The role of individual differences in cognitive training and transfer

Abstract

Working memory (WM) training has recently become a topic of intense interest and controversy. Although several recent studies have reported near- and far-transfer effects as a result of training WM-related skills, others have failed to show far transfer, suggesting that generalization effects are elusive. Also, many of the earlier intervention attempts have been criticized on methodological grounds. The present study resolves some of the methodological limitations of previous studies and also considers individual differences as potential explanations for the differing transfer effects across studies. We recruited intrinsically motivated participants and assessed their need for cognition (NFC; Cacioppo & Petty Journal of Personality and Social Psychology 42:116–131, 1982) and their implicit theories of intelligence (Dweck, 1999) prior to training. We assessed the efficacy of two WM interventions by comparing participants’ improvements on a battery of fluid intelligence tests against those of an active control group. We observed that transfer to a composite measure of fluid reasoning resulted from both WM interventions. In addition, we uncovered factors that contributed to training success, including motivation, need for cognition, preexisting ability, and implicit theories about intelligence.

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Notes

  1. 1.

    Note that Gf data are not reported in Anguera et al. (2012, Exp. 2) because that article was primarily addressing issues of WM and motor function, building on the findings of Experiment 1. However, the Gf data are available as a technical report (Seidler et al., 2010).

  2. 2.

    Kundu et al. (2013) paid participants about $360 for study completion. Note, though, that as in Anguera et al.’s (2012) study, Kundu et al.’s participants showed n-back-related improvement in other outcome measures. Furthermore, the lack of differential group effects in the Raven’s test could also stem from the fact that their control task consisted of Tetris, a game that had previously resulted in training-related improvements in spatial ability itself (e.g., De Lisi & Wolford, 2002; Okagaki & Frensch, 1994; Terlecki, Newcombe, & Little, 2008).

  3. 3.

    Even though the present participants were not paid for participation, the 25 % attrition rate is comparable to or lower than the rates reported in other cognitive training studies. For example, Chooi and Thompson (2012) reported a drop-out rate of 28 %, and Redick et al. (2013) reported a drop-out rate of approximately 42 %.

  4. 4.

    Note that the DST has no parallel test version.

  5. 5.

    Note that the comparisons reported in this paragraph contrasted the entire training group (i.e., including all three interventions) with the groups that had dropped out, withdrawn, or just completed the paid pretest. Within the training group, we found no significant intervention group differences for any of those variables (on either the cognitive measures or the questionnaires).

  6. 6.

    Note that by taking the whole sample into account (N = 195; this sample size reflects all participants who completed all of the baseline assessments, including the paid pretest sample), the same factor structure emerged, explaining 45 % of the total variance, although the APM loaded equally on both factors.

    The DST was included in a first exploratory factor analysis and revealed equal, weak loadings on both factors (<.3). In the reported analysis, DST was not included, and was also discarded from all further analyses involving composite scores.

  7. 7.

    Note that the p values remained significant if the data were analyzed using a repeated measures analysis of variance with Time (pretest, posttest) as a within-subjects factor and Group (single n-back, dual n-back, control) as a between-subjects factor.

  8. 8.

    A slightly different interpretation is that those with malleable mindsets are better learners in both the active control and intervention groups, and thus improve on transfer tasks regardless of the intervention; this would also yield a placebo-like effect.

  9. 9.

    In the previous single n-back intervention, the improvement was 3.6 n-back levels (Jaeggi et al., 2010) versus 3.0 levels in the present study. However, note that the previous sample trained on a visuospatial version, whereas the present sample trained on an auditory version; thus, the improvements might not be entirely comparable.

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Author note

This work has been funded by grants from the ONR and the NSF to J.J., and by a grant from the IES to P.S. We thank our research assistants, especially Chris Cargill, for their invaluable help with data collection. We also thank Michael Kane, Bill Thompson, and Stephen Kosslyn for letting us use their material for the verbal analogies task.

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Correspondence to Susanne M. Jaeggi.

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Jaeggi, S.M., Buschkuehl, M., Shah, P. et al. The role of individual differences in cognitive training and transfer. Mem Cogn 42, 464–480 (2014). https://doi.org/10.3758/s13421-013-0364-z

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Keywords

  • Working memory
  • Reasoning
  • Skill acquisition
  • Individual differences
  • Intelligence