Faces strongly attract early fixations in naturally sampled real-world stimulus materials

Autor(en): Gert, A.L.
Ehinger, B.V. 
Kietzmann, T.C.
König, P. 
Herausgeber: Spencer, S.N.
Stichwörter: Brain dynamics; Eye-tracking; Face perception; Face-attraction bias; Human faces; Linear model; Logistic models; Natural conditions; Real world environments; Real-world; Social information; Stimulus materials, Eye tracking
Erscheinungsdatum: 2020
Herausgeber: Association for Computing Machinery
Journal: Eye Tracking Research and Applications Symposium (ETRA)
Zusammenfassung: 
Faces are an important and salient stimulus in our everyday life. They convey social information and, consequently, attract our attention easily. Here, we investigate this face-attraction-bias in detail and analyze the first fixations made in a free-viewing paradigm. We presented 20 participants with natural, head-centered, live-sized stimuli of indoor scenes, taken during unconstrained free-viewing in a real-world environment. About 70% of first fixations were made on human faces, rather than human heads, non-human faces or the background. This effect was present even though human faces constituted only about 5% of the stimulus area and occurred in a wide variety of positions. With a hierarchical logistic model, we identify behavioral and stimulus' features that explain this bias. We conclude that the face-attraction bias replicates under more natural conditions, reflects high-level properties of faces, and discuss its implications on the measurement of brain dynamics. © 2020 ACM.
Beschreibung: 
Conference of 2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020 ; Conference Date: 2 June 2020 Through 5 June 2020; Conference Code:160051
ISBN: 9781450371346
DOI: 10.1145/3379156.3391377
Externe URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085737825&doi=10.1145%2f3379156.3391377&partnerID=40&md5=b28215fc469af18d46c9a893b6f6cdcd

Zur Langanzeige

Seitenaufrufe

5
Letzte Woche
0
Letzter Monat
0
geprüft am 14.05.2024

Google ScholarTM

Prüfen

Altmetric