A threshold theory for simple detection experiments.

The two-state "high" threshold model is generalized by assuming that (with low probability) the threshold may be exceeded when there is no stimulus. Existing Yes-No data (that rejected the high threshold theory) are compatible with the resulting isosensitivity (ROC) curves, namely, 2 line segments that intersect at the true threshold probabilities. The corresponding 2-alternative forced-choice curve is a 45° line through this intersection. A simple learning process is suggested to predict S's location along these curves, asymptotic means are derived, and comparisons are made with data. These asymptotic biases are coupled with the von Bdk&y-Stevens neural quantum model to show how the theoretical linear psychometric functions are distorted into nonsymmetric, nonlinear response curves.