Humans and animals do not only keep track of time intervals but they can also make decisions about durations. Temporal bisection is a psychophysical task that is widely used to assess the latter ability via categorization of durations as short or long. Many existing models of performance in temporal bisection primarily account for choice proportions and tend to overlook the associated response times. We propose a time-cell neural network that implements both interval timing and temporal categorization. The proposed model can keep track of time intervals based on lurching wave activity, it can learn the reference durations along with their association with different categorization responses, and finally, it can carry out the comparison of arbitrary intermediate durations to the reference durations. We compared the model's predictions about choice behavior and response times to the empirical data previously gathered from rats. We showed that this time-cell neural network can predict the canonical behavioral signatures of temporal bisection performance. Specifically, (a) the proposed model can account for the sigmoidal relationship between the probability of the long choices and the test durations, (b) the superposition of choice functions on a relative time scale, (c) the localization of the point of subjective equality at the geometric mean of the reference durations, and (d) the differential modulation of short and long categorization response times as a function of the test durations.

中文翻译：

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一个简单的三层兴奋抑制神经元网络，用于时间决策。

人和动物不仅可以跟踪时间间隔，还可以决定持续时间。时间二等分是一项心理物理任务，已广泛用于通过将持续时间分为短时间或长时间分类来评估后者的能力。时间分割的许多现有绩效模型主要考虑选择比例，并倾向于忽略相关的响应时间。我们提出了一种实现时间间隔计时和时间分类的时间单元神经网络。所提出的模型可以基于潜伏波活动跟踪时间间隔，可以学习参考持续时间以及它们与不同分类响应的关联，最后可以对任意中间持续时间与参考持续时间进行比较。我们比较了模型 关于选择行为和对先前从大鼠那里收集的经验数据的响应时间的预测。我们表明，该时间单元神经网络可以预测时空平分表现的规范行为特征。具体来说，（a）所提出的模型可以解释长选择概率与测试持续时间之间的S形关系；（b）选择函数在相对时间尺度上的叠加；（c）主观点的定位参考持续时间的几何平均值相等，并且（d）短和长分类响应时间的微分调制作为测试持续时间的函数。我们表明，该时间单元神经网络可以预测时空平分表现的规范行为特征。具体而言，（a）所提出的模型可以解释长选择概率与测试持续时间之间的S形关系；（b）选择函数在相对时间尺度上的叠加；（c）主观点的定位参考持续时间的几何平均值相等，并且（d）短和长分类响应时间的微分调制作为测试持续时间的函数。我们表明，该时间单元神经网络可以预测时空平分表现的规范行为特征。具体来说，（a）所提出的模型可以解释长选择概率与测试持续时间之间的S形关系；（b）选择函数在相对时间尺度上的叠加；（c）主观点的定位参考持续时间的几何平均值相等，并且（d）短和长分类响应时间的微分调制作为测试持续时间的函数。