Where does the meaning actually originate in reader-detected resonance across language?
This reads 'resonance' as the live, frame-based way readers build meaning — and asks whether meaning lives in the words, in the reader, or in the act of detection itself; the corpus locates it firmly in the reader's selective activation, not the text.
This explores where meaning actually comes from when a reader feels words 'click' together — and the collection's sharpest claim is that meaning doesn't originate in the words at all, but in the reader's live detection of which subsets of words activate a shared frame. One note frames meaning-making as a selective, non-additive, non-monotonic operation: you don't sum up word meanings, you sense which combination lights up a frame and suppress the rest How do readers actually build meaning from words?. Resonance, on this view, is an event that happens in the reader — the text supplies the material, but the spark is detected, not transmitted.
The corpus makes this vivid by contrast with how machines fail at exactly this step. Transformers integrate every token through weighted parallel aggregation — they read additively, never selectively suppressing the irrelevant words that frame-detection requires Why do AI systems miss jokes and wordplay so consistently?. That single architectural difference, not a knowledge gap, explains why models miss jokes and wordplay, and why GPT-4 disambiguates only 32% of deliberately ambiguous sentences against humans' 90% Can language models recognize when text is deliberately ambiguous?. If meaning lived in the words, a system that ingests all the words should recover it. It doesn't — which is itself evidence that the meaning was never fully in the text.
There's a deeper twist, though: the same models that can't hold two interpretations at once are eerily good at the relational structure of language. Research showing LLMs operationalize Saussure's *langue* — learning culturally situated discourse purely by compressing relational patterns, no world-reference needed Can language models learn meaning without engaging the world? — suggests a lot of 'meaning' really is latent in the statistical web of how words co-occur. So the picture is split: the relational scaffold can be learned from text alone, but the live, selective *resonance event* — choosing which frame fires right now — seems to require a reader doing something the architecture doesn't do.
And crucially, that reader isn't neutral. Interpretation Modeling work shows the same socially-loaded sentence yields irreducibly multiple valid readings depending on the reader's social position — disagreement carries information, it isn't annotation noise Why do readers interpret the same sentence so differently?. So if you ask 'where does the meaning originate,' part of the answer is: in *which* reader, carrying which frames. Coherence itself is reconstructed live, with the reader simultaneously tracking segments, intentions, and salience as mutually constraining layers How do readers track segments, purposes, and salience together?.
The thing you might not have known you wanted to know: where machine 'meaning' *does* come from is measurably elsewhere. LLMs systematically prefer the higher-frequency phrasing of two equivalent paraphrases — across math, translation, and reasoning — tracking statistical mass from pretraining rather than recognizing meaning Do language models really understand meaning or just surface frequency?. So the collection draws a clean line: in humans, meaning originates in the reader's live frame-resonance; in current AI, the nearest analog originates in pretraining frequency. Same surface fluency, two completely different origins.
Sources 7 notes
Meaning-making is the live detection of which word subsets activate shared frames, not compositional aggregation of individual word meanings. This operation is selective, non-additive, and non-monotonic, fundamentally different from how current AI processes language.
Transformers integrate token information through weighted parallel aggregation rather than selective suppression of irrelevant words. This structural difference explains consistent failures with jokes, wordplay, and frame-dependent meaning—not knowledge gaps, but missing cognitive operations.
AMBIENT benchmark shows GPT-4 correctly disambiguates only 32% of cases versus 90% for humans. This failure spans lexical, structural, and scope ambiguity—revealing that LLMs cannot hold multiple interpretations simultaneously, a fundamental gap hidden by standard benchmarks.
Research shows LLMs learn culturally situated discourse patterns by compressing relational structure from text, demonstrating that fluent language generation requires no external referents or embodied grounding.
Interpretation Modeling research shows that disagreement on socially embedded sentences reflects valid differences in reader perspective, not annotation failure. Structured human disagreement in NLI benchmarks confirms that interpretation distributions carry meaningful information.
Discourse processing demands parallel recognition of linguistic segments, intentional structure, and attentional salience—not sequential processing. These three layers constrain each other during comprehension, and failures in any single layer disrupt overall understanding.
LLMs show consistent preference for higher-frequency surface forms over semantically equivalent rare paraphrases across math, machine translation, commonsense reasoning, and tool calling. This suggests models track statistical mass from pretraining rather than meaning-recognition as their primary mechanism.