Resuming a Dynamic Task Following Increasingly Long Interruptions: The Role of Working Memory and Reconstruction
Bibliographic record
Abstract
Studies examining individual differences in interruption recovery have shown that higher working memory capacity (WMC) attenuated the negative impact of interruption length on resumption, at least in static contexts. In continuously evolving (or dynamic) situations, however, working memory may not be as central to the effective resumption of a task, especially in the case of long interruptions. One of the main theories of task interruption suggests that dynamic task resumption could depend on a reconstruction of the primary task context, that is, a visual examination of the post-interruption environment. To better define the role of working memory and reconstruction processes in interruption recovery, the current study examined the association between (1) dynamic task resumption following interruptions of various lengths and (2) two cognitive abilities chosen to operationalize the processes under study, namely, WMC and visual search capacity. Participants performed a multiple object tracking task which could be uninterrupted or interrupted for 5, 15, or 30 s while the hidden stimuli continued their trajectory. They also completed tasks measuring the two cognitive abilities of interest. The results revealed that WMC contributed to post-interruption accuracy regardless of interruption duration. On the contrary, visual search capacity was related to faster resumption in the 15-s and 30-s interruption conditions only. Those results show that working memory plays a preponderant role in resumption not only in static, but also in dynamic contexts. However, our study suggests that this mechanism must share the limelight with reconstruction following lengthy interruptions in dynamic settings.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".