Your map/territory risk is exactly what this lineage formalizes -- internal maps are necessary but they shape and limit perception. Walter Lippmann (1922) makes "pictures in our heads" the operative reality of public judgment:

https://en.wikipedia.org/wiki/Public_Opinion

Frederic Bartlett (1932) defines schemas as memory structures that pre-shape perception and recall:

https://en.wikipedia.org/wiki/Schema_(psychology)

Jean Piaget explains schema updating via assimilation/accommodation when evidence conflicts with the map:

https://en.wikipedia.org/wiki/Assimilation_(psychology)

Edward Tolman introduces cognitive maps, making "map" literal in psychology:

https://en.wikipedia.org/wiki/Cognitive_map

Marvin Minsky formalizes frames as slot-filled expectations that speed inference but can blind you to anomalies:

https://en.wikipedia.org/wiki/Frame_(artificial_intelligence...

voidhorse: "mental model" vs "theory" is a real distinction in the literature. Kenneth Craik frames small-scale models as internal simulations for reasoning, not public theories:

https://en.wikipedia.org/wiki/Kenneth_Craik

Philip Johnson-Laird formalizes mental models as internal simulations used for inference and prediction:

https://en.wikipedia.org/wiki/Philip_Johnson-Laird

andsoitis: "informal, simplified, personal" models are exactly why systematic errors show up. Daniel Kahneman and Amos Tversky document heuristics and biases when internal maps are over-trusted:

https://en.wikipedia.org/wiki/Heuristics_in_judgment_and_dec...

Repair loop: Seymour Papert's microworlds provide controlled sandboxes for testing and revising models:

https://en.wikipedia.org/wiki/Constructionism_(learning_theo...

Gary Drescher gives a schema mechanism for incremental action/outcome updates that rebuild the map from experience:

https://mitpress.mit.edu/9780262517089/made-up-minds/

If you want to see Drescher operationalized, MOOLLM turns the schema mechanism into working skills. Schema Mechanism is the causal core, Schema Factory adds a deterministic toolchain and context bundles for LLM reasoning, and Play-Learn-Lift is the governance loop that maps ACT/OBSERVE/ATTRIBUTE/SPIN OFF into audited upgrades. This is GOFAI made practical with LLMs filling the old gaps in grounding and explanation.

Drescher's Schema Mechanism as Anthropic Skill:

https://github.com/SimHacker/moollm/blob/main/skills/schema-...

Drescher's Schema Factory as Anthropic Skill:

https://github.com/SimHacker/moollm/blob/main/skills/schema-...

Play=>Learn=>Lift methodology as Anthropic Skill:

https://github.com/SimHacker/moollm/blob/main/skills/play-le...

Here is the exact kind of thing we are talking about -- the YAML Jazz schema examples are live, readable schemas-by-example with causal context, semantic comments, evidence counts, side effects, and marginal attribution notes, including a practical devops edgebox/ingest cluster and a Zork/MUD "learn by dying" cluster so you can see the mechanism at work in real data:

https://github.com/SimHacker/moollm/blob/main/skills/schema-...

  # YAML Jazz schema examples (comments are semantic)
  #
  # These are schemas-by-example: minimal structure, rich intent.
  # Follow canon schema rules where possible, but annotate as needed.
  # Ad hoc fields and side-notes are allowed for partially jelled ideas.

And here is a MOOLLM simulation session explaining Gary Drescher's ideas themselves -- an ethical tribute simulation (not actually real people), grounded in documented work and analyzed source code, and framed for a simulated audience of familiar experts, to show how a Society of Mind meets "The Sims" style ensemble can explain itself:

https://github.com/SimHacker/moollm/blob/main/examples/adven...

Finally, if you want the deeper connections tour written specifically for this thread -- the big-picture synthesis that ties Papert, Minsky, Drescher, Play-Learn-Lift, and live microworlds into one operational map -- dive here:

https://github.com/SimHacker/moollm/blob/main/designs/CONNEC...

Mental Models (2018)