Measuring Individual Differences in Implicit Learning with Artificial Grammar Learning Tasks

Abstract

Abstract. Implicit learning can be defined as learning without intention or awareness. We discuss conceptually and investigate empirically how individual differences in implicit learning can be measured with artificial grammar learning (AGL) tasks. We address whether participants should be instructed to rate the grammaticality or the novelty of letter strings and look at the impact of a knowledge test on measurement quality. We discuss these issues from a conceptual perspective and report three experiments which suggest that (1) the reliability of AGL is moderate and too low for individual assessments, (2) a knowledge test decreases task consistency and increases the correlation with reportable grammar knowledge, and (3) performance in AGL tasks is independent from general intelligence and educational attainment.