N-back Versus Complex Span Working Memory Training
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Working memory (WM) is the ability to maintain and manipulate task-relevant information in the absence of sensory input. While its improvement through training is of great interest, the degree to which WM training transfers to untrained WM tasks (near transfer) and other untrained cognitive skills (far transfer) remains debated and the mechanism(s) underlying transfer are unclear. Here we hypothesized that a critical feature of dual n-back training is its reliance on maintaining relational information in WM. In experiment 1, using an individual differences approach, we found evidence that performance on an n-back task was predicted by performance on a measure of relational WM (i.e., WM for vertical spatial relationships independent of absolute spatial locations), whereas the same was not true for a complex span WM task. In experiment 2, we tested the idea that reliance on relational WM is critical to produce transfer from n-back but not complex span task training. Participants completed adaptive training on either a dual n-back task, a symmetry span task, or on a non-WM active control task. We found evidence of near transfer for the dual n-back group; however, far transfer to a measure of fluid intelligence did not emerge. Recording EEG during a separate WM transfer task, we examined group-specific, training-related changes in alpha power, which are proposed to be sensitive to WM demands and top-down modulation of WM. Results indicated that the dual n-back group showed significantly greater frontal alpha power after training compared to before training, more so than both other groups. However, we found no evidence of improvement on measures of relational WM for the dual n-back group, suggesting that near transfer may not be dependent on relational WM. These results suggest that dual n-back and complex span task training may differ in their effectiveness to elicit near transfer as well as in the underlying neural changes they facilitate.
KeywordsCognitive training Working memory Transfer Alpha power
We wish to thank Cody Elias, Antonio Vergara, Samantha Dunnum, Myranda Gormley, Leon Li, and Carolyn Xue for help with data collection.
This project was supported by a Johns Hopkins University Science of Learning Institute Fellowship to KJB, NIH grant R01 MH082957 to SMC, and grant K23 NS073626 to JBE.
- Bahlmann, J., Blumenfeld, R. S., & D'Esposito, M. (2014). The rostro-caudal axis of frontal cortex is sensitive to the domain of stimulus information. Cerebral Cortex. https://doi.org/10.1093/cercor/bht419.
- Bastiaansen, M., Mazaheri, A., & Jensen, O. (2012). Beyond ERP’s: Oscillatory neuronal dynamics. In S. J. Luck & E. S. Kappenman (Eds.), The Oxford handbook of event-related potential components. USA: Oxford University Press.Google Scholar
- Beatty, E. L., Jobidon, M. E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., et al. (2015). Transfer of training from one working memory task to another: Behavioural and neural evidence. Frontiers in Systems Neuroscience, 9, 86. https://doi.org/10.3389/fnsys.2015.00086.CrossRefPubMedPubMedCentralGoogle Scholar
- Blacker, K. J., Ikkai, A., Lakshmanan, B. M., Ewen, J. B., & Courtney, S. M. (2016). The role of alpha oscillations in deriving and maintaining spatial relations in working memory. Cognitive Affective & Behavioral Neuroscience, 16(5), 888–901. https://doi.org/10.3758/s13415-016-0439-y.CrossRefGoogle Scholar
- Blacker, K. J., Weisberg, S. M., Newcombe, N. S., & Courtney, S. M. (2017). Keeping track of where we are: Spatial working memory in navigation. Visual Cognition, 1–12. https://doi.org/10.1080/13506285.2017.1322652.
