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Ideal observer analysis is a method for investigating how information is processed in a perceptual system. It is also a basic principle that guides modern research in perception. The ''ideal observer'' is a theoretical system that performs a specific task in an optimal way. If there is uncertainty in the task, then perfect performance is impossible and the ideal observer will make errors. ''Ideal performance'' is the theoretical upper limit of performance. It is theoretically impossible for a real system to perform better than ideal. Typically, real systems are only capable of sub-ideal performance. This technique is useful for analyzing psychophysical data (see psychophysics). ==Definition== Many definitions of this term have been offered. Geisler (2003) (slightly reworded): The central concept in ideal observer analysis is the ''ideal observer'', a theoretical device that performs a given task in an optimal fashion given the available information and some specified constraints. This is not to say that ideal observers perform without error, but rather that they perform at the physical limit of what is possible in the situation. The fundamental role of uncertainty and noise implies that ideal observers must be defined in probabilistic (statistical) terms. ''Ideal observer analysis'' involves determining the performance of the ideal observer in a given task and then comparing its performance to that of a real perceptual system, which (depending on the application) might be the system as a whole, a subsystem, or an elementary component of the system (e.g. a neuron). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「ideal observer analysis」の詳細全文を読む スポンサード リンク
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