Valuation and Social Decision Making
Research group


Every day in our lives is plastered with decisions, from the minute (What shall I have for breakfast?) to the grand (Shall I pursue a career in decision neuroscience?). Common theoretical accounts posit that the human brain accomplishes decision making through a series of neural computations, in which the expected future reward of different decision options are compared with one another and then the option with the highest expected value is usually selected. Thus, valuation lies at the heart of the puzzle of human decision making.

Our group is interested in tracking down these values signals in the brain and the representations of choice options using computational modeling of choice behavior in combination with fMRI and EEG. This approach, called model-based fMRI. allows us to identify neural structures that participate in the computation of expected values and their updating through learning. In general, we try to understand valuation by perturbing this process through experimental manipulation, record the change in valuation, and therefore learn how the brain organizes value computations.

We are primarily focussed on social decision-making to understand how social reasoning and Theory of Mind changes the own valuation process by internally simulating the decisions of others. We have developed a novel experimental protocol task in the decision-making domain that mimics the dynamics of the classic “Sally Anne Task”, one of the most commonly used test for Theory of Mind abilities. Using EEG hyperscanning (concurrent recording in two players) with this cooperative decision making task we are investigating intra- and inter-brain connectivities during social reasoning processes and Theory of Mind.

We have developed new experimental protocols to look into how social conformity with others changes the computation of expected values and how the social information form others is integrated into the own decision making process. By serializing the own decision and the decisions of others we can characterize value computations and their updates step-by-step.

One of hallmarks of social decision-making is the recursivity of social reasoning (i.e. What is your opponent going to do? vs. what does your opponent think you are going to do?). This is often referred to as Level-k reasoning. To investigate these different levels of reasoning we have we have modified the classic Matching Pennies game to induce different levels of social reasoning.

We are also investigating how learning and decision-making change the representations of stimuli and decision states in the brain. By using multivariate analysis technique (decoding, representational similarity analysis, pattern component modeling) of fMRI data, we are investigating how unisensory patterns of stimulus representations are integrated into multisensory representation and, importantly, how these higher-order representations are changed through learning. In an extension of this simple reward learning paradigm we are also using second-order conditioning to characterize the representations of a “predictor of a predictor of a reward”.

  • Staff
  • Staff
    Dr. rer. nat. Dipl.-Psych.
    Jan P. Gläscher
    • Arbeitsgruppenleiter

    W34, 3. Etage, Raumnummer 329


    Yuanwei Yao
    Position: Postdoc
    Background: B.Sc. in Psychology, M.Sc. in Psychology, PhD in Psychology

    Phd Students

    Tatia Buidze
    Position: PhD candidate
    Background: B.Sc. in Psychology, M.Sc. in Neurocognitive Psychology

    Sepideh Khoneiveh
    Position: PhD candidate
    Background: B.Sc. in Biomedical Engineering, M.Sc. in Biomedical Engineering

    Student Assistant


    Martin Hebart
    Position: Professor Computational Cognitive Neuroscience and Quantitative Psychiatry at UKGM Psychiatrie und Psychotherapie
    Background: B.Sc. in Neuro-cognitive Psychology, M.Sc. in Neuro-cognitive Psychology,
    PhD in Psychology

    Sam Chien
    Position: PhD candidate (now Postdoc at the Schuck Lab (Neurologe, MPI for Human Development, Berlin)
    Background: B.Sc. in Chemistry, M.Sc. in Biology

    Lei Zhang
    Position: Associate Professor at the School of Psychology University of Birmingham, UK
    Background: B.Sc. in Psychology, M.Sc. in Cognitive Science

    Christoph Korn
    Position: PI of research group " Decision Neuroscience of Human Cooperation "
    Background: B.Sc. in Biological Medicine, M.Sc. in Brain and Mind Science, Ph.D. in Psychology

    Tessa Rusch
    Position: Postdoc
    Background: B.Sc. in Cognitive Science, M.Sc. in Neuro-cognitive Psychology

    Sasa Redzepovic
    Position: PhD candidate
    Background: B.Sc. in Psychology, M.Sc. in Cognitive Neuroscience

