Katrina Wolters L&S Social Sciences
Behavioral signatures of Hidden Markov Model-revealed perceptual modes
This project aims to use advanced computational modeling techniques, such as Hidden Markov Models (HMMs), to better understand fluctuations and biases in humans’ perceptual attention while making visual judgments. By applying these methods to continuous report data and eye-tracking data, we will explore the variability in perceptual biases and gaze behavior among participants. While previous studies have explored differences in task performance and gaze behavior in relation to attention, they often did so by inducing different behavioral states using separate tasks. My project will be among the first to explore these differences using HMMs to infer contrasting perceptual states as they naturally occur within a single, continuous task. I will also be able to evaluate both within-subject and between-subject differences in these behaviors, as HMMs allow us to track fluctuations in behavior and perceptual bias across time, rather than treating each state as an isolated event in a given task. This approach allows us to capture a more dynamic and nuanced picture of perceptual attention and bias, revealing how subtle fluctuations relate to the way we see and interact with the world.
Message To Sponsor
Thank you so much for supporting my research this summer! I’m incredibly excited to explore new questions in the field of vision science, and your generous contribution makes it possible for me to pursue this project in depth. Not only will this experience allow me to dive into fascinating research, but it will also help me gain invaluable skills and knowledge for my future work. I’m truly grateful for your support in making this opportunity possible.