Rehabilitation Research Institute (REVAL), Hasselt University, Hasselt, Belgium.
Department of Imaging and Pathology, Catholic University Leuven, Leuven, Belgium.
Behav Res Methods. 2024 Mar;56(3):2537-2548. doi: 10.3758/s13428-023-02165-7. Epub 2023 Jun 27.
How much data are needed to obtain useful parameter estimations from a computational model? The standard approach to address this question is to carry out a goodness-of-recovery study. Here, the correlation between individual-participant true and estimated parameter values determines when a sample size is large enough. However, depending on one's research question, this approach may be suboptimal, potentially leading to sample sizes that are either too small (underpowered) or too large (overcostly or unfeasible). In this paper, we formulate a generalized concept of statistical power and use this to propose a novel approach toward determining how much data is needed to obtain useful parameter estimates from a computational model. We describe a Python-based toolbox (COMPASS) that allows one to determine how many participants are needed to fit one specific computational model, namely the Rescorla-Wagner model of learning and decision-making. Simulations revealed that a high number of trials per person (more than the number of persons) are a prerequisite for high-powered studies in this particular setting.
从计算模型中获得有用参数估计需要多少数据？解决这个问题的标准方法是进行恢复性研究。在这里，个体参与者真实和估计参数值之间的相关性决定了样本量足够大的时间。然而，根据研究问题的不同，这种方法可能不是最优的，可能导致样本量太小（功率不足）或太大（成本过高或不可行）。在本文中，我们提出了一个统计功效的广义概念，并利用这个概念提出了一种新的方法，用于确定从计算模型中获得有用参数估计需要多少数据。我们描述了一个基于 Python 的工具包（COMPASS），它可以确定需要多少参与者来拟合一个特定的计算模型，即学习和决策的 Rescorla-Wagner 模型。模拟结果表明，每个人的试验次数较多（超过参与者的数量）是在这种特定环境下进行高功效研究的前提条件。
