She studies how we build models of the world and use them in memory, exploration, & planning. She tests neurally plausible algorithms for learning the structure of the environment. Her approach combines reinforcement learning, neural networks, & machine learning with behavioral experiments, fMRI, & electrophysiology.
Ida is interested in the interface of memory representations and control in goal-directed behavior. She research about how memory representations persist, interact, or change in the face of uncertainty about the world and shape learning, planning, and decision making. She also studies memory representations that emerge at a collective level from individual interactions. Her present interests are rooted in her past, in philosophy of mind and artificial intelligence.