Description
When moving to naturalistic domains, the space of possible cognitive models grows exponentially. This further increases the burden on theoreticians, who are already the primary bottleneck in advancing conceptual understanding in the cognitive sciences. There is thus a pressing need to developed tools that can systematically explore the space of cognitive models based on data. While state-of-the-art AI systems can serve as starting points for this exploration, ultimately the models we converge on should be based both on normative considerations (i.e., can they perform naturalistic tasks) as well as descriptive considerations (i.e., do they explain behavior and brain activity).
To address this, I am working on an Automated Neuroscientist that can discover computational theories directly from behavioral and neural data.
