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 recovere. 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）为从选择与反应时数据中推断潜在认知过程提供了强大的框架。虽然传统上EAMs仅限于二元选择，但近期发展已通过圆形扩散模型 \citep{smith2016diffusion} 和空间连续扩散模型 \citep{ratcliff2018decision} 将其扩展到旋转对称的连续响应。然而，这类扩展在应用范围上存在局限，因为许多心理构念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——半圆扩散模型（HCDM）和贝塔漂移扩散模型（BDDM），填补了这一空白。利用实证数据集，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。这些无似然方法规避了对解析似然函数的需求，使得这些复杂模型可用于实际数据分析。结果表明，两种模型均能准确捕捉选择及其反应时的联合分布，并生成可解释且可靠恢复的参数。在响应精度极高或极低的情景下，BDDM提供了更优的拟合效果。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框架扩展至新的应用场景——有界连续自报数据，为研究者提供了建模连续响应认知动态的易用工具包。