
Zayas, V., Greenwald, A. G. and Osterhout, L. (2011), Unintentional covert motor activations predict behavioral effects: Multilevel modeling of trial-level electrophysiological motor activations. Psychophysiology, 48: 208–217. doi: 10.1111/j.1469-8986.2010.01055.x
- Issue published online: 6 JAN 2011
- Article first published online: 22 JUN 2010
- (Received August 20, 2009; Accepted March 9, 2010)
- Motor activations;
- Event-related potentials
The present experiment measured an EEG indicator of motor cortex activation, the lateralized readiness potential (LRP), while participants performed a speeded category classification task. The LRP data showed that visually masked words triggered covert motor activations. These prime-induced motor activations preceded motor activations by subsequent (to-be-classified) visible target words. Multilevel statistical analyses of trial-level effects, applied here for the first time with electrophysiological data, revealed that accuracy and latency of classifying target words was affected by both (a) covert motor activations caused by visually masked primes and (b) spontaneous fluctuations in covert motor activations. Spontaneous covert motor fluctuations were unobserved with standard subject-level (multi-trial) analyses of grand-averaged LRPs, highlighting the utility of multilevel modeling of trial-level effects.