Dopamine modulates egalitarian behavior in humans
Here, we sought to test dopamine’s implication in human social behavior by giving healthy subjects a drug, tolcapone, that increases dopamine levels in the brain. In each session (drug/placebo), volunteers played a Dictator Game in which they chose how to split a pot of money with a social partner another person. We found that while under the influence of the drug, subjects chose more even, but not more generous, allocations.
These results provide direct proof for dopamine’s causal (i.e. not just correlational) involvement in human social decision-making, and they support the idea that inequity is explicitly represented in the human brain. This highlights the important role of egalitarian behavior, a powerful force in promoting prosocial behavior and enabling large-scale cooperation in the human species.
Linking genes to behavior using computational models
Here we combined computational modeling of strategic learning with a pathway approach to characterize association of strategic behavior with variations in the dopamine pathway.
We found that variation in genes that primarily regulate prefrontal dopamine clearance modulated degree of belief learning across individuals. In contrast, variation in genes that primarily regulate striatal dopamine function was associated with learning rate. These findings highlight dissociable roles of frontostriatal systems in strategic learning and support the notion that genetic variation forms an important source of variation in strategic behavior in decision-making.
Dopamine genes influence learning in economic games
In this review we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of physiological processes closer to genotypes, and (ii) striking a balance between discovery and interpretability when dealing with genomic data containing up to millions of markers.
Our proposed approach involves linking models of neural computations that underlie behavior and sets of the genes involved in biochemical processes related to these neural systems. In particular, we focus on value-based decision-making, and discuss how to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior.