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For object tracking by computers, see Video tracking.

In psychology and neuroscience, multiple object tracking (MOT) refers to the ability of humans and other animals to simultaneously monitor multiple objects as they move. It is also the term for certain laboratory techniques used to study this ability.

In an MOT study, several identical moving objects are presented on a display. Some of the objects are designated as targets while the rest serve as 'distractors'. The study participants try to monitor the changing positions of the targets as they and the distractions move about. At the end of the trial, typically the participants are asked to indicate the final positions of the targets.

The results of MOT experiments have revealed limitations on humans' ability to simultaneously monitor multiple moving objects. For example, awareness of features such as color and shape is disrupted by the objects' movement.

Background

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History

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In the 1970s, researcher Zenon Pylyshyn postulated the existence of a "primitive visual process" in the human brain capable of "indexing and tracking features or feature-clusters". Using this process, cognitive processes can continuously refer to, or "track", objects despite movement of the objects causing them to stimulate different visual neurons over time.[1] Data collected with Pylyshyn's MOT protocol and published in 1988 provided the first formal demonstration that the mind can keep track of the changing positions of multiple moving objects.[1]

As a specific theory of this ability, Pylyshyn proposed "fingers of instantiation" theory (FINST), which claims that tracking is mediated by a fixed set of discrete pointers. While FINST theory has been very influential, many studies have found evidence that seems inconsistent with the theory.[2]

Procedure

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Sequence of events in a typical MOT task. Target objects are initially highlighted before becoming identical to the distractors. When the objects stop moving, the challenge is to identify which objects were the targets.

A typical MOT study involves the presentation of between eight and twelve objects. The participant is told to monitor the positions of a subset of the objects, which are referred to as targets. Often the targets are indicated by being presented initially in a distinct color. The targets then become identical in appearance to the other, distractor objects. The targets and distractors move about the screen for several seconds in an unpredictable fashion. The participant is then asked to indicate which of the objects are the targets. The accuracy of the participant's judgments indicates whether the participant mentally updated the positions of the targets as they moved.

To ensure that the task requires participants to mentally update the targets' positions, displays are typically designed such that object paths cause the targets to swap positions with distractors, at least occasionally. With that constraint, MOT task variations have been designed to probe specific aspects of how the mind tracks moving objects. For example, to compare performance in the left to performance in the right visual fields, studies confine some or all the moving objects to one of the visual fields.[3] To avoid any contribution from spatial interference among mental object representations, some studies maintain a minimum distance between objects.[4] Other studies have combined MOT with a concurrent task to investigate whether the two tasks draw on the same mental resource, and have changed target features such as color to assess whether study participants update their representations of those features.

Capacity limits

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MOT study results indicate that the number of targets that people can track is very limited. This reflects a bottleneck in the brain's processing architecture. While at the early, sensory stages of visual processing, dozens of objects may be fully processed, later processes such as those associated with cognition have much more limited capacity to process visual objects.[5]

The specific number of visual objects that people can accurately track varies widely with display parameters, contrary to a common belief that people can track no more than four or five objects. Even for a fixed set of display parameters, rather than there being a clear limit, performance falls gradually with the number of targets.[6] Such findings undermine Pylyshyn's FINST theory that tracking is mediated by a fixed set of discrete pointers.[7]

The above limitations appear to stem from processes specific to the two cerebral hemispheres. The independence of the limits in the two hemifields is demonstrated by findings that when one is tracking the maximum number that can be tracked in the left hemifield (which is processed by the right cerebral hemisphere), one can add targets to the right hemifield (which is processed by the left cerebral hemisphere) at little to no cost to performance.[8][9] For features other than position, capacity seems to be more limited—see § Updating of features other than position.

While the tracking capacity limit is largely set separately by the two cerebral hemispheres, a more unified and cognitive resource also can contribute to tracking. For example, if there is only one target, one can bring one's full cognitive abilities to bear, such as in predicting future positions, to facilitate tracking. When more targets are present, these resources may still play a role.[10]

Spatiotemporal limits

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If the objects of a display are not sufficiently widely spaced, the objects are difficult to identify and select with attention due to spatial crowding, which can prevent tracking.[4][11] High object speeds have a similar effect—faster objects are harder to track, and humans are completely unable to track objects that move sufficiently fast. This "speed limit", however, is much slower than the maximum object speed at which humans can judge the object's movement direction.[9][12] This dissociation between motion perception and object tracking is thought to reflect that direction judgments can be based on low-level and local motion detector responses that do not register the positions of objects.

