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漂移扩散模型解释了反应的可变性和跟踪物体的能力。

Drift-diffusion explains response variability and capacity for tracking objects.

机构信息

Biomedical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

Electrical Engineering Department and Brain Research Center, Sharif University of Technology, Tehran, Iran.

出版信息

Sci Rep. 2019 Aug 2;9(1):11224. doi: 10.1038/s41598-019-47624-4.

Abstract

Being able to track objects that surround us is key for planning actions in dynamic environments. However, rigorous cognitive models for tracking of one or more objects are currently lacking. In this study, we asked human subjects to judge the time to contact (TTC) a finish line for one or two objects that became invisible shortly after moving. We showed that the pattern of subject responses had an error variance best explained by an inverse Gaussian distribution and consistent with the output of a biased drift-diffusion model. Furthermore, we demonstrated that the pattern of errors made when tracking two objects showed a level of dependence that was consistent with subjects using a single decision variable for reporting the TTC for two objects. This finding reveals a serious limitation in the capacity for tracking multiple objects resulting in error propagation between objects. Apart from explaining our own data, our approach helps interpret previous findings such as asymmetric interference when tracking multiple objects.

摘要

能够跟踪我们周围的物体对于在动态环境中规划动作至关重要。然而,目前缺乏用于跟踪一个或多个物体的严格认知模型。在这项研究中,我们要求人类受试者判断一个或两个物体的接触时间(TTC),这些物体在移动后不久就会消失。我们发现,受试者反应的模式具有最佳的误差方差,可以用逆高斯分布来解释,并且与偏向漂移扩散模型的输出一致。此外,我们还证明,在跟踪两个物体时出现的错误模式显示出与受试者使用单个决策变量报告两个物体 TTC 的依赖程度一致。这一发现揭示了跟踪多个物体的能力存在严重限制,导致物体之间的误差传播。除了解释我们自己的数据外,我们的方法还有助于解释之前的发现,例如在跟踪多个物体时出现的不对称干扰。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f008/6677806/6341ea7767fe/41598_2019_47624_Fig1_HTML.jpg

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