Effects of Strategy Training on Progressive Matrices Performance

Two qualitatively different information-processing algorithms for solution of Raven’s Progressive Matrices items have been identified. Whereas the Gestalt algorithm involves spatial operations upon the test stimuli, the Analytic algorithm employs logical operations upon features abstracted from the displays. In this study, training groups were established varying both in the Strength (Weak or Strong) and Type (Gestalt or Analytic) of training at three grade levels. Two sets of post-test measures were given. Ambiguous items were constructed such that more than one correct answer was possible, some being the result of the Gestalt algorithm and others of the Analytic algorithm. Subjects’ performances on the Ambiguous items indicated that strong Analytic training had been particularly effective and was specific to Analytic answer options. The second post-test measure was Set I of the Advanced Progressive Matrices. Performance on these Test items indicated that the effects of strategy training had been maintained, and were due to the facilitation of Analytic item performance by Analytic training. The effects of Strength and Type of training were consistent across Grades. These results support Hunt’s analysis of Raven’s Progressive Matrices items, and demonstrate that strategy training based upon a precise information processing task analysis can be effective in improving Progressive Matrices performance. The implications of these results for intellectual assessment are discussed.