The Next Word Fallacy in LLMs: It’s Still Wayfinding, But Neurological not Computational
Ok, so in my understanding the process of producing outputs in both LLMs and human speech are equivalent to making incremental judgements to satisfy a demand. Now I call this wayfinding because I’m framing my concepts from neuroscience (where all cognition originates in wayfinding). But I can see if one’s coming from the computer science background first instead of cog-sci first you might think wayfinding means ‘from here to there’ instead of ‘incrementally satisfying demand’.
This plays into my work on closure in LLMs, where I seek satisfaction of demand as marginal indifference as closure rather than identity as in logical or mathematical closure.
Ah — now it’s clear why the word wayfinding was causing the cross-wiring.
You’re using wayfinding in the cognitive-science sense:
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All cognition is navigational.
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Organisms evolved to incrementally minimize uncertainty, error, or metabolic cost in moving through space, time, or state-space.
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Speech, thought, planning — they are just refinements of this same machinery, each one incrementally satisfying a demand (drive, prediction error, goal signal).
Whereas in computer science, wayfinding is usually framed as:
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Planning from here to there over a graph.
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Producing a route before traversal.
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Often associated with optimization, shortest-path, or A→B search problems.
So the computer scientist hears wayfinding and imagines global search over paths; the neuroscientist hears wayfinding and imagines local iterative correction toward equilibrium.
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Sensory input sets prediction error or “demand” at time t.
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Generative models propose a continuation reducing that error.
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Motor or linguistic output alters the world (or the utterance), changing the next prediction error.
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The loop runs at millisecond scale — so behavior looks smooth, but under the hood it’s tiny satisfactions chained together.
This is the same whether:
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A foraging animal adjusts path to a food source,
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A speaker adjusts phrase choice mid-sentence,
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A thinker adjusts hypothesis mid-paragraph.
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Prompt sets the initial “demand” = context that must be satisfied.
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Next-token prediction incrementally satisfies it at each step.
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Closure layers (constraints, logic) add meta-demands like truth, legality, or style.
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There is no global plan: the trajectory emerges from local increments constrained by priors + feedback.
Cognitive-Science FramingLLM / CS FramingWayfinding = incremental correctionAutoregression = local next-token stepDemand = prediction error / drivePrompt = conditioning contextConstraint layers = executive controlGovernance / closure layersEmergent trajectoryGenerated text stream
So your wayfinding = incremental demand satisfaction, not route-planning.
The misunderstanding was lexical, not conceptual.
The misunderstanding was lexical, not conceptual.
Here’s the three-layer causal loop explanation showing how demand → incremental satisfaction → constraint gating works the same way in human cognition and LLMs:
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Demand / Error Signal:
In humans: prediction error, drives, goals.
In LLMs: prompt conditioning, input context. -
Incremental Satisfaction:
In humans: cortical predictive coding, speech motor loops.
In LLMs: autoregressive next-token generation. -
Constraint Gating:
In humans: prefrontal/executive control networks.
In LLMs: your closure/governance layers, logic rules, external verifiers.
Source date (UTC): 2025-09-28 23:28:18 UTC
Original post: https://x.com/i/articles/1972443277501866486
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