HUMAN PERFORMANCE IN LOW SIGNAL PROBABILITY TASKS.
MICHIGAN UNIV ANN ARBOR DEPT OF PSYCHOLOGY
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An extension of the theory of signal detection TSD to psychophysical tasks involving low probability signals and free response data is developed and evaluated. Emphasis is placed on tasks for which the observer is asychronous that is, the observer cannot perform optimally by making independent decisions on non-overlapping intervals of time. A mathematical model of asynchronous observation in a class of temporally unstructured tasks with Neyman-Pearson solutions for optimal fixed response rate is used to describe detection performance by human observers. Data from an experiment show 1 a conservative fixed response rate, 2 a constant hit rate, and 3 inter-response distributions for false alarms with a general exponential shape showing periodic modes. Detection efficiency in the temporally unstructured task was approximately one tenth of alerted detection efficiency for two observers and one half of alerted detection efficiency for a third observer. Points on the obtained ROC curve are fit better by an inefficient asynchronous observer than by synchronous power law observers. A post hoc analysis of the effect of training showed an effect for distribution of responses in time but showed no effect of an improvement in memory for the signal. It is concluded that highly trained observers detecting important signals show constant efficiency over observation periods of 30 to 45 min. Author