Progressive Disclosure: Cognitive Load Onboarding Simulator

Onboarding Flow

Chunks revealed per screen 6

Screen pacing (s) 3.5s

Forgetting / decay rate 0.35

Progressive disclosure
gradual reveal vs. instant info-dump

Cohort — live

Avg load / capacity

Active screen

1 / 5

Activation rate

100%

Overload drop-outs

Avg comprehension

100%

Runs completed

How it works

Each of 40 synthetic users has a working-memory capacity C ~ N(7, 1.5) chunks, echoing Miller's "seven plus or minus two". As a screen reveals information, load L accumulates and continuously leaks away (forgetting) as a leaky integrator:

dL/dt = inflow(t) − decay·L

Progressive disclosure: inflow = chunks/screen ÷ pacing   (spread evenly across the screen)
Instant dump:           inflow = 0, with chunks/screen added as one impulse at screen start

At the end of every screen, a user whose load exceeds their own capacity risks abandoning:

excess = max(0, L − C) / C
P(drop this screen) = clamp(1.2 · excess, 0, 0.9)
comprehension(screen) = clamp(1 − excess, 0, 1)

Chunks per screen — information density of each onboarding screen.
Pacing — how long a screen stays up before auto-advancing.
Decay rate — how fast unused information is forgotten between screens.
Progressive disclosure toggle — spreads the same information over time instead of dumping it all at once, lowering peak load without changing total content.

Real-world relevance: this is the working-memory mechanism cited by cognitive-load theory for why paced, drip-fed onboarding tutorials convert better than a single feature-dump screen — the total information is identical, but peak load and overload dropout are not.

Comfortable load Near capacity Overloaded / dropped

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