听觉预测编码的系统神经生理学信号

Psychophysiology Pub Date : 2024-06-01 Epub Date: 2024-02-13 DOI:10.1111/psyp.14544
Manuel Muñoz-Caracuel, Vanesa Muñoz, Francisco J Ruiz-Martínez, Antonio J Vázquez Morejón, Carlos M Gómez
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引用次数: 0

摘要

预测编码框架认为,我们的大脑会持续监测环境变化并更新其预测模型,最大限度地减少预测误差,从而有效地适应环境需求。然而,这些预测现象的潜在神经生理学机制仍不清楚。本研究旨在探索被动和主动听觉处理过程中预测编码过程的系统神经生理学相关性。研究使用基于听觉模式的新奇怪异范式分析了脑电图(EEG)、功能性近红外光谱(fNIRS)和自律神经系统(ANS)测量。研究人员招募了 32 名健康受试者。结果显示,被动和主动条件下有共同的慢速诱发电位,可解释为预测和更新的自动预测过程,与有意识的注意努力无关。在这两种情况下,大脑皮层血流动力学活动和听觉模式违反时的独特诱发电位也存在差异,而只有导致强制性反应的有意识感知才伴有相位性自律神经系统反应。这些结果表明,在面对感官刺激时,预测编码神经资源会根据情境需求进行系统性的分层重新分配。通过主成分分析,可以将记录到的一些信号的可变性联系起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Systemic neurophysiological signals of auditory predictive coding.

Predictive coding framework posits that our brain continuously monitors changes in the environment and updates its predictive models, minimizing prediction errors to efficiently adapt to environmental demands. However, the underlying neurophysiological mechanisms of these predictive phenomena remain unclear. The present study aimed to explore the systemic neurophysiological correlates of predictive coding processes during passive and active auditory processing. Electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and autonomic nervous system (ANS) measures were analyzed using an auditory pattern-based novelty oddball paradigm. A sample of 32 healthy subjects was recruited. The results showed shared slow evoked potentials between passive and active conditions that could be interpreted as automatic predictive processes of anticipation and updating, independent of conscious attentional effort. A dissociated topography of the cortical hemodynamic activity and distinctive evoked potentials upon auditory pattern violation were also found between both conditions, whereas only conscious perception leading to imperative responses was accompanied by phasic ANS responses. These results suggest a systemic-level hierarchical reallocation of predictive coding neural resources as a function of contextual demands in the face of sensory stimulation. Principal component analysis permitted to associate the variability of some of the recorded signals.

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