MODELING SYMMETRY DETECTION WITH BACKPROPAGATION NETWORKS
MODELING SYMMETRY DETECTION WITH BACKPROPAGATION NETWORKS
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DOI：
10.1163/156856894x00080
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发表时间：
1994-01-01
期刊：
影响因子：
--
通讯作者：
STEVENS, C
中科院分区：
文献类型：
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作者：
LATIMER, C;JOUNG, W;STEVENS, C
本文报告了一个小的6 × 6二进制模式的对称性检测项目的实验数据和网络模拟结果。图案关于垂直、水平、正倾斜或负倾斜轴对称，并在计算机屏幕上观察。受试者被鼓励快速准确地做出反应，通过按下四个指定键中的一个来指示对称轴。记录检测时间和错误。反向传播网络被训练为基于对称轴对模式进行分类，并且通过在其输出单元上采用级联激活函数，可以将网络性能与受试者的检测时间进行比较。在对垂直轴和水平轴对称的模式进行了更多的训练后，观察到模拟和人类检测时间函数之间的最佳对应关系。与没有预训练和预训练不对称模式相比，具有单个垂直，水平，正倾斜或负倾斜条的预训练网络加快了对称模式的后续学习。结果进行了讨论的背景下，理论表明，更快的检测对称性的垂直和水平轴可能是由于更早的经验，这些轴上的刺激取向。
This paper reports experimental data and results of network simulations in a project on symmetry detection in small 6 x 6 binary patterns. Patterns were symmetrical about the vertical, horizontal, positive-oblique, or negative-oblique axis, and were viewed on a computer screen. Encouraged to react quickly and accurately, subjects indicated axis of symmetry by pressing one of four designated keys. Detection times and errors were recorded. Back-propagation networks were trained to categorize the patterns on the basis of axis of symmetry, and, by employing cascaded activation functions on their output units, it was possible to compare network performance with subjects' detection times. Best correspondence between simulated and human detection-time functions was observed after the networks had been given significantly more training on patterns symmetrical about the vertical and the horizontal axes. In comparison with no pre-training and pre-training with asymmetric patterns, pre-training networks with sets of single vertical, horizontal, positive-oblique or negative-oblique bars speeded subsequent learning of symmetrical patterns. Results are discussed within the context of theories suggesting that faster detection of symmetries about the vertical and horizontal axes may be due to significantly more early experience with stimuli oriented on these axes.