- Boot, W. R., Simons, D. J., Stothart, C., & Stutts, C. (2013). The pervasive problem of placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. Perspectives on Psychological Science, 8(4), 445–454. https://doi.org/10.1177/1745691613491271.CrossRefPubMedGoogle Scholar
- Colom, R., Roman, F. J., Abad, F. J., Shih, P. C., Privado, J., Froufe, M., et al. (2013). Adaptive n-back training does not improve fluid intelligence at the construct level: Gains on individual tests suggest that training may enhance visuospatial processing. Intelligence, 41(5), 712–727. https://doi.org/10.1016/J.Intell.2013.09.002.CrossRefGoogle Scholar
- Haatveit, B. C., Sundet, K., Hugdahl, K., Ueland, T., Melle, I., & Andreassen, O. A. (2010). The validity of d prime as a working memory index: Results from the “Bergen n-back task”. Journal of Clinical and Experimental Neuropsychology, 32(8), 871–880. https://doi.org/10.1080/13803391003596421.CrossRefPubMedGoogle Scholar
- Hossiep, R., Turck, D., & Hasella, M. (1999). Bochumer Matrizentest (BOMAT) advanced-short version. Gottingen: Hogrefe.Google Scholar
- Ikkai, A., Blacker, K. J., Lakshmanan, B. M., Ewen, J. B., & Courtney, S. M. (2014). Maintenance of relational information in working leads to suppression of the sensory cortex. Journal of Neurophysiology, 112(8), 1903–1915. https://doi.org/10.1152/jn.00134.2014.CrossRefPubMedPubMedCentralGoogle Scholar
- Jaeggi, S. M., Seewer, R., Nirkko, A. C., Eckstein, D., Schroth, G., Groner, R., & Gutbrod, K. (2003). Does excessive memory load attenuate activation in the prefrontal cortex? Load-dependent processing in single and dual tasks: Functional magnetic resonance imaging study. NeuroImage, 19(2 Pt 1), 210–225.CrossRefPubMedGoogle Scholar
- Jaeggi, S. M., Studer-Luethi, B., Buschkuehl, M., Su, Y. F., Jonides, J., & Perrig, W. J. (2010a). The relationship between n-back performance and matrix reasoning—implications for training and transfer. Intelligence, 38(6), 625–635. https://doi.org/10.1016/J.Intell.2010.09.001.CrossRefGoogle Scholar
- Jonides, J., Schumacher, E. H., Smith, E. E., Lauber, E. J., Awh, E., Minoshima, S., & Koeppe, R. A. (1997). Verbal working memory load affects regional brain activation as measured by PET. Journal of Cognitive Neuroscience, 9(4), 462–475. https://doi.org/10.1162/Jocn.19184.108.40.2062.CrossRefPubMedGoogle Scholar
- Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W. (2004). The generality of working memory capacity: A latent-variable approach to verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology: General, 133(2), 189–217. https://doi.org/10.1037/0096-34220.127.116.11.CrossRefGoogle Scholar
- Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006). Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. Journal of Neurophysiology, 95(6), 3844–3851. https://doi.org/10.1152/jn.01234.2005.CrossRefPubMedGoogle Scholar
- Klingberg, T., Fernell, E., Olesen, P. J., Johnson, M., Gustafsson, P., Dahlstrom, K., et al. (2005). Computerized training of working memory in children with ADHD—a randomized, controlled trial. Journal of the American Academy of Child and Adolescent Psychiatry, 44(2), 177–186. https://doi.org/10.1097/00004583-200502000-00010.CrossRefPubMedGoogle Scholar
- Kundu, B., Sutterer, D. W., Emrich, S. M., & Postle, B. R. (2013). Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. The Journal of Neuroscience, 33(20), 8705–8715. https://doi.org/10.1523/JNEUROSCI.5565-12.2013.CrossRefPubMedPubMedCentralGoogle Scholar
- Li, C. H., He, X., Wang, Y. J., Hu, Z., & Guo, C. Y. (2017). Visual working memory capacity can be increased by training on distractor filtering efficiency. Frontiers in psychology, 8.Google Scholar
