Response Inhibition and Error Monitoring during a Visual Go/No-Go Task in Inuit Children Exposed to Lead, Polychlorinated Biphenyls, and Methylmercury
Bibliographic record
Abstract
BACKGROUND: Lead (Pb) and polychlorinated biphenyls (PCBs) are neurotoxic contaminants that have been related to impairment in response inhibition. OBJECTIVES: In this study we examined the neurophysiological correlates of the response inhibition deficits associated with these exposures, using event-related potentials (ERPs) in a sample of school-age Inuit children from Arctic Québec exposed through their traditional diet. METHODS: In a prospective longitudinal study, we assessed 196 children (mean age, 11.3 years) on a visual go/no-go response inhibition paradigm. Pb, PCB, and mercury (Hg) concentrations were analyzed in cord and current blood samples. Hierarchical multiple regression analyses were conducted to examine the associations of contaminant levels to go/no-go performance (mean reaction time, percent correct go, percent correct no-go) and five ERPs [N2, P3, error-related negativity, error positivity (Pe), and correct response positivity (Pc)] after control for confounding variables. RESULTS: Current blood Pb concentrations were associated with higher rates of false alarms and with decreased P3 amplitudes to go and no-go trials. Current plasma PCB-153 concentrations were associated with slower reaction times and with reduced amplitudes of the Pe and Pc response-related potentials. Hg concentrations were not related to any outcome on this task but showed significant interactions with other contaminants on certain outcomes. CONCLUSIONS: These results suggest that Pb exposure during childhood impairs the child's ability to allocate the cognitive resources needed to correctly inhibit a prepotent response, resulting in increased impulsivity. By contrast, postnatal PCB exposure appears to affect processes associated with error monitoring, an aspect of behavioral regulation required to adequately adapt to the changing demands of the environment, which results in reduced task efficiency.
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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 | 0.002 |
| 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".