Voskuilen Chelsea, Ratcliff Roger, Smith Philip L
The Ohio State University, United States.
The University of Melbourne, Australia.
J Math Psychol. 2016 Aug;73:59-79. doi: 10.1016/j.jmp.2016.04.008. Epub 2016 May 24.
Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsing boundaries provides a better account of human data than a model with fixed boundaries. We compared the models using data from four new numerosity discrimination experiments and two previously published motion discrimination experiments. When model selection was based on BIC values, the fixed boundary model was preferred over the collapsing boundary model for all of the experiments. When model selection was carried out using a parametric bootstrap cross-fitting method (PBCM), which takes into account the flexibility of the alternative models and the ability of one model to account for data from another model, data from 5 of 6 experiments favored either fixed boundaries or boundaries with only negligible collapse. We found that the collapsing boundary model produces response times distributions with the same shape as those produced by the fixed boundary model and that its parameters were not well-identified and were difficult to recover from data. Furthermore, the estimated boundaries of the best-fitting collapsing boundary model were relatively flat and very similar to those of the fixed-boundary model. Overall, a diffusion model with decision boundaries that converge over time does not provide an improvement over the standard diffusion model for our tasks with human data.
对猴子的最优性研究和决策研究已被用于支持一种模型，在该模型中，用于评估证据的决策边界会随时间崩溃。本文研究了具有崩溃边界的扩散模型是否比具有固定边界的模型能更好地解释人类数据。我们使用来自四个新的数字辨别实验和两个先前发表的运动辨别实验的数据对模型进行了比较。当基于贝叶斯信息准则（BIC）值进行模型选择时，在所有实验中，固定边界模型都比崩溃边界模型更受青睐。当使用参数自举交叉拟合方法（PBCM）进行模型选择时，该方法考虑了替代模型的灵活性以及一个模型解释另一个模型数据的能力，6个实验中的5个实验的数据支持固定边界或仅有可忽略崩溃的边界。我们发现，崩溃边界模型产生的反应时间分布形状与固定边界模型产生的相同，并且其参数无法很好地识别，难以从数据中恢复。此外，拟合效果最佳的崩溃边界模型的估计边界相对平坦，与固定边界模型的边界非常相似。总体而言，对于我们涉及人类数据的任务，具有随时间收敛的决策边界的扩散模型并未比标准扩散模型有改进。
