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简单玻璃图案感知中的形状与时间整合

Form and Temporal Integration in the Perception of Simple Glass Patterns.

作者信息

Donato Rita, Vicovaro Michele, Nucci Massimo, Roccato Marco, Campana Gianluca, Pavan Andrea

机构信息

Department of General Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy.

Human Inspired Technology Research Centre, University of Padova, Via Luzzati 4, 35121 Padova, Italy.

出版信息

Vision (Basel). 2025 Aug 4;9(3):69. doi: 10.3390/vision9030069.

DOI:10.3390/vision9030069
PMID:40843793
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12372061/
Abstract

This study presents a reanalysis of existing data to clarify how the visual system processes simple dynamic Glass patterns (GPs), with a particular focus on translational configurations. By combining datasets from previous studies, we apply a mixed-effects modeling approach-which offers advantages over the statistical methods used in previous studies-to investigate the contributions of pattern update rate and number of unique frames to perceptual sensitivity. Our findings indicate that the number of unique frames is the most robust predictor of discrimination thresholds, supporting the idea that the visual system integrates global form information across multiple frames-a process consistent with spatiotemporal summation. In contrast, the pattern update rate showed a weaker, though statistically significant, effect. This suggests that faster updates help preserve temporal consistency between frames, facilitating global form extraction. These results align with previous observations on complex dynamic GPs, where discrimination thresholds decrease with more unique frames, suggesting that the summation of form signals across time plays a key role in form-motion perception. By adopting a mixed-effects modeling approach, our reanalysis provides new insights into the mechanisms underlying global form perception in dynamic GPs.

摘要

本研究对现有数据进行了重新分析,以阐明视觉系统如何处理简单的动态格拉斯图案(GPs),特别关注平移配置。通过合并先前研究的数据集,我们应用了一种混合效应建模方法——该方法比先前研究中使用的统计方法具有优势——来研究图案更新率和独特帧数对感知灵敏度的影响。我们的研究结果表明,独特帧数是辨别阈值最可靠的预测指标,支持了视觉系统在多个帧上整合全局形状信息的观点——这一过程与时空总和一致。相比之下,图案更新率的影响较弱,尽管在统计学上具有显著性。这表明更快的更新有助于保持帧之间的时间一致性,促进全局形状提取。这些结果与先前关于复杂动态GPs的观察结果一致,即辨别阈值随着更多独特帧数的增加而降低,这表明跨时间的形状信号总和在形状运动感知中起关键作用。通过采用混合效应建模方法,我们的重新分析为动态GPs中全局形状感知的潜在机制提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/3ba1f1fbce17/vision-09-00069-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/6e8fb0d3de37/vision-09-00069-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/1423e5b57496/vision-09-00069-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/3ba1f1fbce17/vision-09-00069-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/6e8fb0d3de37/vision-09-00069-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/1423e5b57496/vision-09-00069-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dd5a/12372061/3ba1f1fbce17/vision-09-00069-g003.jpg

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