Dynamic binding of identity and location information: a serial model of multiple identity tracking

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.