Aging and the development of automaticity in conjunction search
Bibliographic record
Abstract
In two experiments, younger and older observers carried out feature searches for targets defined by their luminance contrast and orientation. Additionally, they received consistent-mapping (CM) training in luminance contrast by orientation conjunction search, followed by a brief exposure to conjunction search under reversal conditions. In Experiment 1, display size effects on reaction time suggested that both younger and older observers were conducting a parallel search in all conditions and showed equivalent disruption at reversal. Experiment 2 was a substantive replication of the first using more difficult conjunction search displays. In addition to latency, we measured the number, duration, and feature-based selectivity of fixations made during conjunction search. Display size effects were larger than in Experiment 2 and were of equivalent magnitude in younger and older people. There were no age differences in improvement in conjunction search and minimal age differences in disruption following reversal. Both age groups demonstrated early in training that they could select items possessing target features (i.e., the color white), and both age groups demonstrated that they could not completely reverse this selectivity when these features no longer defined the target. These experiments have several implications for models of visual attention and age differences therein.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".