Biasing influences on the motor system during action preparation: a multimodal neuroimaging-computationally informed approach

在生活的几乎每一个方面，适当的行为首先需要处理传入的感官信息。然后，我们必须权衡其相关性，在不同的运动之间做出决定，并选择最合适的行为。例如，面对点球的守门员需要观察球员和球，根据经验和球员的姿势来估计球的可能轨迹，并最终决定朝哪个方向尽可能快地移动。因此，我们使用我们的经验来准备适当的动作，但我们仍然不太清楚过去的经验等信息是如何进入我们的实际动作的。先前的研究表明，在解剖学上和时间上，大脑中潜在的感知和运动过程是重叠的。然而，大脑活动模式、规则和计算在不同的大脑区域之间交换，以实现这种影响，在很大程度上仍然是不确定的。我的建议的中心目标是揭示不同的大脑区域如何与运动系统相互作用，以利用过去的经验和先验信息进行运动选择。在我的实验中，参与者根据视觉线索准备动作。这些线索表明，当随后的视觉信号出现时，需要哪些动作（例如按下按钮）。当线索可靠地预测所需的动作时，反应就会很快。这表明我们利用我们对视觉信息可靠性的经验来有效地准备动作。通过改变这些视觉线索的有效性，人们可以改变对将要执行的动作的不确定性。通过这种方式，我们可以解决我们对世界的体验如何影响我们的行为和大脑的问题。首先，在这些实验中，非侵入性脑刺激将测量运动系统在运动准备过程中的激活状态。这种引起大脑活动的安全方法提供了对大脑区域状态的直接读出。然后我将把大脑刺激和神经成像结合起来。我最近开发了一种综合技术，可以测量大脑刺激对整个大脑活动的影响，具有很高的解剖学精度。参与准备运动的大脑区域将受到刺激。与此同时，神经成像将测量大脑相关区域的活动变化。这将揭示与基于感官经验做出运动相关的大脑区域，并揭示它们之间的影响，这些影响是将我们的经验与我们的行动结合在一起所必需的。最后，脑磁图无创检测人脑电信号，并将根据经验和过去的信息揭示在准备运动时大脑活动的时间过程。似乎很明显，大脑利用过去的信息来指导我们未来的行动。然而，很少有研究正式地试图量化我们对感觉信息的体验如何随着时间的推移塑造运动系统。人们可以通过使用简单的数学模型来量化这种体验。这些提供了所谓的计算信息时间进化的经验表征，可以应用于测试相应的大脑活动变化。使用这样的计算信息表征，例如，关于视觉信息的不确定性，是令人兴奋的，因为它克服了过去100年来困扰认知心理学的一个核心限制：大脑的实际计算对我们来说是隐藏的，只有通过形式表征，我们才能理解大脑为在不确定的世界中有效地指定运动而修饰的过程。

In almost every aspect of life, adequate behavior first requires the processing of incoming sensory information. We then must weigh its relevance, decide between alternative movements, and select the most appropriate behavior. For example, a goalkeeper facing a penalty kick needs to observe player and ball, estimate the likely trajectory of the ball based on experience and postural cues from the player, and finally decide in which direction to move as fast as possible. We therefore use our experience for preparing the appropriate movements, yet it is still poorly understood how information such as past experience gets funnelled into our actual movements. Previous research suggests that the processes underlying perception and movement overlap in the brain, both anatomically and in time. However, the brain activity patterns, rules and computations exchanged by different brain regions to accomplish this influence remain largely undetermined. The central goal of my proposal is to reveal how different brain regions interact with the motor system to use past experience and prior information for movement selection. In my experiments, participants prepare movements based on visual cues. These cues indicate which movement is required (e.g. button presses) when a subsequent visual go-signal is presented. When cues are reliably predicting the required movement, responses are fast. This suggests that we use our experience about the reliability of visual information available to us to prepare movements efficiently. By changing the validity of these visual cues, one can change the uncertainty about which movement will have to be performed. In this way, we can address the question how our experience about the world influences our actions, and our brain. First, non-invasive brain stimulation will measure the activation state of the motor system during movement preparation in these experiments. This safe method for causing brain activity provides a direct read-out of the state of a brain region. I will then combine this brain stimulation with neuroimaging. I have recently developed this combined technique which allows for measuring the impact of brain stimulation on activity across the entire brain with high anatomical precision. Brain regions involved in preparing movements will be stimulated. At the same time, neuroimaging will measure the resulting activity changes in connected brain regions. This will reveal the brain regions relevant for making movements based on sensory experience, and disclose the influences among them which are required to bind together our experience with our actions. Finally, Magnetoencephalography non-invasively detects electrical brain signals in humans, and will reveal the timecourse of brain activity during preparation for movements based on experience and past information. It may seem obvious that the brain uses past information to guide our future movements. However, few studies have formally tried to quantify how our experience about sensory information shapes the motor system over time. One can quantify this experience by using simple mathematical models. These provide so-called computationally informed time-evolving representations of experience, and can be applied to test for corresponding brain activity changes. Using such computationally informed representations, for example, about the uncertainty of visual information, is exciting because it overcomes one of the central limitations that has bedevilled cognitive psychology for the past 100 years: the actual computations of the brain are hidden to us, and only by formal representation can we understand the processes that the brain embellishes for specifying movements efficiently in an uncertain world.