Attention Checks in Decisions From Experience: The Case of Checking Experiments
Bibliographic record
Abstract
Can online participants be compared to laboratory subjects. In the five studies (Ntotal = 1519) reported in this article, we comprehensively compared the behavior of online attentive and inattentive participants; i.e., those who passed or failed a simple attention check. The findings show that in a decisions from experience paradigm (i.e., a multi-trial repeated choice task), even one simple attention check is sufficient to differentiate between attentive and inattentive participants. The validity of this separation is further evidenced in the significant difference in the reported conscientiousness measure.The results fully replicated three previously run lab studies for the attentive participants, but not for the inattentive participants. This highlights the importance of using attention checks to avoid spurious conclusions. There are three explanations for the behavioral differences between the attentive and inattentive participants. Attentive participants exhibited a pre-disposition towards checking. They also had a greater likelihood of noticing distinct outcomes. Finally, the inattentive participants tended to behave more randomly. When checking was beneficial, these three effects pointed in the same direction, leading to a major difference between participants. When checking was detrimental, these effects cancelled each other out, resulting in similar checking rates and indistinguishable behavior.
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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 | 0.027 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".