Dobs Katharina, Martinez Julio, Kell Alexander J E, Kanwisher Nancy
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sci Adv. 2022 Mar 18;8(11):eabl8913. doi: 10.1126/sciadv.abl8913. Epub 2022 Mar 16.
The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.
人类大脑包含多个具有不同功能的区域，这些功能往往高度专业化，从识别面孔到理解语言，再到思考他人的想法。然而，目前尚不清楚大脑皮层为何一开始就表现出这种高度的功能专业化。在这里，我们通过人工神经网络来研究面部感知的情况，以检验以下假设：大脑中人脸识别的功能分离反映了针对更广泛的面部和其他视觉类别视觉识别问题的计算优化。我们发现，在物体识别任务上训练的网络在人脸识别任务中表现不佳，反之亦然，并且针对这两项任务进行优化的网络会自发地将自身分离成用于面部和物体识别的独立系统。然后，我们展示了其他视觉类别也存在不同程度的功能分离，这揭示了一种普遍趋势，即优化（没有内置的特定任务归纳偏差）会导致机器以及我们推测的大脑出现功能专业化。
