Forewarning interruptions in dynamic settings: Can prevention bolster recovery?
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
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).
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it