An Investigation into the Self-deployment of Attentional Reminders
Bibliographic record
Abstract
In a series of studies, we sought to determine whether 1) people will self-deploy attentional reminders when asked to complete an attentionally demanding task (Experiment 1 & 2), 2) people modulate the number of attentional reminders they selected depending on the presence or absence of a continuous distraction (Experiment 1 & 2) and 3) if so, do reminders improve performance on the attentionally demanding task (Experiment 1, 2 & 3). In Experiments 1 and 2 participants were asked to complete an attentionally demanding task (2-back; primary task) and completed the 2-back task on its own (no distraction condition) or while a distracting video was played on the computer screen above the 2-back task stimuli (distraction condition). Critically, participants were given a preview of the 2-back task and the video (if present). After being given a preview of the task, they were asked to set how many (if any) reminders they wanted to receive during the task. We followed this up in Experiment 3, where we removed the choice component. Specifically, in this study, half of the participants received experimenter-set attentional reminders (every 2 minutes) while the other half did not receive any reminders. Findings from Experiments 1 and 2 indicated that people will opt to select attentional reminders when asked to complete an attentionally demanding task, however, their modulation of the reminders was irrespective of the presence or absence of a distracting video. In addition, the attentional reminders people set did not influence performance on the 2-back task. Experiment 3 demonstrated that people who received experimenter-set attentional reminders did not significantly perform better on the 2-back task in the presence of a distracting video. These results suggest that the attentional reminders may influence performance, however, their influence might be dependent on the contingent timing of the deployment of the attentional reminders.
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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 | 0.001 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".