- Linke, A. C., Vincente-Grabovetsy, A., Mitchell, D. J., & Cusack, R. (2011). Encoding strategy accounts for individual differences in change detection measures of VSTM. Neuropsychologia, 49(6), 1476–1486. https://doi.org/10.1016/j.neuropsychologia.2010.11.034.CrossRefPubMedGoogle Scholar
- McKendrick, R., Ayaz, H., Olmstead, R., & Parasuraman, R. (2014). Enhancing dual-task performance with verbal and spatial working memory training: Continuous monitoring of cerebral hemodynamics with NIRS. NeuroImage, 85(Pt 3), 1014–1026. https://doi.org/10.1016/j.neuroimage.2013.05.103.CrossRefPubMedGoogle Scholar
- Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49–100. https://doi.org/10.1006/cogp.1999.0734.CrossRefPubMedGoogle Scholar
- Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J. M. (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Computational Intelligence and Neuroscience, 2011, 1.Google Scholar
- Redick, T. S., Broadway, J. M., Meier, M. E., Kuriakose, P. S., Unsworth, N., Kane, M. J., & Engle, R. W. (2012). Measuring working memory capacity with automated complex span tasks. European Journal of Psychological Assessment, 28(3), 164–171. https://doi.org/10.1027/1015-5759/a000123.CrossRefGoogle Scholar
- Redick, T. S., Shipstead, Z., Harrison, T. L., Hicks, K. L., Fried, D. E., Hambrick, D. Z., et al. (2013). No evidence of intelligence improvement after working memory training: A randomized, placebo-controlled study. Journal of Experimental Psychology-General, 142(2), 359–379. https://doi.org/10.1037/a0029082.CrossRefPubMedGoogle Scholar
- Roux, F., & Uhlhaas, P. J. (2013). Working memory and neural oscillations: Alpha-gamma versus theta-gamma codes for distinct WM information? Trends in Cognitive Sciences. https://doi.org/10.1016/j.tics.2013.10.010.
- Rudebeck, S. R., Bor, D., Ormond, A., O’Reilly, J. X., & Lee, A. C. H. (2012). A potential spatial working memory training task to improve both episodic memory and fluid intelligence. PLoS One, 7(11). https://doi.org/10.1371/journal.pone.0050431.
- Sadaghiani, S., Scheeringa, R., Lehongre, K., Morillon, B., Giraud, A. L., D'Esposito, M., & Kleinschmidt, A. (2012). Alpha-band phase synchrony is related to activity in the fronto-parietal adaptive control network. The Journal of Neuroscience, 32(41), 14305–14310. https://doi.org/10.1523/JNEUROSCI.1358-12.2012.CrossRefPubMedPubMedCentralGoogle Scholar
- Sauseng, P., Klimesch, W., Stadler, W., Schabus, M., Doppelmayr, M., Hanslmayr, S., et al. (2005a). A shift of visual spatial attention is selectively associated with human EEG alpha activity. The European Journal of Neuroscience, 22(11), 2917–2926. https://doi.org/10.1111/j.1460-9568.2005.04482.x.CrossRefPubMedGoogle Scholar
- Sauseng, P., Klimesch, W., Schabus, M., & Doppelmayr, M. (2005b). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology, 57(2), 97–103. https://doi.org/10.1016/j.ijpsycho.2005.03.018.CrossRefPubMedGoogle Scholar
- Thompson, T. W., Waskom, M. L., & Gabrieli, J. D. (2016). Intensive working memory training produces functional changes in large-scale frontoparietal networks. J Cogn Neurosci, 1–14. doi: https://doi.org/10.1162/jocn_a_00916
- van Gerven, M., Bahramisharif, A., Heskes, T., & Jensen, O. (2009). Selecting features for BCI control based on a covert spatial attention paradigm. Neural Networks, 22(9), 1271–1277.Google Scholar
- Vartanian, O., Jobidon, M. E., Bouak, F., Nakashima, A., Smith, I., Lam, Q., & Cheung, B. (2013). Working memory training is associated with lower prefrontal cortex activation in a divergent thinking task. Neuroscience, 236, 186–194. https://doi.org/10.1016/j.neuroscience.2012.12.060.CrossRefPubMedGoogle Scholar