Attentional tuning to events associated with long-term concerns
Bibliographic record
Abstract
Stimulus features such as brightness, color, and subjective arousal level are potent in capturing attention. This capture facilitates perceptual encoding and subsequent processing. Yet, it is still unknown whether stimuli related to long-term personal concerns similarly capture attention. To address this question, we employed an attentional blink (AB) paradigm, where observers detected two targets embedded in a stream of distractors during rapid serial visual presentation. The second target (T2) was either a word related to climate change or a neutral word (Experiment 1). Observers were more likely to detect T2 if it was climate-related than neutral. We refer to this reduced attentional blink for words related to climate change as climate word AB sparing. To examine whether this was driven by emotional arousal, we added a condition with negative emotionally arousing words (Experiment 2). We again found that observers were more likely to detect climate-related and negative T2 words than neutral ones. However, the climate words (e.g. carbon) were rated as less arousing than the negative words (e.g. murder), suggesting that climate word AB sparing was not strictly due to immediate arousal. Finally, to examine whether the sparing was explained by semantic priming, because climate words share a semantic category, we performed a version of the experiment with health-related rather than climate-related words (Experiment 3). Observers were equally likely to detect health-related or neutral targets, suggesting that the climate-word AB sparing was not due to semantic priming. In all experiments, no mention of climate change was made in the instructions. In subsequent surveys, most observers expressed environmental concerns. Thus, the heightened perceptual encoding of climate change stimuli was driven by long-term concerns. In sum, these findings suggest that attentional tuning to salient stimuli can reflect associations with long-term concerns, rather than immediate arousal or priming of a semantic category. Meeting abstract presented at VSS 2015
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".