Related Experiment Video
Updated: Jul 29, 2026

The Deese-Roediger-McDermott (DRM) Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory
Published on: January 31, 2017
Factors that determine false recall: a multiple regression analysis
H L Roediger1, J M Watson, K B McDermott
1Department of Psychology, Washington University, St. Louis, Missouri 63130-4899, USA. roediger@artsci.wustl.edu
Abstract:
In the Deese-Roediger-McDermott (DRM) paradigm, subjects study lists of words that are designed to elicit the recall of an associatively related critical item. The 55 lists we have developed provide levels of false recall ranging from .01 to .65, and understanding this variability should provide a key to understanding this memory illusion. Using a simultaneous multiple regression analysis, we assessed the contribution of seven factors in creating false recall of critical items in the DRM paradigm. This analysis accounted for approximately 68% of the variance in false recall, with two main predictors: associative connections from the study words to the critical item (r = +.73; semipartial r = +.60) and recallability of the lists (r = -.43; semipartial r = -.34). Taken together, the variance in false recall captured by these predictors accounted for 84% of the variance that can be explained, given the reliability of the false recall measures (r = .90). Therefore, the results of this analysis strongly constrain theories of false memory in this paradigm, suggesting that at least two factors determine the propensity of DRM lists to elicit false recall. The results fit well within the theoretical framework postulating that both semantic activation of the critical item and strategic monitoring processes influence the probability of false recall and false recognition in this paradigm.
Related Concept Videos
Factorial Design
Regression Toward the Mean
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
False Memories
One primary source of false memories is misattribution, where individuals incorrectly associate external information with...

