Computational modeling of cognition allows latent psychological variables to be measured by means of adjustable model parameters. The estimation and interpretation of the parameters are impaired, however, if parameters are strongly intercorrelated within the model. We point out that strong parameter interdependencies are especially likely to emerge in models that combine a subjective value function with a probabilistic choice rule—a common structure in the literature. We trace structural parameter interdependencies between value function and choice rule parameters across several prominent computational models, including models on risky choice (cumulative prospect theory), categorization (the generalized context model), and memory (the SIMPLE model of free recall). Using simulation studies with a generic choice model, we show that the accuracy in parameter estimation is hampered in the presence of high parameter intercorrelations, particularly the ability to detect group differences on the parameters and associations of the parameters with external variables. We demonstrate that these problems can be alleviated by using a different specification of stochasticity in the model, for example, by assuming parameter stochasticity or a constant error term. In addition, application to two empirical data sets of risky choice shows that alleviating parameter interdependencies in this way can lead to different conclusions about the estimated parameters. Our analyses highlight a common but often neglected problem of computational models of cognition and identify ways in which the design and application of such models can be improved. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

中文翻译：

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认知计算模型中的结构参数相互依赖性。

认知的计算建模允许通过可调整的模型参数来测量潜在的心理变量。然而，如果参数在模型中高度相关，则参数的估计和解释会受到损害。我们指出，在结合主观价值函数和概率选择规则（文献中常见的结构）的模型中，特别有可能出现强参数相互依赖性。我们在几个著名的计算模型中追踪价值函数和选择规则参数之间的结构参数相互依赖性，包括风险选择模型（累积前景理论）、分类模型（广义上下文模型）和记忆模型（自由召回的简单模型）。使用具有通用选择模型的模拟研究，我们表明，在存在高参数相互相关性的情况下，参数估计的准确性受到阻碍，特别是检测参数的组差异以及参数与外部变量的关联的能力。我们证明，可以通过在模型中使用不同的随机性规范来缓解这些问题，例如，通过假设参数随机性或恒定误差项。此外，对风险选择的两个经验数据集的应用表明，以这种方式减轻参数相互依赖性可以导致关于估计参数的不同结论。我们的分析突出了认知计算模型的一个常见但经常被忽视的问题，并确定了可以改进此类模型的设计和应用的方法。（PsycInfo 数据库记录 (c) 2021 APA，