Seeing and knowing: Attention to illustrations during storybook reading and narrative comprehension in 2‐year‐olds
Bibliographic record
Abstract
Abstract Research (Evans & Saint‐Aubin, ) suggests systematic patterns in how young children visually attend to storybooks. However, these studies have not addressed whether visual attention is predictive of children's storybook comprehension. In the current study, we used eye‐tracking methodology to examine two‐year‐olds' visual attention while being read an unfamiliar storybook. Immediately following reading, they completed a comprehension assessment. Children who visually attended to illustrations depicting key narrative events during the initial reading demonstrated stronger comprehension than children who focused on other areas. Importantly, visual attention to pertinent illustrations was also positively related to parental reports of vocabulary knowledge. Collectively, this supports a reciprocal model of early knowledge development: vocabulary knowledge facilitates visual attention, and visual attention to storybook illustrations facilitates subsequent learning. Highlights The current study examines two‐year‐olds comprehension of storybooks and whether this comprehension is impacted by their visual attention to illustrations and extant vocabulary. This study uses eye‐tracking methodology to examine the relationship between extant vocabulary, visual attention to illustration and comprehension in two‐year‐olds. This study found that two‐year‐olds visual attention to relevant illustration is predicted by their extant vocabulary and predicts comprehension.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".