Caregiver faces capture 6- to 10-year-old children’s attention during an online visual search task.
Bibliographic record
Abstract
Developing attention skills allow children to parse their complex world by orienting to a subset of especially salient or meaningful inputs. Infants and children are biased to orient to faces and have difficulty ignoring faces when they appear as distractors. Although these past findings suggest that faces are more salient than nonsocial stimuli, it is unclear whether specific types of faces capture attention to a greater extent than others. Caregiver faces are one of the most prevalent and socially motivating stimuli in infants' and children's environments, suggesting that they may be biased to orient to caregiver faces to a greater extent than faces in general. Forty-six 6- to 10-year-old children across the United States and Canada completed an online attention capture task in which participants searched for a target within arrays containing multiple distractors. During some trials, either a stranger or the child's caregiver's face appeared as one of the distractors. Children showed consistently poorer performance (i.e., increased omission errors, poorer accuracy, and slower response times) when the caregiver face appeared as a distractor, especially during trials in which the target was present and within larger search arrays. These increased performance costs indicate an enhanced orienting bias to caregiver faces, which may reflect increased motivational salience of these faces. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".