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Selecting and tracking multiple objects
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AI
DOI：10.1002/wcs.1328.png)
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摘要
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When interacting with the world, people can dynamically split attention across multiple objects in the environment, both when the objects are stationary and when the objects are moving. This type of visual processing is commonly studied in lab settings using either static selection tasks or moving tracking tasks. We describe performance limits that are common to both tasks, including limits on capacity, crowding, visual hemifield arrangement, and speed. Because these shared limits on performance suggest common underlying mechanisms, we examine a set of models that might account for limits across both. We also review cognitive neuroscience data relevant to these limits, which can provide constraints on the set of models. Finally, we examine performance limits that are unique to tracking tasks, such as trajectory encoding, and identity encoding. We argue that a complete model of multiple object tracking must account for both those limits shared between static selection and dynamic tracking, as well as limits unique to tracking. It must also provide neurally plausible mechanisms for the underlying processing resources. (C) 2014 John Wiley & Sons, Ltd.
Keyword:
SHORT-TERM-MEMORY
NEURAL MEASURES
ELECTROPHYSIOLOGICAL MEASURES
INDIVIDUAL-DIFFERENCES
ANIMATED TRANSITIONS
ATTENTIONAL TRACKING
SPATIAL ATTENTION
FMRI ACTIVATION
WORKING-MEMORY
RESOURCES
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IF:
3.8
论文数:
613
被引数:
2.8K
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