In addition, MCMC method is capitalized on to perform efficient sampling and to avoid the performance degradation of particle filter-based approaches in multiple object tracking arising from the curse of dimensionality.
Moreover, given the poor temporal resolution of the fMRI technique, these data do not provide information on the temporal brain dynamics involved in multiple object processing.
For example, in multiple object tracking (MOT) tasks participants are asked to pursue with covert attention only (i.e., without moving their gaze from central fixation) a variable number of objects that move independently and unpredictably across the visual display for an extended duration of time (e.g. 10 seconds).
SAIM's starting point is the human ability to identify objects in multiple object scenes.
SAIM models the human ability to perform translation-invariant object identification in multiple object scenes.
Overall VS-SAIM implements a translation-invariant object identification in multiple object scenes.
Chesney, D.L. & Haladjian, H.H. Evidence for a shared mechanism used in multiple-object tracking and subitizing.
The idea of local image entropy was introduced in [24], where the measure of local entropy defines the segmentation boundaries in multiple-object images.
In this study we examine the role of visual learning in multiple-object tracking and characterize how varieties of attention interact with visual learning.
In the present study we tested whether individual variation in CHRNA4, a gene coding for a subcomponent in α4β2 nicotinic receptors in the human brain, interacted with processing load in multiple-object tracking (MOT) and visual search (VS).
In those blocks in which multiple objects were presented, 1 object was always positioned at the center of screen, and 9 other objects were positioned at random locations surrounding it on the display screen, without any overlap between objects.
