Zoe He

Hello! I am a Ph.D. Candidate in Cognitive Science at UC San Diego, where I work at the intersection of computational neuroscience and artificial intelligence. My research seeks to uncover the universal computational principles of intelligence by studying it in both biological and artificial systems.

My work is fundamentally interdisciplinary, spanning two complementary domains:

Representational Alignment Across Brains and AI

I analyze and compare internal representations in large-scale AI models (LLMs, Vision Transformers) and human brains (fMRI) using representational alignment metrics. This approach helps uncover where, how, and why these different systems give rise to intelligent capabilities, with particular focus on cross-modal semantic understanding.

Related papers:

  • He, Z.W., Trott, S., & Khosla, M. (2025). Seeing Through Words, Speaking Through Pixels: Deep Representational Alignment Between Vision and Language Models. EMNLP 2025 (Oral). [pdf]

Computational Modeling of Human Cognition

I design experiments and build Bayesian computational models to understand how the human brain performs learning, reasoning, and decision-making under uncertainty. This work examines the neural and cognitive mechanisms that enable adaptive behavior in dynamic environments.

Related papers:

  • He, Z.W., L’Hôtellier, M., Paunov, A., Guo, D., Meyniel, F., & Yu, A.J. (2024). Pupil size reflects the relevance of reward prediction error and estimation uncertainty in upcoming choice. CogSci 2024 (Oral). [pdf]

  • Paunov, A., L’Hôtellier, M., Guo, D., He, Z., Yu, A., & Meyniel, F. Multiple and subject-specific roles of uncertainty in reward-guided decision-making. bioRxiv Preprint. [pdf] To appear in eLife.

Other Publications and Selected Conference Presentations

  • He, Z.W., Trott, S., & Khosla, M. (2025). Many-to-Many, Yet Convergent: Insights into the alignment of Vision and Language Models. Conference on Cognitive Computational Neuroscience.

  • He, Z.W., L’Hôtellier, M., Paunov, A., Guo, D., Meyniel, F., & Yu, A.J. (2023). A pupillometry study of reward and uncertainties in experience-based decision making. European Conference on Visual Perception.

  • He, Z.W., L’Hôtellier, M., Paunov, A., Guo, D., Meyniel, F., & Yu, A.J. (2022). Role of pupil-linked uncertainties and rewards in value-based decision making. Conference on Cognitive Computational Neuroscience.

  • Paunov, A., L’Hôtellier, M., Guo, D., He, Z., Yu, A., & Meyniel, F. (2022). Information coding in frontoparietal regions reflects individual differences in uncertainty-driven choices. Conference on Cognitive Computational Neuroscience.

  • He, Z.W., & Yu, A.J. (2021). Gender differences in face-based trait perception and social decision making. Proceedings of the Annual Meeting of the Cognitive Science Society. [pdf]