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).
Professor for Computational Cognitive Neuroscience
Lena Esther Ptasczynski
MSc Computational Science
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.
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, Wilbertz G, Hebart MN & Sterzer P (2016). Mesolimbic confidence signals guide perceptual learning in the absence of external feedback. eLife 5: 1–19
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.
We are located at
1) Health and Medical University Potsdam (HMU), Institute for Mind, Brain and Behavior, Olympischer Weg 1, 14471 Potsdam
2) Charité – Universitätsmedizin Berlin, Department of Psychiatry and Neurosciences, Visual Perception Lab, Campus Mitte, 10117 Berlin