Abstract
How people extract visual information from complex scenes provides important information about cognitive processes. Eye tracking studies that have used naturalistic, rather than highly controlled experimental stimuli, reveal that variability in looking behavior is determined by bottom-up image properties such as intensity, color, and orientation, top-down factors such as task instructions and semantic information, and individual differences in genetics, cognitive function and social functioning. These differences are often revealed using areas of interest that are chosen by the experimenter or other human observers. In contrast, we adopted a data-driven approach by using machine learning (Support Vector Machine (SVM) and Deep Learning (DL)) to elucidate factors that contribute to age-related variability in gaze patterns. These models classified the infants by age with a high degree of accuracy, and identified meaningful features distinguishing the age groups. Our results demonstrate that machine learning is an effective tool for understanding how looking patterns vary according to age, providing insight into how toddlers allocate attention and how that changes with development. This sensitivity for detecting differences in exploratory gaze behavior in toddlers highlights the utility of machine learning for characterizing a variety of developmental capacities.
| Original language | English (US) |
|---|---|
| Article number | 6255 |
| Journal | Scientific reports |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 1 2019 |
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Machine learning accurately classifies age of toddlers based on eye tracking. / Dalrymple, Kirsten; Jiang, Ming; Zhao, Qi; Elison, Jed T.
In: Scientific reports, Vol. 9, No. 1, 6255, 01.12.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Machine learning accurately classifies age of toddlers based on eye tracking
AU - Dalrymple, Kirsten
AU - Jiang, Ming
AU - Zhao, Qi
AU - Elison, Jed T
PY - 2019/12/1
Y1 - 2019/12/1
N2 - How people extract visual information from complex scenes provides important information about cognitive processes. Eye tracking studies that have used naturalistic, rather than highly controlled experimental stimuli, reveal that variability in looking behavior is determined by bottom-up image properties such as intensity, color, and orientation, top-down factors such as task instructions and semantic information, and individual differences in genetics, cognitive function and social functioning. These differences are often revealed using areas of interest that are chosen by the experimenter or other human observers. In contrast, we adopted a data-driven approach by using machine learning (Support Vector Machine (SVM) and Deep Learning (DL)) to elucidate factors that contribute to age-related variability in gaze patterns. These models classified the infants by age with a high degree of accuracy, and identified meaningful features distinguishing the age groups. Our results demonstrate that machine learning is an effective tool for understanding how looking patterns vary according to age, providing insight into how toddlers allocate attention and how that changes with development. This sensitivity for detecting differences in exploratory gaze behavior in toddlers highlights the utility of machine learning for characterizing a variety of developmental capacities.
AB - How people extract visual information from complex scenes provides important information about cognitive processes. Eye tracking studies that have used naturalistic, rather than highly controlled experimental stimuli, reveal that variability in looking behavior is determined by bottom-up image properties such as intensity, color, and orientation, top-down factors such as task instructions and semantic information, and individual differences in genetics, cognitive function and social functioning. These differences are often revealed using areas of interest that are chosen by the experimenter or other human observers. In contrast, we adopted a data-driven approach by using machine learning (Support Vector Machine (SVM) and Deep Learning (DL)) to elucidate factors that contribute to age-related variability in gaze patterns. These models classified the infants by age with a high degree of accuracy, and identified meaningful features distinguishing the age groups. Our results demonstrate that machine learning is an effective tool for understanding how looking patterns vary according to age, providing insight into how toddlers allocate attention and how that changes with development. This sensitivity for detecting differences in exploratory gaze behavior in toddlers highlights the utility of machine learning for characterizing a variety of developmental capacities.
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UR - http://www.scopus.com/inward/citedby.url?scp=85064563905&partnerID=8YFLogxK
U2 - 10.1038/s41598-019-42764-z
DO - 10.1038/s41598-019-42764-z
M3 - Article
VL - 9
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 6255
ER -