Cognitive and Behavioral Impacts of Two Decision-Support Modes for Judgmental Bootstrapping
Bibliographic record
Abstract
The Cognitive Shadow is a decision-support system that uses policy capturing to model human operators’ judgment policies and provide online predictions of their decisions. The system can provide support in reaction to a decision mismatch (shadowing mode) or proactively (recommendation mode). The goal of this study was to compare these two modes of operation in their ability to effectively model and support decision-making and to examine impacts on information processing, workload, and trust. Participants took part in an aircraft threat evaluation simulation without decision support or with the Cognitive Shadow (either shadowing or recommendation mode). Dwell time was collected over different areas of the user interface. While the recommendation mode had no advantage over the control group, the shadowing mode resulted in greater human and model accuracy. This mode led to longer dwell time over the parameters zone presenting key information for decision-making. These benefits were maintained even after the tool was removed. Workload was unaffected by the mode, and while trust was initially higher in the recommendation mode, it quickly became equivalent between both modes, overall supporting shadowing as the better configuration for cognitive assistance. Results are discussed in terms of decision processes, operators support, and automation bias.
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.
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".