Abstract
Potsdam Eye-Movement Corpus for Scene Memorization and Search With Color and Spatial-Frequency Filtering.
Highlights
Corpus-based analyses of eye movements represent a powerful approach to further theories on free-viewing, memorization, and search behavior in real-world scenes; for example, when assessing the roles of low-level visual processing and top-down control for eye guidance (Schütt et al, 2019)
In mathematical models of eye-movement control during scene viewing, sequential likelihood methods (Schütt et al, 2017), which are based on the availability of time-ordered fixation sequences, can achieve highest precision in the evaluation of theoretical models (Engbert et al, 2022)
The present paper presents a database with a large new eye-movement corpus for scene viewing under different task instructions and experimental conditions
Summary
Corpus-based analyses of eye movements represent a powerful approach to further theories on free-viewing, memorization, and search behavior in real-world scenes; for example, when assessing the roles of low-level visual processing and top-down control for eye guidance (Schütt et al, 2019). Our database provides eye-movement data from many participants who inspected color or grayscale scenes under different task instructions while visual-cognitive processing difficulty was manipulated. Eye-movement data can be used for analyses of task differences (scene memorization vs search) and the importance of color and spatial frequencies while performing these tasks; as spatial frequencies are attenuated gaze-contingently in central or peripheral vision, the contributions of central and peripheral vision to scene processing can be investigated. The data can be used for validating computational models of attention and eye-movement control during scene viewing; and the corpus data allow time-dependent analyses and analyses on an object- or cluster-based level
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