    Saurabh Steixner-Kumar
    Position: Postdoc
    Background: B.Tech. in Electronics and Communications engineering, M.Sc. in Digital Communications/Signal Processing, Doctorate in Theoretical Medicine/Medical Sciences

  • Research topics

    • Cooperative and competitive social decision-making in humans

    • Social influence on human decision-making

    • Mental models of others and Theory of Mind

    • Interaction of associative learning and cross-modal integration

    • Model-based and model-free learning

    • Delineating cognitive functions with lesion mapping

    Research methods

    • Model-based fMRI (combining computational modeling with neuroimaging)
    • EEG Hyperscanning (concurrent recordings in two participants)
    • Model-based lesion mapping
    • Behavioral genetics
    • Eye-tracking and psychophysiological recordings

  • L Zhang, J Gläscher. (2020) A brain network supporting social influences in human decision-making. Science Advances 6 (34), eabb4159

    S Steixner-Kumar, J Gläscher. (2020) Strategies for navigating a dynamic world. Science 369 (6507), 1056-1057

    T Rusch, S Steixner-Kumar, P Doshi, M Spezio, J Gläscher. (2020) Theory of mind and decision science: Towards a typology of tasks and computational models. Neuropsychologia, 107488

    Guo R, Böhmer W, Hebart M, Chien S, Sommer T, Obermeyer K, Gläscher J (2016). Interaction of instrumental and goal-directed learning modulated prediction error representations in ventral striatum. J Neurosci, 36(50), 12650-12660.

    Chien S, Wiehler A, Spezio M, Gläscher J (2016). Congruence of inherent and acquired values facilitates reward-based decision-making. J Neurosci, 36(18), 5003-5012.

    Hebart MN, Gläscher J (2014). Serotonin and Dopamine differentially affect appetitive and aversive general Pavlovian-to-Instrumental transfer, Psychopharmacology, 232(2), 437-451.

    Gläscher J, Adolphs R, Damasio H, Bechara A, Rudrauf D, Calamia M, Paul LK, Tranel D (2012). Lesion mapping of cognitive control and value-based decision-making in the prefrontal cortex. PNAS, 109(36), 14681-14686.

    Gläscher J, Daw N, Dayan P, O’Doherty JP (2010). States versus Rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning, Neuron, 66(4), 585-595.

  • 2017 - 2020 “Flexibles Lernen unter Stress: neurokognitive Mechanismen und klinische Implikationen” Verbundantrag Landersforschungsförderung Hamburg, total volume: 1.47 M €. Projekt C “Aufgabenspezifischer Stress und flexibles Lernen” (Task-specific stress and flexible lerning), cooperation with Dr. Tobias Sommer, 243 K € (role: PI)

    2011 - 2016
    “Modulation of Value Representation during Human Decision Making: a Neurocomputational Approach”, Bernstein Prize for Computational Neuroscience 2009 from the German Ministry of Education and Research, grant no. 01GQ1006: 1.25 M € (role: PI)

    2014 - 2016
    “Influence of Estrogen on hippocampal and amygdala dependent emotional memory”, DFG Grant SO-952/6-1, Estrogen Grant: 330 K € (role: Co-PI)

    2016 - 2019
    SFB TRR 169 “Crossmodal Learning: Adaptivity, Prediction and Interaction” (Transregional collaborative research center between Hamburg and Beijing, total volume: 11,6 M €) Project B02 “Bayesian analysis of the interaction of learning, semantics and social influence with cross-modal integration”, (role: PI)

    2016 - 2019
    US-German Collaborative Research in Computational Neuroscience: “Computational Modeling of Cooperative Success using Neuroal Signals and Networks”, collaboration with Prof. M. Spezio, Scripps College, CA, co-finance by BMBF and NSF, total volume 660 K €, German share: 372 K € € (role: PI)

  • Ralph Adolphs

    Michael Spezio
    Scripps College

    Nathaniel Daw
    Princeton University

    Klaas Stephan
    ETH Zurich

    Prashant Doshi
    University of Georgia

    Lars Schwabe
    University of Hamburg