As an object's speed is increased, temporal crowding can result and prevent tracking well before the tracking speed limit is reached.[12][13] Temporal crowding refers to an impairment caused by distractors visiting a target's former location within a short interval. The phenomenon was revealed in a study with a display where distractors were evenly-spaced along a circular trajectory that was also shared by a target. Participants could not track three targets if the locations traversed were visited by objects more than three times per second, and this was true even if the objects were moving at a relatively slow speed. This temporal crowding limit on tracking becomes more severe as the number of targets increases.[13][14]

As the spatial, temporal, and speed limits are approached, tracking performance decreases gradually[11][13] and in typical MOT displays, it is unclear which of these limits, or what combination of them, determine the maximum number of targets that can be tracked.[15] For the spatial limit, one study found little to no effect beyond the Bouma's law crowding zone.[4] Many MOT studies do not enforce sufficient spacing between objects to avoid spatial crowding, making spatial crowding likely to be one factor in overall performance.

Role of prediction and trajectory information

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Brains continuously predict some aspects of the future.[16][17] In the case of multiple object tracking, however, several MOT studies have found evidence against extrapolation of future positions.[18][19][20][21][22]

When future positions are predictable, human object tracking performance can be higher than when future positions are unpredictable. However, the benefit seems to disappear when there are more than one or two targets,[23][24][22] suggesting that any prediction happening is more limited in processing capacity than other aspects of object tracking. One issue with those studies, however, it that predictability of objects' future positions appears to be confounded with the objects being distinguishable from each other (on the basis of maintaining particular and different motion directions). In such experiments, the difference in targets' and distractors' motion directions or accelerations may be the facilitator of tracking rather than prediction of future positions.[25] Indeed, distinctiveness of motion directions alone facilitates tracking.[25] Ability to detect a change in a target's trajectory is much worse with each increase in target number. This suggests motion direction is only utilized when there are few targets,[26] and may explain why the predictability benefit is confined to when there are only a few targets.[23][24]

Role of grouping and coordinate frames

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The human brain represents the positions of objects with multiple reference frames or coordinate systems. Early stages of the visual system represent the locations of objects relative to the direction the eyes are pointing (retinotopic coordinates). Some later stages of human visual processing can represent object locations relative to each other or to the scene.

Regarding representation of relative locations, the relative positions of objects can be represented with an imaginary polygon, with each target a different vertex of that polygon. In studies of MOT, Steve Yantis drew participants' attention to the polygon formed by the targets and found that benefited performance,[27] as did setting the targets' trajectories to avoid much disruption of the constantly-morphing polygon. This suggests that shape tracking contributes to accurate performance, at least in some participants.[28] One study measured an electrical brain response (ERP) to a probe that was flashed while the objects were moving. The earliest-detectable part of the neural response to the probe was significantly greater if the probe lay on the polygon defined by the targets rather than inside or outside the polygon.[29] This suggests that at least some of the participants continuously tracked the polygon defined by the targets.

Displays with more complicated statistical relationships among moving targets have been devised to show that regularities in hierarchical relationships are extracted and utilized in multiple object tracking, including nesting of groups of objects within moving reference frames.[30]

Updating of features other than position

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The classic MOT task requires updating of targets' positions but not their other features. People appear to be less able to update the other features of targets, and have difficulty even in maintaining their knowledge of such features as the associated objects move. In one study, Pylyshyn assigned distinct identities to four identical targets, either by giving them names or by giving them easily-identifiable starting positions: the four corners of the screen. In addition to the usual task at the end of the trial of identifying which objects were the targets, participants also were asked about the identity of the targets – which one each was. Contrary to what Pylyshyn expected from his FINST theory, accuracy at identifying which target was which was very low, even when accuracy reporting the targets' positions was high.[31]

To assess maintenance of knowledge of object identities, one series of experiments used cartoon animals as targets and distractors that all moved about the screen. By the end of each trial, the animals came to rest behind cartoons of cacti, so that their identities were no longer visible. Participants were asked where a particular target (e.g., the cartoon rabbit) had gone—that is, which occluder it was hiding behind. In this multiple identity tracking (MIT) task, performance was much worse than in the standard MOT task of reporting target locations irrespective of which target a location belonged to.[32]

