Direction information in multiple object tracking is limited by a graded resource

Why can we track only so many objects? We addressed this question by asking when and how tracking errors emerge. To test the hypothesis that many tracking errors are target/nontarget confusions emerging from close encounters, we compared standard multiple-object tracking trials with trials on which a nontarget turned a random color whenever it approached within 4°of a target. This manipulation significantly improved performance by alleviating the correspondence challenge of a close encounter. Two control experiments showed that color change benefits were not merely due to target recovery. Follow-up experiments demonstrated that color change benefits did not accrue monotonically with distance but, instead, seemed to obey a step function; and an additional experiment demonstrated that, without color changes, the frequency of close encounters predicts tracking performance. Taken together, these experiments suggest that uncertainty about target location imposes the primary constraint on tracking, at times causing errors by leading to confusions between targets and nontargets.

Section: What Constrains Tracking Performance?mentioning

confidence: 99%

Section: What Constrains Tracking Performance?mentioning

confidence: 99%

Close encounters of the distracting kind: Identifying the cause of visual tracking errors

Bae

Flombaum

2012

“…Horowitz and Cohen (2010) suggested independent sampling, which involves a square-root increase in precision with resource. Unfortunately it is not clear whether the percentage of change in tracking accuracy would be linearly related to precision or have some other mapping.…”

Section: Attention and The Tracking Resourcementioning

confidence: 99%

“…A different resource function for tracking was proposed by Horowitz and Cohen (2010). Horowitz and Cohen assessed participants' ability to report, after the display had stopped, the direction that tracked targets had been moving.…”

Section: The Resource-versus-performance Functionmentioning

confidence: 99%

Resource demands of object tracking and differential allocation of the resource

Chen

Howe

Holcombe

2013

The attentional processes for tracking moving objects may be largely hemisphere-specific. Indeed, in our first two experiments the maximum object speed (speed limit) for tracking targets in one visual hemifield (left or right) was not significantly affected by a requirement to track additional targets in the other hemifield. When the additional targets instead occupied the same hemifield as the original targets, the speed limit was reduced. At slow target speeds, however, adding a second target to the same hemifield had little effect. At high target speeds, the cost of adding a same-hemifield second target was approximately as large as would occur if observers could only track one of the targets. This shows that performance with a fast-moving target is very sensitive to the amount of resource allocated. In a third experiment, we investigated whether the resources for tracking can be distributed unequally between two targets. The speed limit for a given target was higher if the second target was slow rather than fast, suggesting that more resource was allocated to the faster of the two targets. This finding was statistically significant only for targets presented in the same hemifield, consistent with the theory of independent resources in the two hemifields. Some limited evidence was also found for resource sharing across hemifields, suggesting that attentional tracking resources may not be entirely hemifield-specific. Together, these experiments indicate that the largely hemisphere-specific tracking resource can be differentially allocated to faster targets.

“…The motion preview yielded no benefit when tracking four targets, but the performance increasingly improved with three or fewer targets. This workload effect is in accord with the following MOT experiment (Horowitz & Cohen, 2010), in which participants were asked directly about the last known direction of a target. The precision of responses declined as the number of targets increased.…”

Section: Abstract Eye Movements and Visual Attention Motion: Integrmentioning

confidence: 83%

Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Lukavský

Děchtěrenko

2016

In everyday life, people often need to track moving objects. Recently, a topic of discussion has been whether people rely solely on the locations of tracked objects, or take their directions into account in multiple object tracking (MOT). In the current paper, we pose a related question: do people utilise extrapolation in their gaze behaviour, or, in more practical terms, should the mathematical models of gaze behaviour in an MOT task be based on objects' current, past or anticipated positions? We used a data-driven approach with no a priori assumption about the underlying gaze model. We repeatedly presented the same MOT trials forward and backward and collected gaze data. After reversing the data from the backward trials, we gradually tested different time adjustments to find the local maximum of similarity. In a series of four experiments, we showed that the gaze position lagged by approximately 110 ms behind the scene content. We observed the lag in all subjects (Experiment 1). We further experimented to determine whether tracking workload or predictability of movements affect the size of the lag. Low workload led only to a small non-significant shortening of the lag (Experiment 2). Impairing the predictability of objects' trajectories increased the lag (Experiments 3a and 3b). We tested our observations with predictions of a centroid model: we observed a better fit for a model based on the locations of objects 110 ms earlier. We conclude that mathematical models of gaze behaviour in MOT should account for the lags.