Neural Hardware → Cognitive Substrate Bridge: Full Analysis

Status: Full bridge analysis. The mechanisms that translate neural hardware {Nr,Sy,Og,Ol,Mb,Td} into cognitive substrate {Rp,Ct,As,Sq,Sy,Ev}. Builds on: analysis-neural-hardware.md (neural hardware domain: 6 primitives), v1_revision/v1_biology_domain_analysis/bio_v2/exploration-cognitive-information-system-full-analysis.md (cognitive substrate: 6 primitives confirmed), v1_revision/v1_biology_domain_analysis/bio_v2/cognitive-bridge-mechanisms.md (cognitive development bridge — the NEXT bridge up, not this one) Parallel to: entity_domain_analysis/analysis-hardware-to-computing-bridge.md (hardware → computing bridge), entity_domain_analysis/analysis-computing-to-entity-bridge.md (computing → entity bridge), v1_revision/v1_biology_domain_analysis/bio_v1/biology-organism-architecture.md (biology → organism developmental bridge) Note: This bridge is the realization bridge — how the cognitive substrate is PHYSICALLY REALIZED in neural hardware. The cognitive DEVELOPMENT bridge (cognitive substrate → cognitive architecture) is analyzed separately in the v1 analysis.

1. What the bridge does

Translates the physical activity of neural hardware {Nr, Sy, Og, Ol, Mb, Td} into the information-processing operations of the cognitive substrate {Rp, Ct, As, Sq, Sy, Ev}.

Below the bridge: neurons fire, synapses transmit, circuits oscillate — electrochemical events in biological tissue. Above the bridge: representations form, categories emerge, associations link, sequences order, symbols abstract, evaluations direct — information processing.

This is the bridge that MAKES COGNITION PHYSICAL. Every cognitive operation is realized as neural hardware activity through these mechanisms. The bridge is what neuroscience studies — the "neural correlates" of cognitive function.

Structural parallel:

• Biology → Organism architecture: molecular machinery → organismal function (via ~12 developmental mechanisms)
• Hardware → Digital computing: electronic circuits → logical operations (via 6 abstraction mechanisms)
• Neural hardware → Cognitive substrate: neural tissue → information processing (via the mechanisms identified here)

Critical distinction from the cognitive development bridge: The cognitive DEVELOPMENT bridge (analyzed in v1 as ~10 mechanisms: perceptual learning, category formation, language acquisition, etc.) is the bridge from cognitive SUBSTRATE to cognitive ARCHITECTURE — from {Rp,Ct,As,Sq,Sy,Ev} to {Kw,Sk,Dc,Pl,Co,Jd,Cr,Si,Id}. That's the next level UP.

THIS bridge is the level BELOW — from neural hardware to cognitive substrate. How neural activity BECOMES representation, categorization, association, sequence, symbolization, evaluation.

2. Bridge primitives

2.1 Identification

Each bridge mechanism translates specific neural hardware pair-bundles into specific cognitive substrate capabilities. The question: what are the IRREDUCIBLE translation mechanisms?

2.2 Reduction test

Is Population Coding (Pc) reducible to other bridge mechanisms? Pc is the FUNDAMENTAL translation — how neural activity becomes information. Without population coding, no bridge mechanism has anything to work with. Hierarchical processing uses populations. Hebbian learning operates on populations. Oscillatory binding synchronizes populations. Pc is the base layer. Keep.

Is Hebbian Learning (Hl) reducible to Population Coding? Pc creates instantaneous representations. Hl creates PERSISTENT STRUCTURE — categories and associations that survive after the triggering activity ends. The distinction: Pc is momentary (pattern exists while neurons fire); Hl is lasting (synaptic changes persist). Separable: you can have population coding without learning (fixed-wiring systems) and learned structure without active population coding (dormant memories). Keep.

