We agree with Blauch, Behrmann, and Plaut (2020) on a number of points, and are reassured that their data bear out our previous findings. We discuss differences in modelling style, and the usefulness of different types of model for supporting psychological understanding. We emphasise the role that within-person variability plays in recognising familiar faces and clarify the range over which it is idiosyncratic. The combination of image analysis with top-down support to cohere different images of the same person seems to be an important characteristic of successful models.

中文翻译：

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人脸识别计算模型的见解：对 Blauch、Behrmann 和 Plaut 的回复。

我们在许多观点上同意 Blauch、Behrmann 和 Plaut（2020）的观点，并且确信他们的数据证实了我们之前的发现。我们讨论建模风格的差异，以及不同类型模型对于支持心理理解的有用性。我们强调人体内的变异性在识别熟悉的面孔中所起的作用，并阐明它的特殊范围。将图像分析与自上而下的支持相结合以凝聚同一个人的不同图像似乎是成功模型的重要特征。