School of Psychological Science, University of Western Australia, Nedlands, WA, Australia.
Air Force Research Laboratory, Wright-Patterson AFB Ohio, Dayton, OH, USA.
Psychon Bull Rev. 2024 Jun;31(3):1057-1077. doi: 10.3758/s13423-023-02418-8. Epub 2023 Dec 4.
Despite the ubiquitous nature of evidence accumulation models in cognitive and experimental psychology, there has been a comparatively limited uptake of such techniques in the applied literature. While quantifying latent cognitive processing properties has significant potential for applied domains such as adaptive work systems, accumulator models often fall short in practical applications. Two primary reasons for these shortcomings are the complexities and time needed for the application of cognitive models, and the failure of current models to capture systematic trial-to-trial variability in parameters. In this manuscript, we develop a novel, trial-varying extension of the shifted Wald model to address these concerns. By leveraging conjugate properties of the Wald distribution, we derive computationally efficient solutions for threshold and drift parameters which can be updated instantaneously with new data. The resulting model allows the quantification of systematic variation in latent cognitive parameters across trials and we demonstrate the utility of such analyses through simulations and an exemplar application to an existing data set. The analytic nature of our solutions opens the door for real-world applications, significantly extending the reach of computational models of behavioral responses.
尽管在认知和实验心理学中，证据积累模型无处不在，但在应用文献中，这种技术的应用相对较少。虽然量化潜在的认知处理特性对自适应工作系统等应用领域具有重要意义，但在实际应用中，积累器模型往往存在不足。造成这些缺点的两个主要原因是认知模型的应用复杂性和所需时间，以及当前模型无法捕捉参数在试验间的系统变化。在本文中，我们开发了一种新的、随试验变化的 Wald 模型扩展，以解决这些问题。通过利用 Wald 分布的共轭性质，我们为门限和漂移参数推导出了计算效率高的解决方案，这些参数可以随着新数据的输入即时更新。所得到的模型允许量化潜在认知参数在试验间的系统变化，我们通过模拟和对现有数据集的示例应用来证明了这种分析的有效性。我们的解决方案具有分析性质，为实际应用开辟了道路，极大地扩展了行为反应的计算模型的应用范围。
