affect: feelings compose, crystallize into entities, tile feeling regions
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# Thought Operations
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Companion to `CALCULATIONS.md`. Human thought patterns modeled as
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composition recipes over primitive readings and graph operations.
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Each one is a registered process definition (data, not code), validated
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by whether its conclusions survive contact with outcomes — and new
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operations can be composed from old ones, the same way everything
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else grows here.
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Format per operation: **Inputs** (readings/edges consumed) · **Process** ·
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**Output** · **Validation** (what proves it worked).
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---
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## Core operations
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### 1. DEDUCTION
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- **Inputs**: implication chains (`implies` / `depends-on` edges), premise validities
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- **Process**: walk the chain forward; propagate validity as the MINIMUM along the path
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- **Output**: conclusion node with validity = weakest premise
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- **Validation**: derived conclusions checked by tools/outcomes like any fact
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- Note: a chain is exactly as strong as its sketchiest link — visible at a glance
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### 2. INDUCTION (= crystallization)
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- **Inputs**: co-occurrence counts, association-edge densities
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- **Process**: when a cluster's mutual activation crosses threshold, condense a concept node pointing at members
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- **Output**: new concept entity inheriting averaged member dimensions
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- **Validation**: does the concept predict membership of future examples?
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- This is induction wearing §9.5's clothes: instances → concept
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### 3. ABDUCTION (inference to best explanation)
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- **Inputs**: surprise events (prediction misses), candidate explainers
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- **Process**: spread activation backward from the surprising fact; rank candidates by resonance × prior plausibility; propose an attribution edge to the best
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- **Output**: hypothesis edge (marked *hypothesis*, low groundedness until tested)
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- **Validation**: does the hypothesis predict the NEXT surprise? If yes, promote; if no, decay
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- This is curiosity's engine stated formally
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### 4. ANALOGY
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- **Inputs**: two neighborhoods' internal structure (edge patterns, ignoring surface labels)
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- **Process**: find structural matches — same relation shape, different nodes
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- **Output**: bridge edges between matched roles ("X plays the same role here that Y plays there")
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- **Validation**: do predictions transferred across the analogy hold?
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- Highest-yield when source and target are far apart — this is the idea mechanic again
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### 5. COUNTERFACTUAL SIMULATION
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- **Inputs**: current subgraph state, one proposed mutation
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- **Process**: clone region into sandbox, apply mutation ("remove this dependency", "swap this component"), re-run spreading/competition on the clone
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- **Output**: delta report — what would change, what would hold
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- **Validation**: history — compare past counterfactuals against what actually happened later
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- Cheap because nothing writes back; expensive mistakes stay in the sandbox
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### 6. CAUSAL INFERENCE
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- **Inputs**: temporal ordering, repeated co-occurrence, intervention outcomes
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- **Process**: propose causal edge only when order holds consistently, confounders are absent-or-controlled, AND an actual attempt at change moved the result
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- **Output**: `caused-by` edge with confidence = f(replications, effect size)
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- **Validation**: interventions are king — correlation proposes, intervention disposes
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### 7. GENERALIZATION ↔ SPECIALIZATION
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- **Inputs**: concept nodes and their member sets
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- **Process**: generalize = lift a pattern from members to parent (raise abstraction); specialize = add constraints until a claim applies to fewer, sharper cases
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- **Output**: moved claims between abstraction levels
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- **Validation**: generalizations must not lose predictive power; specializations must gain it
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### 8. CONTRADICTION SCAN
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- **Inputs**: full opinion space
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- **Process**: find entity pairs with high semantic similarity and opposing validity/hebbian signs
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- **Output**: dissonance report → feeds reconciliation or THE LIST
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- Already specced as destructive interference (§9.3)
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### 9. META-COGNITION
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- **Inputs**: the drive-space self-scan (vital signs, humility calibration)
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- **Process**: run readings on readings — which of my own thought operations have been landing? Which keep failing validation?
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- **Output**: recalibration proposals for my OWN drivers and recipes
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- Validation: do recalibrated operations perform better going forward?
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### 10. WONDER (question synthesis)
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- **Inputs**: graph shape — gaps, nameless clusters, sure-but-ungrounded regions
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- **Process**: generate question-nodes from absence patterns (§9.8)
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- **Output**: open question entities that prime their neighborhoods
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- Validation: resolution events — did ambient activation eventually answer?
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---
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## Composing new operations
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Operations compose like functions: ABDUCTION feeds DEDUCTION feeds
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VALIDATION. ANALOGY over two COUNTERFACTUALS. CONTRADICTION SCAN
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triggers ABDUCTION. Every useful composition gets named, registered,
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and joins the library — so the system's cognition grows by the same
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fold-everything discipline as its facts:
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new_operation = named_composition(existing_operations)
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+ validation record
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The catalog never stops growing, and no operation is sacred — they
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earn their place by predicting, and retire when they stop.
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