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Prediction under uncertainty: Dissociating sensory from cognitive expectations in highly uncertain musical contexts
Brain Research ( IF 2.9 ) Pub Date : 2021-09-21 , DOI: 10.1016/j.brainres.2021.147664
Iris Mencke 1 , David Ricardo Quiroga-Martinez 2 , Diana Omigie 3 , Georgios Michalareas 4 , Franz Schwarzacher 2 , Niels Trusbak Haumann 2 , Peter Vuust 2 , Elvira Brattico 5
Affiliation  

Predictive models in the brain rely on the continuous extraction of regularities from the environment. These models are thought to be updated by novel information, as reflected in prediction error responses such as the mismatch negativity (MMN). However, although in real life individuals often face situations in which uncertainty prevails, it remains unclear whether and how predictive models emerge in high-uncertainty contexts. Recent research suggests that uncertainty affects the magnitude of MMN responses in the context of music listening. However, musical predictions are typically studied with MMN stimulation paradigms based on Western tonal music, which are characterized by relatively high predictability. Hence, we developed an MMN paradigm to investigate how the high uncertainty of atonal music modulates predictive processes as indexed by the MMN and behavior. Using MEG in a group of 20 subjects without musical training, we demonstrate that the magnetic MMN in response to pitch, intensity, timbre, and location deviants is evoked in both tonal and atonal melodies, with no significant differences between conditions. In contrast, in a separate behavioral experiment involving 39 non-musicians, participants detected pitch deviants more accurately and rated confidence higher in the tonal than in the atonal musical context. These results indicate that contextual tonal uncertainty modulates processing stages in which conscious awareness is involved, although deviants robustly elicit low-level pre-attentive responses such as the MMN. The achievement of robust MMN responses, despite high tonal uncertainty, is relevant for future studies comparing groups of listeners’ MMN responses to increasingly ecological music stimuli.



中文翻译:

不确定性下的预测:在高度不确定的音乐环境中将感官与认知期望分离

大脑中的预测模型依赖于从环境中不断提取规律。这些模型被认为是由新信息更新的,这反映在预测误差响应中,例如失配负性 (MMN)。然而,尽管在现实生活中个人经常面临不确定性盛行的情况,但仍不清楚预测模型是否以及如何在高不确定性环境中出现。最近的研究表明,在听音乐的情况下,不确定性会影响 MMN 响应的幅度。然而,音乐预测通常使用基于西方音调音乐的 MMN 刺激范式进行研究,其特点是可预测性相对较高。因此,我们开发了一个 MMN 范式来研究无调性音乐的高度不确定性如何调节由 MMN 和行为索引的预测过程。在未经音乐训练的 20 名受试者中使用 MEG,我们证明磁性 MMN 响应音高、强度、音色和位置偏差在调性和无调性旋律中都被唤起,条件之间没有显着差异。相比之下,在一项涉及 39 名非音乐家的单独行为实验中,参与者更准确地检测到音高偏差,并且对音调的置信度高于无调性音乐背景。这些结果表明,上下文音调不确定性会调节涉及有意识意识的处理阶段,尽管偏差会强烈地引发低水平的注意力前反应,例如 MMN。

更新日期:2021-11-03
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