Our understanding of the sensory environment is contextualized on the basis of prior experience. Measurement of auditory ERPs provides insight into automatic processes that contextualize the relevance of sound as a function of how sequences change over time. However, task-independent exposure to sound has revealed that strong first impressions exert a lasting impact on how the relevance of sound is contextualized. Dynamic causal modeling was applied to auditory ERPs collected during presentation of alternating pattern sequences. A local regularity (a rare p = .125 vs. common p = .875 sound) alternated to create a longer timescale regularity (sound probabilities alternated regularly creating a predictable block length), and the longer timescale regularity changed halfway through the sequence (the regular block length became shorter or longer). Predictions should be revised for local patterns when blocks alternated and for longer patterning when the block length changed. Dynamic causal modeling revealed an overall higher precision for the error signal to the rare sound in the first block type, consistent with the first impression. The connectivity changes in response to errors within the underlying neural network were also different for the two blocks with significantly more revision of predictions in the arrangement that violated the first impression. Furthermore, the effects of block length change suggested errors within the first block type exerted more influence on the updating of longer timescale predictions. These observations support the hypothesis that automatic sequential learning creates a high-precision context (first impression) that impacts learning rates and updates to those learning rates when predictions arising from that context are violated. The results further evidence automatic pattern learning over multiple timescales simultaneously, even during task-independent passive exposure to sound.

中文翻译：

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声音序列处理的多个时间尺度上统计规律的分层学习：动态因果建模研究。

我们对感官环境的理解是基于先前经验的。听觉 ERP 的测量提供了对自动过程的洞察，这些过程将声音的相关性作为序列如何随时间变化的函数进行背景化。然而，与任务无关的声音暴露表明，强烈的第一印象对声音的相关性如何被语境化产生持久的影响。动态因果模型应用于在交替模式序列呈现期间收集的听觉 ERP。局部规律（罕见的 p = .125 与常见的 p = .875 声音）交替产生更长的时间尺度规律（声音概率有规律地交替产生可预测的块长度），并且较长的时间尺度规律在序列中途发生变化（常规块长度变短或变长）。块交替时的局部模式和块长度改变时更长的模式的预测应该被修改。动态因果建模揭示了第一块类型中罕见声音的错误信号的整体精度更高，与第一印象一致。对于两个块，响应于底层神经网络中的错误的连接性变化也不同，在违反第一印象的安排中，预测的修订显着更多。此外，第一种块类型内块长度变化的影响建议错误对较长时间尺度预测的更新产生更大的影响。这些观察结果支持这样一种假设，即自动顺序学习会创建一个高精度的上下文（第一印象），当违反该上下文的预测时，该上下文会影响学习率和对这些学习率的更新。结果进一步证明了同时在多个时间尺度上进行自动模式学习，即使在与任务无关的被动暴露于声音期间也是如此。