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Characterizing perceptual learning with external noise

机译：用外部噪声表征感知学习

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Performance in perceptual tasks often improves with practice. This effect is known as 'perceptual learning,' and it has been the source of a great deal of interest and debate over the course of the last century. Here, we consider the effects of perceptual learning within the context of signal detection theory. According to signal detection theory, the improvements that take place with perceptual learning can be due to increases in internal signal strength or decreases in internal noise. We used a combination of psychophysical techniques (external noise masking and double-pass response consistency) that involve corrupting stimuli with externally added noise to discriminate between the effects of changes in signal and noise as observers learned to identify sets of unfamiliar visual patterns. Although practice reduced thresholds by as much as a factor of 14, internal noise remained virtually fixed throughout training, indicating learning served to predominantly increase the strength of the internal signal. We further examined the specific nature of the changes that took place in signal strength by correlating the externally added noise with observer's decisions across trials (response classification). This technique allowed us to visualize some of the changes that took place in the linear templates used by the observers as learning occurred, as well as test the predictions of a linear template-matching model. Taken together, the results of our experiments offer important new theoretical constraints on models of perceptual learning.
机译：在感知任务中的表现通常会随着练习而提高。这种效应被称为“感知学习”，并且在上个世纪以来一直是引起人们极大兴趣和争论的源头。在这里，我们考虑在信号检测理论的背景下知觉学习的影响。根据信号检测理论，通过感知学习进行的改进可能是由于内部信号强度的增加或内部噪声的减少。当观察者学会识别不熟悉的视觉模式时，我们使用了心理物理技术的组合（外部噪声掩蔽和两次通过响应一致性），其中包括破坏刺激和外部添加噪声，以区分信号和噪声变化的影响。尽管实践将阈值降低了多达14倍，但内部噪声在整个训练过程中几乎保持不变，这表明学习有助于显着提高内部信号的强度。我们通过将外部添加的噪声与观察者在整个试验中的决策（响应分类）相关联，进一步研究了信号强度变化的特殊性质。这种技术使我们可以直观地观察观察者在学习过程中使用的线性模板中发生的某些变化，并测试线性模板匹配模型的预测。总之，我们的实验结果为感知学习模型提供了重要的新理论约束。

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