Vision and Haptics Laboratory, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK.
J Vis. 2022 Jan 4;22(1):5. doi: 10.1167/jov.22.1.5.
Sensory cue integration is one of the primary areas in which a normative mathematical framework has been used to define the "optimal" way in which to make decisions based upon ambiguous sensory information and compare these predictions to behavior. The conclusion from such studies is that sensory cues are integrated in a statistically optimal fashion. However, numerous alternative computational frameworks exist by which sensory cues could be integrated, many of which could be described as "optimal" based on different criteria. Existing studies rarely assess the evidence relative to different candidate models, resulting in an inability to conclude that sensory cues are integrated according to the experimenter's preferred framework. The aims of the present paper are to summarize and highlight the implicit assumptions rarely acknowledged in testing models of sensory cue integration, as well as to introduce an unbiased and principled method by which to determine, for a given experimental design, the probability with which a population of observers behaving in accordance with one model of sensory integration can be distinguished from the predictions of a set of alternative models.
感觉线索整合是一个主要领域，其中一个规范的数学框架被用来定义基于模糊感觉信息做出决策的“最佳”方式，并将这些预测与行为进行比较。这些研究的结论是，感觉线索以统计上最优的方式进行整合。然而，存在许多替代的计算框架，可以通过这些框架来整合感觉线索，其中许多框架可以根据不同的标准被描述为“最优”。现有的研究很少评估相对于不同候选模型的证据，因此无法得出结论，即感觉线索是根据实验者偏好的框架进行整合的。本文的目的是总结和强调在测试感觉线索整合模型时很少被承认的隐含假设，并引入一种无偏且有原则的方法，用于确定在给定的实验设计下，根据一个感觉整合模型进行行为的观察者群体与一组替代模型的预测相区分的概率。
