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在不同强度和延迟条件下的视听检测。

Audiovisual detection at different intensities and delays.

作者信息

Chandrasekaran Chandramouli, Blurton Steven P, Gondan Matthias

机构信息

Department of Electrical Engineering, Stanford University, USA.

Howard Hughes Medical Institute, Stanford University, USA.

出版信息

J Math Psychol. 2019 Aug;91:159-175. doi: 10.1016/j.jmp.2019.05.001. Epub 2019 Jul 2.

DOI:10.1016/j.jmp.2019.05.001
PMID:31404455
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6688765/
Abstract

In the redundant signals task, two target stimuli are associated with the same response. If both targets are presented together, redundancy gains are observed, as compared with single-target presentation. Different models explain these redundancy gains, including race and coactivation models (e.g., the Wiener diffusion superposition model, Schwarz, 1994, Journal of Mathematical Psychology, and the Ornstein Uhlenbeck diffusion superposition model, Diederich, 1995, Journal of Mathematical Psychology). In the present study, two monkeys performed a simple detection task with auditory, visual and audiovisual stimuli of different intensities and onset asynchronies. In its basic form, a Wiener diffusion superposition model provided only a poor description of the observed data, especially of the detection rate (i.e., accuracy or hit rate) for low stimulus intensity. We expanded the model in two ways, by (A) adding a temporal deadline, that is, restricting the evidence accumulation process to a stopping time, and (B) adding a second "nogo" barrier representing target absence. We present closed-form solutions for the mean absorption times and absorption probabilities for a Wiener diffusion process with a drift towards a single barrier in the presence of a temporal deadline (A), and numerically improved solutions for the two-barrier model (B). The best description of the data was obtained from the deadline model and substantially outperformed the two-barrier approach.

摘要

在冗余信号任务中,两个目标刺激与相同的反应相关联。如果两个目标同时呈现,与单目标呈现相比,会观察到冗余增益。不同的模型解释了这些冗余增益,包括竞争模型和共同激活模型(例如,维纳扩散叠加模型,施瓦茨,1994年,《数学心理学杂志》,以及奥恩斯坦 - 乌伦贝克扩散叠加模型,迪德里希,1995年,《数学心理学杂志》)。在本研究中,两只猴子对不同强度和起始异步性的听觉、视觉和视听刺激执行了一个简单的检测任务。在其基本形式中,维纳扩散叠加模型对观察到的数据,尤其是低刺激强度下的检测率(即准确率或命中率),提供的描述很差。我们通过两种方式扩展了该模型:(A)添加一个时间期限,即把证据积累过程限制在一个停止时间内;(B)添加一个代表目标不存在的第二个“否”屏障。我们给出了在存在时间期限的情况下,朝着单个屏障漂移的维纳扩散过程的平均吸收时间和吸收概率的闭式解(A)以及双屏障模型的数值改进解(B)。从期限模型获得了对数据的最佳描述,并且其表现明显优于双屏障方法。

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Dynamic Multisensory Integration: Somatosensory Speed Trumps Visual Accuracy during Feedback Control.动态多感官整合:在反馈控制过程中,体感速度胜过视觉准确性。
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