COGITO

In the COGITO study, 101 younger adults (20–31 years of age) and 103 older adults (65–80 years of age) participated in 100 daily sessions in which they worked on cognitive tasks measuring perceptual speed, episodic memory, and working memory, as well as various self-report measures (see Schmiedek, Lövdén, & Lindenberger, 2010). All participants completed pretests and posttests with baseline measures of cognitive abilities and transfer tasks for the practiced abilities. Brain-related measures were taken from subsamples of the group, including structural magnetic resonance imaging (MRI), functional MRI, and electroencephalo-graphic (EEG) recordings. A central goal of the COGITO study was the comparison of between-person and within-person structures of cognitive abilities. Further, the COGITO study qualifies as a cognitive training study of unusually high dosage and long duration because of its 100 sessions of challenging cognitive tasks. More information on COGITO can be found in the download "Study Description".

Data Description

The images below give an impression of the breadth of measures available in the COGITO study. Further details are shown in the download entitled "Data Description."

Cognition

Cognition cubes

Baseline measures

Self-report

Self-report cubes

Baseline measures

Applying for Use of COGITO Data

The collection, storage, use, and disclosure of personal data are strictly regulated in Germany. For this reason, the COGITO Study data set cannot be put in the public domain. However, parts of the data set can be made available for specific analysis projects under the condition that the relevant data protection rules are met.

Applications to use COGITO data for such projects are welcome. For data requests, please fill out the "Data Transfer Request" (see Downloads) and send the form as an email attachment to Maike Kleemeyer, cogito@mpib-berlin.mpg.de. If your request is granted by the COGITO Steering Committee, a formal contract between the Max Planck Institute for Human Development and your research institution taking data protection into account will need to be completed before the data can be transferred to you.

Principal Investigators

The principal investigators of the original COGITO Study, which started in 2006, were:

• Ulman Lindenberger
• Martin Lövdén
• Florian Schmiedek

At the time, all three were at the Center for Lifespan Psychology, Max Planck Insitute for Human Development, Berlin.

Funding

The study was made possible by a grant from the Innovation Fund of the President of the Max Planck Society (to UL). Additional sources of funding for data analysis and later data collections included the Sofja Kovalevskaja Award administered by the Alexander von Humboldt Foundation and donated by the German Federal Ministry for Education and Research (to ML), and the Gottfried Wilhelm Leibniz Award 2010 of the German Research Foundation (to UL).

COGITO Conference

The international conference "The COGITO Study: Looking at 100 Days Ten Years After" took place in October 2016. World-leading behavioral scientists participated. A special section in the journal Multivariate Behavioral Research on the results was published in 2018.

Multivariate Behavioral Research: Special Section

The COGITO Study: Looking at 100 Days 10 Years After

West, S. G. (2018). Opportunities and issues in modeling intensive longitudinal data: Learning from the COGITO project. Multivariate Behavioral Research, 53(6), 777–781. https://doi.org/10.1080/00273171.2018.1545631

Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2018). The role of time in the quest for understanding psychological mechanisms. Multivariate Behavioral Research, 53(6), 782–805. https://doi.org/10.1080/00273171.2018.1496813

Boker, S. M., & Martin, M. (2018). A conversation between theory, methods, and data. Multivariate Behavioral Research, 53(6), 806–819. https://doi.org/10.1080/00273171.2018.1437017

Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53(6), 820–841. https://doi.org/10.1080/00273171.2018.1446819

Ghisletta, P., Joly-Burra, E., Aichele, S., Lindenberger, U., & Schmiedek, F. (2018). Age differences in day-to-day speed-accuracy tradeoffs: Results from the COGITO study. Multivariate Behavioral Research, 53(6), 842–852. https://doi.org/10.1080/00273171.2018.1463194

Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2018). Improved insight into and prediction of network dynamics by combining VAR and dimension reduction. Multivariate Behavioral Research, 53(6), 853–875. https://doi.org/10.1080/00273171.2018.1516540

Further COGITO Publications

Adolf, J. K., Loossens, T., Tuerlinckx, F., & Ceulemans, E. (2021). Optimal sampling rates for reliable continuous-time first-order autoregressive and vector autoregressive modeling. Psychological Methods, 26(6), 701-718. https://doi.org/10.1037/met0000398

