Attention, Researchers! It Is Time to Take a Look at the Real World
Bibliographic record
Abstract
Theories of attention, too often generated from artificial laboratory experiments, may have limited validity when attention in the natural world is considered. For instance, for more than two decades, conceptualizations of “reflexive” and “volitional” shifts of spatial attention have been grounded in methodologies that do not recognize or utilize the basic fact that people routinely use the eyes of other people as rich and complex attentional cues. This fact was confirmed by our novel discovery that eyes will trigger a reflexive shift of attention even when they are presented centrally and are known to be spatially nonpredictive. This exploration of real-world attention also led to our finding that, contrary to popular wisdom, arrows, like eyes, are capable of producing reflexive shifts of attention—a discovery that brings into question much of the existing attention research. We argue that research needs to be grounded in the real world and not in experimental paradigms. It is time for cognitive psychology to reaffirm the difficult task of studying attention in a manner that has relevance to real-life situations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.010 | 0.035 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".