Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America.
Department of Mathematics, University of California, Berkeley, Berkeley, California, United States of America.
PLoS Comput Biol. 2024 May 15;20(5):e1012119. doi: 10.1371/journal.pcbi.1012119. eCollection 2024 May.
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.
计算认知模型被广泛用于形式化认知过程。模型参数为量化人类信息处理个体差异提供了一种简单的方法。同样，模型比较允许研究人员确定哪些理论，嵌入在不同的模型中，为数据提供了最佳解释。认知建模使用统计工具将模型与数据定量关联，而这些数据通常依赖于计算/估计模型下数据的可能性。然而，对于大量模型来说，这种可能性在计算上是难以处理的。这些相关模型可能体现了合理的认知理论，但由于可用于将它们与数据相关联的工具有限，往往没有得到充分探索。我们通过使用人工神经网络 (ANN) 以简单的方式解决了这一差距，该方法直接将数据映射到模型身份和参数上，从而绕过了可能性估计。我们在具有强试验间依赖性的认知模型类别（例如强化学习模型）上测试了我们的 ANN 作为认知模型拟合工具的实例，这些模型对大多数方法提出了独特的挑战。我们表明，我们可以使用我们的 ANN 方法充分进行参数估计和模型识别，包括那些无法使用传统基于似然的方法拟合的模型。我们进一步讨论了我们在利用基于模拟的方法进行参数估计和模型识别的研究背景下的工作，以及这些方法如何拓宽研究人员可以进行定量研究的认知模型类别。
