For code used in my projects, see my GitHub page. See also Google Scholar, Research Gate and Twitter.

Bachelor seminar (in German)

Neurokognitive Psychologie: Lernen und Entscheiden

Master seminar (in German)

Decision Neuroscience

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. To study the neural and cognitive processes underlying these learning abilites in humans, I apply behavioral and neuroimaging experiments.

Many environments do not immediately provide information that is necessary for succesful 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. In my current main research projects, I examine (1) human learning under perceptual uncertainty and (2) lifespan differences in adaptive learning.

Learning under perceptual uncertainty: Together with my supervisors Hauke Heekeren and Dirk Ostwald, I investigate the computational principles of reward-based learning under perceptual uncertainty. For example, when learning 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 may 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 undertainty

Lifespan differences in adaptive learning: In a collaboration with Matt Nassar (Brown University, Providence) and Ben Eppinger (Concordia University, Montreal) I try to better understand the factors that explain age-related learning differences in dynamically changing environments. During learning in these environments, humans have to flexibly regulate how they combine newly arriving information with information from past experiences. 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, which suggests that they tend to consider fewer past experiences during learning. In a follow-up project we are currently investigating lifespan differences in these learning abilites.

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

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). Default beliefs guide learning under uncertainty in children and older adults. PsyArXiv. View PDF 

Frömer, R., Nassar, M. R., Bruckner, R., Stürmer, B., Sommer, W., and Yeung, N. (2020). I knew that! Response-based outcome predictions and confidence regulate feedback processing and learning. BioRxiv. View PDF

Nassar, M. R., Bruckner, R., and Frank, M., J. (2019). Statistical context dictates the relationship between feedback-related EEG signals and learning. eLife. View 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. View 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