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Dr. Rasmus Bruckner

Rasmus Bruckner

Biological Psychology and Cognitive Neuroscience

Researcher

Address
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 Biological Psychology and
Cognitive Neuroscience
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 seminar (in German)

Neurokognitive Psychologie: Lernen und Entscheiden

Master seminar (in German)

Decision Neuroscience

Many environments do not immediately provide information that is necessary for successful learning and decision making (e.g., where should I invest my money?). Instead, we often need to combine information across many experiences to uncover the regularities in the environment.

Learning under perceptual uncertainty: I investigate the computational principles of reward-based learning under perceptual uncertainty. For example, when exploring which varieties of berries are edible, learning is difficult under perceptual uncertainty about the type of berry (i.e., berries that look very similar). In this project, we have developed and tested formal models that algorithmically describe how humans take perceptual uncertainty into consideration. In ongoing neuroimaging work, we study the neural mechanisms that enable the brain to adjust learning to perceptual uncertainty.

Belief states and categorical-choice biases determine reward-based learning under perceptual uncertainty

Lifespan differences in adaptive learning: I also study the factors that explain age-related learning differences in dynamically changing environments. Humans have to flexibly regulate how they combine newly arriving information with information from past experiences during learning in these environments. This requires a consideration of the possible events that took place (e.g., is the cause of my subpar meal a new chef, or a kitchen accident unlikely to be repeated) and how much has already been learned about an environment (have I previously been in the restaurant or is it the first visit?). In a recent study including younger (20 - 30 years) and older adults (60 - 80 years), we found evidence for a reduced consideration of uncertainty in older adults, suggesting that they tend to consider fewer past experiences during learning. In a follow-up project, we are currently investigating lifespan differences in these learning abilities.

Default beliefs guide learning under uncertainty in children and older adults

Age differences in learning emerge from an insufficient representation of uncertainty in older adults

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. View PDF

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

Bruckner, R., Nassar, M. R., Li, S.-C., and Eppinger, B. (2020). Differences in learning across the lifespan are driven by satisficing. PsyArXivView 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:e46975View 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. bioRxivView PDF

Ostwald, D., Schneider, S., Bruckner, R., and Horvarth, L. (2019). Random field theory-based p-values: a review of the SPM implementation. arXiv. View 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. View 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. View 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. View PDF