The influence of prominence cues in 7- to 10-year-olds’ pronoun resolution: Disentangling order of mention, grammatical role, and semantic role
Bibliographic record
Abstract
Abstract In two visual world experiments we disentangled the influence of order of mention (first vs. second mention), grammatical role (subject vs object), and semantic role (proto-agent vs proto-patient) on 7- to 10-year-olds’ real-time interpretation of German pronouns. Children listened to SVO or OVS sentences containing active accusative verbs (küssen “to kiss”) in Experiment 1 (N = 72), or dative object-experiencer verbs (gefallen “to like”) in Experiment 2 (N = 64). This was followed by the personal pronoun er or the demonstrative pronoun der. Interpretive preferences for er were most robust when high prominence cues (first mention, subject, proto-agent) were aligned onto the same entity; and the same applied to der for low prominence cues (second mention, object, proto-patient). These preferences were reduced in conditions where cues were misaligned, and there was evidence that each cue independently influenced performance. Crucially, individual variation in age predicted adult-like weighting preferences for semantic cues (Schumacher, Roberts & Järvikivi, 2017).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".