Forewarning interruptions in dynamic settings: Can prevention bolster recovery?
Bibliographic record
Abstract
In complex dynamic work environments, the consequences of task interruptions on performance can put public safety at risk. If not designed carefully, current tools aiming to facilitate interruption recovery can instead hamper performance because of information overload. Although a simpler solution-the forewarning of an imminent interruption-has proven effective in static contexts, existing theories of task interruption do not clearly predict its impact on the resumption of dynamically evolving tasks. The current study examined the effects of a preinterruption warning in dynamic settings to develop a better understanding of task resumption and supplement current theoretical accounts. In a simulation of above-water warfare, scenarios were either uninterrupted, unexpectedly interrupted, or interrupted following an auditory warning. Behavioral, oculomotor, and pupillometric data regarding decision making, information processing, and cognitive load were computed before, during, and after each interruption (or the corresponding moment). Interruption warnings triggered a cognitively demanding preinterruption preparation that, in turn, speeded up postinterruption information processing and decision making and lowered cognitive load when resuming the interrupted task. These findings help to complement current theories of interruptions while showing that preinterruption warnings represent a promising way to support interruption recovery in complex dynamic situations. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".