Identification of Multidimensional Objects and Multiple Observation Intervals

In identification tasks, observers provide distinct labels for each of m > 2 possible stimuli. If all stimuli are assumed independent, detection-theory analyses can estimate sensitivity for each stimulus and bias for each response. Data from one identification task can be used to predict the results of an experiment using a subset of the same stimuli using the constant-ratio rule. Multi-interval forced-choice experiments, consisting of sequences of length m , longer than 2, that contain one sample of S 2 and m − 1 samples of S 1 , can be analyzed using either detection theory or Choice Theory. Experiments in which stimuli are both detected and identified can be analyzed using identification operating characteristics (IOCs), which are theoretically related to ROCs for the same data. Identification of stimuli constructed factorially from values on two or more dimensions provide data from which various types of perceptual interaction can be evaluated using Multidimensional Signal Detection Analysis (MSDA).

Biology · Identification (biology) · Computer Science · Neural Networks and Applications