Goal-directed unequal attention allocation during multiple object tracking

Abstract

In standard multiple object tracking (MOT) tasks the relative importance of the targets being tracked is equal. This is atypical of everyday situations in which an individual may need to prioritize one target relative to another and so allocate attention unequally. We report three experiments that examined whether participants could unequally split attention using a modified MOT task in which target priority was manipulated. Specifically, we examined the effect of priority on participants’ magnitude of error and used a distribution mixture analysis to investigate how priority affected both participants’ probability of losing an item and tracking precision. Experiment 1 (trajectory tracking) revealed a higher magnitude of error and higher proportion of guessing for low- compared with high-priority targets. Experiments 2 (trajectory tracking) and 3 (position tracking) examined how fine-grained this ability is by manipulating target priority at finer increments. In line with Experiment 1 , results from both these experiments indicated that participants could split attention unequally. There was some evidence that participants could allocate attention unequally at fine increments, but this was less conclusive. Taken together, these experiments demonstrate participants’ ability to distribute attention unequally across multiple moving objects but suggest some limitation with the flexibility of attention allocation.

A Time-Delay Feedback Neural Network for Discriminating Small, Fast-Moving Targets in Complex Dynamic Environments

“…Note that 1/f −1 (2d) is determined by the time-delay feedback signal and can be tuned by the three feedback parameters α, n 4 , and τ 4 , which will be further discussed in Section IV-D. The above analysis indicates that the time-delay feedback loop shows a preference for fast-moving objects 3 , which is consistent with the biological finding that visual systems of animals generally pay more attention to fast-moving objects than those moving at lower velocities [47]- [49]. At this stage, the STMD model can eliminate slow-moving objects by the proposed time-delay feedback, while continuing to respond to those with higher velocities regardless of their size.…”

“…The results show that the Feedback STMD largely suppresses slow-moving background false positives, whilst retaining the ability to respond to small targets with higher velocities. The behaviour of the developed feedback model is consistent with that of the animal visual systems in which high-velocity objects always receive more attention [47]- [49]. Furthermore, it also enables autonomous robots to effectively discriminate potentially threatening fastmoving small targets from complex backgrounds, a feature required, for example, in surveillance.…”

Examining the roles of working memory and visual attention in multiple object tracking expertise

“…Importantly for current purposes, tracking is achieved at speeds above which sequential visual fixations can be made (Yantis 1992); hence, if objects are processed in a serial fashion, it is through covert attention switching (Posner et al 1987)-that is, decoupling the locus of attention from the foveal parts of the visual field. A splitting of attention across multiple targets, sometimes unequally, requires the use of covert attention (Cavanagh and Alvarez 2005;Crowe et al 2019;Doran et al 2009), and a significant portion of tracking occurs using peripheral vision (Vater et al 2017). Consequently, covert attention plays a major role in tracking, but, previous work has also suggested an important role for overt visual attention (Fehd and Seiffert 2010;Zelinsky and Neider 2008), which may also be a determining factor in MOT performance.…”

Motion disrupts dynamic visual search for an orientation change

“…In a lifeguarding situation, for example, it remains to be seen whether motion of the waves in a wave pool would be detrimental to the detection of a drowning incident, in addition to the motion of the swimmers (potential targets) themselves. In MOT, participants can strategically split their attention unequally (Crowe et al, 2019) and, in visual search, task relevance predicts the gaze of participants monitoring an array of CCTV screens (Howard et al, 2011). Therefore, it is likely that certain locations (e.g.…”