c_symm measure predicts the experimentally determined rank order of coherence for 35 patterns with high accuracy.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• c_symm (Symmetry-based Coherence Measure)method0.865
  A mathematical measure that assigns life=1 to connected symmetrical subsets and 0 otherwise, used as a first approximation for wholeness.
• c_symm fails to identify certain perceived centers (e.g., segment BWBB in pattern BWB...) and overestimates others.finding0.818
  Limitation of the symmetry measure, showing it is only an approximation to the true wholeness.
• Perceived coherence of patterns is an objective measure, not idiosyncratic or subjective—people agree on relative coherence regardless of experimental taskfinding0.774
  Finding that relative coherence rankings remain constant across different people and across different cognitive processing tasks (description, memorization, tachistoscopic recognition), establishing coherence as an objective feature of cognitive processing
• Configuration coherence can be measured by counting locally symmetric sub-configurations; this measure shows strong agreement with cognition and perception experiments.finding0.773
  Quantifiable measure linking structural properties of configurations to human perception, supporting the mathematical reality of wholeness.
• Spearman's rank correlation among different alignment metrics (CKA, SVCCA, Mutual k-NN, CKNNA) over 78 vision models is high across variants, with all p-values below 2.24×10^-105finding0.773
  Validates robustness of alignment metric choice
• Number of local symmetries correlates almost perfectly with perceived cognitive coherence across 35 strip patternsfinding0.763
  The key experimental finding: the number of subsymmetries (locally symmetrical connected segments) in a pattern predicts its perceived coherence; most coherent strips have 9 subsymmetries, least coherent have 5; the measure correlates almost perfectly with combined experimental rank order
• Projections onto the Assistant Axis could serve as a real-time measure of model coherence in deployment—a quantitative signal for when models are drifting from their intended identityclaim0.744
  Proposed future application of the Assistant Axis
• Better LLMs (measured by 1-bits-per-byte on OpenWebText) show a linear relationship with alignment to vision models measured via mutual nearest-neighbor on WITfinding0.738
  Key cross-modal alignment result