Altered development of face recognition among infants born amid the COVID-19 pandemic
Bibliographic record
Abstract
To effectively contain the spread of COVID-19, public health agencies mandated special regulations. Although they protected us from COVID-19, these restrictions have inevitably changed the environment around us. It remains unclear how these changes may have affected early cognitive development among infants born during the pandemic. Thus, this study examined how the COVID-19 restrictions have affected infants' face recognition ability, a hallmark of their cognitive capacities. Specifically, we used the familiarization and visual pair comparison paradigm to examine face recognition performance among infants aged 6 to 14 months amid the second wave of the pandemic (February to July 2021). Experiment 1 investigated the recognition of unmasked faces and found that only younger infants, but not older infants, recognized faces by showing a novelty preference. Experiment 2 examined the recognition of faces wearing masks and found that only older infants, but not younger ones, recognized faces by exhibiting a familiarity preference. These results suggest that with limited interactions during the pandemic, infants could have developed an overly specialized face processing ability that failed to recognize the faces of strangers. Moreover, infants could have obtained more information on masked faces during the pandemic and adapted to the current situation. In Expreiment 3, we further confirmed the restriction on infants' interpersonal experiences with a survey conducted both before and during the pandemic. Overall, these findings demonstrated how the pandemic altered early perceptual development and further confirmed that interpersonal experiences during infancy are critical in their cognitive development.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".