Idea

Humans are able to report their subjective certainty about decisions, actions or perceptions. We believe that such subjective certainty – or confidence – can be used as a learning signal to reinforce neural circuitry involved in these processes. We refer to this form of learning as confidence-based learning.

Our goal is to establish this potential novel form of learning using psychophysical, physiological and neuroimaging measurements. To understand the mechanisms of confidence-based learning, we devise computational models and test them through simulation and data-driven model comparison.

This group is supported by the Deutsche Forschungsgemeinschaft (DFG).

People


Matthias Guggenmos
Professor for Computational Cognitive Neuroscience

E-mail
Matthias Guggenmos

Barbora Wolf
Student assistant

Barbora Wolf

Marek Němeček
Student assistant

Marek Němeček

Henriette Settmacher
Student

Henriette Settmacher

Laura Ullmann
Student assistant

Laura Ullmann

Lena Esther Ptasczynski
MSc Computational Science
Student assistant

Lena Esther Ptasczynski

We are open to students interested in internships, lab rotations or master theses. To apply, please write us an E-mail with a short motivation and CV.

Key publications

Learning with internal feedback: confidence-based learning

Ptaczynski E, Steinecker I, Sterzer P, Guggenmos M (2022). The value of confidence: Confidence prediction errors drive value-based learning in the absence of external feedback. PLOS Computational Biology 18(10): e1010580  

Guggenmos M & Sterzer P (2017). A confidence-based reinforcement learning model for perceptual learning. RLDM 2017: Paper M94     BioRxiv  

Guggenmos M, Wilbertz G, Hebart MN & Sterzer P (2016). Mesolimbic confidence signals guide perceptual learning in the absence of external feedback. eLife 5: 1–19  


Measuring metacognition

Guggenmos M (2022). Reverse engineering of metacognition. eLife 11: e75420  

Guggenmos M (2021). Measuring metacognitive performance: type 1 performance dependence and test-retest reliability. Neuroscience of Consciousness 2021: 1  


Learning with external feedback

Varrier RS, Rothkirch M, Stuke H, Guggenmos M* & Sterzer P* (2020). Unreliable feedback deteriorates information processing in primary visual cortex. NeuroImage 214, 116701  

Varrier RS, Stuke H, Guggenmos M* & Sterzer P* (2019). Sustained effects of corrupted feedback on perceptual inference. Scientific Reports 9: 5537  


Methods: multivariate pattern analysis in neural data analysis

Guggenmos M, Schmack K, Veer I, Lett L, Sekutowicz M, Sebold M, Garbusow M, Sommer C, Wittchen H-U, Zimmermann U, Smolka M, Walter H, Heinz A & Sterzer P (2020). A multimodal neuroimaging classifier for alcohol dependence. Scientific Reports 10, 1–12  

Gayet S, Guggenmos M, Christophel TB, Haynes J, Paffen CLE, Sterzer P, and Van Der Stigchel S (2020). No evidence for mnemonic modulation of interocularly suppressed visual input. NeuroImage 215, 116801  

Sekutowicz M*, Guggenmos M*, Kuitunen P-S, Garbusow M, Sebold M, Pelz P, Priller J, Wittchen H-U, Smolka MN, Zimmermann U, Heinz A, Sterzer P & Schmack K (2019). Neural Response Patterns During Pavlovian-to-Instrumental Transfer Predict Alcohol Relapse and Young Adult Drinking. Biological Psychiatry 86: 857–863  

Guggenmos M, Sterzer P & Cichy RM (2018). Multivariate pattern analysis for MEG: a comparison of dissimilarity measures. NeuroImage 173: 434–447  

Gayet S, Guggenmos M, Christophel TB, Haynes JD, Paffen CLE, Van Der Stigchel S, and Sterzer P (2017). Visual working memory enhances the neural response to matching visual input. Journal of Neuroscience 37, 6638–6647  


Methods: computational modeling

Sebold M, Nebe S, Garbusow M, Guggenmos M, Schad DJ, Beck A, Kuitunen P-S, Sommer C, Neu P, Zimmermann US, Rapp MA, Smolka MN, Huys QJM, Schlagenhauf F & Heinz A (2017). When habits are dangerous - Alcohol expectancies and habitual decision-making predict relapse in alcohol dependence. Biological Psychiatry 82: 846-856  

Ostwald D, Spitzer B, Guggenmos M, Schmidt TT, Kiebel SJ & Blankenburg F (2012). Evidence for neural encoding of Bayesian surprise in human somatosensation. NeuroImage 62: 177–88  


See Google Scholar for a full list of publications.