TY - JOUR
T1 - From eye movements to scanpath networks
T2 - A method for studying individual differences in expository text reading
AU - Ma, Xiaochuan
AU - Liu, Yikang
AU - Clariana, Roy
AU - Gu, Chanyuan
AU - Li, Ping
N1 - Funding Information:
Preparation of this article has been supported by a grant from the US National Science Foundation (BCS-1533625) to PL and RBC, and a Research Startup Fund from the Hong Kong Polytechnic University to PL. The authors wish to express gratitude to Elisabeth Karuza and members in the Brain, Language, and Computation Lab for their assistance to this study.
Publisher Copyright:
© 2022, The Author(s).
PY - 2023/2
Y1 - 2023/2
N2 - Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers’ comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers’ processing of the text. Results showed that low ability and high ability readers’ scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension.
AB - Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers’ comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers’ processing of the text. Results showed that low ability and high ability readers’ scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension.
KW - Eye tracking
KW - Knowledge representation
KW - Network metrics
KW - Reading comprehension
KW - Scanpath
UR - http://www.scopus.com/inward/record.url?scp=85128671003&partnerID=8YFLogxK
U2 - 10.3758/s13428-022-01842-3
DO - 10.3758/s13428-022-01842-3
M3 - Journal article
SN - 1554-3528
VL - 55
SP - 730
EP - 750
JO - Behavior Research Methods
JF - Behavior Research Methods
IS - 2
ER -