Chennu Srivas, Noreika Valdas, Gueorguiev David, Shtyrov Yury, Bekinschtein Tristan A, Henson Richard
School of Computing, University of Kent, Chatham Maritime ME4 4AG, United Kingdom, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, United Kingdom,
Medical Research Council Cognition and Brain Sciences Unit, Cambridge CB2 7EF, United Kingdom.
J Neurosci. 2016 Aug 10;36(32):8305-16. doi: 10.1523/JNEUROSCI.1125-16.2016.
There is increasing evidence that human perception is realized by a hierarchy of neural processes in which predictions sent backward from higher levels result in prediction errors that are fed forward from lower levels, to update the current model of the environment. Moreover, the precision of prediction errors is thought to be modulated by attention. Much of this evidence comes from paradigms in which a stimulus differs from that predicted by the recent history of other stimuli (generating a so-called "mismatch response"). There is less evidence from situations where a prediction is not fulfilled by any sensory input (an "omission" response). This situation arguably provides a more direct measure of "top-down" predictions in the absence of confounding "bottom-up" input. We applied Dynamic Causal Modeling of evoked electromagnetic responses recorded by EEG and MEG to an auditory paradigm in which we factorially crossed the presence versus absence of "bottom-up" stimuli with the presence versus absence of "top-down" attention. Model comparison revealed that both mismatch and omission responses were mediated by increased forward and backward connections, differing primarily in the driving input. In both responses, modeling results suggested that the presence of attention selectively modulated backward "prediction" connections. Our results provide new model-driven evidence of the pure top-down prediction signal posited in theories of hierarchical perception, and highlight the role of attentional precision in strengthening this prediction.
Human auditory perception is thought to be realized by a network of neurons that maintain a model of and predict future stimuli. Much of the evidence for this comes from experiments where a stimulus unexpectedly differs from previous ones, which generates a well-known "mismatch response." But what happens when a stimulus is unexpectedly omitted altogether? By measuring the brain's electromagnetic activity, we show that it also generates an "omission response" that is contingent on the presence of attention. We model these responses computationally, revealing that mismatch and omission responses only differ in the location of inputs into the same underlying neuronal network. In both cases, we show that attention selectively strengthens the brain's prediction of the future.
越来越多的证据表明，人类感知是通过神经过程的层次结构实现的，其中从较高层次向后发送的预测会导致预测误差，这些误差从较低层次向前馈送，以更新当前的环境模型。此外，预测误差的精度被认为受注意力调节。这些证据大多来自这样的范式，即一个刺激与其他刺激的近期历史所预测的刺激不同（产生所谓的“失配反应”）。而在没有任何感觉输入实现预测的情况下（“遗漏”反应），证据较少。在没有混淆的“自下而上”输入的情况下，这种情况可以说是对“自上而下”预测的更直接测量。我们将脑电图（EEG）和脑磁图（MEG）记录的诱发电磁反应的动态因果模型应用于一种听觉范式，在该范式中，我们将“自下而上”刺激的存在与否与“自上而下”注意力的存在与否进行了析因交叉。模型比较表明，失配反应和遗漏反应均由增加的前向和后向连接介导，主要区别在于驱动输入。在这两种反应中，建模结果表明注意力的存在选择性地调节了后向“预测”连接。我们的结果为层次感知理论中假设的纯自上而下预测信号提供了新的模型驱动证据，并突出了注意力精度在加强这种预测中的作用。
人类听觉感知被认为是由一个神经元网络实现的，该网络维持一个模型并预测未来的刺激。对此的许多证据来自于这样的实验，即一个刺激意外地与之前的刺激不同，这会产生一个著名的“失配反应”。但是，当一个刺激意外地完全被遗漏时会发生什么呢？通过测量大脑的电磁活动，我们表明它也会产生一种“遗漏反应”，这种反应取决于注意力的存在。我们通过计算对这些反应进行建模，揭示失配反应和遗漏反应仅在输入到同一基础神经元网络的位置上有所不同。在这两种情况下，我们都表明注意力选择性地加强了大脑对未来的预测。
