Trends in Cognitive Sciences
Tracking multiple targets with multifocal attention
Section snippets
Models of multiple-object tracking
Several models for multiple-object tracking have proposed since Pylyshyn and Storm first reported their results and five of these are described briefly here: grouping, attention switching, multifocal attention, preattentive indexes (FINSTs), and object files (see [12] for a more detailed review of these models). The grouping and switching proposals require only a single focus of attention. Yantis [3], for example, suggested that all the targets are grouped into one higher order object with each
Tracking capacity
Pylyshyn's early results suggested that the limit for tracking independent targets was 4. A recent study [24] shows that arraying the targets in depth increases this capacity somewhat. According to Oksama and Hyona [12], however, there is a lot of variability in the tracking capacity across individuals. They tested 201 subjects and reported that the tracking capacity on trials lasting 5 s was distributed uniformly between 2 to 6, with a mean of 4 (capacity dropped for longer tracking durations).
Attention benefits for targets
There is an advantage for detection of briefly presented probes [15] or identity changes [25] when they appear on targets rather than non-targets. This is evidence that attention is allocated to some or all of the targets and a more detailed analysis of data like these could resolve the issue. Specifically, in the switching and FINST models, attention is on only one target at a time and so the probability of a probe benefiting from attention drops with the reciprocal of the number of targets.
What is tracked in multiple-object tracking?
Recent evidence from Scholl, Pylyshyn and Feldman [33] suggests that the basic unit of tracking is the object. They took a multiple-object tracking display where subjects could successfully track about 4 square targets among distractor squares (Figure 3a,b). They then joined the squares in target-distractor pairs so that each target was now one end of a bar shaped, extended object (see Figure 3c,d). Subjects were unable to track these same 4 target ends when they were parts of the bars. The
Hemifield independence
Whatever the capacity for a given tracking task, a recent study shows that this capacity is split between left and right hemifields [13]. Rather than a limit of, say, 4, subjects demonstrated a limit of 2+2 (see Figure 4), with 2 each in the left and right hemifields. This unexpected hemifield specificity indicates that an early stage of attentive tracking could be based in the retinotopic cortical areas. Certainly, other attention-limited tasks show little or no hemifield independence in
Advantages of studying attention with tracking tasks
In a typical attention task (e.g. [9]), subjects are given a cue indicating a region of interest and a target is then briefly presented at that location (or not, on some small proportion of trials). By contrast, in a tracking task, there are no brief events; items typically remain constant in every respect but their location and the task is to monitor that changing location, making it more akin to the real-world allocation of attention. Multiple-object tracking is also an inherently active task
Tracking and visual memory
Tracking and visual memory are tightly linked and not only in the switching model which relies directly on visual memory for tracking. With attention serving as the gateway to episodic memory, attention can be described as visual memory at time zero. The attention and memory systems do show similar capacity limits and fMRI results for tracking and for visual short term memory show strong parallels. For example, there is a linear increase in the activation of the posterior parietal area as the
Conclusion and future directions
Multiple-object tracking addresses the central question of how attention can be divided. Recent research reveals how many attention ‘spotlights’ or channels can be deployed, whether concurrently or sequentially, and in some tasks, the nature of the target information that can be read out from each while performing the tracking. The trade-off between capacity and feature encoding 25, 26, 12 suggests that attention has a fixed total bandwidth for selection and the bandwidth can be shared across
Acknowledgements
We thank Steve Franconeri for helpful comments. This work was supported in part by NEI grant EY09258 to P.C. and by NRSA grant F31-MH069095–01to G.A.
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