Journal of Cognitive Enhancement

, Volume 1, Issue 4, pp 374–393 | Cite as

Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach

  • Sabrina GuyeEmail author
  • Carla De Simoni
  • Claudia C. von Bastian
Original Article


Cognitive training interventions have become increasingly popular as a potential means to cost-efficiently stabilize or enhance cognitive functioning across the lifespan. Large training improvements have been consistently reported on the group level, with, however, large differences on the individual level. Identifying the factors contributing to these individual differences could allow for developing individually tailored interventions to boost training gains. In this study, we therefore examined a range of individual differences variables that had been discussed in the literature to potentially predict training performance. To estimate and predict individual differences in the training trajectories, we applied Latent Growth Curve models to existing data from three working memory training interventions with younger and older adults. However, we found that individual differences in demographic variables, real-world cognition, motivation, cognition-related beliefs, personality, leisure activities, and computer literacy and training experience were largely unrelated to change in training performance. Solely baseline cognitive performance was substantially related to change in training performance and particularly so in young adults, with individuals with higher baseline performance showing the largest gains. Thus, our results conform to magnification accounts of cognitive change.


Working memory training Individual differences Latent growth curve modeling 



During the work on her dissertation, Sabrina Guye was a pre-doctoral fellow of the International Max Planck Research School on the Life Course (LIFE; participating institutions: MPI for Human Development, Humboldt-Universität zu Berlin, Freie Universität Berlin, University of Michigan, University of Virginia, University of Zurich).

Funding Information

Data reported in this work has been collected with the support of grants awarded to the first and second author from the Suzanne and Hans Biäsch Foundation for Applied Psychology (Ref 2014/32; 2016/08). The first author was further supported by the Forschungskredit of the University of Zurich (FK-16-062), and the second author by the Swiss National Science Foundation (No. 100014_146074). Moreover, both authors were supported by the URPP “Dynamics of Healthy Aging” of the University of Zurich.

Compliance with Ethical Standards

Written informed consent was obtained from all participants. Both studies were approved by the ethics committee of the Department of Psychology of the University of Zurich.

Supplementary material

41465_2017_49_MOESM1_ESM.docx (222 kb)
ESM 1 (DOCX 221 kb)


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University Research Priority Program (URPP) “Dynamics of Healthy Aging”University of ZurichZürichSwitzerland
  2. 2.Department of PsychologyUniversity of ZurichZurichSwitzerland
  3. 3.Department of PsychologyBournemouth UniversityPooleUK
  4. 4.Department of PsychologyUniversity of SheffieldSheffieldUK

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