Bibliographic record
Abstract
Given that the core issues of attention research have been recognized for millenia, we do not know as much about attention as we should. I argue that the reasons for this failure are (1) we create spurious dichotomies, (2) we reify attention, treating it as a cause, when it is an effect, and (3) we equate a collection of facts with a theory. In order to correct these errors, we need a new technical vocabulary that allows for attentional effects to be continuously distributed, rather than merely present or absent, and that provides a basis for quantitative behavioral predictions that map onto neural substrates. The terminology of the Bayesian decision process has already proved useful for structuring conceptual discussions in other psychological domains, such as perception and decision making under uncertainty, and it had demonstrated early success in the domain of attention. By rejecting a reified, causal conception of attention, in favor of theories that produce attentional effects as consequences, psychologists will be able to conduct more definitive experiments. Such conceptual advances will then enhance the productivity of neuroscientists by allowing them to concentrate their data collection efforts on the richest soil.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.008 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".