The Balloon Analogue Risk Task (BART) is a popular task used to measure risk-taking behavior. To identify cognitive processes associated with choice behavior on the BART, a few computational models have been proposed. However, the extant models either fail to capture choice patterns on the BART or show poor parameter recovery performance. Here, we propose a novel computational model, the exponential-weight mean–variance (EWMV) model, which addresses the limitations of existing models. By using multiple model comparison methods, including post hoc model fits criterion and parameter recovery, we showed that the EWMV model outperforms the existing models. In addition, we applied the EWMV model to BART data from healthy controls and substance-using populations (patients with past opiate and stimulant dependence). The results suggest that (1) the EWMV model addresses the limitations of existing models and (2) heroin-dependent individuals show reduced risk preference than other groups, which may have significant clinical implications.

气球模拟风险任务 (BART) 是一项用于衡量冒险行为的流行任务。为了识别与 BART 上的选择行为相关的认知过程，已经提出了一些计算模型。但是，现有模型要么无法捕捉 BART 上的选择模式，要么显示出较差的参数恢复性能。在这里，我们提出了一种新颖的计算模型，即指数权重均值方差 (EWMV) 模型，它解决了现有模型的局限性。通过使用多种模型比较方法，包括事后模型拟合标准和参数恢复，我们表明 EWMV 模型优于现有模型。此外，我们将 EWMV 模型应用于来自健康对照和物质使用人群（既往阿片类药物和兴奋剂依赖的患者）的 BART 数据。