Young children’s updating of mental representations of story characters and events based on verbal and pictorial information
Bibliographic record
Abstract
The ability to create mental models of story events is essential for narrative comprehension, yet little is known about the mechanisms that support children's ability to build and update an integrated mental representation of a story (a 'situation model') as it unfolds. The current study investigated very young children's ability to update their situation model of a simple story from verbal and pictorial information about a physical event, manipulating both the explicitness of verbal information and the informativeness (Study 1) and presence (Study 2) of pictorial information. Sixty-four 2-year-olds (35 girls) and 67 3-year-olds (36 girls) participated in Study 1, and 119 2-year-olds (69 girls) and 81 3-year-olds (43 girls) participated in Study 2. Two- and 3-year-olds updated their mental representation of the physical state of the story protagonist at a rate above chance, regardless of the informativeness of an accompanying picture (Study 1) and the explicitness of verbal information provided (Study 2). However, children's age in months significantly predicted 2-year-olds' performance across studies, and in the absence of a picture, 3-year-olds performed less robustly when receiving implicit than when receiving explicit verbal information. Findings suggest that 2- and 3-year-olds can integrate implicit information into their situation model of a story, even when the accompanying pictorial information is not maximally informative, but that implicit verbal information embedded in a narrative presents challenges for young children's updating when provided without pictorial support.
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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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".