Editing Openai/693cab0b-13a0-8011-949b-27f1d40869c1 (section)

=== Assistant: Short answer: ===
Short answer:
✔ Your LEGO analogy is largely correct for humans (with one refinement).
✔ LLMs have an analogue, but it is distributed, continuous, and non-object-like.

Below is a precise mapping, no hand-waving.

==== ### ====
* Humans think in concepts, not sentences
* Concepts are pre-assembled primitives
* Thinking = re-arranging / binding concepts
* Language comes after internal structuring

This matches cognitive science.

===== Your LEGO blocks are not discrete plastic bricks. =====

They are closer to:
* Neural assemblies
* Overlapping activation patterns
* Soft, fuzzy, context-dependent

So instead of:

<syntaxhighlight>[LEGO_A] + [LEGO_B]

</syntaxhighlight>

Reality is:

<syntaxhighlight>~activation cloud A~ ⊕ ~activation cloud B~

</syntaxhighlight>

==== - Concept ≠ neuron ====
* Concept ≠ word

===== Properties: =====
* Distributed across cortex
* Re-entrant (feedback loops)
* Multimodal (vision + sound + action)
* Context-sensitive

Example: “DOG”
* Visual cortex → shape, fur
* Auditory cortex → bark
* Motor cortex → petting
* Emotion → affection/fear

All bound transiently.

==== <syntaxhighlight>Stimulus ====
  ↓
Concept activation (latent)
  ↓
Concept binding / simulation
  ↓
Stabilized internal model
  ↓
Language motor planning
  ↓
Speech / text

</syntaxhighlight>

Language is late-stage serialization, not thought itself.

==== ### ====

Yes — but alien version.

==== ### ====
* Symbols
* Objects
* Explicit nodes

===== - Directions in embedding space =====
* Subspaces
* Activation patterns across layers

A “concept” in an LLM is:

: 

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| --- | --- | --- |
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==== Not by snapping blocks — by vector algebra. ====

===== - Tokens → embeddings =====
* Attention computes weighted sums
* MLPs perform nonlinear mixing
* Depth = iterative refinement

Mathematically:

<syntaxhighlight>hᵢ₊₁ = f( Σ αⱼ · hⱼ )

</syntaxhighlight>

This is concept superposition, not object placement.

==== ### ====
* Can simulate
* Can imagine
* Can counterfactually manipulate concepts

===== - Can only statistically remix =====
* No internal world-model unless trained explicitly
* No persistent latent state across turns (yet)

So:

: 

==== Because: ====
* Both systems are compositional
* Both delay language until after structuring
* Both use latent representations
* Both rely on pattern reuse

But:
* Humans use dynamics
* LLMs use static weights + runtime attention

==== ### ====
* Stable internal simulation loop
* Self-referential monitoring (metacognition)

LLMs lack:
* Persistent internal workspace
* True concept permanence at inference time

That’s why:
* They don’t “hold” ideas
* They regenerate them every time

==== - ✔ Your LEGO metaphor is directionally correct ====
* ❌ Blocks are not discrete
* ✔ Arrangement happens before language
* ✔ LLMs have concept-like structures
* ❌ LLMs do not manipulate objects, only fields

==== : ====

If you want next:
* How this maps to attention heads
* Why concept superposition causes hallucinations
* Whether adding memory makes LLMs closer to humans
* Why grounding (vision/action) changes everything

Say the axis.