A Quantitative Model-Based Approach to Examining Aging Effects on Information-Integration Category Learning.

Abstract

Information-integration category learning was examined in older and younger adults. Accuracy results indicated that older participants learned less well than younger participants in both linear and nonlinear conditions. Model-based analyses indicated that both groups in the linear condition tended to use information integration but that later in training younger participants were more likely to do so. In contrast, the 2 groups in the nonlinear condition were equally likely to use information integration. Further analysis indicated that younger adults were more accurate than older adults when an information-integration approach was adopted, whereas fewer age-related differences were observed when a rule-based approach was used, suggesting that age can have a negative impact on information-integration category learning processes but less impact on rule-based learning.