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Intra-Person Dynamics Across the Lifespan

The overarching objective of this project is to test theories and explore research designs that articulate human development across different timescales, levels of analysis, and functional domains. The project is based on the premise that a comprehensive understanding of behavioral development across the lifespan requires a person-oriented, multivariate, and longitudinal approach. Only a high density of observations within individuals allows researchers to distinguish among different forms and functions of variability and change. Such high-density data offer great opportunities, but also pose new theoretical and methodological challenges. The project meets these challenges by a strong emphasis on methodology, understood as the productive interplay between substantive research and method development. Regarding the latter, the project collaborates closely with the Formal Methods project.

Team

Gruppe Intra Person Dynamics
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Research Foci

Relationships Between Intraindividual Variability and Change Across Different Domains and Timescales

Investigations with data from the COGITO study (see below) address intraindividual variability and change at timescales that range from moment-to-moment variability in reaction times, day-to-day fluctuations in cognitive performance, to changes over years—like the long-term effects of COGITO’s extensive cognitive training on cognitive abilities and personality traits. Analyses focus on the ways in which constructs are linked within persons over time, such as couplings between day-to-day fluctuations in positive affect and working memory performance.

Using Continuous Time and Moderated Time-Series Models to Analyze Human Development Across Different Timescales and Contexts

While most psychological processes develop continuously over time, we need to rely on discrete measurement occasions to infer them. The goal is thus to reconstruct the mechanisms underlying a continuously unfolding process, such as human development, based on few discrete snapshots in time. Continuous-time models are well suited to achieve this goal. In contrast to popular discrete-time methods, continuous-time models link discrete-time observations to underlying continuous-time parameters by stochastic differential equations. This may not only remove bias due to variability in sampling time and improve comparability across different research designs, it also yields valuable information about the nature of change.

Integrating Within-Person and Between-Person Information in the Search for Causal Mechanisms

Most empirical research in psychology is based on analyzing between-person variation. In contrast, most applied psychology is concerned with variation within individuals. In addition, the mechanisms specified by psychological theories generally operate within, rather than across, individuals. This disconnect between research practice, applied demands, and psychological theories constitutes a major threat to the conceptual integrity of the field. This project is working on reconciling these extreme positions, both conceptually and methodologically.

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. You can find detailed information on this here.

Recent Publications

Hardt, K., Hecht, M., Oud, J. H. L., & Voelkle, M. C. (2019). Where have the persons gone? — An illustration of individual score methods in autoregressive panel models. Structural Equation Modeling, 26, 310–323. https://doi.org/10.1080/10705511.2018.1517355

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

Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2019). The role of time in the quest for understanding psychological mechanisms. Multivariate Behavioral Research. Advance online publication. https://doi.org/10.1080/00273171.2018.1496813

Brandmaier, A. M., Driver, C. C., & Voelkle, M. C. (2018). Recursive partitioning in continuous time analysis. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 259–282). Cham: Springer.

Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods, 23, 774–799. https://doi.org/10.1037/met0000168

Driver, C. C., & Voelkle, M. C. (2018). Understanding the time course of interventions with continuous time dynamic models. In K. van Montfort, J. H. L. Oud, & M. C. Voelkle (Eds.), Continuous time modeling in the behavioral and related sciences (pp. 79–109). Cham: Springer.

Ghisletta, P., Burra, E. J., 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

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

Mueller, S., Wagner, J., Voelkle, M. C., Smith, J., & Gerstorf, D. (2018). The interplay of personality and functional health in old and very old age: Dynamic within-person interrelations across up to 13 years. Journal of Personality and Social Psychology, 115, 1127–1147. https://doi.org/10.1037/pspp0000173

Oud, J. H. L., Voelkle, M. C., & Driver, C. C. (2018). SEM based CARMA time series models for arbitrary N. Multivariate Behavioral Research, 53, 36–56. https://doi.org/10.1080/00273171.2017.1383224

Project Leader

Alumna

Janne Adolf, KU Leuven, Belgium

Key References

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

Driver, C. C., Oud, J. H. L., & Voelkle, M. C. (2017). Continuous time structural equation modeling with R package ctsem. Journal of Statistical Software, 77:5. doi: 10.18637/jss.v077.i05

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. doi: 10.1037/ pag0000177

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. doi: 10.3389/fnagi.2010.00027

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. doi: 10.1080/ 00273171.2014.889593

Lifespan Neural Dynamics Group

The Lifespan Neural Dynamics Group, headed by Douglas Garrett, began its work within this project and is now part of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. For more information, click here.

Lifespan Neural Dynamics Group
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