A model that adopts human fixations explains individual differences in multiple object tracking

Abstract

In many settings "keep your eye on the ball" is good advice. People fixate important objects to obtain high quality information. Perhaps equally often, however, we engage with multiple important, moving, and unpredictable objects. Where should we fixate in these situations, and where do we? Do we for example appropriately center fixations to manage spatial non-uniformity in our visual system? And do we fixate empty space strategically to gain as much information as possible about multiple objects of interest? We explored these issues in the context of Multiple Object Tracking (MOT), wherein observers track several moving objects (targets) within a larger set of moving objects (nontargets), all the objects physically indistinguishable from one another. Among the features that make MOT an interesting paradigm is that it cannot be accommodated by continuous gaze to one important object, because there are multiple such objects in a given trial. Instead, it demands sustained processing of inputs from an entire display and iterated inferences about target versus nontarget identities. MOT therefore demands a strategic interaction between eye movements and cognition: the observer should seek fixation locations that minimize the aggregate probability of confusing any target with any nontarget. Individuals who meet this fixation challenge should perform the task better than those who meet the challenge less effectively. Here we describe a probabilistic model that implements the basic computations needed to do MOT, estimating the positions of targets, predicting their future positions, and inferring correspondences between new inputs and represented targets. The quality of the input received by the model depends on its fixation location at a given moment. We simulated a group of fifty participants who all performed the same MOT trials, with the model adopting each observer's fixation locations in the respective simulations. The model reliably predicted individual participant tracking performances and their relative rankings within the cohort. The results suggest that an individual's relative capability in this cognitively demanding task is in part determined by his/her utilization of eye fixations to control the quality and relevance of incoming visual input.

Keywords: Computational modelling; Eye tracking; Individual differences; Kalman filter; Multiple object tracking.