Suppr超能文献

刺激和行为历史对平滑追踪眼动预测控制的影响。

The influence of stimulus and behavioral histories on predictive control of smooth pursuit eye movements.

机构信息

Faculty of Health and Sport Sciences, University of Tsukuba, Ibaraki, Japan.

Department of Robotic Science and Technology, Chubu University College of Engineering, Kasugai, Japan.

出版信息

Sci Rep. 2021 Nov 16;11(1):22327. doi: 10.1038/s41598-021-01733-1.

Abstract

The smooth pursuit system has the ability to perform predictive feedforward control of eye movements. This study attempted to examine how stimulus and behavioral histories of past trials affect the control of predictive pursuit of target motion with randomized velocities. We used sequential ramp stimuli where the rightward velocity was fixed at 16 deg/s while the leftward velocity was either fixed (predictable) at one of seven velocities (4, 8, 12, 16, 20, 24, or 28 deg/s) or randomized (unpredictable). As a result, predictive pursuit responses were observed not only in the predictable condition but also in the unpredictable condition. Linear mixed-effects (LME) models showed that both stimulus and behavioral histories of the previous two or three trials influenced the predictive pursuit responses in the unpredictable condition. Intriguingly, the goodness of fit of the LME model was improved when both historical effects were fitted simultaneously rather than when each type of historical data was fitted alone. Our results suggest that predictive pursuit systems allow us to track randomized target motion using weighted averaging of the information of target velocity (stimulus) and motor output (behavior) in past time sequences.

摘要

平滑追踪系统具有对眼球运动进行预测性前馈控制的能力。本研究试图探讨在目标运动的速度随机化的情况下,过去试验的刺激和行为历史如何影响预测性追踪的控制。我们使用了顺序斜坡刺激,其中向右的速度固定为 16°/s,而向左的速度则固定在七个速度之一(4、8、12、16、20、24 或 28°/s)或随机化(不可预测)。结果,不仅在可预测条件下观察到了预测性追踪反应,而且在不可预测条件下也观察到了预测性追踪反应。线性混合效应(LME)模型表明,前两个或三个试验的刺激和行为历史都影响了不可预测条件下的预测性追踪反应。有趣的是,当同时拟合两种历史效应时,而不是单独拟合每种类型的历史数据时,LME 模型的拟合优度得到了提高。我们的结果表明,预测性追踪系统允许我们使用过去时间序列中目标速度(刺激)和运动输出(行为)的信息进行加权平均,从而跟踪随机化的目标运动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdaf/8595731/dfa5922be8f0/41598_2021_1733_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验