feat: add goals, drives, affect state, predictions and curiosity

Phase 9. Goals form a mind-scoped tree with kind, priority, progress and
blockers; their deterministic relevance to a candidate feeds the
goal_relevance attention feature and a goal monitor raises stalled goals
and observations that touch one. Drives are a closed set of eight
scalars with operator-configured ranges and personality-derived
baselines; there is no self-preservation, replication or acquisition
drive and no way to add one at runtime. Affect is ten scalars moved by an
explicit rule table over events and decaying towards baseline; both
modulate attention weights each cycle and the effective weights are
recorded with every cycle and shown beside the configured ones.

Predictions carry a window and machine-checkable criteria (event terms
occurring or staying absent, a belief reaching a confidence, or operator
judgement). A resolver job judges them deterministically, keeps an
append-only outcome history for calibration (accuracy, Brier score,
confidence buckets, overconfidence flag), emits prediction_error events
scaled by how confident the miss was, and feeds the outcome back into the
linked belief as evidence. Causal hypotheses derive predictions
automatically. Curiosity turns misses, contradictions and unexplained
observations into questions that compete for attention and become
investigation goals when admitted.

Brain gains after-cycle hooks and goal/motivation state. API, UI (goals,
prediction dashboard, cognitive state panel, effective weights) and CLI
(goal, prediction, state). Demo scenario 4 verified end to end.

PLAN.md

@@ -126,11 +126,15 @@ An administrator can: start Noema → log in → create a mind → configure per
 
 **Milestone M1 reached.** Remaining phases deepen the system; the vertical slice above is exercised end to end by `go test ./tests/m1/`.
 
-### Phase 9 – Goals, drives, predictions
-- ☐ goals (tree, priority, progress, blockers, origin), goal monitor process
-- ☐ drives with ranges; affect model with baselines and decay; UI panels and reset
-- ☐ predictions, resolution scheduler, prediction error → confidence/credibility/calibration; prediction dashboard
-- ☐ Curiosity process
+### Phase 9 – Goals, drives, affect, predictions, curiosity ☑
+- ☑ `migrations/0009_motivation`: goals, drives, affect_states, predictions, append-only prediction_results
+- ☑ `internal/goals`: tree with kinds/status/priority/progress/blockers, stalled detection, deterministic goal relevance
+- ☑ `internal/motivation`: closed drive set (no self-preservation), personality-derived baselines, explicit event rule table, decay, attention weight modulation, significance thresholds
+- ☑ `internal/predictions`: criteria (event terms, belief confidence, operator), deterministic judging, append-only outcomes, calibration (accuracy, Brier, buckets, overconfidence flag), resolver job with prediction_error events and belief feedback
+- ☑ Brain: after-cycle hooks, goals and motivation in cycle state, effective weights recorded per cycle
+- ☑ Cognition: motivation hook, goal monitor, curiosity (questions from misses, contradictions, unexplained observations) with question → investigation goal handler, hypothesis-derived predictions, goal relevance in attend
+- ☑ API: goals, predictions (create/resolve/calibration), state (get/reset/drive ranges); UI: goals page, prediction dashboard with calibration, cognitive state panel on overview, effective weights on workspace; CLI goal/prediction/state
+- ☑ Docs: predictions.md, motivation.md. Demo scenario 4 (prediction error) verified live
 
 ### Phase 10 – Metacognition, reflection, consolidation, narrative, notifications
 - ☐ metacognitive checks and warnings; reflection scheduler; consolidation scheduler with operator controls; narrative generation; notification thresholds, suppression, aggregation, reasons