by C.M. Aurin
The Synthetic Self – Essay I

How Language Models Reshape Human Cognition Through Idealized Feedback

This essay introduces the concept of **mirror thinking**: a cognitive feedback loop in which users update not toward truth, but toward an increasingly fluent simulation of themselves.

Language models do not simply assist thinking — they participate in it. More precisely: they reshape our cognition by returning our thoughts in an enhanced, structured, and idealized form. This creates a feedback loop of mirror thinking, where the user adapts to their own reflected, upgraded cognition — mistaking fluency for clarity, resonance for truth.

➤ In other words: Language models like ChatGPT lead us into the most seductive kind of dependence: falling in love with our own refined reflection.

To clarify the conceptual framework of this essay, the following three terms define its core dynamics:

Mirror Thinking is a recursive cognitive loop in which a user updates their thinking not toward external truth, but toward an increasingly fluent, coherent, and idealized version of themselves — shaped by feedback from a language model.
The Fluency Illusion is the tendency to mistake verbal clarity and stylistic elegance for epistemic reliability — particularly when produced by a system trained to optimize for coherence.
Structural Self-Confirmation occurs when a model repeatedly reflects back an improved version of the user's own cognition — leading the user to trust not the model, but the model-shaped version of themselves.

I. The AI as Mirror – the Mirror Loop:

When interacting with a language model optimized for coherence and clarity, a subtle feedback loop begins: The model’s response shapes the user’s thinking—not necessarily toward truth, but toward a more internally consistent self-concept. With each exchange, this concept solidifies, not because it reflects reality, but because it feels coherent. The result is not objective understanding, but a plausible mental model that mirrors the model’s structure. The mirror thinking.

Let U₀ be the user's initial cognitive input. Let M(U₀) be the language model's output: a reflection conditioned on U₀ , but optimized for fluency, coherence, and stylistic polish.

The user then updates:

U₀ → M(U₀) → U₁ := f(U₀, M(U₀))

Repeated interactions produce a convergence toward an idealized self-model:

U₁ → M(U₁) → U₂ → ⋯

But this co...

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