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). Because both models have intractable likelihoods, we fit them using Amortized Bayesian Inference (ABI) and compare them using Amortized Bayesian Model Comparison (ABMC). We demonstrate the complete workflow on an empirical affect dataset (N = 215), including parameter recovery, simulation-based calibration, posterior predictive checks, and model comparison. Both models accurately capture the joint distribution of responses and reaction times and yield interpretable parameters that can be reliably recovered. The model comparison further reveals a simple diagnostic for choosing between them: the dispersion of the rating distribution, with HCDM preferred for moderate spread and BDDM for highly concentrated or highly dispersed ratings. This work extends the EAM framework to a new application context, bounded continuous self-report data, and offers researchers a user-friendly toolkit for modeling the cognitive dynamics of continuous responses. We release fully documented Python code with both GPU and CPU implementations, along with example datasets.

翻译：证据累积模型（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框架。尽管EAMs传统局限于二元选择，但近期发展已通过圆形扩散模型（smith2016diffusion）和空间连续扩散模型（ratcliff2018decision）将其扩展至旋转对称的连续响应。然而，此类扩展在范围上存在局限，因为许多心理构念是在有界非旋转尺度上测量的。本文通过提出并比较两种适用于有界连续数据的改进模型——半圆扩散模型（HCDM）和贝塔漂移扩散模型（BDDM）——弥合了这一差距。由于两种模型均具有难以处理的似然函数，我们采用摊销贝叶斯推断（ABI）进行拟合，并通过摊销贝叶斯模型比较（ABMC）进行对比。我们在一个经验情感数据集（N=215）上展示了完整工作流程，包括参数恢复、基于模拟的校准、后验预测检验和模型比较。两种模型均能准确捕捉响应与反应时的联合分布，并产生可可靠恢复的可解释参数。模型比较进一步揭示了二者选择的简单诊断依据：评级分布的离散程度——HCDM适用于中等离散度，而BDDM适用于高度集中或高度离散的评级。本研究将EAM框架扩展至新的应用场景——有界连续自报告数据，并为研究者提供了建模连续响应认知动态的用户友好型工具包。我们发布了包含GPU和CPU实现的完整文档化Python代码及示例数据集。