Model of Multiple Identity Tracking (MOMIT) 2.0: Resolving the serial vs. parallel controversy in tracking

Li, Jie; Oksama, Lauri; Hyönä, Jukka

doi:10.1016/j.cognition.2018.10.016

Cited by 20 publications

(40 citation statements)

References 69 publications

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“…When MOMIT selects the wrong object as the target, it can detect and correct the mistake by noticing that the selected object's visual appearance does not match that of the target. Notably, a recent update to MOMIT incorporates parallel processing of visual features, but still requires that targets be serially reattended to refresh their representations (Li, Oksama, & Hyönä, 2019).…”

Section: Serial Trackingmentioning

confidence: 99%

Selection enables enhancement: An integrated model of object tracking

2019

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The diversity of research on visual attention and multipleobject tracking presents challenges for anyone hoping to develop a unified account. One key challenge is identifying the attentional limitations that give rise to competition among targets during tracking. To address this challenge, we present a computational model of object tracking that relies on two attentional mechanisms: serial selection and parallel enhancement. Selection picks out an object for further processing, whereas enhancement increases sensitivity to stimuli in regions where objects have been selected previously. In this model, multiple target locations can be tracked in parallel via enhancement, whereas a single target can be selected so that additional information beyond its location can be processed. In simulations of two psychological experiments, we demonstrate that spatial competition during enhancement and temporal competition for selection can explain a range of findings on multipleobject tracking, and we argue that the interaction between selection and enhancement captured in the model is critical to understanding attention more broadly.Here we summarize five classes of MOT findings, each of which helps to elucidate the limits of human visual attention. Two classes, spatial constraints and temporal constraints, directly address limits on attention. The third class, hemifield advantages, indicates one way of overcoming those limits. The fourth class, dynamic operation, suggests that the demands on attention can change over time during tracking. The final class, sensitivity to motion, demonstrates that processing complex visual features puts additional demands on attention beyond those required simply for tracking targets. Spatial constraintsSeveral studies have found that tracking ability depends on the distance between targets, with performance declining when targets are able to move closer to each other

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Section: Serial Trackingmentioning

confidence: 99%

Selection enables enhancement: An integrated model of object tracking

2019

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“…These studies have led to three types of brain-inspired methods: 1) attention based, 2) memory based, and 3) neural-net based. According to cognitive studies, memory and attention are cognitive processes that are clearly involved in MTT [22]. Tasks measuring visuospatial short-term and working memory as well as attention switching proved to be significant predictors of MTT.…”

Section: Discussionmentioning

confidence: 99%

“…A recent study suggested that MIT performance manifests a more serial pattern when highresolution information is required, and a relatively parallel pattern when low-resolution information suffices for tracking [22]. Authors recorded eye movements and behavioral responses of subjects during tracking multiple distinct faces from the same sex, disks with different colors, and distinct line drawings in separate tasks.…”

Section: Mit Versus Motmentioning

confidence: 99%

“…That is, when tracking faces and drawings, participants visited each target serially, while when tracking color discs, more eye visits to blank areas between targets were observed. When high-resolution information is needed for identifying a target, foveal vision is directed to it; on the other hand, when low-resolution information is sufficient for discriminating the targets, the eyes tend to land on blank areas to sample information from multiple targets covertly [22]. MTT algorithms usually use a serial procedure to track multiple targets.…”

Section: Mit Versus Motmentioning

confidence: 99%

“…Here, we mention some of the consistencies. Human behavioral studies have shown that memory and attention are cognitive processes that are undoubtedly involved in MTT [22]. Several current proposed algorithms benefit from modules with similar function, although their implementation details may not be entirely consistent with how the brain works.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiple-target tracking in human and machine vision

et al. 2020

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Humans are able to track multiple objects at any given time in their daily activities-for example, we can drive a car while monitoring obstacles, pedestrians, and other vehicles. Several past studies have examined how humans track targets simultaneously and what underlying behavioral and neural mechanisms they use. At the same time, computer-vision researchers have proposed different algorithms to track multiple targets automatically. These algorithms are useful for video surveillance, team-sport analysis, video analysis, video summarization, and human-computer interaction. Although there are several efficient biologically inspired algorithms in artificial intelligence, the human multiple-target tracking (MTT) ability is rarely imitated in computer-vision algorithms. In this paper, we review MTT studies in neuroscience and biologically inspired MTT methods in computer vision and discuss the ways in which they can be seen as complementary.

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Tracking the identity of moving words: Stimulus complexity and familiarity affects tracking accuracy

Hyönä

Oksama²,

Rantanen

2019

Applied Cognitive Psychology

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SummaryIn two experiments, participants tracked the identity and location of moving words. The task bears resemblance to one performed by air traffic controllers who track multiple moving aircraft, where they are identified with relatively complex alphanumeric call signs. In Experiment 1, stimulus familiarity was manipulated by comparing the tracking of familiar words and pseudowords. In Experiment 2, also stimulus complexity was varied by having the participants track short and long words. Stimulus complexity affected tracking: Short words were easier to track than long words. Moreover, familiarity of identity affected tracking of short words (short familiar words were easier to track than short pseudo‐words) but not of long words. The results are interpreted within the framework of the model of multiple identity tracking model. Mathematical simulations suggest that observers may not have enough time for full identification of complex identities in a dynamic situation. Practical implications of the results for air traffic control are discussed.

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Model of Multiple Identity Tracking (MOMIT) 2.0: Resolving the serial vs. parallel controversy in tracking

Cited by 20 publications

References 69 publications

Selection enables enhancement: An integrated model of object tracking

Selection enables enhancement: An integrated model of object tracking

Multiple-target tracking in human and machine vision

Tracking the identity of moving words: Stimulus complexity and familiarity affects tracking accuracy

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