Prenatal exposure to antidepressants and depressed maternal mood alter trajectory of infant speech perception
Bibliographic record
Abstract
Language acquisition reflects a complex interplay between biology and early experience. Psychotropic medication exposure has been shown to alter neural plasticity and shift sensitive periods in perceptual development. Notably, serotonin reuptake inhibitors (SRIs) are antidepressant agents increasingly prescribed to manage antenatal mood disorders, and depressed maternal mood per se during pregnancy impacts infant behavior, also raising concerns about long-term consequences following such developmental exposure. We studied whether infants' language development is altered by prenatal exposure to SRIs and whether such effects differ from exposure to maternal mood disturbances. Infants from non-SRI-treated mothers with little or no depression (control), depressed but non-SRI-treated (depressed-only), and depressed and treated with an SRI (SRI-exposed) were studied at 36 wk gestation (while still in utero) on a consonant and vowel discrimination task and at 6 and 10 mo of age on a nonnative speech and visual language discrimination task. Whereas the control infants responded as expected (success at 6 mo and failure at 10 mo) the SRI-exposed infants failed to discriminate the language differences at either age and the depressed-only infants succeeded at 10 mo instead of 6 mo. Fetuses at 36 wk gestation in the control condition performed as expected, with a response on vowel but not consonant discrimination, whereas the SRI-exposed fetuses showed accelerated perceptual development by discriminating both vowels and consonants. Thus, prenatal depressed maternal mood and SRI exposure were found to shift developmental milestones bidirectionally on infant speech perception tasks.
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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 | 0.000 |
| 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".