Tracking of multiple moving objects is commonly assumed to be carried out by a fixed-capacity parallel mechanism. The present study proposes a serial model (MOMIT) to explain performance accuracy in the maintenance of multiple moving objects with distinct identities. A serial refresh mechanism is postulated, which makes recourse to continuous attention switching, a capacity-limited episodic buffer for identity-location bindings, indexed location information stored in the visuospatial short-term memory, and an active role of long-term memory. As identity-location bindings are refreshed serially, a location error is inherent for all other targets except the focally attended one. The magnitude of this location error is a key factor in predicting tracking accuracy. MOMIT's predictions were supported by the data of five experiments: performance accuracy decreased as a function of target set-size, speed, and familiarity. A mathematical version of MOMIT fitted nicely to the observed data with plausible parameter estimates for the binding capacity and refresh time.

中文翻译：

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身份和位置信息的动态绑定：多身份跟踪的串行模型

对多个移动物体的跟踪通常被假定为由固定容量的并联机构执行。本研究提出了一个串行模型（MOMIT）来解释维护具有不同身份的多个移动对象的性能准确性。假设了一种串行刷新机制，它可以求助于连续的注意力切换、用于身份位置绑定的容量有限的情节缓冲区、存储在视觉空间短期记忆中的索引位置信息以及长期记忆的积极作用。由于身份-位置绑定是连续刷新的，因此除了重点关注的目标之外，所有其他目标都固有位置错误。这种位置误差的大小是预测跟踪精度的关键因素。MOMIT 的预测得到了五个实验数据的支持：性能准确度随着目标集大小、速度和熟悉度的变化而下降。MOMIT 的数学版本非常适合观察到的数据，并具有对结合容量和刷新时间的合理参数估计。