How do readers actually build meaning from words?
Does meaning come from adding up word definitions, or from detecting which words activate the same mental frame together? This explores whether composition or resonance better describes how we make sense of language.
The dominant theoretical inheritance for "what is meaning" runs through compositional semantics: the meaning of a sentence is a function of the meanings of its words and their syntactic combination. The theory has its uses, but it under-describes how meaning is actually made in live language use. The bullseye example — where three of four words activate an archery frame while the fourth sits adjacent but unselected — cannot be derived compositionally. Compositionality treats all four words as equally contributing; the meaning emerges from selecting some and suppressing others.
The alternative formulation: meaning is the live detection of resonance. The mind reading a passage does not aggregate word-meanings into sentence-meaning. It scans for frames that some subset of the available words activates together, and the meaning that emerges is the one carried by the strongest-resonating frame. Other words present in the passage are not aggregated into the meaning — they are passed over. The selection is the meaning-making operation; the meaning is what the selected subset adds up to within the activated frame.
This formulation has three implications. First, meaning is not a property of the linguistic surface alone — it is a relation between the surface and the cognitive operation of frame-activation. Different readers can extract different meanings from the same surface depending on which frames they activate. Second, meaning is non-additive — adding more words to a passage does not necessarily add more meaning, and sometimes adds noise that interferes with frame-activation. Third, meaning is non-monotonic — recognizing one frame can suppress others that the same words could have activated.
This bears directly on AI meaning-making. Why do AI systems miss jokes and wordplay so consistently? specifies the AI failure: parallel-aggregative computation does not perform the selective frame-activation that resonance requires. Does the mind selectively activate frames from only some words? specifies the human operation. The theoretical claim here unifies them: meaning is constituted by the selective-resonance operation, not by the linguistic material on which the operation acts.
The strongest counterargument: this is just frame semantics restated. Frame semantics is a major influence, but the resonance formulation adds the specific claim that meaning-making is the live act of selection within the available frame-space — not the application of a selected frame, but the selection itself. The selection is the cognitive work that produces meaning.
Source: Making Sense - brief for co-authored essay on language
Related concepts in this collection
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Does the mind selectively activate frames from only some words?
When we understand wordplay or jokes, do we activate a frame from a subset of available words while suppressing nearby but frame-unrelated words? This matters because it reveals how meaning-making differs from how AI processes language.
the human-side cognitive operation this names theoretically
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Why do AI systems miss jokes and wordplay so consistently?
Exploring whether AI's literal reading of language stems from how transformers process tokens in parallel rather than through selective frame-activation like humans do. Understanding this gap could reveal what cognitive operations current architectures lack.
the AI-side failure this explains
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Why do speakers need to actively calibrate shared reference?
Explores whether using the same words guarantees speakers mean the same thing. Investigates how referential grounding differs across people and what collaborative work is needed to establish true understanding.
adjacent claim about why surface-similarity does not produce shared meaning
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Original note title
meaning is the live detection of resonance across subsets of words in subsets of frames not the sum of word meanings