Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning
Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning
复制标题
DOI：
10.1038/s41562-018-0503-4
复制
发表时间：
2019-03-01
影响因子：
29.9
通讯作者：
Kourtzi，Zoe
中科院分区：
文献类型：
--
作者：
Karlaftis，Vasilis M.;Giorgio，Joseph;Kourtzi，Zoe
成功的人类行为取决于大脑从信息流中提取有意义的结构并对未来事件做出预测的能力。每个人用于了解环境统计数据的决策策略可能存在显着差异，但我们不知道其中的原因。在这里，我们研究了在没有明确奖励的情况下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，个体会根据时间统计的变化而改变决策策略，并参与可分离的回路：提取与运动皮质纹状体回路的可塑性相关的精确序列统计数据，同时选择与视觉、动机和执行皮质纹状体回路的可塑性相关的最可能的结果。结合功能和结构连接的图形指标，我们提供了证据表明这些回路中依赖于学习的变化可以预测个体决策策略。我们的研究结果提出了大脑可塑性机制，可以调节个体解释可变环境结构的能力。
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.