Eye-movement during naturalistic stimulation
Table of Contents
This project aims to advance understanding of how human gaze behavior organizes perception and guides cognition in complex, dynamic environments. The project focuses on developing principled methods to capture, characterize, and compare eye-movement patterns elicited by prolonged, naturalistic stimuli, with particular attention to robustly identifying meaningful events (saccades, fixations, pursuits) and quantifying temporal and spatial correspondences across observers and contexts. By prioritizing ecological validity, the work seeks to bridge laboratory paradigms and real-world visual behavior to reveal how attention and perceptual processing unfold over extended, natural stimulation.
A central goal is to integrate gaze dynamics with concurrent neural and behavioral measures to elucidate the links between moment-to-moment eye movements and underlying brain activity and cognitive states. The project emphasizes reproducibility and methodological rigor: developing analysis approaches that tolerate noise and individual variability, establishing quantitative metrics for sequence similarity, and creating workflows that enable systematic benchmarking across datasets and experimental conditions. Ultimately, the research aims to provide scalable, comparable frameworks for investigating attention, perception, and individual differences in naturalistic settings.
Part of #
Objectives #
- Integrate psychology with informatics (Long-term goal)
- Probe brain function with naturalistic, high-dimensional studies (Long-term goal)
- Promote open science through shared data and collaboration (Deliverable)
People #
Currently associated #
- Michael Hanke (Creator, Lead)
- Asim Dar (Research assistant)
- Jörg Stadler (Contributor)
- Nico Adelhöfer (Research assistant)
- Daniel Kottke (Research assistant)
- Adina Wagner (Researcher)
Outputs #
- A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation
- A practical guide to functional magnetic resonance imaging with simultaneous eye tracking for cognitive neuroimaging research
- REMoDNaV: Robust Eye-Movement Classification for Dynamic Stimulation
- multimatch_gaze: The MultiMatch algorithm for gaze comparison in Python