Implications of search accuracy for serial self-terminating models of search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In two experiments, the data from a total of 35 participants was compared to predictions from two competing serial search models, one with perfect memory and one with no memory. Reaction times were collected from an unlimited exposure duration, 4-alternative, target identification search task with array sizes of 8, 12, or 16 items. From the linear function relating RT to number of distractors a hypothetical “inspection time/item” was computed under the assumption of SSTS. Target identification accuracy was obtained from the same participants using the same displays, but with pre- and post-masks used to limit total inspection time (to 120, 180, 300, and 540 ms). Neither the memoryful nor memoryless model provided a good enough fit to the data to support the assumption that serial inspection is the primary process determining search time. Despite direct evidence for serial allocation of attention in visual search, these results suggest that scholars should seriously consider the possibility of hybrid search models in which serial and parallel strategies operate jointly.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it