cognitive-loop-map

name	cognitive-loop-map
description	Use this to reconstruct how someone repeatedly moves from intent to learning, and how that loop shifts by task type.

Cognitive Loop Map

The Big Idea

The deliverable is HOW work gets thought through, not which adjective describes the thinker. Reconstruct transitions, count dominant paths, then map where the loop bends under different conditions.

The Loop

Trace each episode through:

INTENT -> PROBLEM REPRESENTATION -> PLANNING -> INFORMATION GATHERING -> DECISION -> EXECUTION -> MONITORING -> CORRECTION -> VALIDATION -> LEARNING

For every transition, find real instances. Note the common path and the common skips. A skipped VALIDATION stage is a finding, not an accident.

The Condition Table

Then split by condition. The loop usually changes shape:

Condition	What to compare
Unfamiliar task	Where does uncertainty go first
Familiar task	Which stages get compressed or skipped
Bug	Entry point, hypothesis discipline
Architecture work	Framing depth, option generation
Frontend / visual work	Iteration pattern, verification style
Production incident	Speed vs evidence trade
Research task	Depth, stopping rule
High-stakes operation	Deliberation, reversibility checks
Failed first attempt	Hand off to failure-loops
Different AI model	Same human, changed loop - attribute carefully

Method

1. Pick 20-40 representative episodes across conditions.
2. Mark each stage transition as present, skipped, or outsourced to AI.
3. Draw the dominant loop per condition.
4. Name the two or three transitions that most often go wrong.

When It Backfires

• Inferring the loop from polished final summaries instead of raw moves.
• Confusing the model's loop with the human's. Attribute first (episode-method).
• Presenting one idealized loop when there are really three: calm mode, pressure mode, delegated mode.

One-Line Memory

Map transitions, not traits.