Age and demographic effects on implicit learning in a large sample

Abstract

This paper explores implicit learning using artificial grammar learning (AGL) measured across the lifespan (5 to 90 years of age) with a large sample (N=946). The data was gathered by Professor Hasker Davis at the University of Colorado, Colorado Springs, Colorado. This research assigned participants into age groups and tested whether they can significantly learn the AGL, changes existed between the age groups in AGL, and if implicit learning is more durable than explicit learning using a repeated measures ANOVA. Follow??-up polynomial contrast analyses reveal a linear trend from Early Childhood to Young-Adulthood and a quadratic trend from Adolescent to Eldest Adult. An episodic memory task revealed a quadratic trend between Early Childhood to Eldest Adulthood. A hierarchical regression analysis controlling for education and gender revealed education removed curvilinear age prediction of grammaticality, and episodic memory also was not predictive of performance. Hypotheses for results are found in the text.