About

Hello! This is Jin.

I am a first-year PhD student working with Marvin Chun at Yale University. I obtained a M.A. in Psychology from the University of Chicago working with Yuan Chang (YC) Leong and then worked as a research specialist with Monica Rosenberg. I received undergraduate training from Peking University, where I double-majored in Psychology and Environmental Sciences and was mentored by Xin Zhang. At SANS2025 in Chicago, I owed Hayoung Song and JeongJun Park a drink which I will pay them back at SANS2026 (or SFN2025) in San Diego.

Research Program

I am a computational cognitive neuroscientist studying the neurocomputational mechanisms of everyday human cognition using fMRI, machine learning, deep learning, large language models and naturalistic paradigms. Below I feature some main research threads:

I. Decoding human cognition from neural dynamics in naturalistic contexts

The human mind wanders frequently. How does the wandering mind reflect functional brain organization and everyday behavior? We developed predictive models of spontaneous thoughts from resting-state neural dynamics, which generalized to reveal robust associations with (1) neural synchrony during movie-watching and (2) cognitive abilities, psychiatric symptoms, and personality traits along a holistic, positive-negative axis of covariation. This suggests spontaneous thoughts may offer a unique window into the inner mental world, capturing individual idiosyncrasies and shaping daily subjective experience (Ke et al., in prep).

In another line of work, we demonstrated that predictive models trained on whole-brain functional connectivity patterns reliably predicted moment-to-moment fluctuations in emotional arousal during movie-watching and generalized across diverse datasets and individuals, suggesting dynamic functional connectivity may encode a generalizable neural representation of emotional arousal during narrative perception (Ke et al., PLoS Comp Bio, 2025). Our ongoing work further shows that varieties of arousal - wakefulness arousal (EEG spectral slope at rest), autonomic arousal (pupil during rest and podcast), and emotional arousal (ratings during movies) - are cross-predictable, suggesting a universal, overlapping neural representation across different varieties of arousal (Bhattacharyya et al., in prep).

To further unpack the arousal representation and its consequences on real-world memory, we combined fMRI, graph theory, text analyses, and pupillometry and revealed that emotional arousal enhances memory encoding by strengthening functional integration across brain networks (Park et al., bioRxiv, 2025). Together, these work advance a cross-level understanding of emotional memories that bridges large-scale brain network dynamics, affective states and ongoing cognition.

II. Narrative comprehension and its consequences on social cognition

We experience a subjective feeling of “aha” at moments of new understanding. How does our brain support this process in our daily life? We scanned 36 participants watching the first episode of This Is Us with subjective report of insight moments and impressions on the characters. We observed a cortical reinstatement of causally related events, which drives neural pattern shifts ~ 2s prior to the aha moment (Song et al., bioRxiv, 2025). Such neural pattern shifts at insight moments of new character comprehension reflect social impression updating (Ke et al., in prep). Together, these work reveal the cognitive and neural mechanisms of narrative comprehension and its consequences on social cognition.

III. Brain-computer interface

Much of my ongoing & future graduate research will focus on building non-invasive brain-computer interfaces to restore or enhance human cognitive abilities. Please stay tuned for this section.