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首页> 美国卫生研究院文献>Frontiers in Neuroscience >Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias
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Mismatch negativity (MMN) to pitch change is susceptible to order-dependent bias

机译：音高变化的失配负性（MMN）易受阶跃依赖偏差的影响

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摘要

Pattern learning facilitates prediction about upcoming events. Within the auditory system such predictions can be studied by examining effects on a component of the auditory-evoked potential known as mismatch negativity (MMN). MMN is elicited when sound does not conform to the characteristics inferred from statistical probabilities derived from the recent past. Stable patterning in sequences elevates confidence in automatically generated perceptual inferences about what sound should come next and when. MMN amplitude should be larger when sequence is highly stable compared to when it is more volatile. This expectation has been tested using a multi-timescale paradigm. In this study, two sounds of different duration alternate roles as a predictable repetitive “standard” and rare MMN-eliciting “deviation.” The paradigm consists of sound sequences that differ in the rate at which the roles of two tones alternate, varying from slowly changing (high stability) to rapidly alternating (low stability). Previous studies using this paradigm discovered a “primacy bias” affecting how stability in patterning impacts MMN amplitude. The primacy bias refers to the observation that the effect of longer-term stability within sequences only appears to impact MMN to the sound first encountered as deviant (the sound that is rare when the sequence commences). This study determines whether this order-driven bias generalizes to sequences that contain two tones differing in pitch. By manipulating (within-subjects) the order in which sounds are encountered as deviants the data demonstrate the two defining characteristics of primacy bias: (1) sequence stability only ever impacts MMN amplitude to the first-deviant sound; and (2) within higher stability sequences, MMN is significantly larger when a sound is the first compared to when it is the second deviant. The results are consistent with a general order-driven bias exerting modulating effects on MMN amplitude over a longer timescale.
机译：模式学习有助于预测即将发生的事件。在听觉系统中，可以通过检查对听觉诱发电位的一部分（称为失配负性（MMN））的影响来研究此类预测。当声音不符合根据最近的统计概率推断出的特征时，会引发MMN。序列中的稳定模式提高了自动生成关于下一声以及何时发出声音的感知推断的信心。当序列高度稳定时，MMN振幅应比不稳定时更大。已经使用多时标范式对这一期望进行了测试。在这项研究中，不同持续时间的两种声音交替充当可预测的重复“标准”和罕见的MMN引起的“偏差”。该范式由声音序列组成，这些声音序列在两个音调的角色交替速率上有所不同，从缓慢变化（高稳定性）到快速交替（低稳定性）不等。先前使用此范例的研究发现了“主要偏见”，影响了图案形成的稳定性如何影响MMN幅度。首要偏差是指这样的观察结果，即序列内的长期稳定性的影响似乎只会将MMN影响到首先遇到的声音异常（序列开始时很少出现的声音）。这项研究确定了这种顺序驱动的偏见是否可以推广到包含两个音高不同的音调的序列。通过（在对象内）操纵声音在偏离时的顺序，数据证明了原始偏差的两个定义特征：（1）序列稳定性仅会影响MMN幅度对第一偏离声音的影响； （2）在较高的稳定性序列中，第一种声音的MMN与第二种声音的MMN相比要大得多。结果与在较长时间尺度上对MMN振幅施加调制效应的一般阶跃驱动偏置一致。

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