The deficit in updating the locations of featural and identity information may reflect a more general deficit in updating the locations of objects in visual short-term memory. In a study using a shell game in which the shells hid brightly-colored balls of wool, pairs of shells were swapped at a slow rate of once a second, but accuracy judging which shell contained a particular color fell to 80% accuracy when there were four swaps in a simple three-shell display, compared to 95% accuracy for four swaps with a two-shell display.[33]

The concept of an "object file" is that of a record in the brain that stores the features of a visual object, with the location record updated as the object moves.[34] In the original studies that were motivated by this idea, one feature an object disappears and the object moves to a new location. The feature is then presented in the new location, and people respond faster to that feature than to features that were not previously presented as part of the object. This finding of priming indicates that an object file was created and updated by the brain. One might expect this to tap into the same processing as that assessed by the MIT task. The relationship between the two is unclear, however, as there is evidence that attentional tracking occurs can occur along a different trajectory than that which is the basis of updating the memory of an object's features.[35]

In the studies mentioned so far, the objects involved did not change any of their features besides their positions, so the task was to maintain knowledge of (unchanging) features while updating their positions. Change blindness studies show that in many circumstances, people do poorly at noticing that features have changed. A famous demonstration involves placing a blank screen between the presentation of two versions of a screen to mask the flicker that would otherwise be associated with a change. Change blindness also occurs when the flicker evoked by the change is masked by the objects' motion.[36][37] That, however, may only mean that nothing is comparing the features present before and after the change; it does not necessarily mean that object representations are not updated, so other studies are needed.

A related issue is whether tracking can occur on the basis not only of smooth changes in the position of an object, but also on the basis of smooth changes in an object's other features. In a tracking experiment in which two objects were always spatially superposed, the objects maintained their separate identities based on smooth continuity of their colors, orientations, and spatial frequencies. The participants could only track one such object,[38] suggesting no ability to capitalize on spatiotemporal feature continuity for features other than position, although this has not yet been tested for cases in which the targets do not overlap (overlap may trigger figure-ground interference).

Difficulty tracking unusual objects and object parts

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Many objects have clearly-visible parts. A dumbbell, for example, has a central bar part and has the weights at the bar's ends. Even when such parts are conspicuous, people can have difficulty tracking an individual part of multiple objects. When individual ends of multiple dumbbell-shaped drawings are designated as targets, tracking performance is poor.[39][40] Performance was even worse when participants attempted to track one end of multiple moving lines, where the lines were uniform without distinct parts. Evidently, the mental processes that underlie tracking of multiple objects operate on a particular type of object representation that differs from what we can consciously recognize. Possibly the representation used for tracking is shared by that used when searching for a particular colored shape that is hidden among many other shapes; visual search is hindered by connecting targets to distractors.[41][42]

For some types of "objects" that are not segmented as such by early visual processing, not even a single instance can be tracked. Stuart Anstis has shown that people are unable to track the intersection of two lines sliding over each other, except possibly at very slow speeds.[43]

Some things change shape as they move, such as liquids and slinkys. For slinky-like objects that moved by extending their leading edges to a point and then retracting their trailing edges, Kristy vanMarle and Brian Scholl found that tracking performance was poor.[44] The underlying reason for this is unclear, but reporting the location of even a lone object is impaired by growth or contraction of the object, which may contribute to the tracking failure.[39]

Interference with concurrent performance of other tasks

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Overlap among the processes underlying mental abilities can be revealed by what types of concurrent tasks interfere with each other. Attempting to track multiple visual objects typically interferes with other tasks,[45] even for tasks with stimuli in other modalities.[46][47] Unfortunately, it can be difficult to determine whether this reflects processing somewhat specific to our ability to track or instead reflects the processing necessary to initiate and sustain a wide variety of tasks.