Is Hierarchical Processing (Hp) reducible to Population Coding + Organization? Hp does something specific: ABSTRACTION through layered processing. Simple features (edges, colors) in early layers combine into complex features (faces, objects) in later layers. This isn't just "populations in organized space" — it's a specific computational process (feature extraction through convergent feedforward processing + top-down feedback modulation). The FUNCTION of Hp (abstraction) doesn't follow from Pc + Og without the specific feedforward/feedback processing pattern. Keep.

Is Oscillatory Binding (Ob) reducible to Oscillation itself? Ol is a neural hardware primitive (rhythmic activity exists as physical phenomenon). Ob is a BRIDGE mechanism — it uses oscillatory synchronization to BIND distributed features into unified cognitive representations. The hardware oscillates; the bridge mechanism uses that oscillation to create perceptual unity. Ob converts a physical phenomenon (phase synchronization) into a cognitive operation (representational binding). Keep.

Is Sequence Generation (Sg) a special case of Oscillatory Binding? Ob binds SIMULTANEOUS features. Sg produces ORDERED temporal patterns. Sequences can be generated without oscillatory binding (simple chain activation: A→B→C without oscillatory phase), and oscillatory binding can occur without sequence generation (binding features at the same time point). Keep both.

Is Attentional Selection (As) reducible to Reward Signaling + Oscillatory Binding? Reward signaling assigns VALUE. Attentional selection determines which representations are CURRENTLY ACTIVE. You can attend to something without evaluating it (orienting to a novel stimulus before assessing its value). And you can evaluate something without attending to it (implicit valuation of unattended stimuli). Attentional selection has its own mechanisms: thalamic gating, prefrontal sustained activity, competitive inhibition. Keep.

Is Predictive Processing (Pp) reducible to Hierarchical Processing + Hebbian Learning? Hp does feedforward extraction. Pp adds GENERATIVE MODELS — the brain actively predicts incoming sensory data and processes only the ERRORS (prediction violations). This is a specific computational pattern (prediction → comparison → error propagation) that goes beyond passive hierarchical extraction. Pp produces causal models of the world; Hp produces feature hierarchies. Keep.

Is Motor Decoding (Md) just the reverse of Sensory Encoding? Se converts physical → neural. Md converts neural → physical. They're symmetric but not identical: sensory encoding is largely feedforward (stimulus → receptors → cortex), while motor decoding involves complex feedback loops (motor cortex → spinal cord → muscles → proprioceptive feedback → cortex). Md has its own internal structure (motor planning, inverse models, error correction) that doesn't reduce to reversed Se. Keep both.

2.3 Count assessment

10 bridge mechanisms. This matches the pattern:

• Biology → Organism architecture: ~12 developmental mechanisms
• Entity system → App architecture: 12 system extensions
• Cognitive substrate → Cognitive architecture: ~10 cognitive development mechanisms (v1)
• Neural hardware → Cognitive substrate: 10 mechanisms

Bridge mechanisms cluster in the ~10-12 range across all analyzed bridges. 10 is within range.

3. Partial levels for each bridge mechanism

Population Coding (Pc)

Phase transition: Pc2→Pc3. Sparse distributed coding. Below: the brain uses rate codes — more active neurons = more of something. Above: the brain uses IDENTITY of active neurons — which specific neurons fire matters, not just how many. This enables much higher information capacity (combinatorial vs linear).

Hebbian Learning (Hl)

Phase transition: Hl2→Hl3. STDP. Below: learning depends on temporal co-occurrence at a coarse scale (hundreds of milliseconds). Above: learning depends on PRECISE spike timing (milliseconds) — causal order is captured (pre before post = strengthen; post before pre = weaken). This is where the brain starts learning CAUSAL STRUCTURE, not just correlations.

Hierarchical Processing (Hp)

Phase transition: Hp2→Hp3. Feedback connections. Below: processing is one-way — stimulus drives response, no top-down influence. Above: EXPECTATIONS shape perception — what you expect to see influences what you do see. Feedback connections are what make perception CONSTRUCTIVE rather than passive.

