계산뇌인지과학 연구실

We study how the brain and AI systems navigate uncertainty. This is where intelligence is most needed, yet least understood, perhaps due to its complexity. To tackle this problem, we take a unique approach combining two traditions, hitherto largely separate: Bayesian and neural network modeling. We test our models with behavioral experiments of our own and neural data from collaborators. With them, we aim to advance the foundational understanding of computational cognitive neuroscience and of brain disorders, including insomnia and mood disorders. Ultimately, we hope to improve how we manage uncertainty where we can, and to offer consolation where we cannot—by showing that what appears suboptimal may, under cognitive constraints, be the best one can do. a normative theory that jointly predicts the deformation in the neural representation as well as bias and variability in homing behavior. an ideal observer model that takes as input the video from the 1st-person perspective and egocentric motion, and outputs the posterior belief over the agent’s allocentric location, which allows it to be applied to any setup without handcrafted features, as demonstrated by our re-analysis of historical results. Episodic memory: memory of a unique episode is retained with a graded sense of uncertainty, which has not been considered quantitatively in the domain of episodic memory. We showed that not only is this uncertainty used in causal inference, as reflected in explicit choices, but also betrayed by gazes even after accounting for the explicit choices (CCN 2023). Dual-task: two decisions about one object cannot be made simultaneously; they are made one by one, and evidence for each accumulates in an interleaved fashion. We showed this by developing novel behavioral tasks & efficient d