Adolf, J. K., Voelkle, M. C., Brose, A., & Schmiedek, F. (2017). Capturing context-related change in emotional dynamics via fixed moderated time series analysis. Multivariate Behavioral Research, 52(4), 499–531. https://doi.org/10.1080/00273171.2017.1321978

Bardach, L., Lohmann, J., Horstmann, K. T., Zitzmann, S., & Hecht, M. (2024). From Intellectual Investment Trait Theory to Dynamic Intellectual Investment Trait and State Theory: Theory extension, methodological advancement, and empirical illustration. Journal of Research in Personality, 108, Article 104445. https://doi.org/10.1016/j.jrp.2023.104445

Bellander, M., Bäckman, L., Liu, T., Schjeide, B.-M. M., Bertram, L., Schmiedek, F., Lindenberger, U., & Lövdén, M. (2015). Lower baseline performance but greater plasticity of working memory for carriers of the val allele of the COMT Val158Met polymorphism. Neuropsychology, 29(2), 247–254. https://doi.org/10.1037/neu0000088

Brose, A., de Roover, K., Ceulemans, E., & Kuppens, P. (2015). Older adults’ affective experiences across 100 days are less variable and less complex than younger adults’. Psychology and Aging, 30(1), 194–208. https://doi.org/10.1037/a0038690

Brose, A., Lindenberger, U., & Schmiedek, F. (2013). Affective states contribute to trait reports of affective well-being. Emotion, 13(5), 940–948. https://doi.org/10.1037/a0032401

Brose, A., Lövdén, M., & Schmiedek, F. (2014). Daily fluctuations in positive affect positively co-vary with working memory performance. Emotion, 14(1), 1–6. https://doi.org/10.1037/a0035210

Brose, A., Scheibe, S., & Schmiedek, F. (2013). Life contexts make a difference: Emotional stability in younger and older adults. Psychology and Aging, 28(1), 148–159. https://doi.org/10.1037/a0030047

Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition and Emotion, 29(3), 527–538. https://doi.org/10.1080/02699931.2014.916252

Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2011). Normal aging dampens the link between intrusive thoughts and negative affect in reaction to daily stressors. Psychology and Aging, 26(2), 488–502. https://doi.org/10.1037/a0022287

Brose, A., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Daily variability in working memory is coupled with negative affect: The role of attention and motivation. Emotion, 12(3), 605–617. https://doi.org/10.1037/a0024436

Brose, A., Schmiedek, F., Lövdén, M., Molenaar, P. C. M., & Lindenberger, U. (2010). Adult age differences in covariation of motivation and working memory performance: Contrasting between-person and within-person findings. Research in Human Development, 7(1), 61–78. https://doi.org/10.1080/15427600903578177

Brose, A., Voelkle, M. C., Lövdén, M., Lindenberger, U., & Schmiedek, F. (2015). Differences in the between-person and the within-person structures of affect are a matter of degree. European Journal of Personality, 29(1), 55–71. https://doi.org/10.1002/per.1961

Bulteel, K., Mestdagh, M., Tuerlinckx, F., & Ceulemans, E. (2018). VAR(1) based models do not always outpredict AR(1) models in typical psychological applications. Psychological Methods, 23(4), 740–756. https://doi.org/10.1037/met0000178

Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Clustering vector autoregressive models: Capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, Article 1540. https://doi.org/10.3389/fpsyg.2016.01540

Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016). Using raw VAR regression coefficients to build networks can be misleading. Multivariate Behavioral Research, 51(2–3), 330–344. https://doi.org/10.1080/00273171.2016.1150151

Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour, 3, 478–491. https://doi.org/10.1038/s41562-019-0555-0

Grandy, T. H., Garrett, D. D., Schmiedek, F., & Werkle-Bergner, M. (2016). On the estimation of brain signal entropy from sparse neuroimaging data. Scientific Reports, 6, Article 23073. https://doi.org/10.1038/srep23073

Grandy, T. H., Werkle-Bergner, M., Chicherio, C., Lövdén, M., Schmiedek, F., & Lindenberger, U. (2013). Individual alpha peak frequency is related to latent factors of general cognitive abilities. NeuroImage, 79, 10–18. https://doi.org/10.1016/j.neuroimage.2013.04.059

Grandy, T. H., Werkle-Bergner, M., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Peak individual alpha frequency qualifies as a stable neurophysiological trait marker in healthy younger and older adults. Psychophysiology, 50(6), 570–582. https://doi.org/10.1111/psyp.12043

