Does Predictability Play a Role in Task Management? An Experimental Study With a Financial Trading Simulation
Bibliographic record
Abstract
In many complex time-critical tasks such as financial trading, cyber security monitoring, and patient monitoring in critical care, external interruptions and multiple-task situations disrupt the flow of tasks performed by operators leading to errors and accidents. There is an abundance of work reported on interruptions, which informs system designers and researchers on the potential cost of interruptions at different points within a task. However, a gap exists in our understanding of the relationship between interruption disruptiveness and the predictability of events that require an operator's response. To understand this better, we conducted an experiment involving 22 participants and a financial trading task. The experiment involved two levels of predictability (low and high) and two levels of task load (low and high). The experiment showed that task load had an overall negative effect on events. The results also showed that interruptions negatively affected responses to predictable events. However, we found that interruptions did not affect responses to unpredictable events. Overall, our research suggests that to leverage the role of predictability, the goal-activation model should be used to determine the impact of various design options about visual cues and predictable-trend durations. The research also reveals that unpredictable events may be cognitively different from predictable events when understanding the influence of interruptions on work, suggesting that interruption management tools may need to treat the situational context (predictable or unpredictable) differently, in providing a supportive workflow for the management of interruptions.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".