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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: https://vimeo.com/612895009
Abstract with figure: https://dx.doi.org/10.12751/nncn.bc2021.c007
  • 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 (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 (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 (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

Collaborators

2021 (c) Yul HR Kang.