Dispositional affect predicts temporal attention costs in the attentional blink paradigm
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Bibliographic record
Abstract
Abstract Theories suggest that positive affect broadens attention, whereas negative affect focuses attention. This position has been supported by studies showing that positive affect leads to more diffuse spatial attention while negative affect leads to more focused spatial attention. Recently, researchers have used the attentional blink (AB) paradigm to show that induced positive affect may also lead to more diffuse temporal attention, allowing greater accuracy for targets presented within a short time interval. The present study investigated whether dispositional affect could modulate temporal attentional diffusion using the AB paradigm. Consistent with the diffusion hypothesis, greater positive affect was associated with smaller AB magnitude, whereas greater negative affect was associated with larger AB magnitude. Thus, dispositional affect can modulate the costs of attentional selection over brief time intervals. Keywords: Attentional blinkAttentionAffectPositiveNegativeDiffuse Acknowledgements This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Foundation for Innovation (CFI), and Ontario Innovation Trust (OIT) to the second author. We thank Kirk Stokes and Carleen Gicante for their assistance with data collection. Notes 1The pattern of zero-order correlations between PA, NA, and AB magnitude observed when averaging across stimulus types were also observed for each of the three stimulus types individually. 2Response bias (β, the willingness to say "yes" to the presence of an X) was not significantly correlated with any of the affect measures and was not examined further. 3Results were consistent even when overall T1 accuracy and T2 sensitivity were included as predictors in the regression models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it