Oscillatory Binding (Ob)

Phase transition: Ob2→Ob3. Cross-regional binding. Below: features within a sensory modality are integrated (visual features bound into objects). Above: features ACROSS modalities are integrated (visual + auditory + tactile = unified multisensory percept). This is where perception becomes UNIFIED rather than modality-specific.

Sequence Generation (Sg)

Phase transition: Sg2→Sg3. Cognitive sequences. Below: sequences are MOTOR — ordered physical actions. Above: sequences are MENTAL — ordered representations without motor output. Mental time travel, planning, imagining event sequences. This is where sequence generation becomes INTERNAL, supporting cognitive operations beyond action.

Phase transition: Sg4→Full Sg. Compositional. Below: sequences are flat (even if complex). Above: sequences have INTERNAL STRUCTURE — phrases within sentences within paragraphs, sub-plans within plans. Recursive embedding. This is what enables language's infinite generativity.

Reward Signaling (Rs)

Phase transition: Rs1→Rs2. Prediction error. Below: reward signals reflect current hedonic state (feels good/bad NOW). Above: reward signals reflect PREDICTION VIOLATIONS — the brain computes the DIFFERENCE between expected and received reward. This is the basis of reinforcement learning and is what makes organisms adaptive (they learn from surprise, not just from pleasure/pain).

Sensory Encoding (Se)

Phase transition: Se3→Se4. Topographic mapping. Below: sensory information arrives as feature values. Above: sensory information preserves SPATIAL STRUCTURE — a map of visual space, a map of sound frequency, a map of body surface. This is where the brain starts building spatial models of the world through the encoding itself.

Motor Decoding (Md)

Phase transition: Md3→Md4. Skilled execution. Below: voluntary but crude — can reach and grasp but not precisely. Above: fine-grained motor control enabling SKILLED PERFORMANCE — the motor system becomes a precision instrument.

Phase transition: Md4→Full Md. Communicative output. Below: motor output serves physical goals (get food, avoid predator). Above: motor output serves SYMBOLIC goals — producing speech sounds, forming written characters, making gestures that carry arbitrary meaning. This is where the motor system becomes the OUTPUT CHANNEL for the cognitive substrate's symbolic operations (Sy → externalized through Md-Full).

Attentional Selection (At)

Phase transition: At2→At3. Executive control. Below: attention is reactive — driven by stimulus salience or simple voluntary focus. Above: attention is STRATEGIC — can be sustained against distraction, switched between tasks, deployed based on goals rather than stimuli. This requires prefrontal maturation and is the last attentional capacity to develop (adolescence/early adulthood).

Predictive Processing (Pp)

Phase transition: Pp2→Pp3. Causal models. Below: predictions are about what comes NEXT in a sensory stream (pattern completion). Above: predictions are about what CAUSES what — generative models of the world's causal structure. This is where prediction becomes UNDERSTANDING rather than mere anticipation.

4. Dependencies between bridge mechanisms

Pc → (nothing; foundation — population coding is the base translation)
Se → (nothing; independent root — sensory encoding provides initial input)
Hl → Pc (learning operates on population-coded representations)
Hp → Pc (hierarchy processes population-coded signals)
Ob → Pc, Ol-hardware (binding synchronizes population-coded features)
Sg → Pc (sequences are ordered activations of population codes)
Rs → Pc (reward signals modulate population-coded representations)
At → Pc, Rs (attentional selection uses reward signals to prioritize)
Pp → Hp (predictive processing requires hierarchical organization)
Md → Pc, Sg (motor decoding reads out population codes as action sequences)

DAG

Se (root 1 — sensory input)
  ↘
    Pc (root 2 — population coding, the base translation)
      ├── Hl (learning)
      ├── Hp → Pp (hierarchy → prediction)
      ├── Ob (binding)
      ├── Sg → Md (sequences → motor output)
      ├── Rs → At (reward → attention)
      └── ... (all depend on Pc)

Hub: Population Coding (Pc). Everything depends on having neural activity patterns that encode information. Without population coding, none of the other bridge mechanisms have material to work with.

