Mental Fatigue Detection

1. Hypothesis

A multimodal model combining response variability, correction behaviour and interaction rhythm will detect fatigue earlier than self-report alone.

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: task duration; workload intensity; break schedule; signal combination Dependent variables: fatigue classification; prediction lead time; performance decline Confounders: sleep; caffeine; health; stress; chronotype; device differences Measures: psychomotor vigilance; response-time variability; correction rate; Karolinska Sleepiness Scale; model sensitivity and specificity 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 Mental Fatigue Detection as a testable research proposition. Problem basis: Current approaches to mental fatigue detection 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 fatigue classification; prediction lead time; performance decline while avoiding material deterioration in independence, confidence calibration or delayed performance. Proposed observations: psychomotor vigilance; response-time variability; correction rate; Karolinska Sleepiness Scale; model sensitivity and specificity. 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. Mental Fatigue Detection warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “A multimodal model combining response variability, correction behaviour and interaction rhythm will detect fatigue earlier than self-report alone.” 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.