Traditional eye movement research has in large part been dependent on static, post-experimental analysis of aggregate first-order metrics (e.g., fixations, fixation durations, etc.). Advances in eyetracking methodology call for dynamical evaluation of second-order metrics (e.g., K or gaze entropy) from the time course of collected gaze and eventually in real time.We consider such analysis of these gaze-based indicators for their response during visual search performed by two distinct user groups: Healthy Controls (HC) or those with (Mild) Cognitive Impairment (CI). Analysis of the time course of gaze transition entropy and K with Generalized Additive Models (GAMs) shows differing visual scanning strategies on two types of stimuli. On a jumbled image, the HC group adopted a more focal and less predictable strategy compared to the CI group. The effect was reversed on an image of a classical painting.
Dynamical Time Course Analysis of Real-Time Gaze Metrics
Cavallo, Marco;Cecchetti, Sonja
2026-01-01
Abstract
Traditional eye movement research has in large part been dependent on static, post-experimental analysis of aggregate first-order metrics (e.g., fixations, fixation durations, etc.). Advances in eyetracking methodology call for dynamical evaluation of second-order metrics (e.g., K or gaze entropy) from the time course of collected gaze and eventually in real time.We consider such analysis of these gaze-based indicators for their response during visual search performed by two distinct user groups: Healthy Controls (HC) or those with (Mild) Cognitive Impairment (CI). Analysis of the time course of gaze transition entropy and K with Generalized Additive Models (GAMs) shows differing visual scanning strategies on two types of stimuli. On a jumbled image, the HC group adopted a more focal and less predictable strategy compared to the CI group. The effect was reversed on an image of a classical painting.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