Secondary hub: Hierarchical Processing (Hp). Predictive processing builds on hierarchical structure. Abstract category formation requires hierarchical extraction.

Two roots: Se (sensory encoding — external input) and Pc (population coding — the base translation). Se provides the raw material; Pc provides the computational format.

Dependency-derived build-up

Step 1: Se + Pc — sensory input encoded as population activity
Step 2: + Hl — activity patterns become persistent through learning
Step 3: + Hp — hierarchical feature extraction begins
Step 4: + Ob + Sg — binding and sequencing emerge
Step 5: + Rs — reward signaling adds evaluative dimension
Step 6: + At + Pp — attentional control and predictive models develop
Step 7: + Md — motor output enables externalization

This build-up roughly matches the developmental order in infant cognition:

1. Sensory responses present at birth (Se + Pc)
2. Habituation/sensitization learning within days (Hl)
3. Perceptual hierarchies develop over months (Hp)
4. Object unity (binding) and action sequences by 6 months (Ob + Sg)
5. Reward-based learning active from birth but becomes sophisticated over years (Rs)
6. Executive attention and predictive models develop gradually through childhood (At + Pp)
7. Fine motor and communicative motor output develops over years (Md)

5. Pair-bundle exercise patterns

5.1 Which neural hardware pairs each bridge mechanism exercises

5.2 Which cognitive substrate primitives each bridge mechanism produces

5.3 The many-to-many mapping

Multiple bridge mechanisms → same cognitive substrate primitive:

• Rp (representation) is produced by FIVE mechanisms: Pc, Hp, Ob, Se, Pp — reflecting that representation is the most fundamental cognitive operation, supported by multiple neural mechanisms
• Ct (categorization) is produced by THREE: Hl, Hp, Pp
• Ev (evaluation) is produced by TWO: Rs, At
• Sq (sequence) is produced by TWO: Sg, (Md externalized)
• As (association) is produced by TWO: Hl, Pp
• Sy (symbolization) is produced by ONE: Md (externalized, Full level) — but enabled by the integration of all other mechanisms

The Sy (symbolization) gap: No single bridge mechanism directly produces symbolization. This is consistent with Sy's status as the most mysterious cognitive primitive — it's NOT a simple neural hardware translation. Symbolization requires the INTEGRATION of population coding (distributed representations) + Hebbian learning (stable sign-meaning associations) + sequence generation (combinatorial symbol strings) + reward signaling (symbols become valued/meaningful). It's an emergent property of multiple bridge mechanisms operating together at high partial levels.

This parallels the biology case where consciousness isn't produced by any single developmental mechanism but emerges from the integrated operation of multiple mechanisms at high levels.

6. Hub analysis

6.1 Identifying hub mechanisms

Population Coding (Pc) is the clear hub — all other mechanisms depend on it. It's the base translation: how neural activity becomes INFORMATION. Without population coding, there's nothing to learn (Hl), no features to extract (Hp), nothing to bind (Ob), nothing to sequence (Sg), nothing to evaluate (Rs), nothing to attend to (At), nothing to predict (Pp), and nothing to decode into action (Md).

Sensory Encoding (Se) is the input root — provides the initial content that population coding formats.

Hierarchical Processing (Hp) is the secondary hub — predictive processing depends on it, and it's the primary mechanism for producing abstract representations and categories.

6.2 Hub triad: {Pc, Hl, Hp}

The three mechanisms that together produce the core cognitive substrate:

• Pc provides the representational FORMAT (distributed population codes)
• Hl provides PERSISTENCE (learned synaptic structure = categories + associations)
• Hp provides ABSTRACTION (hierarchical feature extraction = abstract representations + categories)

Together, Pc + Hl + Hp produce the foundations of Rp + Ct + As — the three cognitive substrate primitives that the cognitive substrate's core triad {Rp, Ct, Sy} draws from. (Sy — symbolization — requires additional mechanisms and emerges later.)

