PDF(Pubmed)
Abstract：
In decisions under risk, more numerate people are typically more likely to choose the option with the highest expected value (EV) than less numerate ones. Prior research indicates that this finding cannot be explained by differences in the reliance on explicit EV calculation. The current work uses the attentional Drift Diffusion Model as a unified computational framework to formalize three candidate mechanisms of pre-decisional information search and processing-namely, attention allocation, amount of deliberation, and distorted processing of value-which may differ between more and less numerate people and explain differences in decision quality. Computational modeling of an eye-tracking experiment on risky choice demonstrates that numeracy is linked to how people allocate their attention across the options, how much evidence they require before committing to a choice, and also how strongly they distort currently non-attended information during preference formation. Together, especially the latter two mechanisms largely mediate the effect of numeracy on decision quality. Overall, the current work disentangles and quantifies latent aspects of the dynamics of preference formation, explicates how their interplay may give rise to manifest differences in decision quality, and thereby provides a fully formalized, mechanistic explanation for the link between numeracy and decision quality in risky choice.
摘要：
在有风险的决策中，与数字较少的人相比，数字较多的人通常更有可能选择具有最高期望值(EV)的选项。先前的研究表明，这一发现不能通过对显式EV计算的依赖差异来解释。当前的工作使用注意力漂移扩散模型作为统一的计算框架，形式化了决策前信息搜索和处理的三种候选机制-即，注意力分配，大量的审议，和扭曲的价值处理-这可能在更多和更少的人之间有所不同，并解释了决策质量的差异。关于风险选择的眼动跟踪实验的计算模型表明，算术与人们如何在选项中分配注意力有关，他们在做出选择之前需要多少证据，以及他们在偏好形成过程中扭曲当前无人值守信息的程度。一起,尤其是后两种机制在很大程度上介导了算术对决策质量的影响。总的来说,当前的工作解开并量化了偏好形成动力学的潜在方面，解释了它们之间的相互作用如何导致决策质量的明显差异，从而提供了一个完全形式化的，风险选择中算术与决策质量之间联系的机制解释。
