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

Abstract

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.

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Notes

  1. 1.

    In the older sample, the 33-item version was administered. To match the younger samples, we only included the 16 items from the short version in the present analyses.

  2. 2.

    As the two measures for self-efficacy were not correlated (r = 0.03, p = .715), we analyzed both measures separately rather than computing a composite score.

  3. 3.

    Estimated means are determined by the factor mean of the intercept μ i and pattern coefficients λ and were computed by the formula: estimated mean = μ i + λ × μ s (see Kline 2016 for details).

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Acknowledgements

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

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.

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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.

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Guye, S., De Simoni, C. & von Bastian, C.C. Do Individual Differences Predict Change in Cognitive Training Performance? A Latent Growth Curve Modeling Approach. J Cogn Enhanc 1, 374–393 (2017). https://doi.org/10.1007/s41465-017-0049-9

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Keywords

  • Working memory training
  • Individual differences
  • Latent growth curve modeling