用自组织神经网络映射检测人体对称性不确定性
Human Symmetry Uncertainty Detected by a Self-Organizing Neural Network Map
摘要：
生物和物理系统中的对称性是由进化过程驱动的自组织的产物，或者是受约束的机械系统。基于对称性的特征提取或神经网络表示可以揭示大型图像数据库中最具信息量的内容。尽管人工智能在规则模式的识别和分类方面取得了重大成就，但不确定性问题仍然是模糊数据的主要挑战。在这项研究中，我们提出了一个人工神经网络，检测人类观察员的对称性不确定性状态。为此，我们利用一个神经网络度量的生物启发自组织映射的输出，量化误差（SOM QE）。具有完美几何镜像对称但非均匀外观的形状对，由给定对中的形状内或跨形状的色调、饱和度或亮度的局部变化引起，如这里所示，相对于对称性，对是响应的较长选择RT。这些数据一致地反映在SOM QE的变化，从无监督的神经网络分析相同的刺激图像。因此，神经网络度量能够响应于模式来检测和缩放人类对称性不确定性。这种能力与度量紧密相关，这些度量已被证明对大型和高度复杂的图像数据中的局部对比度和颜色变化具有选择性。
英文摘要：
Symmetry in biological and physical systems is a product of self organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry based feature extrac-tion or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To this end, we exploit a neural network metric in the output of a biologically inspired Self Organizing Map, the Quantization Error (SOM QE). Shape pairs with perfect geometric mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within or across the shapes in a given pair produce, as shown here, longer choice RT for yes responses relative to symmetry. These data are consistently mirrored by the variations in the SOM QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metrics proven selectivity to local contrast and color variations in large and highly complex image data.
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