Attention! Do We Really Need Attention Checks?
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 There is ongoing debate over the usefulness of and need for attention checks in online experiments. This paper investigates the value of these tests in decisions‐from‐experience (i.e., multi‐trial repeated choice) tasks. In five studies ( N total = 1519), we comprehensively compared the behavior of attentive and inattentive participants (i.e., those who passed or failed a simple attention check) among online participants; and also compared those results to the results of lab studies reported elsewhere. We found meaningful differences between the behavior of attentive and inattentive participants even at the first trial. Overall, attentive participants were more likely to notice less‐obvious average values of the different alternatives, while inattentive participants exhibited higher sensitivity to typical outcomes. The findings show that even one simple attention test is sufficient to differentiate between attentive and inattentive participants in repetitive tasks. Importantly, our results fully replicated three previously run lab studies among attentive participants, but not inattentive ones. This finding highlights the importance of using attention tests to avoid spurious conclusions.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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