Ecological influences and visual attention to infant phenotypes
Bibliographic record
Abstract
In humans, the allocation of resources, such as parental care and attention, is vital to offspring survival. Life history strategies are influenced by cues in the environment, particularly those that signal the availability of resources. What has yet to be determined is how individuals allocate resources to infants as a function of perceived ecological harshness and life history strategy. In the current research we hypothesized that perceived ecology would influence infant ratings (Study 1), and that visual attention to infant phenotypes would be associated with life history strategies (Study 2). Study 1 investigated the effect of ecological conditions (control vs. harsh) on preferences to infant phenotypes (i.e., underweight, average weight, overweight). Participants (N = 246) were less likely to rate infants favorably under a harsh ecological condition. Study 2 investigated visual perception in processing infant images. Using an eye-tracking task, participants (N = 239) viewed images of infants while their eye movements were recorded. Participants displayed an early attentional bias (i.e., first fixation duration) to the head of the infant and focused most of their visual attention to the torso of infants (i.e., total visit duration). The results of the both studies indicate that ecological factors play an important role in rating infants, and data from eye-tracking demonstrates that phenotypes influence the amount of attention given to infants.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".