Decoding motor patterns as a function of word length.
<p>(A) Smoothed frequency of first fixation (dff) position according to the position in the word (relative position in characters, characters are overlaid with the graph for representational purpose, the first letter starts in the position 0 and end in the position 1 and so on) for good decode...
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2025
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| Summary: | <p>(A) Smoothed frequency of first fixation (dff) position according to the position in the word (relative position in characters, characters are overlaid with the graph for representational purpose, the first letter starts in the position 0 and end in the position 1 and so on) for good decoders (in blue, upper part of the line) and poor decoders (in orange, lower part) for each word of the 5-words sentence “The mouse looks at the rabbit” in French: “La souris regarde le lapin”). The frequency refers to the number of children in each group whose first fixation fell into a given segment of the word and frequency histograms were smoothed into Gaussian curves using algorithm describe in Materials & Methods section. We can observe that in long words (like “regarde”), the peak of density for poor decoders didn’t fall toward the center of the word but over the first two letters, whereas for good decoders the peak fell between the fourth and the fifth letter. (B) Position of the first digital fixation on the word (expressed in number of characters) according to children’s decoding score using the BELO test (47), and for word length. The position of the first digital fixation depends on the word length: it occurs around the 1st letter in short words (in light grey), and around the 3rd letter in long words (dark grey). Word length effect is stronger in good decoders than in poor decoders. (C) Average number of fixations in good (blue dots) and poor decoders (red dots) as a function of word length (expressed in number of characters) in both training sessions. Regression lines show the slope for good (in blue) or poor (in red) decoders. The number of fixations increased with word length. This effect was stronger in poor decoders than in good decoders. Here only two examples of word were shown (“lapin” and “regarde”).</p> |
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