Decision Quality

Decision quality describes the accuracy component of a speeded choice, isolated from raw response time. Ratcliff's diffusion decision model (Ratcliff, 1978) formalizes decisions as a noisy accumulation of evidence toward one of several response boundaries, showing that speed and accuracy trade off against each other through where that boundary is set. Two players with identical reaction times can differ substantially in decision quality depending on how conservative or aggressive their evidence threshold is.

Reference: Ratcliff, 1978. The NeuroRank implementation holds the canonical form and scales interference via task-irrelevant stimulus density.

1. Picking the correct priority target in a five-versus-five fight when three enemies are simultaneously in range.
2. Choosing to reload, swap, or push in an Apex Legends ring fight with incomplete information.
3. Itemization decisions in a MOBA that require weighing several viable options under a shot clock.

NeuroRank's Decision Quality dimension is built from choice-RT accuracy (40%) and go/no-go commission errors (60%) in the Reaction module, isolating selection accuracy from the raw speed captured separately by the Reaction Speed dimension.