7. Phase transitions in the bridge

7.1 Bridge-level phase transitions

These are transitions in bridge mechanism partial levels that GATE the emergence of cognitive substrate capabilities:

Se3→Se4 (topographic mapping) gates Rp spatial structure: Below: representations are feature lists (object has color X, shape Y, size Z). Above: representations PRESERVE SPATIAL STRUCTURE (object at location, spatial relationships between objects). This is where the cognitive substrate starts building spatial world models.

Hl2→Hl3 (STDP) gates causal Ct and As: Below: learning captures temporal CORRELATION (things that happen together). Above: learning captures temporal ORDER (A causes B, not B causes A). This is where categorization and association become CAUSAL rather than merely correlational.

Hp2→Hp3 (feedback connections) gates constructive Rp: Below: representations are data-driven (bottom-up only). Above: representations are HYPOTHESIS-DRIVEN (top-down expectations shape perception). This is where the cognitive substrate starts CONSTRUCTING its representations rather than passively receiving them.

Sg4→Full Sg (compositional sequences) gates Sy: Below: sequences are flat chains. Above: sequences have hierarchical embedding — RECURSIVE STRUCTURE. This is THE gate for language. Without compositional sequence generation, symbolic language cannot emerge. This is the neural hardware prerequisite for Sy-Full.

Rs1→Rs2 (prediction error) gates adaptive Ev: Below: evaluation is hedonic (feels good/bad now). Above: evaluation is ADAPTIVE (computes difference between expectation and reality). This is where the cognitive substrate starts LEARNING FROM SURPRISE rather than just responding to pleasure/pain.

7.2 The composite gate for symbolization

Sy (symbolization) requires MULTIPLE bridge mechanisms at high levels simultaneously:

• Pc3+ (sparse distributed coding — distinct tokens)
• Hl3+ (STDP — precise sign-meaning associations learned)
• Sg-Full (compositional sequences — recursive syntax)
• Rs3+ (multi-dimensional value — symbols become meaningful/valued)
• Hp3+ (feedback hierarchy — top-down symbolic expectations)
• Md-Full (communicative motor — speech/gesture production)

This composite gate explains why symbolization is so RARE in the animal kingdom — it requires many bridge mechanisms simultaneously at high partial levels. Only humans (and possibly a few other species at partial Sy levels) clear all the gates.

8. Dependencies, filter, and coherent sub-lattice

8.1 Dependencies (summary)

Se → (nothing; input root)
Pc → (nothing; translation root)
Hl → Pc
Hp → Pc
Ob → Pc
Sg → Pc
Rs → Pc
At → Pc, Rs
Pp → Hp (which depends on Pc)
Md → Pc, Sg

8.2 Coherent sub-lattice

Two independent roots: Se and Pc.

With Pc present, all of {Hl, Hp, Ob, Sg, Rs} are independently available. At requires Pc + Rs. Pp requires Pc + Hp. Md requires Pc + Sg.

Valid subsets with Pc present:

• Core 5 {Hl, Hp, Ob, Sg, Rs}: each independent, 2^5 = 32 subsets
• For each: At can be in if Rs is in. Pp can be in if Hp is in. Md can be in if Sg is in.
• So for each subset of the core 5: At is available iff Rs ∈ S, Pp iff Hp ∈ S, Md iff Sg ∈ S.
• 3 dependent mechanisms, each gated by one core mechanism.

For each of 32 core-5 subsets: count valid additions from {At, Pp, Md}.

• At: can add iff Rs present (16 subsets have Rs → 16 subsets can optionally add At → 16×2 + 16×1 = 48... let me be more careful)

Let me count systematically. Variables: {Hl, Hp, Ob, Sg, Rs, At, Pp, Md}, all requiring Pc present.

Constraints:

• At → Rs
• Pp → Hp
• Md → Sg

These are 3 independent pairwise constraints. Unconstrained: 2^8 = 256. Invalid subsets:

• At without Rs: At in, Rs out, 6 others free = 2^6 = 64
• Pp without Hp: Pp in, Hp out, 6 others free = 64
• Md without Sg: Md in, Sg out, 6 others free = 64
• At∧Pp without Rs∧Hp: overlap... use inclusion-exclusion.

