Within and beyond an integrated framework of attentional capture: A perspective from cognitive-affective neuroscience
Bibliographic record
Abstract
The integrative framework proposed by Luck and colleagues [Luck, S. J., Gaspelin, N., Folk, C. L., Remington, R. W., & Theeuwes, J. (2021). Progress toward resolving the attentional capture debate. Visual Cognition, 29(1), 1–21. https://doi.org/10.1080/13506285.2020.1848949] represents major progress in the field of attention research, and remaining areas of disagreement provide an opportunity to test hypotheses by drawing on other research traditions. From the perspective of research in cognitive-affective neuroscience, we first suggest ways in which recent analytic innovations in human neuroimaging can be used to test hypotheses proposed by Folk and Remington about how biases within distinct brain systems may be integrated within a control state. We then shift focus and extend recent critiques of the generalizability of vision-centred frameworks of emotional guidance of attention to the integrative framework, citing evidence that vision-based frameworks of attentional capture do not necessarily extend to other sensory modalities.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".