Neural circuit mechanisms of social decision-making

To understand the neural mechanisms of decision-making, research has focused on how a single animal makes choices in a well controlled environment. However, real life decisions are rarely made by a single agent in isolation. Instead, decisions reflect dynamic interactions between multiple decision makers, and success depends on predicting the actions of others. Recent advances in computer vision make it feasible to monitor multiple animals with real-time video tracking, closed-loop task design; and extract features of behavioural motifs during social interactions. Taking advantage of this technology, we have developed a quantitative framework to study the neural circuit mechanisms of social decision-making in the mouse, where multiple mice engage in game theoretic interactions in an ethologically relevant social foraging task. To maximise reward and social utility, animals should not only track the current position and choice of their opponent, but also predict their future actions. We use quantitative analysis and modelling of multi-player behaviour to compare animals' actual strategies to these optimal solutions. Converging evidence have implicated the prefrontal cortex (PFC) and the amygdala in these computations. But many questions remain about the detailed and causal contributions of the PFC-amygdala circuits. The latent variables in our models can be mapped to internal states of the animal to generate testable predictions for neural recordings and constrain interpretation of perturbations of the PFC-amygdala circuit. We will use multi-regional and pathway-specific methods of in vivo imaging, electrophysiology and optogenetics in our quantitative social foraging task to test how affective, reward, and social information within and between PFC and amygdala facilitate agentspecific representation in the brain. This will in turn expose general principles of how social animals build a model of the world during adaptive and goal-directed behaviour.