Wellcome Trust Centre for Neuroimaging, University College London, London, UK.
Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London, UK.
Nat Hum Behav. 2018 Sep;2(9):670-681. doi: 10.1038/s41562-018-0423-3. Epub 2018 Sep 7.
When confronted with complex inputs consisting of multiple elements, humans use various strategies to integrate the elements quickly and accurately. For instance, accuracy may be improved by processing elements one at a time or over extended periods; speed can increase if the internal representation of elements is accelerated. However, little is known about how humans actually approach these challenges because behavioural findings can be accounted for by multiple alternative process models and neuroimaging investigations typically rely on haemodynamic signals that change too slowly. Consequently, to uncover the fast neural dynamics that support information integration, we decoded magnetoencephalographic signals that were recorded as human subjects performed a complex decision task. Our findings reveal three sources of individual differences in the temporal structure of the integration process-sequential representation, partial reinstatement and early computation-each having a dissociable effect on how subjects handled problem complexity and temporal constraints. Our findings shed new light on the structure and influence of self-determined neural integration processes.
当面对由多个元素组成的复杂输入时,人类会使用各种策略来快速、准确地整合这些元素。例如,逐个或长时间处理元素可以提高准确性;如果加速元素的内部表示,速度可以提高。然而,由于行为学发现可以用多种替代的过程模型来解释,并且神经影像学研究通常依赖于变化太慢的血流动力学信号,因此,人们对人类实际上如何应对这些挑战知之甚少。为了揭示支持信息整合的快速神经动力学,我们对人类受试者在执行复杂决策任务时记录的脑磁图信号进行了解码。我们的研究结果揭示了整合过程中个体差异的三个来源——顺序表示、部分恢复和早期计算——它们对被试如何处理问题复杂性和时间约束具有不同的影响。我们的研究结果为自主神经整合过程的结构和影响提供了新的视角。