Predicting performance on the Raven's Matrices: The roles of associative learning and retrieval efficiency

Abstract

Previous studies have shown that performance on Williams and Pearlberg's (2006) complex associative learning task is a good predictor of fluid intelligence. This task is similar in structure to that used in studying the fan effect (Anderson, 1974), as both tasks involve forming multiple associations and require retrieval in the face of interference. The purpose of the present study was to investigate the relations among complex associative learning, working memory, and fluid intelligence. Specifically, we asked whether retrieval efficiency, as measured by the fan effect, could account for the relation between complex associative learning and performance on Raven's Advanced Progressive Matrices. Consistent with previous findings, complex associative learning predicted Raven's performance, but the fan effect did not account for this relation. Notably, the learning phase of the fan effect task was significantly correlated with both complex associative learning and Raven's performance, providing further support for the importance of learning as a predictor of fluid intelligence.