A dynamic neural field model of multi-object tracking

Abstract

The Multi-Object Tracking (MOT) task has been a central tool for studying object-based attention and the nature of the connection between on-line percepts and the location of objects in a scene. Despite a long empirical tradition, there are few formal models that specify the processes that underlie performance in this dynamic task. Here we propose a Dynamic Neural Field (DNF) model that captures the central empirical effects in this literature and integrates multi-object tracking with other known characteristics of the visuo-spatial cognitive system. Our DNF model has three layers of spatially-tuned neurons. The first layer—the perceptual layer—receives input from the world and forms activation “peaks” that are stabilized by input. The second layer—the working memory layer—receives both direct input from the world as well as input from the perceptual layer. This field is able to form self-sustaining peaks that maintain activation even when input is removed. Finally, both the perceptual and working memory layers are reciprocally connected to a shared layer of inhibitory interneurons. To explain the model's performance, consider a single trial that begins with the presentation of 6 objects, 3 of which are cued. In response to this input, the DNF model forms 6 stabilized peaks of activation in the perceptual layer and three working memory peaks due to the strong input at three locations. These activation peaks are maintained by locally-excitatory and laterally-inhibitory interactions. Consequently, once the inputs begin moving the activation peaks in working memory track their associated inputs provided that the inputs are within the range of locally-excitatory interactions. Moreover, the peaks maintain their fidelity via lateral inhibition even when they come near other peaks. We will show how this basic architecture can perform the MOT task and show proximity effects as well as the ability to track objects behind occluders.