Berkovich Rotem, Meiran Nachshon
Ben Gurion University of the Negev, Beer-Sheva, Israel.
J Cogn. 2024 Aug 16;7(1):65. doi: 10.5334/joc.394. eCollection 2024.
In recent years, a growing body of research uses Evidence Accumulation Models (EAMs) to study individual differences and group effects. This endeavor is challenging because fitting EAMs requires constraining one of the EAM parameters to be equal for all participants, which makes a strong and possibly unlikely assumption. Moreover, if this assumption is violated, differences or lack thereof may be wrongly found. To overcome this limitation, in this study, we introduce a new method that was originally suggested by van Maanen & Miletić (2021), which employs Bayesian hierarchical estimation. In this new method, we set the scale at the population level, thereby allowing for individual and group differences, which is realized by fixing a population-level hyper-parameter through its priors. As proof of concept, we ran two successful parameter recovery studies using the Linear Ballistic Accumulation model. The results suggest that the new method can be reliably used to study individual and group differences using EAMs. We further show a case in which the new method reveals the true group differences whereas the classic method wrongly detects differences that are truly absent.
近年来，越来越多的研究使用证据积累模型（EAMs）来研究个体差异和群体效应。这项工作具有挑战性，因为拟合EAMs需要将其中一个EAM参数对所有参与者设定为相等，这做出了一个强有力且可能不太可能成立的假设。此外，如果这个假设被违反，可能会错误地发现差异或没有差异。为了克服这一局限性，在本研究中，我们引入了一种最初由范·马南和米莱蒂奇（2021年）提出的新方法，该方法采用贝叶斯层次估计。在这种新方法中，我们在总体水平上设定尺度，从而允许个体和群体差异，这是通过其先验固定一个总体水平的超参数来实现的。作为概念验证，我们使用线性弹道积累模型进行了两项成功的参数恢复研究。结果表明，新方法可以可靠地用于使用EAMs研究个体和群体差异。我们进一步展示了一个案例，其中新方法揭示了真正的群体差异，而经典方法错误地检测到了实际上不存在的差异。
