An Electrophysiological Index of Perceptual Goodness

A. Makin, Damien Wright, Giulia Rampone, L. Palumbo, M. Guest, Rhiannon Sheehan, Helen Cleaver, Marco Bertamini
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引用次数: 52

Abstract

A traditional line of work starting with the Gestalt school has shown that patterns vary in strength and salience; a difference in “Perceptual goodness.” The Holographic weight of evidence model quantifies goodness of visual regularities. The key formula states that W = E/N, where E is number of holographic identities in a pattern and N is number of elements. We tested whether W predicts the amplitude of the neural response to regularity in an extrastriate symmetry-sensitive network. We recorded an Event Related Potential (ERP) generated by symmetry called the Sustained Posterior Negativity (SPN). First, we reanalyzed the published work and found that W explained most variance in SPN amplitude. Then in four new studies, we confirmed specific predictions of the holographic model regarding 1) the differential effects of numerosity on reflection and repetition, 2) the similarity between reflection and Glass patterns, 3) multiple symmetries, and 4) symmetry and anti-symmetry. In all cases, the holographic approach predicted SPN amplitude remarkably well; particularly in an early window around 300–400 ms post stimulus onset. Although the holographic model was not conceived as a model of neural processing, it captures many details of the brain response to symmetry.
感知良善的电生理指标
从格式塔学派开始的传统工作表明,模式的强度和显著性各不相同;“感性善良”的区别。全息证据权模型量化了视觉规律的良好性。关键公式是W = E/N,其中E是图案中全息身份的数量,N是元素的数量。我们测试了W是否能预测脑外对称敏感网络对规则性的神经反应幅度。我们记录了由对称性产生的事件相关电位(ERP),称为持续后验负性(SPN)。首先,我们重新分析了已发表的工作,发现W解释了SPN振幅的大部分方差。然后,在四项新的研究中,我们证实了全息模型的具体预测:1)数值对反射和重复的不同影响,2)反射和玻璃图案之间的相似性,3)多重对称性,以及4)对称和反对称。在所有情况下,全息方法都能很好地预测SPN振幅;特别是在刺激开始后300-400毫秒的早期窗口。虽然全息模型没有被设想为神经处理的模型,但它捕捉到了大脑对对称反应的许多细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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