The Impact of Social Stress on Cold and Hot Attention for Youth with Learning Disabilities: A Behavioural and ERP Investigation
Bibliographic record
Abstract
Youth with learning disabilities (LD) often experience challenge with attention, a keystone process critical for development and effective functioning in academic, social, emotional, and behavioural spheres. Difficulties experienced across these domains can heighten levels of daily stress. To date, little research has examined the impact of stress on attention in youth with LD. Further, research completed with similar populations has employed stress-induction tasks that may be less developmentally salient for youth. This three-study dissertation addresses these limitations in a sample of youth with LD (n = 13) and without LD (n = 12). The first study explores the impact of LD status on baseline differences in attention on a “cold” (non-emotionally salient) attention task. Behavioural performance accuracy and event-related potentials (ERPs) captured from electroencephalographic recordings serve as indices of attention. The second study explores the impact of a social stressor (the Cyberball paradigm) on self-reported ostracism and neural indices of attention and emotion regulation, and if youth’s response differs depending on the presence of an LD. The third study explores LD-related differences in ‘hot’ (emotionally evocative) attention by examining differences in behavioural and ERP indices before and after exposure to the social stressor. Across the three studies, findings indicated that attention differed by LD status. Group differences in attention were found in both emotionally neutral (“cold”) and stress-induced (“hot”) contexts, with significantly greater attentional challenges noted in the LD group. Youth with LD were also found to experience greater ostracism than youth without LD in the context of intermittent rejection. These results suggest that the impact of social stress on neural indices of attention is greater for youth with LD, and that youth with LD are more sensitive to occasional rejection cues, even before rejection is experienced persistently. Findings are discussed alongside methodological considerations for future research examining stress and attention, including the potential benefits of examining group-level grand average ERP waveforms over the more traditional examination of peak ERP amplitudes. This research furthers our understanding of the challenges associated with LD, and along with future research may lead to improvements in psychosocial interventions that promote positive developmental trajectories for these youth.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".