By inclusion-exclusion: |invalid| = |At∧¬Rs| + |Pp∧¬Hp| + |Md∧¬Sg| - |At∧¬Rs ∧ Pp∧¬Hp| - |At∧¬Rs ∧ Md∧¬Sg| - |Pp∧¬Hp ∧ Md∧¬Sg| + |At∧¬Rs ∧ Pp∧¬Hp ∧ Md∧¬Sg| = 64 + 64 + 64 - 16 - 16 - 16 + 4 = 148

Valid with Pc present: 256 - 148 = 108. (Equivalently, the 3 disjoint pairwise constraints each leave 3 of 4 states valid, with Hl, Ob free: 3 × 3 × 3 × 2² = 108.)

Including Se independently: Se can appear with any subset or not. So multiply: 108 × 2 = 216 subsets with Pc.

Subsets without Pc: only {} and {Se} are valid (nothing else can appear without Pc). = 2.

Total valid: 216 + 2 = 218 of 2^10 = 1024.

Filter: 218/1024 = 21.3%.

This is moderate — more constrained than either the neural hardware domain (32.8%) or the cognitive substrate domain (26.6%), reflecting the bridge's role as an INTERFACE with its own dependency structure.

Reconciliation note: the earlier figure (226/1024 = 22.1%) was an inclusion-exclusion arithmetic slip: 64+64+64-16-16-16+4 = 192-48+4 = 148, not 144 — so valid-with-Pc = 256-148 = 108 (not 112) and the total is 218/1024 = 21.3% (not 226/22.1%). Independently BFS-verified. Separately, the JSON data/bridges/neural-to-cognitive-bridge.v1.json dependency model was also wrong and did not match this analysis's Step 4 / Step 8.1 model: it was missing Rs⇒Pc, Atn⇒Rs, Md⇒Pc, Md⇒Sg (Md had no dependency at all) and carried a spurious Pp⇒Hl (this analysis lists only Pp → Hp). Both were corrected to the model above; BFS over the corrected presence-dependency set = 218/1024 = 21.3%, and filter_stringency was updated 226→218. filter_stringency is unverified descriptive metadata not enforced by validate/check_bounds, so both the arithmetic slip and the model divergence had silently persisted; the standing filter-discipline rule (independently BFS-recompute every file) catches both. The qualitative claims survive: 21.3% remains the tightest of the analyzed bridges and more constrained than the neural-hardware (32.8%) and cognitive-substrate (26.6%) domains.

9. Cross-domain comparison

9.1 Bridge mechanism counts

Neural hardware → cognitive substrate has 10 mechanisms, within the typical range. The filter (21.3%, BFS-verified) is the tightest of the analyzed bridges, reflecting the many dependencies (most mechanisms depend on the hub Pc, and three mechanisms have additional dependencies).

9.2 The parallel to biology → organism

The parallel is strong. Both bridges translate a 6-primitive realization domain into an information-processing domain through ~10-12 mechanisms, with a hub mechanism, developmental ordering, and phase transition gates.

9.3 The Sy (symbolization) composite gate as the cognitive "eukaryogenesis"

In biology, the transition from prokaryote to eukaryote required MULTIPLE simultaneous developments (membrane complexity, organelles, cytoskeleton, nuclear envelope). It was a composite gate that took ~2 billion years to cross.

In cognition, the transition to symbolization (Sy) requires MULTIPLE simultaneous bridge mechanisms at high levels (Pc3+, Hl3+, Sg-Full, Rs3+, Hp3+, Md-Full). It was a composite gate that took ~300 million years of primate evolution to cross (or ~600 million years from the first centralized brains).

Composite gates — where multiple capabilities must be simultaneously present — are where evolutionary/developmental breakthroughs happen. They're RARE because the probability of all gates being cleared simultaneously is the product of individual gate probabilities.

