Karlaftis Vasilis M, Giorgio Joseph, Vértes Petra E, Wang Rui, Shen Yuan, Tino Peter, Welchman Andrew E, Kourtzi Zoe
Department of Psychology, University of Cambridge, Cambridge, UK.
Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK.
Nat Hum Behav. 2019 Mar 1;3:297-307. doi: 10.1038/s41562-018-0503-4.
Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.
成功的人类行为取决于大脑从信息流中提取有意义结构并对未来事件进行预测的能力。个体在用于了解环境统计信息的决策策略上可能存在显著差异，但我们对此知之甚少。在这里，我们研究了在没有明确奖励的情况下参与学习时间序列的大脑网络是否因个体采用的决策策略而异。我们证明，个体根据时间统计的变化改变其决策策略，并参与不同的神经回路：提取确切的序列统计与运动皮质纹状体回路的可塑性有关，而选择最可能的结果与视觉、动机和执行皮质纹状体回路的可塑性有关。结合功能和结构连接性的图形指标，我们提供证据表明这些回路中依赖学习的变化可预测个体决策策略。我们的研究结果提出了介导个体解释可变环境结构能力的大脑可塑性机制。
