The Neural Representation of Time: An Information-Theoretic Perspective
The Neural Representation of Time: An Information-Theoretic Perspective
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DOI：
10.1162/neco_a_00280
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发表时间：
2012-06-01
影响因子：
2.9
通讯作者：
Herrmann, J. Michael
中科院分区：
文献类型：
--
作者：
Hass, Joachim;Herrmann, J. Michael
在时间感知的心理物理实验中，一个突出的发现是韦伯定律，即时间误差与持续时间的线性比例。重现这种尺度的能力已被作为时间感知的神经计算模型有效性的标准。然而，韦伯定律的起源仍然是未知的，目前只有少数模型一般再现它。在这里，我们使用信息理论框架，将时间感知的神经元机制视为随机过程来研究韦伯定律在时间感知中的统计起源及其经常观察到的偏差。在大脑能够计算时间的最佳估计的假设下，我们发现韦伯定律只有在估计基于过程方差的时间变化时才准确成立。相比之下，如果使用过程均值的系统变化进行估计，则时间误差随时间呈亚线性缩放，就像大多数时间感知模型中的情况一样，而基于时间相关性的估计会导致超线性缩放。如果有几个时间信息源可用，那么这种时间信息的层次结构是保留的。此外，我们考虑了多个随机过程的情况，研究了基于协方差的模型和基于同步链的模型的例子。这种方法表明，现有的时间感知神经计算模型可以分为基于均值、方差和相关性的过程，并允许对结果时间误差的缩放进行预测。
A prominent finding in psychophysical experiments on time perception is Weber's law, the linear scaling of timing errors with duration. The ability to reproduce this scaling has been taken as a criterion for the validity of neurocomputational models of time perception. However, the origin of Weber's law remains unknown, and currently only a few models generically reproduce it. Here, we use an information-theoretical framework that considers the neuronal mechanisms of time perception as stochastic processes to investigate the statistical origin of Weber's law in time perception and also its frequently observed deviations. Under the assumption that the brain is able to compute optimal estimates of time, we find that Weber's law only holds exactly if the estimate is based on temporal changes in the variance of the process. In contrast, the timing errors scale sublinearly with time if the systematic changes in the mean of a process are used for estimation, as is the case in the majority of time perception models, while estimates based on temporal correlations result in a superlinear scaling. This hierarchy of temporal information is preserved if several sources of temporal information are available. Furthermore, we consider the case of multiple stochastic processes and study the examples of a covariance-based model and a model based on synfire chains. This approach reveals that existing neurocomputational models of time perception can be classified as mean-, variance-and correlation-based processes and allows predictions about the scaling of the resulting timing errors.