Decision Quality Under Load

1. Hypothesis

Ranked options with explicit uncertainty and a forced independent judgement step will outperform single-answer recommendations under load.

2. Experiment design

Design: controlled repeated-measures human performance study; baseline, active comparison, calibrated AI condition, user-controlled condition, failure or handback scenario and delayed follow-up Methods: controlled task experiments; repeated-measures studies; ecological momentary assessment; interaction telemetry; interviews; pre-registered analysis; active comparison; subgroup and accessibility analysis; delayed retention or longitudinal follow-up; adverse-effect capture; reproducibility testing; participant debrief Independent variables: recommendation format; uncertainty display; load level; time pressure Dependent variables: decision accuracy; calibration; override quality; confidence Confounders: expertise; risk tolerance; stress; prior trust in AI; stakes Measures: decision accuracy; Brier score; appropriate override rate; confidence calibration; response time Success criteria: Statistically and practically meaningful improvement; preserved or improved human skill and agency; acceptable burden; no disproportionate subgroup harm; reproducible performance; effective handback, correction and recovery. Failure conditions: No meaningful benefit; gains mask reduced understanding or skill; dependency, fatigue, stress or exclusion exceeds benefit; effects fail to transfer or persist; user control is ineffective; correction or handback fails.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Decision Quality Under Load as a testable research proposition. Problem basis: Current approaches to decision quality under load are fragmented, poorly calibrated or insufficiently measured, making it difficult to distinguish real benefit from substitution, novelty or surveillance effects. Directional expectation: If the hypothesis is supported, the intervention condition should improve decision accuracy; calibration; override quality; confidence while avoiding material deterioration in independence, confidence calibration or delayed performance. Proposed observations: decision accuracy; Brier score; appropriate override rate; confidence calibration; response time. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 36/100; Novelty 78/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Decision Quality Under Load warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “Ranked options with explicit uncertainty and a forced independent judgement step will outperform single-answer recommendations under load.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Cognitive load theory; working-memory models; executive-control research; vigilance and task-switching literature; neuroergonomics. It also records: NIH Cognitive Health — https://www.nih.gov/; NIOSH Total Worker Health — https://www.cdc.gov/niosh/twh/; ISO 10075 mental workload — https://www.iso.org/; OECD AI Principles — https://oecd.ai/. Evidence register status: “Seeded; authoritative source register refreshed; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Cognitive load theory; working-memory models; executive-control research; vigilance and task-switching literature; neuroergonomics. References: NIH Cognitive Health — https://www.nih.gov/; NIOSH Total Worker Health — https://www.cdc.gov/niosh/twh/; ISO 10075 mental workload — https://www.iso.org/; OECD AI Principles — https://oecd.ai/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.