Dr. Rasmus Bruckner
Learning Lab
Group Leader | Researcher
Room JK 25/218
14195 Berlin
Office hours
By arrangement
Research Interests
I develop and apply computational models of learning and decision making. The main focus of my research is on experience-driven learning in uncertain environments that require sophisticated information processing. I use behavioral and neuroimaging experiments to study the neural and cognitive processes underlying these learning abilities in humans.
| Since 2024 | Post-doc Uni Hamburg and Co-PI DFG Research Unit 5389 |
| Since 2020 | Post-doc FU Berlin |
| 2015 - 2020 | Dr. rer. nat. Psychology ("Summa cum laude") Freie Universität Berlin International Max Planck Research School LIFE Max Planck School of Cognition Max Planck Institute for Human Development Berlin |
| 2012 - 2015 | M.Sc. Psychology, Humboldt University Berlin |
| 2008 - 2011 | B.Sc. Psychology, Radboud University Nijmegen |
Bachelor seminars (in German)
Grundlagen und Methoden der Allgemeinen Psychologie: Seminar (2022/23)
Empirisch-Experimentelles Praktikum (2022-2024)
Neurokognitive Psychologie: Lernen und Entscheiden (2020/21)
Master seminar (in German)
Decision Neuroscience (2020-2022)
Master Cognitive Neuroscience (in English)
Decision Neuroscience (2022-2024)
Publications
Satti, M. H., Wille, K., Nassar, M. R., Cichy, R. M., Schuck, N. W., Dayan, P., & Bruckner, R. (2025). Absence of systematic effects of internalizing psychopathology on learning under uncertainty. bioRXiv. <Link>
Ganesh, P., Cichy, R. M., Finke, C., & Bruckner, R. (2025). Limited validity of learning rates: Cognitive ability predicts learning performance, not learning rates. PsyArXiv. <Link>
Bruckner, R., Nassar, M. R., Li, S.-C., & Eppinger, B. (2025). Differences in learning across the lifespan emerge via resource-rational computations. Psychological Review, 132(3):556–580. <Link>
Bruckner, R., Heekeren, H. R., & Nassar, M. R. (2025). Understanding learning through uncertainty and bias. Communications Psychology 3, 24. <Link>
Ganesh, P., Donner, T. H., Cichy, R. M., Schuck N. W., Finke, C., & Bruckner, R. (2024). Pupil-linked arousal encodes uncertainty-weighted prediction errors. <Link PsyArXiv>
Ganesh, P., Cichy, R. M., Schuck N. W., Finke, C., & Bruckner, R. (2024). Adaptive integration of perceptual and reward information in an uncertain world. eLife, 13:RP99266. <Link>
Bruckner, R. & Nassar, M. R. (2024). Decision-making under uncertainty. Encyclopedia of the Human Brain, 2nd edition (Academic Press). <Link>
O'Leary, J. D., Bruckner, R., Autore, L., & Ryan, T. J. (2024). Natural forgetting reversibly modulates engram expression. eLife, 12:RP92860. <Link>
Koch, C., Zika, O., Bruckner, R., & Schuck, N. W. (2024). Influence of surprise on reinforcement learning in younger and older adults. PLoS Computational Biology, 20(8), e1012331. <Link>
Dabas, A., Bruckner, R., Schultz, H., Benoit, R. G.(2024). Learning from imagined experiences via an endogenous prediction error. bioRxiv <Link>
Yao, Y.-W., Song, K.-R., Schuck, N. W., Li, X., Fang, X.-Y., Zhang, J.-T., Heekeren, H. R., & Bruckner, R. (2023). The dorsomedial prefrontal cortex represents subjective value across effort-based and risky decision-making. NeuroImage, 279:120326. <Link>
Pupillo, F., & Bruckner, R. (2023). Signed and unsigned effects of prediction error on memory: Is it a matter of choice? Neuroscience & Biobehavioral Reviews, 153:105371. <Link>
Pupillo, F., Ortiz-Tudela, J., Bruckner, R., & Shing, Y. L. (2023). The effect of prediction error on episodic memory encoding is modulated by the outcome of the predictions. npj Science of Learning, 8(18). <PDF>
Frömer, R., Nassar, M. R., Bruckner, R., Stürmer, B., Sommer, W., & Yeung, N. (2021). Response-based outcome predictions and confidence regulate feedback processing and learning. eLife, 10:e62825. <PDF>
Bruckner, R., Heekeren, H. R., & Ostwald, D. (2020). Belief states and categorical-choice biases determine reward-based learning under perceptual uncertainty. bioRxiv. <PDF>
Nassar, M. R., Bruckner, R., & Frank, M., J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife, 8:e46975. <PDF>
Ostwald, D., Schneider, S., Bruckner, R., & Horvarth, L. (2019). Power, positive predictive value, and sample size calculations for random field theory-based fMRI inference. bioRxiv. <PDF>
Ostwald, D., Schneider, S., Bruckner, R., & Horvarth, L. (2019). Random field theory-based p-values: a review of the SPM implementation. arXiv. <PDF>
van den Bos, W., Bruckner, R., Nassar, M. R., Mata, R., & Eppinger, B. (2018). Computational neuroscience across the lifespan: Promises and pitfalls. Developmental Cognitive Neuroscience, 33:42–53. <PDF>
Nassar, M. R., Bruckner, R., & Eppinger, B. (2016). What do we GANE with age? [Invited peer commentary]. Behavioral and Brain Sciences, 39:e218. <Link>
Nassar, M. R., Bruckner, R., Gold, J. I., Li, S.-C., Heekeren, H. R., & Eppinger, B. (2016). Age differences in learning emerge from an insufficient representation of uncertainty in older adults. Nature Communications, 7:11609. <PDF>
Eppinger, B. & Bruckner, R. (2015). Towards a mechanistic understanding of age-related changes in learning and decision making: A neuro-computational approach. New York: Academic Press. <PDF>