Research

The emergence of decisions in the brain

In psychology and neuroscience, decision-making models were established that allow predicting which decision is made but also how long the decision process takes. Our group addresses the question of which cognitive mechanisms of emerging decisions can be linked to the functioning of specific brain regions.

Core publications:

Gluth, S., Kern, N., Kortmann, M., & Vitali, C. (2020). Value-based attention but not divisive normalization influence decisions with multiple alternatives. Nature Human Behaviour. doi.org/10.1038/s41562-020-0822-0

Gluth, S., Rieskamp, J., & Büchel, C. (2012). Deciding when to decide: time-variant sequential sampling models describe the emergence of decisions in the human brain. Journal of Neuroscience, 32, 10686-10698.

Review article:

Busemeyer, J.R., Gluth, S., Rieskamp, J., & Turner, B. (2019). Cognitive and neural bases of multi-attribute, multi-alternative value-based decisions. Trends in Cognitive Sciences, 23, 251–263.

Reward-based learning and the dopamine system

We can improve our decisions by learning. Of particular importance are rewards, which tell us how appropriate a decision has been. The brain's dopamine system processes rewards and thus influences our future behavior. At the Center for Decision Neuroscience, we ask how learning processes and decision-making models can be combined.

Core publications:

Fontanesi, L., Gluth, S., Rieskamp, J., & Forstmann, B.U. The role of dopaminergic nuclei in predicting and experiencing gains and losses: A 7T human fMRI study. bioRxiv.

Spektor, M.S., Gluth, S., Fontanesi, L. & Rieskamp, J. (2019). How similarity between choice options affects decisions from experience: The accentuation-of-differences model. Psychological Review, 126, 54-88.

Gluth, S., Hotaling, J.M., & Rieskamp, J. (2017). The attraction effect modulates reward prediction errors and intertemporal choices. Journal of Neuroscience, 37, 371-382.

Decisions and memory

For many decisions we have to retrieve relevant information from memory. Yet, we do not fully understand whether and how our memory changes our decisions. In our group, we address this question and look at the role of brain regions that are critical for processing memories (e.g. hippocampus).

Core publications:

Weilbächer, R.A., Kraemer, P.M., & Gluth, S. (in press) The reflection effect in memory-based decisions. Psychological Science.

Gluth, S., Sommer, T., Rieskamp, J., & Büchel, C. (2015). Effective connectivity between hippocampus and ventromedial prefrontal cortex controls preferential choices from memory. Neuron, 86, 1078-1090.

Review article:

Weilbächer, R.A., & Gluth, S. (2017). The interplay of hippocampus and ventromedial prefrontal cortex in memory-based decision making. Brain Sciences, 7, 4.

Methods of model-based cognitive neuroscience

Neuroimaging and psychophysiological data offer us insights into cognitive processes on a trial-by-trial level. We develop methods to estimate trial-specific parameters of cognitive models for linking these estimates to brain data and improving our understanding of the neural basis of cognition.

Core publications:

Gluth, S., & Meiran, N. (2019). Leave-One-Trial-Out, LOTO, a general approach to link single-trial parameters of cognitive models to neural data. eLife, 8, e42607.

Gluth, S., & Rieskamp, J. (2017). Variability in behavior that cognitive models do not explain can be linked to neuroimaging data. Journal of Mathematical Psychology, 76, 104-117.

Research methods

  • Computational modeling: We apply mathematical models to describe processes of decision making and learning in detail. The goal is to not only predict what people choose but also how they make their decisions, for instance, how quickly they choose and how they move their eyes during the emerging decision.
  • fMRI: Using fMRI, we are able to localize mechanisms of decision making in the brain and to describe interactions between different brain regions
  • EEG: In order to capture temporal aspects of neural decision processes, we employ the EEG technology which measures electrical signals at the scalp
  • Eye-tracking: With measuring eye movements, we examine the role of attention for decision making