To date, cognitive intervention research has provided mixed but nevertheless promising evidence with respect to the effects of cognitive training on untrained tasks (transfer). However, the mechanisms behind learning, training effects and their predictors are not fully understood. Moreover, individual differences, which may constitute an important factor impacting training outcome, are usually neglected. We suggest investigating individual training performance across training sessions in order to gain finer-grained knowledge of training gains, on the one hand, and assessing the potential impact of predictors such as age and fluid intelligence on learning rate, on the other hand. To this aim, we propose to model individual learning curves to examine the intra-individual change in training as well as inter-individual differences in intra-individual change. We recommend introducing a latent growth curve model (LGCM) analysis, a method frequently applied to learning data but rarely used in cognitive training research. Such advanced analyses of the training phase allow identifying factors to be respected when designing effective tailor-made training interventions. To illustrate the proposed approach, a LGCM analysis using data of a 10-day working memory training study in younger and older adults is reported.

No abstract

Empirical examinations of the hypothesis of dedifferentiation of cognitive abilities in old and very old age (a) do not account for possible retest effects, which consequently may yield biased estimates of age effects, and (b) focus on time-independent relations (e.g., number of latent constructs, correlations between latent or measured variables). The authors applied a structural equation model with statistical control for retest effects to investigate the dynamic relations between a marker of perceptual speed (cross out) and a marker of verbal fluency (category-fruits). Longitudinal data are from 5 waves of the Swiss Interdisciplinary Longitudinal Study on the Oldest Old (N = 377, baseline age range = 79.5- 84.5 years). The authors found that, independently of retest effects, performance on the cross-out task affected changes in performance on the category task while the opposite did not hold true. This analytical technique could be applied to various markers of broad fluid-mechanic and broad crystallized-pragmatic components of cognition.

These findings confirm the hypothesis that WM capacity explains age differences in text processing, while it is itself accounted for by the efficiency of inhibiting irrelevant information and by speed of processing.

Our data indicate that personality factors and depression are related, independently of patients' age. Differences in this relationship are mainly due to the intensity of depressive symptoms rather than the patients' life period. They also stress the need to consider physical health, level of dependency and severity of symptoms when studying the relationship between personality traits and mood disorders.