Bibliographic record
Abstract
The term ’attention’ has been a drag on our science ever since the early days of experimental psychology. Our frequent offerings and sacrifices (articles and the debates they provoke), and our unwillingness to abandon our belief in this reified entity indicates the aptness of the Jamesian phrase ”idol of the tribe.” While causal accounts of attention are empty, attention might be, as suggested by Hebb, a useful label. It could be used to indicate that some experimental observable is not immediately explained by the excitation of receptor cells. However, labeling of something as ’attention’ means there is something to be explained; not that something has been explained. Common experimental manipulations used to provoke visual selective attention: instructions, cues, and reward are in fact the guide to explaining away ’attention’. The observations provoked by such manipulations frequently induce behavioral performance differences not explainable in terms of differences in retinal stimulation. These manipulations are economically summarized as components of a process in which base rates, evidence, value, and plausibility combine to determine perceptual experience. After briefly reviewing the history of how attention has been confusing from the start, I will summarize the notion of conceptual fragmentation and show how it applies. I will then review how the traditional conditions of an attentional experiment provide the basis for a superior, attention free, account of the phenomena of interest, and I will present some of the opportunities for the use of more formal descriptions that should lead to better theoretically motivated experimental investigations.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".