Address correspondence to: John J. Allen, Ph.D., Department of Psychology. University of Arizona, Tucson. AZ 85721.
The Identification of Concealed Memories Using the Event-Related Potential and Implicit Behavioral Measures: A Methodology for Prediction in the Face of Individual Differences
Article first published online: 30 JAN 2007
Volume 29, Issue 5, pages 504–522, September 1992
How to Cite
Allen, J. J., Iacono, W. G. and Danielson, K. D. (1992), The Identification of Concealed Memories Using the Event-Related Potential and Implicit Behavioral Measures: A Methodology for Prediction in the Face of Individual Differences. Psychophysiology, 29: 504–522. doi: 10.1111/j.1469-8986.1992.tb02024.x
This research was supported, in part, by NIMH research training fellowship 5T32-MHI 7069–07. The authors would like to thank Scott Sponheim, John Ficken, and Laurie Dunn for serving as raters. Thanks also to Shen Boril for assistance in running subjects. And thank you to Laurie Dunn for helpful comments on earlier versions of this manuscript. Portions of the present data set were presented at the 1989, 1990, and 1991 annual conventions of the Society for Psychophysiological Research.
- Issue published online: 30 JAN 2007
- Article first published online: 30 JAN 2007
- (Manuscript received November 21, 1990; accepted for publication June 4, 1991)
- Event-related potentials;
- Memory assessment;
- Individual differences;
- Bayesian classification
The development and validation of an event-related potential (ERP) memory assessment procedure is detailed. The procedure identifies learned material with high rates of accuracy, whether or not subjects give intentional responses indicating they had previously learned it. Because the traditional analysis of variance approach fails to provide probabilistic conclusions about any given individual, Bayesian posterior probabilities were computed, indicating the probability for each and every person that material was learned. The method was developed on a sample of 20 subjects, and then cross-validated on two additional samples of 20 subjects each. Across the three samples, the method correctly defined over 94% of learned material as learned, and misclassified 4% of the unlearned material. Additionally, in a simple oddball task performed by the same subjects, the method classified rare and frequent material with perfect accuracy. Finally, combining two implicit behavioral measures—mean reaction time and the number of incorrect responses—in Bayesian fashion yielded classification accuracy that actually exceeded that of the ERP-based procedure overall, but the two methods provided identical accuracy in classifying the most critical material as recognized.