Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany,
Department of Psychology.
J Neurosci. 2018 Aug 29;38(35):7600-7610. doi: 10.1523/JNEUROSCI.0307-18.2018. Epub 2018 Jul 20.
Learning the statistical structure of the environment is crucial for adaptive behavior. Humans and nonhuman decision-makers seem to track such structure through a process of probabilistic inference, which enables predictions about behaviorally relevant events. Deviations from such predictions cause surprise, which in turn helps improve inference. Surprise about the timing of behaviorally relevant sensory events drives phasic responses of neuromodulatory brainstem systems, which project to the cerebral cortex. Here, we developed a computational model-based magnetoencephalography (MEG) approach for mapping the resulting cortical transients across space, time, and frequency, in the human brain ( = 28, 17 female). We used a Bayesian ideal observer model to learn the statistics of the timing of changes in a simple visual detection task. This model yielded quantitative trial-by-trial estimates of temporal surprise. The model-based surprise variable predicted trial-by-trial variations in reaction time more strongly than the externally observable interval timings alone. Trial-by-trial variations in surprise were negatively correlated with the power of cortical population activity measured with MEG. This surprise-related power suppression occurred transiently around the behavioral response, specifically in the beta frequency band. It peaked in parietal and prefrontal cortices, remote from the motor cortical suppression of beta power related to overt report (button press) of change detection. Our results indicate that surprise about sensory event timing transiently suppresses ongoing beta-band oscillations in association cortex. This transient suppression of frontal beta-band oscillations might reflect an active reset triggered by surprise, and is in line with the idea that beta-oscillations help maintain cognitive sets. The brain continuously tracks the statistical structure of the environment to anticipate behaviorally relevant events. Deviations from such predictions cause surprise, which in turn drives neural activity in subcortical brain regions that project to the cerebral cortex. We used magnetoencephalography in humans to map out surprise-related modulations of cortical population activity across space, time, and frequency. Surprise was elicited by variable timing of visual stimulus changes requiring a behavioral response. Surprise was quantified by means of an ideal observer model. Surprise predicted behavior as well as a transient suppression of beta frequency-band oscillations in frontal cortical regions. Our results are in line with conceptual accounts that have linked neural oscillations in the beta-band to the maintenance of cognitive sets.
学习环境的统计结构对于适应性行为至关重要。人类和非人类决策者似乎通过概率推理过程来跟踪这种结构,从而对行为相关事件进行预测。对这些预测的偏差会引起惊讶,而惊讶反过来又有助于提高推理能力。对行为相关感官事件时间的惊讶会引起调制脑干系统的相位反应,这些系统投射到大脑皮层。在这里,我们开发了一种基于计算模型的脑磁图 (MEG) 方法,用于在人类大脑中(=28,17 名女性)跨空间、时间和频率映射由此产生的皮质瞬变。我们使用贝叶斯理想观察者模型来学习简单视觉检测任务中变化时间的统计信息。该模型产生了逐次试验的时间惊喜的定量估计。基于模型的惊喜变量比单独观察到的外部间隔时间更能预测逐次试验的反应时间变化。惊喜的逐次试验变化与用 MEG 测量的皮质群体活动的功率呈负相关。这种与惊喜相关的功率抑制在行为反应周围呈瞬态发生,特别是在β频带中。它在顶叶和前额叶皮层中达到峰值,远离与改变检测的明显报告(按钮按压)相关的运动皮质中β功率的抑制。我们的结果表明,对感官事件时间的惊讶会暂时抑制关联皮层中正在进行的β波段振荡。这种与惊讶相关的额β波段振荡的短暂抑制可能反映了由惊讶触发的主动重置,这与β振荡有助于维持认知集的观点一致。大脑不断跟踪环境的统计结构,以预测行为相关事件。对这些预测的偏差会引起惊讶,这反过来又会驱动投射到大脑皮层的皮质下脑区的神经活动。我们在人类中使用脑磁图来绘制出跨空间、时间和频率的皮质群体活动与惊讶相关的调制。通过需要行为反应的视觉刺激变化的可变定时来引发惊讶。通过理想观察者模型来量化惊讶。惊讶不仅预测了行为,还预测了额叶皮质区域β频带振荡的短暂抑制。我们的结果与将β频带中的神经振荡与认知集的维持联系起来的概念解释一致。