Temporal preparation is the cognitive function that takes place when anticipating future events. This is commonly considered to involve a process that maximizes preparation at time points that yield a high hazard. However, despite their prominence in the literature, hazard-based theories fail to explain the full range of empirical preparation phenomena. Here, we present the formalized multiple trace theory of temporal preparation (fMTP), an integrative model which develops the alternative perspective that temporal preparation results from associative learning. fMTP builds on established computational principles from the domains of interval timing, motor planning, and associative memory. In fMTP, temporal preparation results from associative learning between a representation of time on the one hand and inhibitory and activating motor units on the other hand. Simulations demonstrate that fMTP can explain phenomena across a range of time scales, from sequential effects operating on a time scale of seconds to long-term memory effects occurring over weeks. We contrast fMTP with models that rely on the hazard function and show that fMTP’s learning mechanisms are essential to capture the full range of empirical effects. In a critical experiment using a Gaussian distribution of foreperiods, we show the data to be consistent with fMTP’s predictions and to deviate from the hazard function. Additionally, we demonstrate how changing fMTP’s parameters can account for participant-to-participant variations in preparation. In sum, with fMTP we put forward a unifying computational framework that explains a family of phenomena in temporal preparation that cannot be jointly explained by conventional theoretical frameworks.

中文翻译：

---

FMTP：跨时间尺度的时间准备的统一计算框架。

时间准备是在预测未来事件时发生的认知功能。这通常被认为涉及在产生高风险的时间点最大限度地准备的过程。然而，尽管它们在文献中很突出，但基于危害的理论无法解释所有的经验准备现象。在这里，我们提出了时间准备 (fMTP) 的形式化多迹理论，这是一种综合模型，它发展了时间准备由联想学习产生的另一种观点。f MTP 建立在间隔计时、运动规划和联想记忆领域的既定计算原理之上。在fMTP，时间准备是一方面时间表示与另一方面抑制和激活运动单位之间的联想学习的结果。模拟表明，f MTP 可以解释一系列时间尺度的现象，从以秒为单位的时间尺度上的连续效应到发生在数周内的长期记忆效应。我们将f MTP 与依赖于风险函数的模型进行对比，并表明f MTP 的学习机制对于捕捉全方位的经验效应至关重要。在使用前周期高斯分布的关键实验中，我们显示数据与f一致MTP 的预测和偏离危险函数。此外，我们演示了更改f MTP 的参数如何解释参与者与参与者之间的准备差异。总而言之，我们通过f MTP 提出了一个统一的计算框架，该框架可以解释时间准备中的一系列现象，而传统的理论框架无法共同解释这些现象。