New semantic and serial clustering indices for the California Verbal Learning Test-Second Edition: Background, rationale, and formulae

The original California Verbal Learning Test (CVLT) employed a semantic clustering index that used the words recalled during a given trial as the baseline for calculating expected values of chance clustering (recall-based expectancy). Although commonly used in cognitive psychology, clustering indices that use recall-based calculations of expectancy are implied by the assumption that organizational processes do not occur until after words are retrieved from memory. This assumption contradicts the generally held assumptions among neuropsychologists that (1) organization is an antecedent to recall, and (2) increases in the use of organizational strategies will result in better recall performance. After reviewing a brief history of clustering metrics, we used Monte Carlo simulations, informative examples, and patient data to examine clustering indices that use the word list as a baseline for calculating expectancy and propose these list-based expectancy measures as a refinement of the clustering indices used on the original CVLT. These indices are used on the recently published CVLT-II.