Signal-detection models for multidimensional stimuli: Probability distributions and combination rules
Probabilistic independence among multiple random variables (e.g., among the outputs of multiple spatial-frequency channels) has been invoked to explain two effects found with many kinds of stimuli: increments in detection performance due to ``probability summation'' and decrements in detection and identification performance due to ``extrinsic uncertainty.'' Quantitative predictions of such effects, however, depend on the precise assumptions. Here we calculate predictions from multidimensional signal-detection theory assuming any of several different probability distributions characterizing the random variables (including two-state, Gaussian, exponential, and double-exponential distributions) and either of two rules for combining the multiple random variables into a single decision variable (taking the maximum or summing them).
decrements in detection and identification performance
multidimensional signal-detection theory
scientific article; zbMATH DE number 4016673