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Dr. Rasmus Bruckner

Rasmus Bruckner

Learning Lab

Group Leader | Researcher

Habelschwerdter Allee 45
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 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

Pupillo, F., Ortiz-Tudela, J., Bruckner, R., and 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. <PDF>

O'Leary, J. D., Bruckner, R., Autore, L., & Ryan, T. J. (2023). Natural forgetting is modulated by experience. bioRxiv. <Link>

Yao, Y.-W., Song, K.-R., Schuck, N. W., Li, X., Fang, X.-Y., Zhang, J.-T., Heekeren, H. R., & Bruckner, R. (2022). The dorsomedial prefrontal cortex represents subjective value across effort-based and risky decision-making. PsyArXiv. <Link>

Bruckner, R., Heekeren, H. R., and Nassar, M. R. (2022). Understanding learning through uncertainty and bias. PsyArXiv. <Link>

Frömer, R., Nassar, M. R., Bruckner, R., Stürmer, B., Sommer, W., and Yeung, N. (2021). Response-based outcome predictions and confidence regulate feedback processing and learning. eLife, 10:e62825. <PDF>

Bruckner, R., Heekeren, H. R., and Ostwald, D. (2020). Belief states and categorical-choice biases determine reward-based learning under perceptual uncertainty. bioRxiv. <PDF>

Bruckner, R., Nassar, M. R., Li, S.-C., and Eppinger, B. (2020). Differences in learning across the lifespan are driven by satisficing. PsyArXiv. <PDF>

Nassar, M. R., Bruckner, R., and 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., and 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., and 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., and Eppinger, B. (2018). Computational neuroscience across the lifespan: Promises and pitfalls. Developmental Cognitive Neuroscience, 33:42–53. <PDF>

Nassar, M. R., Bruckner, R., and 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., and Eppinger, B. (2016). Age differences in learning emerge from an insufficient representation of uncertainty in older adults. Nature Communications, 7:11609. <PDF>

Eppinger, B. and Bruckner, R. (2015). Towards a mechanistic understanding of age-related changes in learning and decision making: A neuro-computational approach. NewYork: Academic Press. <PDF>