A Threshold Theory for Simple Detection Experiments.

Abstract

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 prob-abilities. 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. A classic postulate of psychophysics is that some stimuli or differences between stimuli never manage to affect the central decision making centers; others, of course, do. In a phrase, peripheral thresholds were assumed to exist. At least three types have been distinguished: absolute, difference, and detection. It is not, however, clear that there is any real difference among them. Absolute thresholds seem to be the same as detection ones except that the only noise is internal, and many difference threshold experiments differ from de-tection experiments only in the nature of the background stimulus, e.g., a pure tone or noise. Recently the literal interpretation of the threshold postulate has been