Grosse Rueschkamp, J. M., Kuppens, P., Riediger, M., Blanke, E. S., & Brose, A. (2020). Higher well-being is related to reduced affective reactivity to positive events in daily life. Emotion, 20(3), 376–390. https://doi.org/10.1037/emo0000557

Hecht, M., Hardt, K., Driver, C. C., & Voelkle, C. M. (2019). Bayesian continuous-time Rasch models. Psychological Methods, 24(4), 516–537. https://doi.org/10.1037/met0000205

Hertzog, C., Lövdén, M., Lindenberger, U., & Schmiedek, F. (2017). Age differences in coupling of intraindividual variability in mnemonic strategies and practice-related associative recall improvements. Psychology and Aging, 32, 557–571. https://doi.org/10.1037/pag0000177

Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A.-L., de Mooij, S. M. M., Moutoussis, M., Goodyer, I. M., Bullmore, E., Jones, P. B., Fonagy, P., the Neuroscience in Psychiatry Network (NSPN) Consortium, Lindenberger, U., & Dolan, R. J. (2018). Developmental cognitive neuroscience using latent change score models: A tutorial and applications. Developmental Cognitive Neuroscience, 33, 99–117. https://doi.org/10.1016/j.dcn.2017.11.007

Kühn, S., Schmiedek, F., Schott, B., Ratcliff, R., Heinze, H.-J., Düzel, E., Lindenberger, U., & Lövden, M. (2011). Brain areas consistently linked to individual differences in perceptual decision-making in younger as well as older adults before and after training. Journal of Cognitive Neuroscience, 23(9), 2147–2158. https://doi.org/10.1162/jocn.2010.21564

Kühn, S., Schmiedek, F., Brose, A., Schott, B. H., Lindenberger, U., & Lövdén, M. (2013). The neural representation of intrusive thoughts. Social Cognitive and Affective Neuroscience, 8(6), 688–693. https://doi.org/10.1093/scan/nss047

Lövdén, M., Bodammer, N. C., Kühn, S., Kaufmann, J., Schütze, H., Tempelmann, C., Heinze, H.-J., Düzel, E., Schmiedek, F., & Lindenberger, U. (2010). Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia, 48(13), 3878–3883. https://doi.org/10.1016/j.neuropsychologia.2010.08.026

Lövdén, M., Schmiedek, F., Kennedy, K. M., Rodrigue, K. M., Lindenberger, U., & Raz, N. (2013). Does variability in cognitive performance correlate with frontal brain volume? NeuroImage, 64, 209–215. https://doi.org/10.1016/j.neuroimage.2012.09.039

Lydon-Staley, D. M., Ram, N., Brose, A., & Schmiedek, F. (2017). Reduced impact of alcohol use on next-day tiredness in older relative to younger adults: A role for sleep duration. Psychology and Aging, 32(7), 642–653. https://doi.org/10.1037/pag0000198

Neubauer, A. B., Brose, A., & Schmiedek, F. (2023). How within-person effects shape between-person differences: A multilevel structural equation modeling perspective. Psychological Methods, 28(5), 1069–1086. https://doi.org/10.1037/met0000481

Noack, H., Lövdén, M., Schmiedek, F., & Lindenberger, U. (2013). Age-related differences in temporal and spatial dimensions of episodic memory performance before and after hundred days of practice. Psychology and Aging, 28(2), 467–480. https://doi.org/10.1037/a0031489

Potter, S., Gerstorf, D., Schmiedek, F., Drewelies, J., Wolff, J., & Brose, A. (2022). Health sensitivity in the daily lives of younger and older adults: Correlates and longer-term change in health. Aging & Mental Health, 26(6), 1261–1269. https://doi.org/10.1080/13607863.2021.1913475

Raz, N., Schmiedek, F., Rodrigue, K. M., Kennedy, K. M., Lindenberger, U., & Lövdén, M. (2013). Differential brain shrinkage over six months shows limited association with cognitive practice. Brain and Cognition, 82(2), 171–180. https://doi.org/10.1016/j.bandc.2013.04.002

Sander, J., Schmiedek, F., Brose, A., Wagner, G. G., & Specht, J. (2017). Long-term effects of an extensive cognitive training on personality development. Journal of Personality, 85(4), 454–463. https://doi.org/10.1111/jopy.12252

