Estimating the dynamic role of attention via random utility
Estimating the dynamic role of attention via random utility
复制标题
利用随机效用估计注意力的动态作用
DOI：
10.1007/s40881-019-00062-4
复制
发表时间：
2019-08-01
影响因子：
1.9
通讯作者：
Webb, Ryan
中科院分区：
文献类型：
--
作者：
Smith, Stephanie M.;Krajbich, Ian;Webb, Ryan
在做决定的时候，人们倾向于在各种选择之间来回看，直到最终做出选择。眼球追踪研究已经证实，这些注意力的转移与选择结果密切相关。为了理解选择过程的动态，以及注意力的影响，一个主要的框架是信息的顺序抽样。然而，在这些模型中，现有的注意力参数估计方法计算成本高，过于灵活，产生的估计精度和偏差未知。在这里，我们提出了一种依赖于顺序抽样模型和随机实用新型（RUM）之间的联系的估计方法。该方法使用熟悉的计量经济学工具（即逻辑回归），与现有方法相比，在一小部分计算时间内，产生的估计值似乎是无偏的，相对精确。因此，RUM似乎是估计注意力对选择的影响的有用工具。
When making decisions, people tend to look back and forth between the alternatives until they eventually make a choice. Eye-tracking research has established that these shifts in attention are strongly linked to choice outcomes. A predominant framework for understanding the dynamics of the choice process, and thus the effects of attention, is sequential sampling of information. However, existing methods for estimating the attention parameters in these models are computationally costly and overly flexible, and yield estimates with unknown precision and bias. Here we propose an estimation method that relies on a link between sequential sampling models and random utility models (RUM). This method uses familiar econometric tools (i.e., logistic regression) and yields estimates that appear to be unbiased and relatively precise compared to existing methods, in a small fraction of the computation time. The RUM thus appears to be a useful tool for estimating the effects of attention on choice.