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Optimal multisensory integration leads to optimal time estimation

机译:最佳的多传感器集成可导致最佳的时间估计

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Our brain compensates sensory uncertainty by combining multisensory information derived from an event, and by integrating the current sensory signal with the prior knowledge about the statistical structure of previous events. There is growing evidence that both strategies are statistically optimal; however, how these two stages of information integration interact and shape an optimal percept remains an open question. In the present study, we investigated the perception of time as an amodal perceptual attribute. The central tendency, a phenomenon of biasing the current percept toward previous stimuli, is used to quantify and model how the prior information affects the current timing behavior. We measured the timing sensitivity and the central tendency for unisensory and multisensory stimuli with sensory uncertainty systematically manipulated by adding noise. Psychophysical results demonstrate that the central tendency increases as the uncertainty increases, and that the multisensory timing improves both the timing sensitivity and the central tendency bias compared to the unisensory timing. Computational models indicate that the optimal multisensory integration precedes the optimal integration of prior information causing the central tendency. Our findings suggest that our brain incorporates the multisensory information and prior knowledge in a statistically optimal manner to realize precise and accurate timing behavior.
机译:我们的大脑通过结合从事件中获得的多感官信息,以及通过将当前的感官信号与有关先前事件的统计结构的先验知识相结合来补偿感觉不确定性。越来越多的证据表明这两种策略在统计上都是最优的。然而,这两个信息整合阶段如何相互作用并形成最佳感知仍然是一个悬而未决的问题。在本研究中,我们调查了对时间的感知,这是一种无情态的感知属性。集中趋势是一种将当前感知偏向先前刺激的现象,用于量化和建模先前信息如何影响当前计时行为。我们通过添加噪声系统地操纵了感官不确定性,测量了单感和多感刺激的时序敏感性和中心趋势。心理物理学结果表明,中心趋势随着不确定性的增加而增加,并且与单感觉时机相比,多感觉时机同时改善了时机敏感性和中心趋势偏差。计算模型表明,最佳的多感官整合优先于先验信息的最佳整合,从而导致集中趋势。我们的发现表明,我们的大脑以统计上最优的方式整合了多感官信息和先验知识,以实现精确而准确的计时行为。

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