Lateral Thinking: Spreading-Activation Idea Network

Lateral Entry

Last entry: —

Diffusion Parameters

Memory decay τ 0.75

Insight threshold θ 0.55

Spread speed 5

Network State

Active nodes

Network energy

0.00

Insights found

Entries injected

How it works

Edward de Bono's random entry technique breaks fixation on a problem by forcing an unrelated, randomly-chosen word into the thinking process and following whatever associations it triggers. This simulator models that with a real cognitive-science mechanism: a spreading-activation network (Collins & Loftus, 1975), the same model used to explain how one concept primes another in human memory.

Forty concept nodes sit in 3D space, wired with short local "obvious association" edges plus a handful of long-range lateral edges that jump across the semantic space. Every tick, activation energy diffuses outward:

A_i(t+1) = clamp( max( A_i(t)·τ,  g · Σ_j A_j(t)·w_ij ), 0, 1)

τ = memory decay (higher = ideas linger longer)
g = fixed propagation gain (0.9)
w_ij = 1 / (1 + distance(i,j))   — closer concepts couple more strongly

Click Random Entry to inject full activation (A=1) into a random concept node — exactly de Bono's forced random word. Watch the glow diffuse through the web. When enough of it converges on the white Problem node and crosses the insight threshold θ, that counts as an insight: a genuinely new, indirect connection between the random stimulus and the problem, discovered purely through the network's own wiring rather than a direct, obvious link.

Memory decay τ — how slowly activation fades; higher values let weak signals accumulate into stronger ones over multiple entries.
Insight threshold θ — how much activation the Problem node needs before a connection counts as a genuine insight.
Spread speed — how often the diffusion step runs.

Problem node Concept node Lateral link

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