EnglishDeutsch

COGITO

Logo of the COGITO-Study

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.

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.

Cognition Cubes

COGITO's Cognitive Cubes
© COGITO, MPIB

Klicken Sie zur Vergrößerung auf das +.

Baseline Measures

COGITO's Cognitive Baseline Measures
© COGITO, MPIB

Cognition

Self-Report Cubes

COGITO's Self-report Cubes
© COGITO, MPIB

Self-Report

Baseline Measures

COGITO's Self-report Baseline Measures
© COGITO, MPIB

Self-Report

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 COGITO Data Transfer Request below and send the form as an email attachment to Charles Driver (see Contact, top right). 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, are:

  • 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).

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, 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, 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, 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, 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, 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, 853–875. https://doi.org/10.1080/00273171.2018.1516540

Publications

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, 499–531. https://doi.org/10.1080/00273171.2017.1321978

Bellander, M., Bäckman, L., Liu, T., Schjeide, B.-M. M., Bertram, L., Schmiedek, F., ... 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, 247–254. https://doi.org/10.1037/neu0000088

Boker, S. M., & Martin, M. (2018). A conversation between theory, methods, and data. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2018.1437017

Brose, A., Lindenberger, U., & Schmiedek, F. (2013). Affective states contribute to trait reports of affective well-being. Emotion, 13, 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, 1–6. 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, 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, 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, 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, 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, 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, 55–71. https://doi.org/10.1002/per.1961

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. Advance online publication. https://doi.org/10.1080/00273171.2018.1516540

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. Advance online publication. https://doi.org/10.1080/00273171.2018.1463194

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: 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, 570–582. https://doi.org/10.1111/psyp.12043

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. Advance online publication. https://doi.org/10.1080/00273171.2018.1446819

Hecht, M., Hardt, K., Driver, C. C., & Voelkle, C. M. (2019). Bayesian continuous-time Rasch models. Psychological Methods. Advance online publication. 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., ... 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., ... 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, 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, 688–693. https://doi.org/10.1093/scan/nss047

Lövdén, M., Bodammer, N. C., Kühn, S., Kaufmann, J., Schütze, H., Tempelmann, C., ... Lindenberger, U. (2010). Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia, 48, 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, 642–653. https://doi.org/10.1037/pag0000198

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, 467–480. https://doi.org/10.1037/a0031489

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, 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, 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, 59–67. https://doi.org/10.1024/1662-9647/a000013

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: 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, 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, 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: 1475. https://doi.org/10.3389/fpsyg.2014.01475

Shing, Y. L., Schmiedek, F., Lövdén, M., & Lindenberger, U. (2012). Memory updating practice across 100 days in the COGITO study. Psychology and Aging, 27, 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, 193–213. https://doi.org/10.1080/00273171.2014.889593

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, 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, 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, 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

Contact

Charles Driver, MPI for Human Development
COGITO [at] mpib-berlin [dot] mpg [dot] de (Contact)

Steering Committee

Annette Brose, Humboldt-Universität zu Berlin
Ulman Lindenberger, MPI for Human Development
Martin Lövdén, Karolinska Institutet
Florian Schmiedek, DIPF | Leibniz Institute for Research and Information in Education

Key Publication

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: 27. https://doi.org/10.3389/fnagi.2010.00027

Film

A documentary about the study by Joachim Lühning.

COGITO Conference 2016

An international conference entitled "The COGITO Study: Looking at 100 Days Ten Years After" took place in October 2016. Various world-leading behavioral scientists participated. More information can be found here.

Cover Programmheft
© Foto: David Ausserhofer