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Conchordal: Emergent Harmony via Direct Cognitive Coupling in a Psychoacoustic Landscape

Koichi Takahashi

Abstract

This paper introduces Conchordal, a bio-acoustic instrument for generative composition whose sonic agents are governed by artificial life dynamics within a psychoacoustic fitness landscape. The system is built on Direct Cognitive Coupling (DCC), a design principle requiring that generative dynamics operate directly within a landscape derived from psychoacoustic observables and read from that landscape without symbolic harmonic rules. The environment integrates roughness and harmonicity into a continuous consonance field without presupposing discrete scales or explicit harmonic rules. Agents adjust pitch through local proposal-and-accept dynamics under a crowding penalty, regulate survival via consonance-dependent metabolism, and entrain temporally through Kuramoto-style phase coupling. Four experiments are reported: (1) consonance search produces structured polyphony with enriched consonant intervals; (2) consonance-dependent metabolism yields survival differentials that vanish when recharge is disabled; (3) a minimal hereditary adaptation assay shows that parent-guided respawn plus metabolic selection can accumulate more structured polyphony without adult hill-climbing; and (4) a shared oscillatory scaffold organizes rhythmic timing under external forcing. A supplementary mechanism check reports one possible composer-configurable bridge by which spectral state can modulate temporal coupling. These findings show that a psychoacoustically derived landscape serves as an effective artificial-life terrain, yielding self-organization, selection, synchronization, and lineage-level accumulation in a non-traditional computational medium. At the level of the model, the same landscape therefore functions both as ecological terrain and as an internal proxy for musical coherence.

Conchordal: Emergent Harmony via Direct Cognitive Coupling in a Psychoacoustic Landscape

Abstract

This paper introduces Conchordal, a bio-acoustic instrument for generative composition whose sonic agents are governed by artificial life dynamics within a psychoacoustic fitness landscape. The system is built on Direct Cognitive Coupling (DCC), a design principle requiring that generative dynamics operate directly within a landscape derived from psychoacoustic observables and read from that landscape without symbolic harmonic rules. The environment integrates roughness and harmonicity into a continuous consonance field without presupposing discrete scales or explicit harmonic rules. Agents adjust pitch through local proposal-and-accept dynamics under a crowding penalty, regulate survival via consonance-dependent metabolism, and entrain temporally through Kuramoto-style phase coupling. Four experiments are reported: (1) consonance search produces structured polyphony with enriched consonant intervals; (2) consonance-dependent metabolism yields survival differentials that vanish when recharge is disabled; (3) a minimal hereditary adaptation assay shows that parent-guided respawn plus metabolic selection can accumulate more structured polyphony without adult hill-climbing; and (4) a shared oscillatory scaffold organizes rhythmic timing under external forcing. A supplementary mechanism check reports one possible composer-configurable bridge by which spectral state can modulate temporal coupling. These findings show that a psychoacoustically derived landscape serves as an effective artificial-life terrain, yielding self-organization, selection, synchronization, and lineage-level accumulation in a non-traditional computational medium. At the level of the model, the same landscape therefore functions both as ecological terrain and as an internal proxy for musical coherence.

Paper Structure

This paper contains 22 sections, 9 equations, 5 figures, 1 table.

Figures (5)

  • Figure 1: Psychoacoustic landscape around 220 Hz. (A) Harmonicity $H_{01}$; (B) roughness $R_{01}$; (C) consonance field $C_{\text{field}}$; (D) consonance density $C_{\text{density}}$. Grey: 0.5-oct grid; dashed: integer ratios. Simple-ratio peaks in $H$ and troughs in $R$ create attractor structure in both views. Reduced density outside $\pm 1$ oct is a finite-order property ($N\!=\!16$).
  • Figure 2: Consonance search. Top---(A) Mean LOO consonance $C_{\text{score}}$ over time (95% CI); vertical line = phase switch, shaded band = burn-in excluded from statistics. (B) Scene consonance $G(F)$ (Eq. \ref{['eq:consonance-core']}) after singleton subtraction (95% CI). (C) Representative local-search trajectories (cents from drone). (D) Final unique pitch bins (95% CI). Bottom---(E) Pairwise interval histogram (25 ct bins): local-search concentrates mass at consonant intervals; random-walk spreads broadly. (F) Entropy of the 5 ct interval distribution (95% CI).
  • Figure 3: Consonance as selection pressure. (A, B) Kaplan--Meier survival by early consonance ($C_{\mathrm{firstK}}$) median split: with recharge, high-consonance agents survive longer; without recharge, the separation disappears. (C, D) Lifetime versus early consonance shows the same contrast. Curves pool events across 20 seeds for display; inference is seed-level ($n=20$).
  • Figure 4: Hereditary adaptation through lineage-biased respawn and metabolic selection (20 matched seeds, 4 conditions). (A) Mean leave-one-out contextual $C_{\text{score}}$ over simulation steps (95% CI). (B) Pitch heatmap for heredity$+$selection over simulation steps. (C) Final leave-one-out contextual $C_{\text{score}}$ by condition (95% CI). (D) Final just-intonation proximity score by condition (95% CI). Teal denotes heredity and rose denotes matched random; dark colors indicate selection-on and light colors indicate no-selection.
  • Figure 5: Temporal scaffold assay (20 seeds $\times$ 3 conditions). (A) Group mean PLV to the canonical 2 Hz (120 BPM) beat over time (95% CI). (B) Seed-averaged onset phase histogram relative to the canonical beat (bin probabilities normalized within seed, then averaged across seeds; shaded bands indicate 95% CI across seeds). (C) Seed-level onset vector strength: shared rhythm produces the strongest phase concentration, scrambled forcing is weaker, and off is lowest.