Bibliographic record
Abstract
Abstract Making a good decision often takes time, and in general, taking more time improves the chances of making the right choice. During the past several decades, the process of making decisions in time has been described through a class of models in which sensory evidence about choices is accumulated until the total evidence for one of the choices reaches some threshold, at which point commitment is made and movement initiated. Thus, if sensory evidence is weak (and noise in the signal increases the probability of an error), then it takes longer to reach that threshold than if sensory evidence is strong (thus helping filter out the noise). Crucially, the setting of the threshold can be increased to emphasize accuracy or lowered to emphasize speed. Such accumulation-to-bound models have been highly successful in explaining behavior in a very wide range of tasks, from perceptual discrimination to deliberative thinking, and in providing a mechanistic explanation for the observation that neural activity during decision-making tends to build up over time. However, like any model, they have limitations, and recent studies have motivated several important modifications to their basic assumptions. In particular, recent theoretical and experimental work suggests that the process of accumulation favors novel evidence, that the threshold decrease over time, and that the result yields improved decision-making in real, natural 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".