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 in the brain and the representations of
choice options using computational modeling of choice behavior in combination with fMRI and
more recently with EEG. This approach 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. More
recently, we have been focussing decision making in real-time social interactions and seek to
understand how social reasoning about others affects our own choices. Moreover, we also
investigate how stimulus properties, pharmacological manipulations, behavioral genetics are
biasing human valuation.

For instance, under this general framework of experimental perturbations of valuation, we are
investigating how inherent values of option cues (e.g. facial attractiveness, emotional valence,
political statements) influence the learning of new expected values through rewards and
punishments. This phenomenon, termed “value congruence”, describes the effect that when the
inherent and the learned value are similar (either both are high or low), performance improves and the resulting expected value is learned more quickly.

We are also addressing the issue from the opposite perspective by asking, how learning expected values changes state representations 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 representation are changed through learning.

Recently, we have 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 novel experimental protocols to look into how conformity with others changes the computation of expected values and how the social information form others is integrated into the own decision making process. Furthermore, we have modified the classic Matching Pennies game to induce different levels of social reasoning (i.e. What is your opponent going to do? vs. what does your opponent think you are going to do?) Finally, we have develop an innovative cooperative decision making task 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 investing intra- and inter-brain connectivities during social reasoning processes and Theory of Mind.

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

    W34, 3. Etage, Raumnummer 329


    Christoph Korn
    Position: Postdoc
    Background: B.Sc. in Biological Medicine, M.Sc. in Brain and Mind Science, Ph.D. in Psychology

    I am interested how value-based decision-making is guided by homeostatic demands and how game-theoretic principles of social interaction are computed and represented in the human brain. In addition, in my current work I am also investigating how learning shapes multi-sensory cue integration using uni- and multivariate fMRI analyses and connectivity analyses in cross-modal learning tasks.

    Saurabh Kumar

    Phd Students

    Sam Chien
    Position: PhD candidate
    Background: B.Sc. in Chemistry, M.Sc. in Biology

    "I’m interested in machine learning/AI, especially Bayesian inference, deep learning, and reinforcement learning. My current project incorporates reinforcement learning based computation models in conjunction with fMRI to further understand value based decision making process in the brain."

    Lei Zhang
    Position: PhD candidate
    Background: B.Sc. in Psychology, M.Sc. in Cognitive Science

    "My research applies knowledge from cognitive neuroscience, psychology and computational modeling to gain a comprehensive understanding of how the brain computes value and processes social information when making decisions. For that, I use functional magnetic resonance imaging (fMRI) and Bayesian hierarchical modeling."

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

    "I am interested in understanding how we represent other humans’ mental states (mentalizing). I use EEG hyperscanning and computational modeling to approach this topic. To gain additional information from hyperscanning in comparison to single person EEG, we developed an interactive paradigm that requires a continuous representation of the mental processes of the other person."

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

    I am interested in investigating the interaction of multisensory integration and value-based decision-making using multivariate pattern analysis of fMRI. In particular, I am interested how unisensory pattern are modified through value-based learning and how that propagates to cue integration in multimodal brain regions.

    Julia Spilcke-Liss
    Position: doctoral candidate in Medicine
    Background: medical student

    "I am interested in how the brain processes cross-modal integration and congruence. My research project is investigating how semantic congruence between an visual image and an auditory sound affects multi-sensory integration in the human brain, which I am investigating with fMRI."


    Martin Hebart
    Position: Postdoc (former lab member)
    Background: B.Sc. in Neuro-cognitive Psychology, M.Sc. in Neuro-cognitive Psychology,
    PhD in Psychology

  • 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

  • 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.

    Gläscher J, Rudrauf D, Colom R, Paul LK, Tranel D, Damasio H, Adolphs R (2010). The distributed network for general intelligence revealed by lesion mapping. PNAS, 107(10), 4705-4709.

    Gläscher J, Tranel D, Paul LK, Rudrauf D, Rorden C, Hornaday A, Grabowski T, Damasio H, Ralph Adolphs (2009). Lesion Mapping of Cognitive Abilities Linked to Intelligence, Neuron, 61(5), 681-691.

    Gläscher J, Hampton AN, O’Doherty J (2009). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making, Cerebral Cortex, 19(2), 483-495.

  • 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