One exception to the usual finding of interference with other tasks is that an auditory pitch discrimination task was found to not interfere with visual multiple object tracking.[48] With a task designed as an auditory analog of tracking rather than just requiring discrimination of a few pitches, however, Daryl Fougnie et al. found that the task interfered approximately as much with visual object tracking as did a visual feature-tracking task. This suggests that auditory and visual tracking are limited by a common processing resource.[49]

Neural basis

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Neuroimaging studies find that activation of areas of the parietal cortex increases with the number of objects tracked, which is consistent with the suggestion that the parietal cortex plays a role in humans' limited tracking capacity.[50][51][52] Activation of other brain areas also seems to increase with target load, but the particular areas may be less consistent across studies than the parietal cortex finding. The size of participants' pupils also increases with the number of objects tracked. The pupil size increase, which also is caused by mental effort in other tasks, may reflect norepinephrine release from the locus coeruleus.[52][53]

Objects presented to the left visual hemifield are processed initially by the right cerebral hemisphere, while stimuli presented to the right visual hemifield are processed initially by the left cerebral hemisphere. The independent capacity limits in the two hemifields are very similar, although there may be a small right-hemifield advantage.[54] A right hemifield advantage would be consistent with a contribution by both parietal cortices to tracking that hemifield, which was suggested because both parietal cortices are thought to contribute to other attentional functions in the right hemifield.[55]

The neural basis of MOT has also been studied using electroencephalography (EEG). One such study found a robust correlation between tracking performance and the effect of number of targets on the N2pc event-related potential and also on contralateral delay activity.[56] Multiple brain areas contribute to these signals, so such studies have not yet allowed researchers to determine exactly which brain areas mediate tracking.

Human variation and development

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If a person is tested multiple times, their scores are usually similar to each other.[57][58][59][60] This suggests that the variation in the number of objects people seem able to track (for one version of the task, capacities ranged between one and six targets)[61][62] reflects real variation in ability. A caveat is that studies have failed to assess how much of this could be due to variation in individuals' motivation, but one study tested only top military recruits, a sample that was likely to be highly motivated, and also found substantial variation between individuals.[62]

Most research has been conducted on healthy undergraduates at universities in Western countries, so we don't know much about other populations. Comparing children of different ages, however, two studies in North America found a marked increase with age in the number of objects the children could track, from 6 or 7 years old to adulthood.[63][64] People with autism spectrum disorders have been found to have poorer MOT performance than typically-developing people. This was attributed to a deficit in attentional selection in autism.[65][66]

Adults with Williams Syndrome have profound deficits on certain spatial assembly tasks, such as copying a four-block checkerboard pattern.[67] For multiple object tracking, their performance is similar to typically-developing four- or five-year-old children.[68][65][66] In contrast, their ability to remember the locations of MOT targets if they don't move is more comparable to typically-developing 6-year-olds, which has led to the suggestion that maintaining attentional selection is a particular problem in Williams Syndrome.[69]

Among older typically-developing adults, MOT performance falls steeply with age.[14][70][71] Age-related increases in spatial crowding[72] and temporal crowding[14] likely contribute to this.

Several papers report that video game players perform substantially better in MOT tasks than those who do not play video games.[64][73] However, it has been suggested that this could be an artifact of research practices such as selective publication of results.[74]

Covariation of object tracking ability with other abilities

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While some have used MOT in an attempt to ensure study participants sustain their attention over a long interval, a study with a large number of participants found little correlation with a continuous performance task specifically designed to measure lapses in attention.[75] MOT may, then, be forgiving of lapses in attention, which is consistent with findings that for typical displays, participants can perform well in MOT even if they are occasionally briefly interrupted, with their tracking processes able to pick up where they left off.[45][76]

One approach to investigating which tasks share underlying processing is to test participants on several different tasks to determine which tasks have the highest correlations across individuals. The results of studies that have done this with MOT have not been entirely consistent with each other, so which tasks yield the highest correlation with MOT performance is not yet clear. However, multiple studies find that visual working memory is one of the most highly-correlated tasks.[57][59] That correlation is consistent with findings that working memory tasks are among the best predictors of performance in a range of tasks.[77] This may reflect shared mechanisms such as maintaining goal-relevant information in memory (possibly including which objects are the targets) and disengaging from outdated or irrelevant information.[78]

Use in ability testing and training

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Some professional sports teams use laboratory-style MOT tests for ability assessment and for training.[79] Associates of the company that makes the "NeuroTracker" MOT product claim that NeuroTracker is a "cognitive enhancer" that improves a variety of abilities relevant to performance on the sports field, but the evidence in the studies purporting to show this is weak.[80] Another reason for skepticism of such claims is the poor track record of other commercial "brain training" products advertised for their cognitive-enhancing effects.[79][81]

