Abstract
Limitations on higher-order cognitive resources have been proposed as constraints on human performance in multiple object tracking (MOT). We report results from experiments testing a possible account of these cognitive limitations – observers rationally allocate attention to object locations to reduce the probability of spatial interference, they accomplish this reduction via increased spatial precision at attended locations, and drop targets within trials when their attention resource pool is overwhelmed. Our experiments used a standard MOT display following (Alvarez & Franconeri, 2007), but made two modifications to the standard design that allowed us to (i) probe the spatial precision with which subjects track individual objects during MOT, and (ii) whether subjects were aware that they had dropped any targets during trials where they made errors. We also conducted computational experiments, augmenting an existing model (Vul et al, 2009) with a central controller that assigns a limited attention resource to low-level object trackers, proportionately reducing their perceptual noise to minimize the chances of local perceptual confusion. We report three main findings. First, our computational model replicates the pattern of human errors on a per-trial basis to a considerably greater extent than a simple spatial interference model. Second, observers can localize targets with greater precision than non-targets, and that, contra simple spatial interference arguments, they localize targets with greater precision in crowded positions than when they are in open space. Third, observers were significantly less likely to report surprise (implying they knew they had dropped at least one target) for trials where our model predicted greater instances of attention resource scarcity. These results together support the case for rational attention allocation attenuating perceptual confusion during MOT trials, and limitations on this attention pool causing at least some of the difficulties that human observers encounter in multiple object tracking.
Meeting abstract presented at VSS 2015