美国匹兹堡大学William R. Stauffer研究小组发现，计算复杂度导致持续的思考。2023年4月24日出版的《自然—神经科学》发表了这项成果。

研究人员利用两个非人灵长类动物执行组合优化任务，以识别有价值的子集并满足预定义约束。它们行为揭示了组合推理的证据，当考虑一个项目的低复杂度算法能提供最佳解决方案时，动物会采取低复杂度推理策略。当需要更多计算资源时，动物们则采用寻找最佳组合的高复杂度算法。思考时间反映了计算复杂性带来的要求——高复杂度算法需要更多的操作，随之而来的是动物所需思考时间的增加。

模仿低复杂度和高复杂度算法的递归神经网络也反映了行为思考时间，并用于支持经济思考的特定算法计算。这些发现揭示了基于算法推理的证据，并为研究持续思考神经生理学基础建立了范本。

据悉，经济思考是缓慢、费力和有意识地寻找解决困难经济问题的办法。尽管这种考虑对于做出正确的决策至关重要，但对潜在的推理策略和神经生物学机制仍然知之甚少。

附：英文原文

Title: Computational complexity drives sustained deliberation

Author: Hong, Tao, Stauffer, William R.

Issue&Volume: 2023-04-24

Abstract: Economic deliberations are slow, effortful and intentional searches for solutions to difficult economic problems. Although such deliberations are critical for making sound decisions, the underlying reasoning strategies and neurobiological substrates remain poorly understood. Here two nonhuman primates performed a combinatorial optimization task to identify valuable subsets and satisfy predefined constraints. Their behavior revealed evidence of combinatorial reasoning—when low-complexity algorithms that consider items one at a time provided optimal solutions, the animals adopted low-complexity reasoning strategies. When greater computational resources were required, the animals approximated high-complexity algorithms that search for optimal combinations. The deliberation times reflected the demands created by computational complexity—high-complexity algorithms require more operations and, concomitantly, the animals deliberated for longer durations. Recurrent neural networks that mimicked low- and high-complexity algorithms also reflected the behavioral deliberation times and were used to reveal algorithm-specific computations that support economic deliberation. These findings reveal evidence for algorithm-based reasoning and establish a paradigm for studying the neurophysiological basis for sustained deliberation.

DOI: 10.1038/s41593-023-01307-6

Source: https://www.nature.com/articles/s41593-023-01307-6