Abstract The ACT-R cognitive architecture is a computational framework for developing, simulating and testing comprehensive theories of cognition. By far, the most common method of evaluating ACT-R models involves generating predictions through Monte Carlo simulation and comparing those predictions to aggregated human data. This approach has several limitations, including computational inefficiency, the potential for averaging artifacts, and difficulty representing uncertainty in parameter estimates. In this paper, we demonstrate the fundamentals of developing models of ACT-R within a Bayesian framework. Instantiating ACT-R in a Bayesian framework has many advantages, including the ability to use modern parameter estimation and model comparison techniques, the ability to compare ACT-R to other closed-form models, increased computational efficiency, and the ability to perform a deeper mathematical analysis of model properties. We develop model variants of the classic fan experiment, beginning with a simple baseline model of ACT-R’s declarative memory system and progressing through increasingly complex variants until reaching a moderately complex general model. Our hope is that this will highlight connections between computational and mathematical approaches to formal modeling and facilitate new and exciting research.

中文翻译：

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在统计框架内开发基于记忆的 ACT-R 模型

摘要 ACT-R 认知架构是一个用于开发、模拟和测试综合认知理论的计算框架。到目前为止，评估 ACT-R 模型的最常用方法包括通过蒙特卡罗模拟生成预测并将这些预测与汇总的人类数据进行比较。这种方法有几个限制，包括计算效率低下、平均伪影的可能性以及难以表示参数估计中的不确定性。在本文中，我们展示了在贝叶斯框架内开发 ACT-R 模型的基础知识。在贝叶斯框架中实例化 ACT-R 有许多优点，包括能够使用现代参数估计和模型比较技术，能够将 ACT-R 与其他封闭形式的模型进行比较，提高计算效率，以及对模型属性进行更深入数学分析的能力。我们开发了经典风扇实验的模型变体，从 ACT-R 陈述性记忆系统的简单基线模型开始，然后通过越来越复杂的变体进行，直到达到中等复杂的通用模型。我们希望这将突出计算和数学方法之间的形式建模方法之间的联系，并促进新的和令人兴奋的研究。