Individual Differences and Reliability of Paired Associates Learning in Younger and Older Adults Philippe Rast and Daniel Zimprich University of Zurich The authors modeled individual nonlinear trajectories of learning using structured latent growth curves based on an exponential function with 3 parameters: initial performance, learning rate, and asymptotic performance. The 3 parameters showed reliable individual differences and the between-parameter correlations indicated that participants with high learning rates recalled more items initially. The asymptotic performance was unrelated to the learning rate and the initial performance. In addition, age and speed of information processing were included in the analyses. Age mainly affected negatively the asymptotic and the initial performance whereas speed of information processing affected the learning rate positively. Reliability estimates based on 2 similar learning conditions were moderate overall. Keywords: individual differences, age differences, learning, reliability Supplemental materials: http://dx.doi.org/10.1037/a0016138.supp Learning typically follows a nonlinear trajectory: If perfor- mance is diagrammed as a function of the number of practice repetitions, the so-called learning curve emerges, which follows a gradually increasing, albeit negatively accelerated, trajectory (cf. Ritter & Schooler, 2001). Although the bulk of learning research has focused on group-based data and thus on an average learning curve, little is known about individual differences in verbal learn- ing and factors that influence these between-person differences. The paired associates (PA) learning task has been prototypal for investigating individual differences in associative learning. PA tasks are structured in terms of stimulus and response items that are presented contemporarily, with the task being to learn to respond with the response element when the stimulus is presented (e.g., Nelson & Dunlosky, 1994). Ideally, in a PA procedure participants start with no knowledge of the stimulus–response association and finish with these pairs coded in associative mem- ory (cf. Cerella, Onyper, & Hoyer, 2006). Comparable to other memory-related domains, associative learning appears to decline from adulthood into old age. On average, older adults show lower learning rates and lower recall performance compared with younger adults (Cerella et al., 2006; Kausler, 1991, 1994; Salt- house, 1994; Winn, Elias, & Marshall, 1976). There is evidence that the PA task tends to make these age differences more pro- nounced, due to older adults’ difficulties in merging unrelated attributes (Naveh-Benjamin, 2000). Many learning processes can be described by three prominent characteristics (cf. Meredith & Tisak, 1990): They have an initial level; they reach a plateau where learning has an upper asymptote; and they incorporate a rate of learning, a nonlinear slope that describes the speed with which the asymptote is reached, starting from the initial level. Learning curves usually are inherently non- linear, which renders standard—that is, linear or quadratic— growth models inappropriate. 1 In contrast to polynomial functions (McCullagh & Nelder, 1989), nonlinear functions fit the data adequately when limiting behavior is expected. On the basis of earlier work (Browne & Du Toit, 1991), Browne (1993) suggested applying “structured latent curve models” for learning data, which impose specific nonlinear constraints on the pattern matrix of otherwise standard latent growth curve models. Zimprich, Rast, and Martin (2008) followed this approach to model individual differences at the initial level, the asymptotic performance, and the rate of verbal learning in a representative sample of 364 older adults between 65 and 80 years of age. The authors used data from a verbal learning test comprising five learning and recall trials of 27 unrelated words administered in the first wave of the Zurich Longitudinal Study on Cognitive Aging (Zimprich, Martin, et al., 2008). The variances in the verbal learning parameters were all large compared with their standard errors, implying that there were reliable individual differences with respect to the means of the three learning parameters. The authors argued that individual learning trajectories should not be collapsed across individuals, because this would discount relevant informa- tion. In addition, asymptotic performance and learning rate were significantly but negatively correlated, showing that those with a higher asymptotic performance tended to have a slower rate of 1 In polynomial functions, all parameters enter the equation linearly, and the curvature is achieved by adding or subtracting polynomials from each other or from constants. Higher order polynomials follow the observed data closely but often fit badly at the extremes of the observed range of the axis of abscissa, even more so for values which are beyond the observed range (Royston & Altman, 1994). Philippe Rast and Daniel Zimprich, Department of Psychology, Univer- sity of Zurich, Zurich, Switzerland. The research described in this article was supported by a grant from the University of Zurich (Forschungskredit 2004) to Daniel Zimprich. Correspondence concerning this article should be addressed to Philippe Rast, Department of Psychology, University of Zurich, CH-8050 Zurich, Switzerland. E-mail: p.rast@psychologie.uzh.ch Psychology and Aging © 2009 American Psychological Association 2009, Vol. 24, No. 4, 1001–1006 0882-7974/09/$12.00 DOI: 10.1037/a0016138 1001