Comparison of the Traditional Recall-Based versus a New List-Based Method for Computing Semantic Clustering on the California Verbal Learning Test: Evidence from Alzheimer's Disease

Abstract

For over 50 years, cognitive psychologists and neuropsychologists have relied almost exclusively on a method for computing semantic clustering on list-learning tasks (recall-based formula) that was derived from an outdated assumption about how learning occurs. A new procedure for computing semantic clustering (list-based formula) was developed for the CVLT-II to correct the shortcomings of the traditional method. In the present study we compared the clinical utility of the traditional recall-based method versus the new list-based method using results from the original CVLT administered to 87 patients with Alzheimer's disease and 86 matched normal control participants. Logistic regression and score distribution analyses indicated that the new list-based method enhances the detection of differences in semantic-clustering ability between the groups.

Keywords:

• Learning
• Memory
• Neuropsychology
• CVLT

Acknowledgments

We wish to thank the Alzheimer's Disease Research Center (ADRC) at the University of California, San Diego, School of Medicine for providing the CVLT data used in this study. Dr. Delis is a co-author of the CVLT and receives royalties from this test.