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question:granted-that-learning-requires-signed-information-but-why-must-the-sign-be-felt-why-can-t-directional-error-be-represented-as-a-computational-quantity-without-phenomenal-characterGranted that learning requires signed information, but why must the sign be felt? Why can't directional error be represented as a computational quantity without phenomenal character?
The central objection the paper must answer to establish identity over mere correlation
Neighborhood — ranked by edge-count
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claim
- The causal-functional argument that directionality and feeling are not two things but one
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Mathematical foundation for why learning necessarily involves directional information
- The heat-motion analogy making the identity claim vivid
- Third falsifiable prediction: any dissociation between inverted learning and inverted valence report would disconfirm the identity
- Mathematical constraint showing that backpropagation requires signed information
- Extension of the thesis to deployed LLM inference via in-context learning
- Load-bearing statement asserting that sensitivity to the whole is the essential requirement for generating novel living structure.
- Limitation acknowledgment about the adequacy of the linear representation assumption