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Training Change Detection Leads to Substantial Task-Specific Improvement


Previous research has demonstrated that adaptive training of working memory can substantially increase performance on the trained task. Such training effects have been reported for performance on simple span tasks, complex span tasks, and n-back tasks. Another task that has become a popular vehicle for studying working memory is the change-detection paradigm. In a typical change-detection trial, one has to determine whether a set of stimuli is identical to a set that was presented just previously. Here, we developed an adaptive training regimen comprised of increasingly difficult change-detection trials to assess the degree to which individuals’ change-detection performance can be improved with practice. In contrast to previous work, our results demonstrate that participants are able to dramatically improve their performance in change detection over the course of 10 training sessions. We attribute this improvement to the current training method that adaptively adjusted the set size of the change-detection task to the proficiency of the trainee. Despite these considerable training effects, an exploratory investigation revealed that these improvements remained highly task specific and may not generalize to untrained tasks.

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  1. We note at this point that definitions of WM often stress the involvement of storage and processing components (e.g., Oberauer 2005). The change-detection paradigm is commonly described as a WM task, although it relies more on a storage than a processing component.

  2. In a two-back task for example, a one-back target and a three-back target both constitute a lure.

  3. The corresponding RGB values were black (0, 0, 0), blue (0, 0, 255), green (0, 128, 0), purple (128, 0, 128), red (255, 0, 0), white (255, 255, 255), and yellow (255, 255, 0).

  4. Note that due to the variation in computer hardware, the stimulus size (as expressed in degrees of visual angle) could vary between participants because of different screen sizes and screen resolutions.

  5. Concerning the modeling approaches, we note that for the spatial resolution task, the appropriateness of the model is reflected in both the small number of “error” trials detected (M = 4.9 out of 115 trials; SD = 4.4) and the high test-retest reliability of the resulting precision statistics (1/sd of pre-training vs post-training point distributions was correlated r = 0.81; after each participant was fitted with the model the corresponding 1/SD precision score correlation improved to r = 0.88). The correlation between the number of trials categorized as guesses was also significantly correlated pre- and post-test (r = 0.65; t(38) = 5.2, p < 0.001). For the color resolution task, substantially more trials were categorized as guesses (M = 20.8 out of 125 trials; SD = 10.8), and the precision value (kappa) fitted to each participant (assuming no guessing) had a similar test-retest correlation (r = 0.56 for the raw scores; r = 0.52 for the mixture model). Interestingly, the correlation between the number of trials categorized as guess was higher than the precision scores (r = 0.73).

  6. Note that we present BF10 in the tables.


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The authors would like to thank Nelson Cowan for comments on the initial results of this experiment.


This work was supported by the Office of Naval Research Grant N00014-09-0213 and the Institute of Education Sciences Grant R324A090164

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Correspondence to Martin Buschkuehl.

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SMJ has an indirect financial interest in the MIND Research Institute. MB is employed at MIND Research Institute whose interest is related to this work.

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Buschkuehl, M., Jaeggi, S.M., Mueller, S.T. et al. Training Change Detection Leads to Substantial Task-Specific Improvement. J Cogn Enhanc 1, 419–433 (2017).

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  • Working memory
  • Visual array comparison
  • Practice