Attending, learning, and socioeconomic disadvantage: developmental cognitive and social neuroscience of resilience and vulnerability
Bibliographic record
Abstract
We review current findings associating socioeconomic status (SES), development of neurocognitive functions, and neurobiological pathways. A sizeable interdisciplinary literature was organized through a bifurcated developmental trajectory (BiDeT) framework, an account of the external and internal variables associated with low SES that may lead to difficulties with attention and learning, along with buffers that may protect against negative outcomes. A consistent neurocognitive finding is that low-SES children attend to information nonselectively, and engage in late filtering out of task-irrelevant information. Attentional preferences influence the development of latent inhibition (LI), an aspect of learning that involves reassigning meaningful associations to previously learned but irrelevant stimuli. LI reflects learning processes clarifying the relationship between neurobiological mechanisms related to attention and socioeconomic disadvantage during child development. Notably, changes in both selective attention and typical LI development may occur via the mesocorticolimbic dopamine (MsCL-DA) system. Chaotic environments, social isolation, and deprivation associated with low SES trigger stress responses implicating imbalances in the MsCL-DA and consolidating anxiety traits. BiDeT describes plausible interactions between socioemotional traits and low-SES environments that modify selective attention and LI, predisposing individuals to vulnerability in cognitive development and academic achievement. However, positive role models, parental style, and self-regulation training are proposed as potential promoters of resilience.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".