While it is unlikely that training on laboratory-style MOT tasks yields broad mental benefits, when more rigorous studies are done, it is possible that firm evidence may support the use of tasks related to MOT for screening or training purposes for specific purposes. Regarding screening, however, one study found that laboratory MOT performance did not predict driving test performance as well as the Montreal Cognitive Assessment, a trail-making task, or a useful field-of-view task.[82] A multiple object avoidance (MOA) task, involving steering a ball with a computer mouse to prevent it from colliding with other moving balls on a computer screen, was found to correlate better with driving performance than MOT.[83] In another study, strong positive correlations with MOA performance were found with driving simulator performance and years of driving experience.[84] This may be because MOA includes control of movement, which is necessary for driving, but is not required for MOT.[85]

Theories and models

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Published computational models fit some aspects of tracking results, with most focusing on the pattern of performance decline with increasing number of targets, and some modeling the dissociation between position and non-position features. No published theory purports to explain all four of the following: the difficulty with tracking parts of objects, the role of temporal interference, the dissociation between position and non-positional features, and the pattern of performance decline with increasing number of targets.

Serial versus parallel processing

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The independence of tracking in the left and right hemifields suggests that position updating in each hemifield occurs independently of and in parallel with position updating in the other hemifield (see § Capacity limits). Within a hemifield, it is not yet completely clear whether tracking of multiple objects happens in parallel or instead the target positions are updated one-by-one, but most recent theorists agree with Pylyshyn's original FINST theory that positions are updated in parallel.[86][87][88][89] A finding that gives some support to the alternative of serial switching is the marked increase in temporal interference as the number of targets tracked increases. In particular, the amount of increase in time needed between when a target leaves a location and a distractor takes its place is approximately predicted by the theory that attention must visit each moving target one-by-one to update its location.[13]

Some who theorize that position updating occurs simultaneously for multiple targets draw a contrast with features other than position, stating that they are updated by a process that must serially switch among the targets.[86][87][88][89] A model by Lovett, Bridewell, & Bello published in 2019, for example, includes a parallel process to track changes in position and connect to visual pointers that are shared with visual short-term memory and other visual attention tasks. A serial selection process is also included, which operates on only one object at a time and enables access to a target's motion history and other features.[88]

Slots versus resources

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Central to Pylyshyn's FINST theory is that a small set of discrete pointers mediate multiple object tracking. Subsequent researchers have suggested that rather than discrete pointers, a mental resource that is more continuous is divided among the targets.[90][91] This dispute is similar to the "slots versus resources" debate in the study of working memory. A continuous resource naturally explains the smooth decline in performance with number of targets, although there is no agreement about what precisely about tracking becomes worse when less resource is provided. Possibilities include spatial resolution, temporal resolution, the maximum speed of the tracker, or all three (see § Spatiotemporal limits).

References

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This article was adapted from the following source under a CC BY 4.0 license (2023) (reviewer reports): Alex O. Holcombe (15 April 2023). "Multiple object tracking" (PDF). WikiJournal of Science. 6 (1): 3. doi:10.15347/WJS/2023.003. ISSN 2470-6345. Wikidata Q115162234.

