Substantial differences in attentional disengagement between Prolific and MTurk samples: Implications for experimental outcomes
Bibliographic record
Abstract
Abstract Attention often disengages from primary tasks in favor of secondary tasks (i.e., multitasking) and task-unrelated thoughts (i.e., mind wandering). In this study, we assessed whether attentional disengagement, in the context of a standard cognitive task, can substantially differ between samples collected via commonly used online participant recruitment platforms, Prolific Academic (Prolific) and Amazon’s Mechanical Turk (MTurk). Initially, eighty participants were recruited through Prolific to perform an attention task in which the risk of losing points for incorrect responses was varied (i.e., high risk = 80% chance of loss, low risk = 20% chance of loss). Attentional engagement was assessed by measuring task performance along with self-reported mind wandering and multitasking. On Prolific, we observed surprisingly low levels of disengagement. We then conducted the same experiment on MTurk. Strikingly, MTurk participants exhibited greater levels of disengagement than did Prolific participants. In addition, there was an interaction between risk and platform, with the high-risk group exhibiting less disengagement, in terms of better task performance, than the low-risk group, but only on MTurk. Platform differences in trait inattention and relations among study variables were also observed. Therefore, choice of recruitment platform could impact results, particularly those that relate to attentional disengagement.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".