Schmiedek, F., Bauer, C., Lövdén, M., Brose, A., & Lindenberger, U. (2010). Cognitive enrichment in old age: Web-based training programs. GeroPsych, 23(2), 59–67. https://doi.org/10.1024/1662-9647/a000013

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2009). On the relation of mean reaction time and intraindividual reaction time variability. Psychology and Aging, 24(4), 841–857. https://doi.org/10.1037/a0017799

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: Findings from the COGITO study. Frontiers in Aging Neuroscience, 2, Article 27. https://doi.org/10.3389/fnagi.2010.00027

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2013). Keeping it steady: Older adults perform more consistently on cognitive tasks than younger adults. Psychological Science, 24(9), 1747–1754. https://doi.org/10.1177/0956797613479611

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). Younger adults show long-term effects of cognitive training on broad cognitive abilities over 2 years. Developmental Psychology, 50(9), 2304–2310. https://doi.org/10.1037/a0037388

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). A task is a task is a task: Putting complex span, n-back, and other working memory indicators in psychometric context. Frontiers in Psychology, 5, Article 1475. https://doi.org/10.3389/fpsyg.2014.01475

Schmiedek, F., Lövdén, M., & Lindenberger, U. (2020). Training working memory for 100 days: The COGITO Study. In J. M. Novick, M. F. Bunting, M. R. Dougherty, & R. W. Engle (Eds.), Cognitive and working memory training: Perspectives from psychology, neuroscience, and human development (pp. 40–57). Oxford University Press.

Schmiedek, F., Lövdén, M., Ratcliff, R., & Lindenberger, U. (2023). Practice-related changes in perceptual evidence accumulation correlate with changes in working memory. Journal of Experimental Psychology: General, 152(3), 763–779. https://doi.org/10.1037/xge0001290

Schmiedek, F., Lövdén, M., von Oertzen, T., & Lindenberger, U. (2020). Within-person structures of daily cognitive performance differ from between-person structures of cognitive abilities. PeerJ, 8, Article e9290. https://doi.org/10.7717/peerj.9290

Shing, Y. L., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Memory updating practice across 100 days in the COGITO study. Psychology and Aging, 27(2), 451–461. https://doi.org/10.1037/a0025568

Voelkle, M. C., Brose, A., Schmiedek, F., & Lindenberger, U. (2014). Towards a unified framework for the study of between-person and within-person structures: Building a bridge between two research paradigms. Multivariate Behavioral Research, 49(3), 193–213. https://doi.org/10.1080/00273171.2014.889593

von Oertzen, T., Schmiedek, F., & Voelkle, M. C. (2020). Ergodic subspace analysis. Journal of Intelligence, 8(1), Article 3. https://doi.org/10.3390/jintelligence8010003

Werkle-Bergner, M., Grandy, T. H., Chicherio, C., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2014). Coordinated within-trial dynamics of low-frequency neural rhythms controls evidence accumulation. Journal of Neuroscience, 34(5), 8519–8528. https://doi.org/10.1523/jneurosci.3801-13.2014

Wolff, J. K., Brose, A., Lövdén, M., Tesch-Römer, C., Lindenberger, U., & Schmiedek, F. (2012). Health is health is health? Age differences in intraindividual variability and within-person versus between-person factor structures of self-reported health complaints. Psychology and Aging, 27(4), 881–891. https://doi.org/10.1037/a0029125

Wolff, J. K., Lindenberger, U., Brose, A., & Schmiedek, F. (2016). Is available support always helpful for older adults? Exploring the buffering effects of state and trait social support. Journals of Gerontology: Psychological Sciences, 71(1), 23–34. https://doi.org/10.1093/geronb/gbu085

Wolff, J. K., Schmiedek, F., Brose, A., & Lindenberger, U. (2013). Physical and emotional well-being and the balance of needed and received emotional support: Age differences in a daily diary study. Social Science & Medicine, 91, 67–75. https://doi.org/10.1016/j.socscimed.2013.04.033

Youn, C., Grotzinger, A. D., Lill, C. M., Bertram, L., Schmiedek, F., Lövdén, M., Lindenberger, U., Nivard, M., Harden, K. P., & Tucker-Drob, E. M. (2022). Genetic associations with learning over 100 days of practice. npj Science of Learning, 7, Article 7. https://doi.org/10.1038/s41539-022-00121-2