  1. ^ a b Pylyshyn, Z. W.; Storm, R. W. (1988). "Tracking multiple independent targets: Evidence for a parallel tracking mechanism". Spatial Vision. 3 (3): 179–197. doi:10.1163/156856888X00122. PMID 3153671. S2CID 1433436.
  2. ^ Scholl, Brian J. (2008). "What Have We Learned about Attention from Multiple-Object Tracking (and Vice Versa)?". In Dedrick, Don; Trick, Lana (eds.). Computation, cognition, and Pylyshyn. MIT Press. pp. 49–78. doi:10.7551/mitpress/8135.003.0005. ISBN 9780262255196.
  3. ^ Edwards, Grace; Berestova, Anna; Battelli, Lorella (2021-09-29). "Behavioral gain following isolation of attention". Scientific Reports. 11 (1): 19329. Bibcode:2021NatSR..1119329E. doi:10.1038/s41598-021-98670-w. ISSN 2045-2322. PMC 8481494. PMID 34588526.
  4. ^ a b c Holcombe, A. O.; Chen, W.- Y.; Howe, P. D. L. (2014-08-01). "Object tracking: Absence of long-range spatial interference supports resource theories". Journal of Vision. 14 (6): 1. doi:10.1167/14.6.1. ISSN 1534-7362. PMID 25086084.
  5. ^ Holcombe, Alex O. (2023). Attending to moving objects. Cambridge University Press. Section 2. doi:10.1017/9781009003414. ISBN 9781009003414. S2CID 256170538.
  6. ^ Holcombe 2023, Section 3.
  7. ^ Alvarez, George A.; Franconeri, Steven L. (2007-10-30). "How many objects can you track?: Evidence for a resource-limited attentive tracking mechanism". Journal of Vision. 7 (13): 14.1–10. doi:10.1167/7.13.14. ISSN 1534-7362. PMID 17997642.
  8. ^ Alvarez, George A.; Cavanagh, Patrick (August 2005). "Independent Resources for Attentional Tracking in the Left and Right Visual Hemifields". Psychological Science. 16 (8): 637–643. doi:10.1111/j.1467-9280.2005.01587.x. ISSN 0956-7976. PMID 16102067. S2CID 590734.
  9. ^ a b Holcombe, Alex O.; Chen, Wei-Ying (May 2012). "Exhausting attentional tracking resources with a single fast-moving object". Cognition. 123 (2): 218–228. doi:10.1016/j.cognition.2011.10.003. hdl:2123/7868. PMID 22055340. S2CID 20494664.
  10. ^ Holcombe 2023, Section 6.
  11. ^ a b Intriligator, James; Cavanagh, Patrick (November 2001). "The Spatial Resolution of Visual Attention". Cognitive Psychology. 43 (3): 171–216. doi:10.1006/cogp.2001.0755. PMID 11689021. S2CID 18050760.
  12. ^ a b Verstraten, Frans A.J; Cavanagh, Patrick; Labianca, Angela T (December 2000). "Limits of attentive tracking reveal temporal properties of attention". Vision Research. 40 (26): 3651–3664. doi:10.1016/S0042-6989(00)00213-3. PMID 11116167. S2CID 12270476.
  13. ^ a b c d Holcombe, A. O.; Chen, W.-Y. (2013-01-09). "Splitting attention reduces temporal resolution from 7 Hz for tracking one object to". Journal of Vision. 13 (1): 12. doi:10.1167/13.1.12. ISSN 1534-7362. PMID 23302215.
  14. ^ a b c Roudaia, Eugenie; Faubert, Jocelyn (2017-09-01). "Different effects of aging and gender on the temporal resolution in attentional tracking". Journal of Vision. 17 (11): 1. doi:10.1167/17.11.1. ISSN 1534-7362. PMID 28862709.
  15. ^ Holcombe 2023, Section 4.
  16. ^ Clark, Andy (2016). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford. doi:10.1093/acprof:oso/9780190217013.001.0001. ISBN 978-0-19-021701-3. OCLC 904011681.{{cite book}}: CS1 maint: location missing publisher (link)
  17. ^ Hohwy, Jakob (2013). The predictive mind (First ed.). Oxford. doi:10.1093/acprof:oso/9780199682737.001.0001. ISBN 978-0-19-150519-5. OCLC 868923880.{{cite book}}: CS1 maint: location missing publisher (link)
  18. ^ Franconeri, Steven L.; Pylyshyn, Zenon W.; Scholl, Brian J. (May 2012). "A simple proximity heuristic allows tracking of multiple objects through occlusion". Attention, Perception, & Psychophysics. 74 (4): 691–702. doi:10.3758/s13414-011-0265-9. ISSN 1943-3921. PMID 22271165. S2CID 256119018.
  19. ^ Keane, B; Pylyshyn, Z (June 2006). "Is motion extrapolation employed in multiple object tracking? Tracking as a low-level, non-predictive function☆". Cognitive Psychology. 52 (4): 346–368. doi:10.1016/j.cogpsych.2005.12.001. PMID 16442088. S2CID 5771001.
