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Update: The video of my World Wide NeuRise talk (April 6, 2022) on spatial uncertainty during navigation is now available here, from 34:20. (I recommend the talk before mine as well.)

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:

Bernstein talk video:
Abstract with figure:
  • 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. I 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 (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. I showed this by developing novel behavioral tasks & efficient drift-diffusion models, which fit the joint distribution of choices & reaction times (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, which I showed by developing a novel behavioral task & analysis to predict & cross-validate the accuracy of the decision given 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 the Vision Training Grant from the NEI)
  • MD, Seoul National University, South Korea
  • Summer/Winter Schools for International Olympiad in Informatics, South Korea


2021 (c) Yul HR Kang.