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Label overlap across semantic categories. Heatmap showing the percentage of mutual label assignments. Each row shows the shared labels for all fixations bearing a given label. For example, the bottom row shows which percentage of Bodies fixations were also labelled as Face , Text , Touched and so on. Note, here we have calculated the proportion of fixations of a given category that is also labelled another category either (1) because the objects have several labels or (2) the fixation fell on several objects of different labels. The heatmap was created using MATLAB <t>R2019B</t> (MathWorks).
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Label overlap across semantic categories. Heatmap showing the percentage of mutual label assignments. Each row shows the shared labels for all fixations bearing a given label. For example, the bottom row shows which percentage of Bodies fixations were also labelled as Face , Text , Touched and so on. Note, here we have calculated the proportion of fixations of a given category that is also labelled another category either (1) because the objects have several labels or (2) the fixation fell on several objects of different labels. The heatmap was created using MATLAB <t>R2019B</t> (MathWorks).
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Image Search Results


Label overlap across semantic categories. Heatmap showing the percentage of mutual label assignments. Each row shows the shared labels for all fixations bearing a given label. For example, the bottom row shows which percentage of Bodies fixations were also labelled as Face , Text , Touched and so on. Note, here we have calculated the proportion of fixations of a given category that is also labelled another category either (1) because the objects have several labels or (2) the fixation fell on several objects of different labels. The heatmap was created using MATLAB R2019B (MathWorks).

Journal: Scientific Reports

Article Title: Free viewing biases for complex scenes in preschoolers and adults

doi: 10.1038/s41598-023-38854-8

Figure Lengend Snippet: Label overlap across semantic categories. Heatmap showing the percentage of mutual label assignments. Each row shows the shared labels for all fixations bearing a given label. For example, the bottom row shows which percentage of Bodies fixations were also labelled as Face , Text , Touched and so on. Note, here we have calculated the proportion of fixations of a given category that is also labelled another category either (1) because the objects have several labels or (2) the fixation fell on several objects of different labels. The heatmap was created using MATLAB R2019B (MathWorks).

Article Snippet: All pre-processing steps and statistical analyses were computed using MATLAB R2019B (MathWorks).

Techniques: