Yichen Li

ORCID: 0000-0003-0435-5522
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About
Contact & Profiles
Research Areas
  • Functional Brain Connectivity Studies
  • Child and Animal Learning Development
  • Advanced MRI Techniques and Applications
  • Face Recognition and Perception
  • Neural dynamics and brain function
  • Advanced Neuroimaging Techniques and Applications
  • Data Visualization and Analytics

Harvard University
2022-2023

Boston College
2019

Courant Institute of Mathematical Sciences
2019

New York University
2019

Massachusetts Institute of Technology
2019

Harvard University Press
2019

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may use principled mental shortcuts, such as object simplification, similar to models developed by engineers for real-time simulations. We hypothesize that simplified approximations tracking action (the

10.1037/xge0001439 article EN Journal of Experimental Psychology General 2023-06-12

Noise is a major challenge for the analysis of fMRI data in general and connectivity analyses particular. As researchers develop increasingly sophisticated tools to model statistical dependence between signal different brain regions, there risk that these models may capture artifactual relationships are result noise. Thus, choosing optimal denoising methods crucial step maximize accuracy reproducibility models. Most comparisons require knowledge ground truth: what 'real signal'. For this...

10.1371/journal.pone.0222914 article EN cc-by PLoS ONE 2019-09-24

Category-selectivity is a fundamental principle of organization perceptual brain regions. Human occipitotemporal cortex subdivided into areas that respond preferentially to faces, bodies, artifacts, and scenes. However, observers need combine information about objects from different categories form coherent understanding the world. How this multi-category encoded in brain? Studying multivariate interactions between regions with fMRI artificial neural networks, we found angular gyrus shows...

10.31234/osf.io/qbx4m preprint EN 2019-11-14

People make fast and reasonable predictions about the physical behavior of everyday objects. To do so, people may be using principled approximations, similar to models developed by engineers for purposes real-time simulations. We hypothesize that use simplified object approximations tracking action (the "body" representation), as opposed fine-grained forms recognition "shape" representation). used three classic psychophysical tasks (causality perception, collision detection, change...

10.31234/osf.io/vebu5 preprint EN 2022-02-02
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