The impact of visual load on performance in a human-computation game
Why this work is in the frame
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Bibliographic record
Abstract
It is well-known that tasks imposing high cognitive load, i.e., the mental effort required to carry out a task, place a strain on people's ability to perform. In light of this, the present study investigates whether poor performance also occurs in human-computation games. That is, do players perform better in game designs that increase the visual information presented? These designs have the advantage of exposing players to more of the solution space, but may come with the caveat of imposing a higher cognitive load. We present a case study by considering alternative layouts differing in the amount of visual information given to players in a human-computation game. The findings of the study seem to support the idea that presenting more information is beneficial to players. This is surprising result that challenges prevailing beliefs about cognitive load, and invites more detailed, future investigation.
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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