The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT-R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT-R parameters without requiring the modeler to build and fit an entire ACT-R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT-R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT-R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT-R and LBA lends a more concrete interpretation to ACT-R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science.

中文翻译：

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捕捉认知模型中的动态性能：使用线性弹道累加器估计 ACT-R 记忆参数


控制我们行为的参数不断变化。在认知模型中准确捕捉这些动态对建模者提出了挑战。在这里，我们演示了 ACT-R 的陈述性记忆到线性弹道累加器 (LBA) 的映射，这是一个描述证据积累过程之间竞争的数学模型。我们表明，这种映射提供了一种推断单个 ACT-R 参数的方法，而不需要建模者构建和拟合整个 ACT-R 模型。可以使用 LBA 的现有参数估计方法，而不是传统上进行的计算量大的参数扫描。我们进行了参数恢复研究，以确认 LBA 可以从模拟数据中恢复 ACT-R 参数。然后，作为概念证明，我们使用 LBA 从经验数据集中估计 ACT-R 参数。由此产生的参数估计为观察到的随时间变化和个体之间的行为差​​异提供了认知上有意义的解释。此外，我们发现ACT-R和LBA之间的映射为ACT-R的延迟因子参数提供了更具体的解释，即作为响应谨慎性的度量。这项工作有助于将形式建模方法整合到认知科学中。