Performance of normative and approximate evidence accumulation on the dynamic clicks task
Performance of normative and approximate evidence accumulation on the dynamic clicks task
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DOI：
10.1101/541045
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发表时间：
2019-02
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影响因子：
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通讯作者：
Adrian E. Radillo;Alan Veliz-Cuba;K. Josić;Z. Kilpatrick
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作者：
Adrian E. Radillo;Alan Veliz-Cuba;K. Josić;Z. Kilpatrick
许多心理物理学任务的目的是揭示哺乳动物是如何在一个不断变化的世界中做出决定的。在这里，我们考察了这类任务中理想和接近理想观察者的特征。我们会问，绩效何时以及如何取决于任务参数和设计，以及，反过来，观察者的绩效告诉我们他们的决策过程。在动态点击任务中，受试者听到两种不同频率的泊松点击流（左和右）。当受试者正确识别出频率较高的那一面时，他们就会得到奖励，因为这一面的变化是不可预测的。我们证明了任务参数的简化集定义了参数空间中的区域，其中最优但不是接近最优的观察者保持恒定的响应精度。我们还表明，对于一系列任务参数，近似规范模型必须经过精细调整才能达到接近最佳的性能，这说明了区分规范模型及其近似的潜在方法。此外，我们表明，使用负对数似然函数和0/1损失函数来拟合这些类型的模型是不等价的：0/1损失导致参数恢复中的偏差，随着感官噪声的增加而增加。这些发现提出了梳理模型的方法，这些模型在精确调整时难以区分，并指出了实验设计、模型拟合和结果数据解释中的一般陷阱。
The aim of a number of psychophysics tasks is to uncover how mammals make decisions in a world that is in flux. Here we examine the characteristics of ideal and near–ideal observers in a task of this type. We ask when and how performance depends on task parameters and design, and, in turn, what observer performance tells us about their decision-making process. In the dynamic clicks task subjects hear two streams (left and right) of Poisson clicks with different rates. Subjects are rewarded when they correctly identify the side with the higher rate, as this side switches unpredictably. We show that a reduced set of task parameters defines regions in parameter space in which optimal, but not near-optimal observers, maintain constant response accuracy. We also show that for a range of task parameters an approximate normative model must be finely tuned to reach near-optimal performance, illustrating a potential way to distinguish between normative models and their approximations. In addition, we show that using the negative log-likelihood and the 0/1-loss functions to fit these types of models is not equivalent: the 0/1-loss leads to a bias in parameter recovery that increases with sensory noise. These findings suggest ways to tease apart models that are hard to distinguish when tuned exactly, and point to general pitfalls in experimental design, model fitting, and interpretation of the resulting data.