Predictions drive neural representations of visual events ahead of incoming sensory information

Predictions drive neural representations of visual events ahead of incoming sensory information
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DOI:
10.1073/pnas.1917777117
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发表时间:
2020-03-31
影响因子:
11.1
通讯作者:
Hogendoorn, Hinze
Hogendoorn, Hinze
中科院分区:
综合性期刊1区
文献类型:
--
作者:
Blom, Tessel;Feuerriegel, Daniel;Hogendoorn, Hinze

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通过视觉系统传递感官信息需要时间。由于这些延迟,大脑获得的视觉信息总是滞后于当前事件的时间。补偿这些延迟对于在动态环境中运行至关重要,因为与移动对象(例如,接球)需要对物体进行实时定位。大脑可能实现这一目标的一种方法是通过预测预期的事件。使用时间分辨解码的脑电图(EEG)数据,我们表明,视觉系统代表了预期的未来位置的移动对象,表明预测机制激活相同的神经表征传入感觉输入。重要的是,这种激活在对应于刺激位置的感觉输入能够到达之前是明显的。最后,我们证明,当预测的事件没有发生,感官信息到达太晚,以防止视觉系统代表什么是预期的,但从来没有提出。总之,我们展示了视觉系统如何实现预测机制,预先激活感官表征,并认为这可能使它能够补偿自己的时间限制,使我们能够在真实的时间与动态视觉环境进行交互。
The transmission of sensory information through the visual system takes time. As a result of these delays, the visual information available to the brain always lags behind the timing of events in the present moment. Compensating for these delays is crucial for functioning within dynamic environments, since interacting with a moving object (e.g., catching a ball) requires real-time localization of the object. One way the brain might achieve this is via prediction of anticipated events. Using time-resolved decoding of electroencephalographic (EEG) data, we demonstrate that the visual system represents the anticipated future position of a moving object, showing that predictive mechanisms activate the same neural representations as afferent sensory input. Importantly, this activation is evident before sensory input corresponding to the stimulus position is able to arrive. Finally, we demonstrate that, when predicted events do not eventuate, sensory information arrives too late to prevent the visual system from representing what was expected but never presented. Taken together, we demonstrate how the visual system can implement predictive mechanisms to preactivate sensory representations, and argue that this might allow it to compensate for its own temporal constraints, allowing us to interact with dynamic visual environments in real time.