Steering Law: Tunnel Task Simulator

Corridor Parameters

Corridor width W₀ 2.2

Path curviness (amplitude) 3.0

Path frequency 1.4

Run Status

Drag the puck to the green start gate, then Start run

Elapsed time

0.00 s

Progress

Index of Difficulty

— bits

Predicted MT

— s

Last run time

— s

Throughput

— bits/s

How it works

The steering law (Accot & Zhai, 1997) predicts how long it takes to move a pointer through a constrained path — a menu, a scrollbar track, a drag handle, a tunnel-shaped UI target. Unlike Fitts's Law, which scores one discrete point-target, the steering law integrates difficulty continuously along the whole path:

ID = ∫(0→L) ds / W(s)
MT = a + b · ID

Here W(s) is the corridor's local width at arc-length s, ID is the path's Index of Difficulty in "bits", and a, b are empirical constants (this demo uses a = 0.15 s, b = 0.11 s/bit — typical mouse-pointer values from the HCI literature). ID is a property of the corridor alone, computed once from its shape; it does not depend on your trajectory.

Corridor width — the baseline half-width of the tunnel; narrower corridors raise ID and predicted MT.
Curviness / frequency — the corridor follows a sine-wave centerline; more amplitude and tighter frequency lengthen the true arc-length and raise ID further.
Start run — arms the timer; it starts the instant the puck crosses the green start gate and stops the instant it crosses the red finish gate, as long as the puck never leaves the walls.
Leaving the corridor at any point fails the run (steering demands staying inside W(s) continuously, not just hitting an endpoint).
Throughput = ID ⁄ measured MT, in bits/second — the standard way HCI research compares steering performance across devices and users.

Real-world relevance: this exact model is used to evaluate scrollbar and slider ergonomics, stylus/pen-menu "marking menus", surgical-robot teleoperation paths, and any drag-through-a-channel interaction.

Start gate Finish gate Corridor walls

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