Current Projects
During foraging, knowing when to leave and move on to another environment (e.g. food patch) is essential. The most important quantity for this is the rate of reward (i.e. reward per time unit). By tracking instantaneous reward rates an agent can make basic foraging decisions. But if they can go further and predict future reward levels (reward trends), they can adapt to more complex environments that humans thrive in. Yet, the neural or cognitive computations for reward trend estimation for environment decisions are largely unknown.
Dorsal anterior cingulate has been particularly highlighted as relevant for time-sensitive patch leaving behaviour as it has ramping activity towards a leaving threshold during such choices. At the same time, how dACC represents reward predictions allows inferring reward trends.
When engaging in sequential behaviours, being able to anticipate the future is very useful. We build a cognitive model that estimates the value of the future beyond the next outcome when making decisions. We used this model to investigate the neural substrates underlying prospective decisiom making and how people differ in their sequential decision making based on clinical dimensions.
In ecological environments, many decisions are not based on information explicitly shown, but learned from experience over time. In macaques, we investigated the mechanisms of tracking surprising reward experiences that should be conducive to new reward learning, again identifying OFC.
To probe the role of goal directed exploration in learning and its neural substrates we designed a sequential exploration task measuring fMRI signals in macaques.
As humans, many of the most important ecological high-level cognitive processes our brain is capable of are linked to social ability. Our social projects aim at understanding the basic computations in prefrontal cortex underlying our ability to reason with varying reference frames and competing world models, which is important for social but can also be exploited for non-social decision making and problem solving. For example, we have looked at the neural computations of tracking shared and privileged evidence and the construction of self and other beliefs during social decision making.