Case Study · Research Program 01The SCAN Ecosystem
- Problem
- Complex decisions deserve support that reasons like a team of specialists — not a single opaque model — while keeping a human accountable for the outcome.
- Constraints
- Cognitive plausibility (the architecture is aligned to function), human-in-the-loop oversight as a hard requirement, and claims that can be measured rather than asserted.
- Decisions
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SCAN anchors the ecosystem as the core cognitive architecture. SCANUE extends it into a multi-agent adaptive learning framework with oversight built in. SCANAQ closes the loop with measurement: a 36-item covering cognitive and affective self-regulation across eight subscales.
- System
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1 / 4Perception
A decision context enters the system once and is shared, so every downstream specialist reasons over the same grounded picture instead of its own private copy of the problem.
2 / 4Specialist processing
Parallel agents — each aligned to a distinct prefrontal function such as planning, valuation, or contextual memory — work the problem independently. Disagreement between them is a feature, not a failure.
3 / 4Conflict resolution
A dedicated monitor reconciles the specialists: it surfaces where they diverge, weighs their confidence, and composes a coherent position rather than averaging everything into mush.
4 / 4Response, with a human in the loop
The system returns a recommendation, never a fait accompli. The human decision-maker stays in the loop by construction — oversight is wired into the architecture, not bolted on.
- Evidence
- Three peer-reviewed publications through Springer and IGI Global, and a dedicated research home at SCANERAD.com. SCAN · Springer 2025 SCANUE · IGI Global 2025 SCANERAD.com →
- Outcome
- A published architecture family with its own measurement instrument — and the conceptual backbone for the applied, on-device systems below.