Nonlinear probability weighting allows cumulative prospect theory (CPT) to account for key phenomena in decision making under risk (e.g., certainty effect, fourfold pattern of risk attitudes). It describes the impact of risky outcomes on preferences in terms of a rank-dependent nonlinear transformation of their objective probabilities. The attentional Drift Diffusion Model (aDDM) formalizes the finding that attentional biases toward an option can shape preferences within a sequential sampling process. Here we link these two influential frameworks. We used the aDDM to simulate choices between two options while systematically varying the strength of attentional biases to either option. The resulting choices were modeled with CPT. Changes in preference due to attentional biases in the aDDM were reflected in highly systematic signatures in the parameters of CPT’s weighting function (curvature, elevation). In a re-analysis of a large set of previously published data, we demonstrate that attentional biases are also empirically linked to patterns in probability weighting as suggested by the simulations. Our analyses also revealed a previously overlooked link between patterns in probability weighting and response times. These findings highlight that distortions in probability weighting can arise from simple option-specific attentional biases in information search, and suggest an alternative to common interpretations of weighting-function parameters in terms of probability sensitivity and optimism. They also point to novel, attention-based explanations for empirical phenomena associated with characteristic shapes of CPT’s probability-weighting function (e.g., certainty effect, description–experience gap). The results advance the integration of two prominent computational frameworks for decision making. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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非线性概率加权可以反映顺序抽样中的注意力偏差。

非线性概率加权允许累积前景理论 (CPT) 解释风险决策中的关键现象（例如，确定性效应、风险态度的四重模式）。它根据客观概率的秩相关非线性变换描述了风险结果对偏好的影响。注意漂移扩散模型 (aDDM) 形式化了以下发现，即对选项的注意偏差可以在顺序采样过程中塑造偏好。在这里，我们将这两个有影响力的框架联系起来。我们使用 aDDM 来模拟两个选项之间的选择，同时系统地改变对任一选项的注意力偏差强度。由此产生的选择是用 CPT 建模的。由于 aDDM 中的注意力偏差而导致的偏好变化反映在 CPT 加权函数（曲率、仰角）参数的高度系统特征中。在对大量先前发布的数据进行重新分析时，我们证明了注意力偏差也与模拟所建议的概率加权模式有经验联系。我们的分析还揭示了概率加权模式与响应时间之间先前被忽视的联系。这些发现强调，概率加权的扭曲可能源于信息搜索中简单的特定于选项的注意偏差，并建议在概率敏感性和乐观方面对加权函数参数的常见解释进行替代。他们还指小说，对与 CPT 概率加权函数的特征形状相关的经验现象的基于注意力的解释（例如，确定性效应、描述-经验差距）。结果推动了两个突出的决策计算框架的集成。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）