Parallel interactive processing (PIP) represents an approach in which specific context generates interactive relationships between general attributes. This article summarizes previous research that demonstrates how such relationships influence inference making in categorization. This is followed by evidence that the approach can be extended to other areas of cognition, including probability judgments. PIP was successful in fitting data that revealed the prevalence of the conjunction fallacy as well as other probability estimation data. PIP provided better fits overall than the signed summation model and the configural weighted average model. The quantum probability model provided good fits for the conjunction fallacy data but not for other probability judgments.

中文翻译：

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并行交互处理是理解复杂信息处理的一种方式：联合谬误和其他示例。

并行交互处理（PIP）代表一种方法，其中特定上下文在一般属性之间生成交互关系。本文总结了以前的研究，这些研究证明了这种关系如何影响分类中的推理。其次，有证据表明该方法可以扩展到其他认知领域，包括概率判断。PIP成功地拟合了揭示合取谬误的普遍性的数据以及其他概率估计数据。与有符号求和模型和配置加权平均模型相比，PIP提供了更好的总体拟合度。量子概率模型为合取谬误数据提供了很好的契合，但没有为其他概率判断提供契合。