Evidence accumulation models (EAMs) provide a powerful framework for inferring latent cognitive processes from choice and reaction time data. While EAMs are traditionally limited to binary choices, recent developments have extended them to rotationally symmetric continuous responses via the circular diffusion model \citep{smith2016diffusion} and the spatially continuous diffusion model \citep{ratcliff2018decision}. Yet, such extensions are limited in scope, as many psychological constructs are measured on bounded non-rotational scales. In this paper, we bridge this gap by presenting and comparing two adaptations designed for bounded continuous data: the Half-Circular Diffusion Model (HCDM) and the Beta Drift Diffusion Model (BDDM). Using an empirical dataset, we demonstrate a complete methodological workflow for parameter estimation and model selection using Amortized Bayesian Inference (ABI) and Amortized Bayesian Model Comparison (ABMC). These likelihood-free methods bypass the need for analytical likelihood functions, making these complex models accessible for practical data analysis. Our results indicate that both models accurately capture the joint distribution of choices and their reaction times and yield interpretable parameters that can be reliably recovered. The BDDM provides a superior fit in scenarios characterized by very high or very low response precision. To facilitate the adoption of these methods in experimental psychology, we provide fully documented code and example datasets. This work extends the EAM framework to a new application context, the bounded continuous self-report data, offering researchers a user-friendly toolkit for modeling the cognitive dynamics of continuous responses.

翻译：证据积累模型（EAMs）为从选择和反应时间数据中推断潜在认知过程提供了强大框架。尽管EAMs传统上局限于二元选择，但最近的发展通过圆形扩散模型（smith2016diffusion）和空间连续扩散模型（ratcliff2018decision）将其扩展到旋转对称的连续响应。然而，此类扩展的范围有限，因为许多心理构造是在有界非旋转量表上测量的。本文通过提出并比较两种针对有界连续数据的适配模型来填补这一空白：半圆形扩散模型（HCDM）和贝塔漂移扩散模型（BDDM）。利用经验数据集，我们展示了使用摊销贝叶斯推断（ABI）和摊销贝叶斯模型比较（ABMC）进行参数估计和模型选择的完整方法论工作流程。这些无似然方法绕过了对解析似然函数的需求，使这些复杂模型可用于实际数据分析。我们的结果表明，两种模型均能准确捕获选择及其反应时间的联合分布，并产生可解释且可可靠恢复的参数。在响应精度极高或极低的情景下，BDDM提供了更优的拟合。为促进这些方法在实验心理学中的应用，我们提供了完整文档化的代码和示例数据集。本研究将EAM框架扩展到新的应用场景——有界连续自我报告数据，为研究人员提供了用于建模连续响应认知动力学的用户友好型工具包。