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Learning to predict: Exposure to temporal sequences facilitates prediction of future events

机译:学习预测:暴露于时间序列有助于预测未来事件

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摘要

Previous experience is thought to facilitate our ability to extract spatial and temporal regularities from cluttered scenes. However, little is known about how we may use this knowledge to predict future events. Here we test whether exposure to temporal sequences facilitates the visual recognition of upcoming stimuli. We presented observers with a sequence of leftwards and rightwards oriented gratings that was interrupted by a test stimulus. Observers were asked to indicate whether the orientation of the test stimulus matched their expectation based on the preceding sequence. Our results demonstrate that exposure to temporal sequences without feedback facilitates our ability to predict an upcoming stimulus. In particular, observers’ performance improved following exposure to structured but not random sequences. Improved performance lasted for a prolonged period and generalized to untrained stimulus orientations rather than sequences of different global structure, suggesting that observers acquire knowledge of the sequence structure rather than its items. Further, this learning was compromised when observers performed a dual task resulting in increased attentional load. These findings suggest that exposure to temporal regularities in a scene allows us to accumulate knowledge about its global structure and predict future events.
机译:以前的经验被认为有助于我们从混乱的场景中提取空间和时间规律的能力。但是,对于如何使用这些知识预测未来事件知之甚少。在这里,我们测试暴露于时间序列是否有助于视觉识别即将到来的刺激。我们为观察者提供了一系列向左和向右定向的光栅,这些光栅被测试刺激打断了。要求观察者根据前面的顺序来说明测试刺激的方向是否符合他们的期望。我们的结果表明,暴露于没有反馈的时间序列有助于我们预测即将到来的刺激的能力。特别是,观察到结构化但随机序列后的观察者的性能得到了改善。改进的性能持续了很长时间,并且普遍适用于未经训练的刺激方向,而不是具有不同全局结构的序列,这表明观察者应该了解序列结构而不是其项目。此外,当观察者执行双重任务导致注意力增加时,这种学习就受到了损害。这些发现表明,暴露于场景中的时间规律可让我们积累有关其全局结构的知识并预测未来事件。

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