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.