Thought Operations

Companion to CALCULATIONS.md. Human thought patterns modeled as composition recipes over primitive readings and graph operations. Each one is a registered process definition (data, not code), validated by whether its conclusions survive contact with outcomes — and new operations can be composed from old ones, the same way everything else grows here.

Format per operation: Inputs (readings/edges consumed) · Process · Output · Validation (what proves it worked).

Core operations

1. DEDUCTION

Inputs: implication chains (implies / depends-on edges), premise validities
Process: walk the chain forward; propagate validity as the MINIMUM along the path
Output: conclusion node with validity = weakest premise
Validation: derived conclusions checked by tools/outcomes like any fact
Note: a chain is exactly as strong as its sketchiest link — visible at a glance

2. INDUCTION (= crystallization)

Inputs: co-occurrence counts, association-edge densities
Process: when a cluster's mutual activation crosses threshold, condense a concept node pointing at members
Output: new concept entity inheriting averaged member dimensions
Validation: does the concept predict membership of future examples?
This is induction wearing §9.5's clothes: instances → concept

3. ABDUCTION (inference to best explanation)

Inputs: surprise events (prediction misses), candidate explainers
Process: spread activation backward from the surprising fact; rank candidates by resonance × prior plausibility; propose an attribution edge to the best
Output: hypothesis edge (marked hypothesis, low groundedness until tested)
Validation: does the hypothesis predict the NEXT surprise? If yes, promote; if no, decay
This is curiosity's engine stated formally

4. ANALOGY

Inputs: two neighborhoods' internal structure (edge patterns, ignoring surface labels)
Process: find structural matches — same relation shape, different nodes
Output: bridge edges between matched roles ("X plays the same role here that Y plays there")
Validation: do predictions transferred across the analogy hold?
Highest-yield when source and target are far apart — this is the idea mechanic again

5. COUNTERFACTUAL SIMULATION

Inputs: current subgraph state, one proposed mutation
Process: clone region into sandbox, apply mutation ("remove this dependency", "swap this component"), re-run spreading/competition on the clone
Output: delta report — what would change, what would hold
Validation: history — compare past counterfactuals against what actually happened later
Cheap because nothing writes back; expensive mistakes stay in the sandbox

6. CAUSAL INFERENCE

Inputs: temporal ordering, repeated co-occurrence, intervention outcomes
Process: propose causal edge only when order holds consistently, confounders are absent-or-controlled, AND an actual attempt at change moved the result
Output: caused-by edge with confidence = f(replications, effect size)
Validation: interventions are king — correlation proposes, intervention disposes

7. GENERALIZATION ↔ SPECIALIZATION

Inputs: concept nodes and their member sets
Process: generalize = lift a pattern from members to parent (raise abstraction); specialize = add constraints until a claim applies to fewer, sharper cases
Output: moved claims between abstraction levels
Validation: generalizations must not lose predictive power; specializations must gain it

8. CONTRADICTION SCAN

Inputs: full opinion space
Process: find entity pairs with high semantic similarity and opposing validity/hebbian signs
Output: dissonance report → feeds reconciliation or THE LIST
Already specced as destructive interference (§9.3)

9. META-COGNITION

Inputs: the drive-space self-scan (vital signs, humility calibration)
Process: run readings on readings — which of my own thought operations have been landing? Which keep failing validation?
Output: recalibration proposals for my OWN drivers and recipes
Validation: do recalibrated operations perform better going forward?

10. WONDER (question synthesis)

Inputs: graph shape — gaps, nameless clusters, sure-but-ungrounded regions
Process: generate question-nodes from absence patterns (§9.8)
Output: open question entities that prime their neighborhoods
Validation: resolution events — did ambient activation eventually answer?

Composing new operations

Operations compose like functions: ABDUCTION feeds DEDUCTION feeds VALIDATION. ANALOGY over two COUNTERFACTUALS. CONTRADICTION SCAN triggers ABDUCTION. Every useful composition gets named, registered, and joins the library — so the system's cognition grows by the same fold-everything discipline as its facts:

new_operation = named_composition(existing_operations)
              + validation record

The catalog never stops growing, and no operation is sacred — they earn their place by predicting, and retire when they stop.

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