Jean-Claude Dreher

Although social decision making is ubiquitous and central to human society, its underlying neural mechanisms remain poorly understood. There is a need for understanding social decision processes at different levels, bridging the gap between fundamental computational principles and the brain system level. In particular, the fact that complex social decision making relies on probabilistic knowledge about the possible outcomes of choices and on the intentions and cooperativeness of other individuals has been underappreciated.

The current project seeks a better understanding of the psychological and neurobiological basis of social decision-making in humans. We propose to develop a new theoretical computational neuroscience framework of social decision making. We will adopt a combination of interdisciplinary and multidisciplinary perspective, combining Bayesian models and data from multimodal neuroimaging, i.e. intracranial recordings (iEEG) in humans and fMRI, to investigate the neural mechanisms of decision making processes in social context. The general goal is to characterize the computational principles and the neural mechanisms underlying social decision making. Our main hypothesis is that when we are in an interactive social setting, our brain performs Bayesian inferences using probabilistic representations of other individuals’ intentions and cooperativeness. We will use such probabilistic approaches as Bayesian inference and partially observable Markov decision processes (POMDP) to model ways in which we might predict hypothetical action outcomes, the intentions of others and whether the other is cooperative or competitive. This theoretical framework will be tested by computer simulations and validated on data previously obtained using model-based fMRI experiments using Bayesian models of social interactions in healthy adults, which should specify the relationships between the neural mechanisms, behavior and the patterns of brain activation. The locations of neural circuits active when people make decisions in a social setting have been identified. Key components include the ventromedial prefrontal cortex (vmPFC), the dorsomedial (dmPFC) and dorsolateral prefrontal cortex (DLPFC), parts of superior temporal sulcus (STS) including the temporo-parietal junction (TPJ) and the anterior cingulate gyrus (ACCg).

The specific goals of this proposal are to (i) unveil the computational mechanisms underlying the basic neural processes involved in social decision making and integrate them within the more general framework offered by POMDP models that explain how actions are selected in different contexts; (ii) Characterize the neural mechanisms for inferring other's intended action. In addition to discover whether Bayesian inferences can provide the insights regarding the computational algorithms adopted by the brain to infer the intentions of others, the ground-breaking nature of this research is to (i) establish a mechanistic foundation for understanding the neurocomputational mechanisms underlying social choice behaviour; (ii) provide a multilevel understanding of social decisions, from the system-level architecture to the level of neuronal populations. Providing a strong theoretical framework for social decision making should have important comprehensive implications from a scientific, technological, social, psychological and philosophical standpoint. In this way, new questions may be generated in each of these disciplines that would never have been considered without our transdisciplinary approach.