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Perceptual learning shapes multisensory causal inference via two distinct mechanisms

机译:知觉学习通过两种不同的机制塑造多感官因果推理

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摘要

To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this ‘temporal binding window’ can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source.
机译:为了准确表示环境,我们的大脑必须整合来自同一来源的感官信号,同时将来自独立来源的感官信号隔离开来。执行此任务的合理策略是将积分限制在时空一致的线索上。但是,由于多感官信号会经历不同的传输和处理延迟,因此大脑必须对时间差异保持一定程度的耐受性。最近的研究表明,可以通过感知学习来减小“时间绑定窗口”的宽度,但是,对于这些依赖于经验的影响的机制知之甚少。在这里,在单独的实验中,我们在进行视听时间辨别任务训练之前和之后测量人类参与者的时间和空间绑定窗口。我们表明训练以以下两种形式对多感官整合产生了两种截然不同的影响:(i)时间绑定窗口的特定变窄,不会转移到空间绑定;(ii)所有时空之间的交叉模式交互作用的大小普遍减少差异。这些影响自然来自于因果推理的贝叶斯模型,其中学习提高了视听时序估计的精度,同时降低了来自共同来源的刺激的先前期望。

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