用于处理面的链接聚合代码（修订版）
A Linked Aggregate Code for Processing Faces (Revised Version)
摘要：
一个模型的脸表示，启发的生物学的视觉系统，比较实验数据的感知面部相似性。面部表示模型使用聚集的初级视觉皮层（V1）细胞反应，拓扑连接到覆盖面部的网格，允许在两个面部图像的相应点的形状和纹理的比较。当一组相对相似的面孔被用作刺激时，这种链接聚合代码（LAC）预测人类在相似性判断实验中的表现。当使用可感知类别的面孔时，诸如明显的性别和种族等维度出现在未经训练的LAC模型中。混合类别任务的LAC相似性度量的维度结构显示了一些心理上合理的特征，但也突出了模型与人类相似性判断之间的差异。人类的判断表现出种族知觉偏见，这是不共享的LAC模型。结果表明，LAC为基础的相似性度量可以提供一个肥沃的起点，进一步建模研究的脸表示在更高的视觉领域，包括研究的发展偏见，在面对的看法。
英文摘要：
A model of face representation, inspired by the biology of the visual system, is compared to experimental data on the perception of facial similarity. The face representation model uses aggregate primary visual cortex (V1) cell responses topographically linked to a grid covering the face, allowing comparison of shape and texture at corresponding points in two facial images. When a set of relatively similar faces was used as stimuli, this Linked Aggregate Code (LAC) predicted human performance in similarity judgment experiments. When faces of perceivable categories were used, dimensions such as apparent sex and race emerged from the LAC model without training. The dimensional structure of the LAC similarity measure for the mixed category task displayed some psychologically plausible features but also highlighted differences between the model and the human similarity judgements. The human judgements exhibited a racial perceptual bias that was not shared by the LAC model. The results suggest that the LAC based similarity measure may offer a fertile starting point for further modelling studies of face representation in higher visual areas, including studies of the development of biases in face perception.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
