Simulations are an indispensable step in the cycle of theory development and refinement, helping researchers formulate precise definitions, generate models, and make accurate predictions. This paper introduces the Pavlovian Associative Learning Models Simulator (PALMS), a Python environment to simulate Pavlovian conditioning experiments. In addition to the canonical Rescorla-Wagner model, PALMS incorporates several attentional learning approaches, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelley's Hybrid, and a novel extension of the Rescorla-Wagner model with a unified variable learning rate that integrates Mackintosh's and Pearce and Hall's opposing conceptualisations. The simulator's graphical interface allows for the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. Moreover, it uniquely enables the simulation of experiments involving hundreds of stimuli, as well as the computation of configural cues and configural-cue compounds across all models, thereby considerably expanding their predictive capabilities. PALMS operates efficiently, providing instant visualisation of results, supporting rapid, precise comparisons of various models' predictions within a single architecture and environment. Furthermore, graphic displays can be easily saved, and simulated data can be exported to spreadsheets. To illustrate the simulator's capabilities and functionalities, we provide a detailed description of the software and examples of use, reproducing published experiments in the associative learning literature. PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The simulator source code and the latest multiplatform release build are accessible as a GitHub repository at https://github.com/cal-r/PALMS-Simulator

翻译：仿真是理论发展与完善循环中不可或缺的环节，有助于研究者制定精确定义、构建模型并作出准确预测。本文介绍了巴甫洛夫联想学习模型模拟器（PALMS），这是一个用于模拟巴甫洛夫条件反射实验的Python环境。除经典的Rescorla-Wagner模型外，PALMS整合了多种注意学习模型，包括Pearce-Kaye-Hall模型、扩展Mackintosh模型、Le Pelley混合模型，以及一种新颖的Rescorla-Wagner模型扩展——该扩展采用统一变量学习率，融合了Mackintosh模型与Pearce-Hall模型的对立理论框架。该模拟器的图形界面支持以字母数字格式输入完整实验设计，其格式与实验神经科学家常用的范式相似。此外，它独特地实现了对涉及数百种刺激的实验模拟，并能跨所有模型计算构型线索与构型线索复合体，从而显著扩展了模型的预测能力。PALMS运行高效，可即时可视化结果，支持在单一架构和环境中对多种模型预测进行快速精确的比较。同时，图形化结果可轻松保存，模拟数据可导出至电子表格。为展示模拟器的功能特性，我们提供了软件详述及使用示例，复现了联想学习文献中已发表的实验。PALMS采用开源GNU宽通用公共许可证3.0授权。模拟器源代码及最新跨平台发行版本可通过GitHub仓库获取：https://github.com/cal-r/PALMS-Simulator