The relationship between artificial entities’ human likeness and aesthetic preference is thought to be best modelled by an -shaped cubic “uncanny valley” function, which however suffers from conceptual criticisms and lack of parsimony. Here it is argued that uncanniness effects may instead be modelled by a linear function of deviation moderated by perceptual specialization. The two models are compared in an experiment with five incrementally distorted face types (cartoon, CG, drawing, real, robot). Recognition performance for upright and inverted faces were used as a specialization measure. Specialization significantly moderated the linear effect of distortion on uncanniness, and could explain the data better than a conventional uncanny valley. The uncanny valley may thus be better understood as a moderated linear function of specialization sensitizing the uncanniness of deviating stimuli. This simpler yet more accurate model is compatible with neurocognitive theories and can explain uncanniness effects beyond the conventional uncanny valley.

中文翻译：

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重新思考恐怖谷作为一个有调节的线性函数：感知专业化增加了面部扭曲的恐怖程度

人造实体的人类相似性和审美偏好之间的关系被认为最好通过形状立方“恐怖谷”函数来建模，然而，该函数受到概念批评和缺乏简约性的困扰。这里有人认为，恐怖效应可以通过由知觉专业化调节的偏差线性函数来建模。在实验中使用五种增量扭曲的面部类型（卡通、CG、绘画、真实、机器人）对这两个模型进行了比较。直立和倒立面部的识别性能被用作专业化衡量标准。专业化显着缓和了扭曲对恐怖的线性影响，并且可以比传统的恐怖谷更好地解释数据。因此，恐怖谷可以更好地理解为专业化的调节线性函数，使偏离刺激的恐怖变得敏感。这个更简单但更准确的模型与神经认知理论兼容，可以解释传统恐怖谷之外的恐怖效应。