Abstract In the redundant signals task, participants respond, in the same way, to stimuli of several sources, which are presented either alone or in combination (redundant signals). The responses to the redundant signals are typically much faster than to the single signals. Several models explain this effect, including race and coactivation models of information processing. Race models assume separate channels for the two components of a redundant signal, with the response time determined by the faster of the two channels. Because the slower processing times in one channel are canceled out by faster processing in the other channel, responses to redundant signals are, on average, faster than to single signals. In contrast, coactivation models relate the redundancy gain to some kind of integrated processing of the redundant information. The two models can be distinguished using the race model inequality (Miller, 1982) on the response time distribution functions. Miller’s prediction was derived for experiments with 100% accuracy, and despite corrections for guesses and omitted responses, it is limited to easy tasks with negligible error rates. In this article we generalize Miller’s inequality to non-trivial experimental tasks in which incorrect responses may occur systematically. The method is illustrated using data from difficult discrimination tasks with Go/Nogo and choice responses.

中文翻译：

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在困难的冗余信号任务中测试竞赛模型

摘要 在冗余信号任务中，参与者以相同的方式对多个来源的刺激做出反应，这些来源单独或组合呈现（冗余信号）。对冗余信号的响应通常比对单个信号快得多。几个模型解释了这种影响，包括信息处理的种族和共激活模型。Race 模型假设冗余信号的两个分量有不同的通道，响应时间由两个通道中较快的通道决定。由于一个通道中较慢的处理时间被另一通道中较快的处理所抵消，因此对冗余信号的响应平均比对单个信号快。相比之下，共激活模型将冗余增益与冗余信息的某种集成处理联系起来。可以使用响应时间分布函数上的种族模型不等式 (Miller, 1982) 区分这两种模型。Miller 的预测是针对 100% 准确率的实验得出的，尽管对猜测和遗漏的响应进行了修正，但它仅限于错误率可以忽略的简单任务。在本文中，我们将米勒的不等式推广到非平凡的实验任务，在这些任务中可能会系统地发生不正确的反应。该方法使用来自具有 Go/Nogo 和选择响应的困难辨别任务的数据进行说明。在本文中，我们将米勒的不等式推广到非平凡的实验任务，在这些任务中可能会系统地发生不正确的反应。该方法使用来自具有 Go/Nogo 和选择响应的困难辨别任务的数据进行说明。在本文中，我们将米勒的不等式推广到非平凡的实验任务，在这些任务中可能会系统地发生不正确的反应。该方法使用来自具有 Go/Nogo 和选择响应的困难辨别任务的数据进行说明。