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I am a computational cognitive neuroscientist studying the role of uncertainty in navigation, learning, and decision-making as a postdoc in Máté Lengyel’s group at the University of Cambridge. Uncertainty has been largely ignored in studies of navigation, presumably due to the complexity of the setup. I bring my experience in studies of decision-making, where uncertainty is well characterized, into the studies of spatial and non-spatial navigation. I analyze data from experimental collaborators and from the literature, and perform behavioral experiments myself. In doing so, I have tackled the complexity of the navigation task with tools from physics, machine learning, and robotics. With excellent collaborators (below), I showed that:

  • Spatial navigation: grid fields (often called a “ruler in the brain”), which were thought to “deform” depending on the geometry of the environment, in fact represent uncertainty about the animal’s own location (Kang et al. Cosyne 2020 & 2021; Cosyne 2020 Presenters Travel Award; Bernstein 2021 Contributed Talk).
    • Our normative theory jointly predicts the deformation in the neural representation as well as bias and variability in homing behavior.
    • Our model takes as input the video from the 1st-person perspective and egocentric motion vectors, which allows it to be applied to any setup without handcrafted features, as demonstrated by our re-analysis of historic 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. This uncertainty is later used to weight the reliability of the memory on retrieval, and guides choices & gazes based on causal inference (Kang et al. CCN 2019).
  • 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 using novel behavioral tasks and drift-diffusion models (Kang et al. 2021 eLife; Excellent Poster Award: Korean Association for Computational Neuroscience; Cosyne 2021 Contributed Talk).
  • Conscious awareness: people become aware that they reached a decision when evidence for the decision is accumulated up to a threshold, and we can predict the accuracy of their decision from the timing of the awareness using drift-diffusion models (Kang et al. 2017 Current Biology; News piece in the Independent).

When inspiration calls, I like making artworks, in and outside science.

Education & Professional Appointments

  • Junior Research Fellow (2019-present), Wolfson College, University of Cambridge
  • Postdoc (2018-present), Computational and Biological Learning Lab, Department of Engineering, University of Cambridge (Máté Lengyel group)
  • PhD in Neuroscience (2018) focusing on decision-making models, Columbia University (Michael Shadlen lab; Supported by Vision Training Grant from NEI)
  • MD, Seoul National University, South Korea
  • Summer/Winter Schools for International Olympiad in Informatics, South Korea


2021 (c) Yul HR Kang.