The semantic category-based grouping in the Multiple Identity Tracking task

Object-based attention during scene perception elicits boundary contraction in memory

Boundary contraction and extension are two types of scene transformations that occur in memory. In extension, viewers extrapolate information beyond the edges of the image, whereas in contraction, viewers forget information near the edges. Recent work suggests that image composition influences the direction and magnitude of boundary transformation. We hypothesize that selective attention at encoding is an important driver of boundary transformation effects, selective attention to specific objects at encoding leading to boundary contraction. In this study, one group of participants (N = 36) memorized 15 scenes while searching for targets, while a separate group (N = 36) just memorized the scenes. Both groups then drew the scenes from memory with as much object and spatial detail as they could remember. We asked online workers to provide ratings of boundary transformations in the drawings, as well as how many objects they contained and the precision of remembered object size and location. We found that search condition drawings showed significantly greater boundary contraction than drawings of the same scenes in the memorize condition. Search drawings were significantly more likely to contain target objects, and the likelihood to recall other objects in the scene decreased as a function of their distance from the target. These findings suggest that selective attention to a specific object due to a search task at encoding will lead to significant boundary contraction.

Multiple-target tracking in human and machine vision

Humans are able to track multiple objects at any given time in their daily activities—for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneously and what underlying behavioral and neural mechanisms they use. At the same time, computer-vision researchers have proposed different algorithms to track multiple targets automatically. These algorithms are useful for video surveillance, team-sport analysis, video analysis, video summarization, and human–computer interaction. Although there are several efficient biologically inspired algorithms in artificial intelligence, the human multiple-target tracking (MTT) ability is rarely imitated in computer-vision algorithms. In this paper, we review MTT studies in neuroscience and biologically inspired MTT methods in computer vision and discuss the ways in which they can be seen as complementary.

Multiple-target tracking (MTT) is a challenging task vital for both a human’s daily life and for many artificial intelligent systems, such as those used for urban traffic control. Neuroscientists are interested in discovering the underlying neural mechanisms that successfully exploit cognitive resources, e.g., spatial attention or memory, during MTT. Computer-vision specialists aim to develop powerful MTT algorithms based on advanced models or data-driven computational methods. In this paper, we review MTT studies from both communities and discuss how findings from cognitive studies can inspire developers to construct higher performing MTT algorithms. Moreover, some directions have been proposed through which MTT algorithms could raise new questions in the cognitive science domain, and answering them can shed light on neural processes underlying MTT.

The Global Properties of Objects Play the Main Role in Facilitating Multiple Object Tracking Performance

Previous research has revealed the uniqueness-facilitation effect in the multiple object tracking (MOT) task: simple distinct identities and surface features of moving targets could facilitate attentional tracking. By adapting compound stimuli, the present study investigated whether the global or local properties played the main role in the uniqueness-facilitation effect in the MOT task. The uniqueness of local properties, of global properties or of both local and global properties were considered. Observers’ tracking performance in alternative conditions were compared with that in the homogeneous condition wherein all stimuli have identical local and global properties. Results from two experiments suggest that the global properties played the key role in facilitating tracking. The distinctiveness of local properties can also facilitate tracking with global properties being homogeneous. However, when the stimuli’s global properties are distinct from each other—whether the local properties being unique or not—observers’ tracking performance can achieve the same level as that in the unitary-uniqueness condition wherein the moving objects were distinct unitary letters. These results revealed a global superiority effect in the MOT task. Finally, the facilitation effects of the global and local properties might depend on the stimulus sparsity. When the compound stimuli had fewer local elements, the uniqueness facilitation effect on tracking decreased.

The effects of colour complexity and similarity on multiple object tracking performance

Surface features can be used during multiple object tracking (MOT). Previous studies suggested that surface features might be stored in visual working memory to assist object tracking, and attentive tracking and visual working memory share common attentional resources. However, it is still unknown whether features of both the target and distractor sets will be stored, or features of the target and distractor sets are processed differently. Moreover, how feature distinctiveness and similarity between the target and distractor sets affect tracking and allocation of attentional resources are still not clear. First, we manipulated the colour complexity of the target set (CT) and the colour complexity of the distractor set (CD), respectively, in two experiments, where colours of the target and distractor sets were always distinct, to test their effects on tracking performance. If features of the target and distractor sets are stored, manipulating feature complexity of the target and distractor sets would significantly affect tracking performance. Second, this study tested whether tracking performance was affected by different levels of distinctiveness between the target and distractor sets (DTD) and explored how distinctiveness affected tracking and allocation of attentional resources. Results showed that DTD and CT significantly affect tracking performance and allocation of attentional resources, but not CD. These results indicated that when targets and distractors have distinct features, only the surface features of the targets are maintained in visual working memory. And when targets have the same colour with the distractors, they are more difficult and consume more attentional resources to track.