Book Chapter (32)

2018
Book Chapter
Schmiedek, F., & Lindenberger, U. (2018). Methodologische Grundlagen. In W. Schneider & U. Lindenberger (Eds.), Entwicklungspsychologie (8th compl. rev. ed., pp. 99–117). Beltz.
Book Chapter
Schneider, W., & Lindenberger, U. (2018). Gedächtnis. In W. Schneider & U. Lindenberger (Eds.), Entwicklungspsychologie (8th compl. rev. ed., pp. 423–444). Beltz.
2016
Book Chapter
Flagel, S. B., Pine, D. S., Ahmari, S. E., First, M. E., Friston, K. J., Mathys, C., Redish, A. D., Schmack, K., Smoller, J. W., & Thapar, A. (2016). A novel framework for improving psychiatric diagnostic nosology. In A. D. Redish & J. A. Gordon (Eds.), Computational psychiatry: New perspectives on mental illness (Strüngmann Forum Reports) (pp. 169–199). MIT Press.
Book Chapter
Kühn, S., & Lindenberger, U. (2016). Research on human plasticity in adulthood: A lifespan agenda. In K. W. Schaie & S. L. Willis (Eds.), Handbook of the psychology of aging (8th ed., pp. 105–123). Academic Press. https://doi.org/10.1016/B978-0-12-411469-2.00006-6
Book Chapter
Mathys, C. (2016). How could we get nosology from computation? In A. D. Redish & J. A. Gordon (Eds.), Computational psychiatry: New perspectives on mental illness (Strüngmann Forum Reports) (pp. 121–135). MIT Press.

Conference Paper (8)

2024
Conference Paper
Mostajeran, F., Schneider, S., Bruder, G., Kühn, S., & Steinicke, F. (2024). Analyzing cognitive demands and detection thresholds for redirected walking in immersive forest and urban environments. In 2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR) (pp. 61–71). IEEE. https://doi.org/10.1109/VR58804.2024.00030
2022
Conference Paper
Gagne, C., & Dayan, P. (2022). Two steps to risk sensitivity. In M. Ranzato, A. Beygelzimer, P. S. Liang, J. W. Vaughan, & Y. Dauphin (Eds.), Advances in Neural Information Processing Systems 34: 35th Conference on Neural Information Processing Systems (NeurIPS 2021) (pp. 22209–22220). Curran.
2020
Conference Paper
Lindenberger, U. (2020). Human cognitive aging: Maintenance versus dedifferentiation. In The 8th International Winter Conference on Brain-Computer Interface, Feb. 26-28, 2020, High 1 Resort, Korea (pp. 1–2). IEEE. https://doi.org/10.1109/BCI48061.2020.9061660
2019
Conference Paper
Altmann, A., & Mourao-Miranda, J. (2019). Evidence for bias of genetic ancestry in resting functional MRI. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 275–279). IEEE. https://doi.org/10.1109/ISBI.2019.8759284
Conference Paper
Mihalik, A., Brudfors, M., Robu, M., Ferreira, F. S., Lin, H., Rau, A., Wu, T., Blumberg, S. B., Kanber, B., Tariq, M., Garcia, M. E., Zor, C., Nikitichev, D. I., Mourão-Miranda, J., & Oxtoby, N. P. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual fluid intelligence scores from structural MRI using probabilistic segmentation and Kernel ridge regression. In K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 133–142). Springer. https://doi.org/10.1007/978-3-030-31901-4_16
Conference Paper
Oxtoby, N. P., Ferreira, F. S., Mihalik, A., Wu, T., Brudfors, M., Lin, H., Rau, A., Blumberg, S. B., Robu, M., Zor, C., Tariq, M., Garcia, M. E., Kanber, B., Nikitichev, D. I., & Mourão-Miranda, J. (2019). ABCD Neurocognitive Prediction Challenge 2019: Predicting individual residual fluid intelligence scores from cortical grey matter morphology. In K. M. Pohl, W. K. Thompson, E. Adeli, & M. G. Linguraru (Eds.), Adolescent brain cognitive development neurocognitive prediction (pp. 114–123). Springer. https://doi.org/10.1007/978-3-030-31901-4_14
2018
Conference Paper
Ferreira, F. S., Rosa, M. J., Moutoussis, M., Dolan, R. J., Shawe-Taylor, J., Ashburner, J., & Mourao-Miranda, J. (2018). Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships. In 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 12-14 June 2018, Singapore (pp. 1–4). https://doi.org/10.1109/PRNI.2018.8423947
Conference Paper
Wu, C. M., Schulz, E., Garvert, M. M., Meder, B., & Schuck, N. W. (2018). Connecting conceptual and spatial search via a model of generalization. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), COGSCI 2018: Changing / minds. 40th Annual Cognitive Science Society Meeting, Madison, Wisconsin, USA, July 25-28 (pp. 1183–1188). Cognitive Science Society.

Thesis - PhD (10)

2023
Thesis - PhD
Malamud, J. (2023). Dynamics of mood and cognition in depression and anxiety: A computational approach [PhD Thesis, University College London].
Thesis - PhD
Peikert, A. (2023). Towards transparency and Open Science: A principled perspective on computational reproducibility and preregistration [PhD Thesis, Humboldt-Universität zu Berlin]. https://doi.org/10.18452/27056
2022
Thesis - PhD
Dubois, M. (2022). Computational and cognitive mechanisms of exploration heuristics [PhD Thesis, University College London].
Thesis - PhD
Nour, M. M. (2022). Cognitive and neural map representations in schizophrenia [PhD Thesis, University College London].
Thesis - PhD
Wittkuhn, J. L. (2022). Investigating neural replay of task representations in the human brain using fMRI [PhD Thesis, Freie Universität Berlin]. https://doi.org/10.17169/refubium-34672
2021
Thesis - PhD
Arnold, M. (2021). Score-based approaches to heterogeneity in psychological models [PhD Thesis, Humboldt-Universität zu Berlin]. https://doi.org/10.18452/24146
(published online 2022: https://doi.org/10.18452/24146) .
Thesis - PhD
Ciranka, S. (2021). Computational mechanisms of social influence during adolescence [PhD Thesis, Freie Universität Berlin]. https://doi.org/10.17169/refubium-33128
(published online 2022: https://doi.org/10.17169/refubium-33128).
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