Summary

Bridge mechanisms: 10

Key structural features

• Hub: Population Coding (Pc) — the base translation from neural activity to information
• Hub triad: {Pc, Hl, Hp} — format + persistence + abstraction
• Two roots: Se (sensory input) and Pc (computational format)
• Filter: 21.3% (218/1024, BFS-verified — corrected from a 226/22.1% inclusion-exclusion slip; JSON dependency model also corrected) — tightest of analyzed bridges
• Build-up matches infant development order
• Composite gate for symbolization — 6 mechanisms at high levels simultaneously
• Many-to-many mapping: Multiple mechanisms → same cognitive primitive; Rp supported by 5 mechanisms
• Sy produced by NO single mechanism — requires integration of multiple mechanisms at high partial levels (composite gate)

Activation mapping and reconciliation

Activation mapping (data/bridges/neural-to-cognitive-bridge.v1.json, all discriminating; one-home-per-construct): per the bridge rule (#30), the sole JSON-flagged phase_transition Hp3 carries a single-driver partial_level.emergent (constructive/hypothesis-driven representation; §3 Hp2→Hp3 + §7.1 + §6.1 secondary hub). The Step-6.2 hub triad {Pc,Hl,Hp} (the file's existing emergent_property) carries a composition.emergent (presence τ — format + persistence + abstraction → Rp+Ct+As foundations). A new {Pc,Hl,Sg,Rs,Hp,Md} higher composition is added to host §7.2's explicit symbolization composite gate (the bridge's signature emergent, framed in §9.3 as the cognitive "eukaryogenesis" — clears the defining-functional-unit bar emphatically; Step-7.2-requires-construct add, sibling of the Step-10-requires-construct rule). Its conjunction τ maps the analyst's Pc3+/Hl3+/Sg-Full/Rs3+/Hp3+/Md-Full to the v1-compressed JSON levels Pc3/Hl3/Sg4/Rs2/Hp3/Md4. This composite folds in the individual §7.1 component gates (Se3→Se4, Hl2→Hl3, Hp2→Hp3, Sg4→FullSg, Rs1→Rs2) — one-home: §7.1/§7.2 frame them as load-bearing precisely as the joint symbolization gate, so they are documented here, not separately declared (parallels the neural-hardware Ol fold-in; non-fabrication #19 — most are not JSON-flagged and the v1 compression makes standalone JSON-level mapping ambiguous). A full-10 higher composition is added per the per-bridge template (presence τ — §4 build-up endpoint; no explicit analyst "at all Full"). Non-over-flag: the heavy pairs {Pc,Hl}, {Hp,Pp}, {Sg,Md} and the medium {Atn,Hp} get no standalone emergent (the bridge rule anchors on flagged PTs + the hub triad + the analyst's explicit composite gate; the heavy pairs are structural connectivity, not separately-predicted emergents — parallel to the neural-hardware Sy-Mb non-over-flag).

Reconciliation (structure-fix, both polarities): see the §8.2 reconciliation note. The stored filter_stringency (226/22.1%) copied an inclusion-exclusion arithmetic slip in §8.2 (192−48+4 = 148, not 144 → valid-with-Pc 108 not 112 → 218/1024 = 21.3%, not 226/22.1%), and the JSON dependencies model independently diverged from this analysis's Step-4 model (missing Rs⇒Pc, Atn⇒Rs, Md⇒Pc, Md⇒Sg; spurious Pp⇒Hl). Both corrected BFS over the corrected model = 218 (independently confirmed by the disjoint-pairs decomposition 2·3³·2² + 2 = 218). This is the compound case of the standing filter-discipline finding — a file can have a wrong analysis hand-computation and a wrong JSON model simultaneously; only an independent BFS of the reconciled model is authoritative.

Referenced by the model

Cited as a source by 1 model record (browse the model census):

• neural-to-cognitive-bridge — bridge cognition/sc1