Social Decision-Making

Most of our decisions are made in a social context and are therefore influenced by other people. Picture yourself at a lunch counter and the 4 people in front of you all order the same meal. Does that change your own choice what to have for lunch? This is an example for an observational social influence on decision-making. You observe what others choose and that triggers a re-valuation process for the available options. However, you are not competing with the other people for a limited resource (all menu items are still available to you).

This changes in a competitive or cooperative decision context. If you are at an auction and bidding on a popular item, you will try to guess how long the others will keep in the bidding race. Likewise, when you and another person have to coordinate your decision to win a card game like "sheepshead" or "Doppelkopf:, you will also try to think about what the other person know and what s/he will do, so you can adjust your choice. In other words, you are trying to come up with a model of the person and his decision-making process. This capability is commonly referred as Theory of Mind and it involves the capacity to build and maintain a mental model of the other person. This involvesthinking about the thoughts and intentions of others, to put yourself in another person's shoes (perspective taking), and to think recursively about your and another person's over several steps (like in a game of chess). Recursive social reasoning is one of the hallmarks of social decision-making. It involves thoughts like "I am thinking about you, who is thinking about me, who is thinking about you ..." Although this figure of thought could potentially go on ad infinitum human cognitive capacity is limited. When tested experimentally, human participants often accomplish two levels of recursivity (although recent this limit has been called into question).

Whereas observational, competitive and cooperative decision-making can be all seen as trying to maximize the own reward or profit, altruistic decision-making blatantly violates this axiomatic principle of behavioral economics. Yet, altruistic behavior is quite common throughout human society and also throughout the animal kingdom. While some explanations construe the rewards of altruistic behavior in the psychological rather than in the economic domain ("I give money to the beggar to feel good about myself."), this explanation falls short in instances, when personal suffering is consciously accepted to help another person.

Key Research Question

  1. How do we integrate information from others' choices into our own valuation process (e.g. social conformity)? What is the neural basis of it? And how does this integration changes the neural connectivities?
  2. How do we develop a mental model of another player during cooperative and competitive interactions? What is the neural implementation of such a mental model? How does this Theory-of-Mind capacity change intra-brain and inter-brain connectivity recorded with EEG hyperscanning?
  3. What is the depth of social reasoning that can we engage in (e.g. "thinking about you, thinking about me, thinking about you, ...")? Do more levels of social reasoning require increasing prefrontal computations?
  4. What is the utility model of altruistic decision-making that clear defies the self-centered, egoistic utility model of "rational" economic decision making? What personality variables and genetic dispositions facilitate altruistic decision-making?

Value Congruence

The rewards and satisfactions that we encounter every day will endow the cues that predict these rewards with a positive expected values. Likewise the displeasure of punishments that we received from different experiences will lead to negative expected values for the cues predicting these punishments. But these predictive cues are usually not completely neutral. They carry an inherent value that is either innate or las been learned previously, possibly a long time ago in early childhood. Nevertheless, these inherent values can interact with the expected values that are learned from repeated associations with positive or negative outcomes. The phenomenon is quite general and the supposed effect is often employed in advertisement, when an attractive model is presented with a product, or political campaigns ads, when opponent is deliberately shown with negative imagery. The expectation is that the inherent value of the model will make the product more attractive, or that the negative imagery will devalue the political opponent.

This projects investigates one possibility of this interaction between inherent and acquired expected values. Value congruence refers to situation when both the inherent and the acquire value are both positive (an inherently positive cue predicts are reward) or negative (an inherently negative cue predict a punishment). Value incongruence describes the situation in which the inherent value of a cue and the acquired value through learning disagree. We investigate this interaction in great detail and ask whether the inherent value affects the affects the learning of new expected value or the choice of either option. We employ computational modeling, behavioral testing and fMRI, to uncover the behavioral and neural basis of the interaction of inherent and acquired expected values.

Key Research Questions

  1. Does congruence between the valence of a decision cue and the to-be-learned value facilitate the acquisition of newly acquired expected values?
  2. Does value congruence affect learning (of expected value) per se, or "just" the choice of a particular decision cue?
  3. What are commonalities and specialities of value congruence in different decision domains (perceptual properties, emotional arousal, political convictions)?

Cross-modal integration and Learning

Can the expected values of cues that we learn through repeated associations with a reward change the way how we perceptually integrate these stimuli? Does the efficiency with which we integrate stimuli from different modalities influence how we learn about rewards that are associated with them? These question lie at the heart of a new research project that is investigating the interaction of cross-modal integration and value-based decision-making in great detail.

Each experience that we encounter every day  - e.g. sitting in an espresso bar at the harbor of a small Mediterranean fishing village on a sunny day - is comprised of different sensory inputs: images, sounds, smells, tastes, touches. Each of this sensory input is processed in a different primary brain region. Cross-modal integration is the awe-inspiring capability of the brain to bind information from these different sensory modalities together to form a coherent percept.

Cross-modal integration is governed by at least three principles: the spatial rule, which states that if information form different modalities originates from the same spatial location, cross-modal integration is stronger. Similarly, the temporal rule states, if stimuli from different modalities are present closely together in time, cross-modal integration is stronger and more efficient. Finally, the principle of inverse effectiveness, states that cross-modal integration will be stronger, if the uni-modal representations are weak or diffuse. We intend to created stimuli according to the first two rules and investigate, whether stronger or more efficient integration will benefit value-based learning and decision-making.

There are specific areas in the brain that are (among other things) dedicated to the integration of stimuli from different modalities (e.g. temporal parietal junction (TPJ) and superior temporal sulcus (STS)). The primary sensory cortices of different modalities are connected with these integration areas and feed the processed stimulus information to them. In these integration areas this stimulus information is combined. We think that depending for instance on the saliency or the expected value of these different stimuli, one modality will be weighted more heavily during integration and will come to dominate the resulting percept. We intend to manipulate the balance of different modalities during integration by associating the different stimuli with rewards or punishments. We expect that the modality with a higher expected value will come to dominate the cross-modal integration, whereas the modality with a negative expected value will be de-weighted during integration.

Key Research Question

  1. Does the acquisition of expected values through reward learning affect the cross-modal integration of two multi-modal stimuli that serve as decision cues?
  2. Does the strength or the efficiency of cross-modal integration affect the learning of the expected values of these multi-sensory cues?