Janelia Research Campus, 19700 Helix Dr., Ashburn, VA 20147, USA.
Department of Brain and Cognitive Sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA.
Curr Opin Neurobiol. 2016 Apr;37:99-105. doi: 10.1016/j.conb.2016.01.014. Epub 2016 Feb 11.
Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships - all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation.
尽管神经科学取得了重大进展，但智力的神经基础仍未得到很好的理解。可以说，智力最难以捉摸的方面是能够做出超出个人经验的稳健推断的能力。动物能够对物体进行分类，学会发声，甚至可以估计因果关系——所有这些都是在面对经常模棱两可和稀疏的数据的情况下进行的。这种归纳跳跃被认为是大脑推断出控制环境的潜在结构的能力的结果。然而，我们对支持有效结构学习和归纳推理的神经计算知之甚少。最近在开发计算框架方面取得的进展，这些框架可以支持有效的结构学习和归纳推理，可能会为理解潜在的组成过程提供一些启示，并为揭示它们的神经实现铺平道路。
