Brain-like functional specialization emerges spontaneously in deep neural networks
The last quarter century of cognitive neuroscience has revealed numerous cortical regions in humans with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what other people are thinking. But 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 the computational requirements of the task. We find that networks trained on generic object recognition perform poorly on face recognition and vice versa, and further that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. Thus, generic visual features that suffice for object recognition are apparently suboptimal for face recognition and vice versa. 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.