Bio

Dr. Yiyu Wang is a T32 postdoctoral researcher at the Department of Anesthesiology, Stanford School of Medicine. Her research combines computational models and neuroimaging techniques to characterize the neural architecture underlying complex human experiences in emotion and pain. Her current work focuses on leveraging deep learning, foundation models, and explainable AI to improve neuroimaging-based markers as well as multi-modal markers of chronic pain.

Professional Education

  • BS, University of Washington, Psychology (2017)
  • PhD, Northeastern University, Psychology (2023)

Stanford Advisors

Contact

  • Academic
    yiyuw@stanford.edu
    University - Scholar Department: Anesthesiology, Perioperative and Pain Medicine Position: Postdoctoral Scholar

Additional Info

All Publications

  • Neural Predictors of Fear Depend on the Situation. The Journal of neuroscience : the official journal of the Society for Neuroscience Wang, Y., Kragel, P. A., Satpute, A. B. 2024; 44 (46)

    Abstract

    The extent to which neural representations of fear experience depend on or generalize across the situational context has remained unclear. We systematically manipulated variation within and across three distinct fear-evocative situations including fear of heights, spiders, and social threats. Participants (n = 21; 10 females and 11 males) viewed ∼20 s clips depicting spiders, heights, or social encounters and rated fear after each video. Searchlight multivoxel pattern analysis was used to identify whether and which brain regions carry information that predicts fear experience and the degree to which the fear-predictive neural codes in these areas depend on or generalize across the situations. The overwhelming majority of brain regions carrying information about fear did so in a situation-dependent manner. These findings suggest that local neural representations of fear experience are unlikely to involve a singular pattern but rather a collection of multiple heterogeneous brain states.

    View details for DOI 10.1523/JNEUROSCI.0142-23.2024

    View details for PubMedID 39375037

    View details for PubMedCentralID PMC11561869

  • A Computational Neural Model for Mapping Degenerate Neural Architectures. Neuroinformatics Khan, Z., Wang, Y., Sennesh, E., Dy, J., Ostadabbas, S., van de Meent, J. W., Hutchinson, J. B., Satpute, A. B. 2022; 20 (4): 965-979

    Abstract

    Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFA's utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI data and the implications of applying NTFA to do so.

    View details for DOI 10.1007/s12021-022-09580-9

    View details for PubMedID 35349109

    View details for PubMedCentralID PMC9588472

  • Neural Topographic Factor Analysis for fMRI Data Sennesh, E., Khan, Z., Wang, Y., Dy, J., Satpute, A., Hutchinson, J., van de Meent, J., Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

    View details for Web of Science ID 001207696403013

  • Neurocomputational mechanisms underlying motivated seeing. Nature human behaviour Leong, Y. C., Hughes, B. L., Wang, Y., Zaki, J. 2019

    Abstract

    People tend to believe that their perceptions are veridical representations of the world, but also commonly report perceiving what they want to see or hear. It remains unclear whether this reflects an actual change in what people perceive or merely a bias in their responding. Here we manipulated the percept that participants wanted to see as they performed a visual categorization task. Even though the reward-maximizing strategy was to perform the task accurately, the manipulation biased participants' perceptual judgements. Motivation increased neural activity selective for the motivationally relevant category, indicating a bias in participants' neural representation of the presented image. Using a drift diffusion model, we decomposed motivated seeing into response and perceptual components. Response bias was associated with anticipatory activity in the nucleus accumbens, whereas perceptual bias tracked category-selective neural activity. Our results provide a computational description of how the drive for reward leads to inaccurate representations of the world.

    View details for DOI 10.1038/s41562-019-0637-z

    View details for PubMedID 31263289

https://orcid.org/0000-0002-8241-0366