Behavioral Inhibition and Dual Mechanisms of Anxiety Risk: Disentangling Neural Correlates of Proactive and Reactive Control
Bibliographic record
Abstract
Abstract Background Behavioral inhibition (BI) is a temperament style characterized by heightened reactivity and negative affect in response to novel people and situations, and it is a strong predictor of anxiety problems later in life. However, not all BI children develop anxiety problems and mounting evidence suggests that how one manages their cognitive resources (cognitive control) influences anxiety risk. The present study tests whether more (proactive control) or less (reactive control) planful cognitive strategies moderate relations between BI and anxiety. Methods Participants included 144 adolescents (55.9% female) whose temperament was assessed during toddlerhood. In adolescence ( M age = 15.4 years), participants completed an AX Continuous Performance Test while EEG was recorded in order to disentangle neural activity related to proactive (cue-locked P3b) and reactive (probe-locked N2) control. Results BI was associated with greater total anxiety scores only among adolescents with smaller ΔP3bs and larger ΔN2s – a pattern consistent with decreased reliance on proactive strategies and increased reliance on reactive strategies. Additionally, a larger ΔP3b was associated with greater total anxiety scores. Conclusions BI relates to risk for anxiety specifically among adolescents who rely less on proactive strategies and more on reactive control strategies. Results further suggest that proactive control differentiates a BI-related etiological pathway to anxiety from a more general pathway to anxiety occurring regardless of BI level. Thus, developmental context (i.e., temperament) moderates the association between anxiety and proactive control. The present study is the first to characterize how proactive and reactive control uniquely relate to pathways toward anxiety risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".