Stochastic General Recognition Theory

随机一般识别理论是一个精确的数学 形式化的几个想法，一直流行， 心理学和认知心理学多年。 其中一 反复暴露于任何复杂的刺激并不总是 引发同样的知觉状态;第二个是知觉的 分析这种刺激的系统由若干个 相互作用的并行操作的通道。 的 理论对反应作出具体的数值预测 概率和反应时间在各种认知 和知觉实验，如识别和 分类 此外，它有可能解释 不同刺激暴露持续时间的影响， 一个刺激与另一个刺激，以及短刺激间隔的影响 的间隔 这项研究将包括一些实验 其结果由理论预测;因此，这些 实验将对这一理论进行严格的检验。 一组 的实验将探讨如何结构的一个类别 影响分类时间;另一组将研究如何 在知觉处理过程中的不同类型的相互作用 影响分类， 识别性能 这些实验将增加我们的 了解人类如何识别和分类复杂的 模式. 这反过来又可能会提高我们对一些事物的理解 阅读和学习障碍，也可能导致更多的 机器的精确模式识别。

Stochastic general recognition theory is a precise mathematical formalization of several ideas that have been popular in perceptual and cognitive psychology for many years. One of these is that repeated exposure to any complex stimulus does not always elicit the same perceptual state; a second is that the perceptual system that analyzes such a stimulus is composed of several mutually interacting channels that operate in parallel. The theory makes specific numerical predictions about response probabilities and response times in a wide variety of cognitive and perceptual experiments, such as identification and categorization. In addition, it has the potential to account for the effects of varying stimulus exposure duration, the masking of one stimulus with another, and the effects of short interstimulus intervals. This research will include a number of experiments whose results are predicted by the theory; thus, these experiments will provide a rigorous test of the theory. One set of experiments will investigate how the structure of a category affects categorization time; another set will investigate how different types of interactions during the perceptual processing of separate stimulus components affect categorization and identification performance. These experiments will increase our understanding of how humans identify and categorize complex patterns. This, in turn, may improve our understanding of some reading and learning disabilities and may also lead to more accurate pattern recognition by machine.