Conveying Symbolic Relations: Children's Ability to Evaluate and Create Informative Legends
Bibliographic record
Abstract
Symbols are used regularly in our daily lives, but in order for a symbol to serve its intended purpose, its meaning must be conveyed in some way (Myers & Liben, 2012). Across two studies, this research examined 4- to 6-year-olds' understanding of how the relations between symbols and their referents are effectively conveyed using legends. To investigate this issue, a novel task was developed in which it was necessary to convey the arbitrary correspondence between symbols (the shapes on top of a set of boxes) and a set of referents (cards with shapes on them), so that an unknowing other would know which card went inside each box. Study One was an investigation of children’s ability to evaluate legends that either effectively or ineffectively conveyed symbol-referent relations. Children’s performance was examined in relation to age, the ability to detect ambiguity (Ambiguous Messages and Droodle tasks), and Executive Function skills (Inhibitory Control, Working Memory, Planning tasks). The results provide evidence that both the ability to detect ambiguity and Executive Function uniquely relate to children’s ability to evaluate legends. Study Two investigated a new group of children’s ability to create a legend to convey symbol-referent pairs, in relation to the same cognitive skills considered in Study One. In addition, to examine the impact of exposure to effective legends, half of the children who did not create an effective legend were then presented with legends created by the experimenter, while the other half served as the baseline group. Children who received this exposure, relative to those in the baseline group, significantly improved their legend creations and transferred this improvement to a new set of stimuli. This study found evidence that ambiguity detection was related both to legend creation on children’s first attempt, and children’s ability to improve following exposure. However, Executive Function performance did not relate to legend production. Taken together, these studies provide insight on factors that relate to children’s developing understanding of how symbol meanings are effectively conveyed, and argue for the important role of being able to detect ambiguity.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".