Overt attentional correlates of memorability of scene images and their relationships to scene semantics
Author(s) -
Muxuan Lyu,
Kyoung Whan Choe,
Omid Kardan,
Hiroki Kotabe,
John M. Henderson,
Marc G. Berman
Publication year - 2020
Publication title -
journal of vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.126
H-Index - 113
ISSN - 1534-7362
DOI - 10.1167/jov.20.9.2
Subject(s) - fixation (population genetics) , consistency (knowledge bases) , computer science , artificial intelligence , computer vision , semantics (computer science) , medicine , population , environmental health , programming language
Computer vision-based research has shown that scene semantics (e.g., presence of meaningful objects in a scene) can predict memorability of scene images. Here, we investigated whether and to what extent overt attentional correlates, such as fixation map consistency (also called inter-observer congruency of fixation maps) and fixation counts, mediate the relationship between scene semantics and scene memorability. First, we confirmed that the higher the fixation map consistency of a scene, the higher its memorability. Moreover, both fixation map consistency and its correlation to scene memorability were the highest in the first 2 seconds of viewing, suggesting that meaningful scene features that contribute to producing more consistent fixation maps early in viewing, such as faces and humans, may also be important for scene encoding. Second, we found that the relationship between scene semantics and scene memorability was partially (but not fully) mediated by fixation map consistency and fixation counts, separately as well as together. Third, we found that fixation map consistency, fixation counts, and scene semantics significantly and additively contributed to scene memorability. Together, these results suggest that eye-tracking measurements can complement computer vision-based algorithms and improve overall scene memorability prediction.
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