A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information
A tutorial on cue combination and Signal Detection Theory: Using changes in sensitivity to evaluate how observers integrate sensory information
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DOI：
10.1016/j.jmp.2016.04.006
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发表时间：
2016-08-01
影响因子：
1.8
通讯作者：
Jones, Pete R.
中科院分区：
文献类型：
--
作者：
Jones, Pete R.
许多感官输入包含多个信息源（“提示”），例如两种不同频率的声音，或者嘴唇移动时听到的一致声音。通常，每个提示都会提供同一物理属性的单独估计，例如物体的大小或位置。理想的观察者可以利用这些冗余的感官信息来提高其感知判断的准确性。例如，如果每个线索都被建模为独立的高斯随机变量，那么组合 Ncues 应该可以提高检测/辨别灵敏度。或者，效率较低的观察者可能仅根据可用信息的子集做出决策，因此从访问多个信息源中获得很少或根本没有好处。在这里，我们使用信号检测理论来制定和比较各种线索组合模型，其中许多模型通常用于解释经验数据。我们提醒读者注意每个模型固有的关键假设，并提供用于推导定量预测的公式。还提供了用于模拟每个模型的代码，从而可以量化预期的测量误差水平。基于这些结果，结果表明，在定性不同的组合模型之间，预测的灵敏度通常差异很小。这意味着仅凭敏感性不足以理解决策效率，并且讨论了其含义。 (C) 2016 Elsevier Inc. 保留所有权利。
Many sensory inputs contain multiple sources of information ('cues'), such as two sounds of different frequencies, or a voice heard in unison with moving lips. Often, each cue provides a separate estimate of the same physical attribute, such as the size or location of an object. An ideal observer can exploit such redundant sensory information to improve the accuracy of their perceptual judgments. For example, if each cue is modeled as an independent, Gaussian, random variable, then combining Ncues should provide up to a improvement in detection/discrimination sensitivity. Alternatively, a less efficient observer may base their decision on only a subset of the available information, and so gain little or no benefit from having access to multiple sources of information. Here we use Signal Detection Theory to formulate and compare various models of cue-combination, many of which are commonly used to explain empirical data. We alert the reader to the key assumptions inherent in each model, and provide formulas for deriving quantitative predictions. Code is also provided for simulating each model, allowing expected levels of measurement error to be quantified. Based on these results, it is shown that predicted sensitivity often differs surprisingly little between qualitatively distinct models of combination. This means that sensitivity alone is not sufficient for understanding decision efficiency, and the implications of this are discussed. (C) 2016 Elsevier Inc. All rights reserved.