Inferring Mental Representations from Gaze Using Vision-Language Models
Ongoing Python CLIP PyTorch
Using CLIP embeddings to decode the semantic content of mental models from eye-tracking data during naturalistic viewing
Research Projects
My research investigates how people perceive, segment, and remember ongoing events using eye-tracking, fMRI, and computational modeling. I combine behavioral experiments with machine learning approaches (vision-language models, neural networks) to understand the cognitive and neural mechanisms underlying event cognition.
Human Movement Analysis Dashboard
Active Demo Python JavaScript HTML/CSS
Interactive visualization platform for pose estimation and pantomimed action recognition research
Predictive Looking Errors and Event Segmentation
Published Python R
Investigating how gaze anticipation errors reveal event boundaries during movie watching — Published in Journal of Experimental Psychology: General
Segmentation as Proactive Control: Gaze Patterns and Event Boundaries
Published Python R
Gaze entropy increases predictively before event boundaries, revealing proactive cognitive control during naturalistic viewing
Incremental vs. Global Updating of Event Representations
Dissertation Project Python R MATLAB
Investigating whether event model updating occurs gradually or suddenly during narrative comprehension — Dissertation Project
How Action Performance Influences Segmentation and Memory
In Progress Python R
Investigating the role of mimicry and action execution in event perception and memory encoding
Structured Event Memory Model: A Computational Framework
Published Python TensorFlow
Python 3 + TensorFlow 2+ implementation of the SEM cognitive model for event memory research
Other Projects
Applied research and translational work demonstrating how cognitive science findings can be translated into practical tools and applications.
MedShift: Research-Based Medication Memory App
Concept Demo JavaScript HTML CSS Vanilla JS
A UX demo applying EMRC theory from cognitive neuroscience to help patients remember medication changes