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A comparison of serial order short-term memory effects across verbal and musical domains

机译:术语和音乐域序列顺序短期记忆效应的比较

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摘要

Recent studies suggest that the mechanisms involved in the short-term retention of serial order information may be shared across short-term memory (STM) domains such as verbal and visuospatial STM. Given the intrinsic sequential organization of musical material, the study of STM for musical information may be particularly informative about serial order retention processes and their domain-generality. The present experiment examined serial order STM for verbal and musical sequences in participants with no advanced musical expertise and experienced musicians. Serial order STM for verbal information was assessed via a serial order reconstruction task for digit sequences. In the musical domain, serial order STM was assessed using a novel melodic sequence reconstruction task maximizing the retention of tone order information. We observed that performance for the verbal and musical tasks was characterized by sequence length as well as primacy and recency effects. Serial order errors in both tasks were characterized by similar transposition gradients and ratios of fill-in:infill errors. These effects were observed for both participant groups, although the transposition gradients and ratios of fill-in:infill errors showed additional specificities for musician participants in the musical task. The data support domain-general serial order STM effects but also suggest the existence of additional domain-specific effects. Implications for models of serial order STM in verbal and musical domains are discussed.
机译:最近的研究表明,参与序列信息短期信息的短期保留的机制可以在短期内存(STM)域中共享,例如口头和粘面空间STM。鉴于音乐材料的内在顺序组织,对音乐信息的STM的研究可能特别是关于序列订单保留过程及其域一般性的信息。目前的实验检查了与没有先进的音乐专业知识和经验丰富的音乐家的参与者口头和音乐序列的序列订单STM。通过用于数字序列的串行重建任务来评估用于口头信息的串行顺序STM。在音乐域中,使用新的旋律序列重建任务来评估串行顺序STM,最大化音调信息的保留。我们观察到,口头和音乐任务的性能以序列长度以及最初和新近效应为特征。两个任务中的串行顺序错误是以类似的转位梯度和填充率的特征在于:填充错误。对于两个参与者组观察到这些效果,尽管填充的渐变梯度和比率:填充误差显示了音乐专员的音乐家参与者的其他特异性。数据支持域 - 常规序列订单STM效果,但也建议存在额外的域特定效果。讨论了术语和音乐域中的序列订单STM模型的影响。

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