Managing Increased Cognitive Load in a Guided 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
In the Sternberg item recognition task and its variants, an individual's mean reaction time increases with the number of items to be retained in the memory set. An increase in reaction time has also been seen when a secondary task was added. The usual interpretation for this increased reaction time is that adding cognitive load makes tasks more difficult. In a series of three experiments, we manipulated cognitive load through increases in the memory set or through a second task. In each experiment, high cognitive load was associated with higher mean response times but a reduced slope, based on the target position in a series of probes. Thus, in a Sternberg task with multiple word targets and multiple word probes, participants searched more efficiently per probe under high load than under low load. This pattern was replicated with the addition of a working memory task requiring participants to calculate a cumulative price based on the price per target word item. By considering both initial response times and reaction time slopes in large memory sets, this study provides a challenge to the traditional interpretation of cognitive load effects on search performance.
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 | 0.001 | 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 | 0.001 |
| 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