The sensory consequences of speaking: parametric neural cancellation during speech in auditory cortex.
The sensory consequences of speaking: parametric neural cancellation during speech in auditory cortex.
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DOI：
10.1371/journal.pone.0018307
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Schiller NO
中科院分区：
文献类型：
--
作者：
Christoffels IK;van de Ven V;Waldorp LJ;Formisano E;Schiller NO
当我们说话时，我们为自己提供听觉语音输入。言语的有效监控通常被假设为依赖于将来自内部运动命令（前向模型）的预测感觉结果与实际感觉反馈相匹配。在本文中，我们使用功能性磁共振成像测试了前向模型假设。我们管理一个公开的图片命名任务中，我们参数化地降低了噪声掩蔽的口头反馈的质量。在没有明显言语的情况下呈现相同的听觉输入作为听力控制条件。我们的研究结果表明，预测和实际的感觉反馈之间的匹配结果在抑制取消的听觉活动，因为正常的未掩蔽的反馈说话减少听觉皮层的活动相比，听控制条件。此外，在自发语音过程中，听觉皮层的激活随着自发语音反馈质量的降低而增加。我们的结论是，在说话早期听觉皮层参与匹配外部信号与内部产生的模型或预测的感觉后果，其所在地可能驻留在听觉或更高阶的大脑区域。在早期听觉皮层的匹配可以提供一种非常敏感的监测机制，该机制在非常早期的处理水平上突出语音产生错误，并且可以有效地确定语音输入的自我代理。
When we speak, we provide ourselves with auditory speech input. Efficient monitoring of speech is often hypothesized to depend on matching the predicted sensory consequences from internal motor commands (forward model) with actual sensory feedback. In this paper we tested the forward model hypothesis using functional Magnetic Resonance Imaging. We administered an overt picture naming task in which we parametrically reduced the quality of verbal feedback by noise masking. Presentation of the same auditory input in the absence of overt speech served as listening control condition. Our results suggest that a match between predicted and actual sensory feedback results in inhibition of cancellation of auditory activity because speaking with normal unmasked feedback reduced activity in the auditory cortex compared to listening control conditions. Moreover, during self-generated speech, activation in auditory cortex increased as the feedback quality of the self-generated speech decreased. We conclude that during speaking early auditory cortex is involved in matching external signals with an internally generated model or prediction of sensory consequences, the locus of which may reside in auditory or higher order brain areas. Matching at early auditory cortex may provide a very sensitive monitoring mechanism that highlights speech production errors at very early levels of processing and may efficiently determine the self-agency of speech input.
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