  20. ^ Howard, Christina J.; Masom, David; Holcombe, Alex O. (September 2011). "Position representations lag behind targets in multiple object tracking". Vision Research. 51 (17): 1907–1919. doi:10.1016/j.visres.2011.07.001. PMID 21762715. S2CID 14555811.
  21. ^ Howard, Christina J.; Holcombe, Alex O. (April 2008). "Tracking the changing features of multiple objects: Progressively poorer perceptual precision and progressively greater perceptual lag". Vision Research. 48 (9): 1164–1180. doi:10.1016/j.visres.2008.01.023. PMID 18359501. S2CID 8485280.
  22. ^ a b Fencsik, David E.; Klieger, Sarah B.; Horowitz, Todd S. (May 2007). "The role of location and motion information in the tracking and recovery of moving objects". Perception & Psychophysics. 69 (4): 567–577. doi:10.3758/BF03193914. ISSN 0031-5117. PMID 17727110. S2CID 24515387.
  23. ^ a b Howe, P. D. L.; Holcombe, A. O. (2012-12-10). "Motion information is sometimes used as an aid to the visual tracking of objects". Journal of Vision. 12 (13): 10. doi:10.1167/12.13.10. ISSN 1534-7362. PMID 23232339.
  24. ^ a b Luu, Tina; Howe, Piers D. L. (August 2015). "Extrapolation occurs in multiple object tracking when eye movements are controlled". Attention, Perception, & Psychophysics. 77 (6): 1919–1929. doi:10.3758/s13414-015-0891-8. ISSN 1943-3921. PMID 25893469. S2CID 256207631.
  25. ^ a b Wang, Yang; Vul, Edward (2021-03-26). "The role of kinematic properties in multiple object tracking". Journal of Vision. 21 (3): 22. doi:10.1167/jov.21.3.22. ISSN 1534-7362. PMC 7998010. PMID 33769442.
  26. ^ Tripathy, Srimant P.; Barrett, Brendan T. (2004-12-09). "Severe loss of positional information when detecting deviations in multiple trajectories". Journal of Vision. 4 (12): 1020–1043. doi:10.1167/4.12.4. ISSN 1534-7362. PMID 15669909.
  27. ^ Yantis, Steven (July 1992). "Multielement visual tracking: Attention and perceptual organization". Cognitive Psychology. 24 (3): 295–340. doi:10.1016/0010-0285(92)90010-Y. PMID 1516359. S2CID 974635.
  28. ^ Merkel, Christian; Stoppel, Christian M.; Hillyard, Steven A.; Heinze, Hans-Jochen; Hopf, Jens-Max; Schoenfeld, Mircea Ariel (2014-01-01). "Spatio-temporal Patterns of Brain Activity Distinguish Strategies of Multiple-object Tracking". Journal of Cognitive Neuroscience. 26 (1): 28–40. doi:10.1162/jocn_a_00455. ISSN 0898-929X. PMID 23915053. S2CID 11744449.
  29. ^ Merkel, Christian; Hopf, Jens-Max; Schoenfeld, Mircea Ariel (February 2017). "Spatio-temporal dynamics of attentional selection stages during multiple object tracking". NeuroImage. 146: 484–491. doi:10.1016/j.neuroimage.2016.10.046. PMID 27810524. S2CID 3389532.
  30. ^ Bill, Johannes; Pailian, Hrag; Gershman, Samuel J.; Drugowitsch, Jan (2020-09-29). "Hierarchical structure is employed by humans during visual motion perception". Proceedings of the National Academy of Sciences. 117 (39): 24581–24589. Bibcode:2020PNAS..11724581B. doi:10.1073/pnas.2008961117. ISSN 0027-8424. PMC 7533882. PMID 32938799.
  31. ^ Pylyshyn, Zenon (October 2004). "Some puzzling findings in multiple object tracking: I. Tracking without keeping track of object identities". Visual Cognition. 11 (7): 801–822. doi:10.1080/13506280344000518. ISSN 1350-6285. S2CID 14717612.
  32. ^ Horowitz, Todd S.; Klieger, Sarah B.; Fencsik, David E.; Yang, Kevin K.; Alvarez, George A.; Wolfe, Jeremy M. (February 2007). "Tracking unique objects". Perception & Psychophysics. 69 (2): 172–184. doi:10.3758/BF03193740. ISSN 0031-5117. PMID 17557588. S2CID 8138353.
  33. ^ Pailian, Hrag; Carey, Susan E.; Halberda, Justin; Pepperberg, Irene M. (December 2020). "Age and Species Comparisons of Visual Mental Manipulation Ability as Evidence for its Development and Evolution". Scientific Reports. 10 (1): 7689. Bibcode:2020NatSR..10.7689P. doi:10.1038/s41598-020-64666-1. ISSN 2045-2322. PMC 7203154. PMID 32376944.
  34. ^ Kahneman, Daniel; Treisman, Anne; Gibbs, Brian J (April 1992). "The reviewing of object files: Object-specific integration of information". Cognitive Psychology. 24 (2): 175–219. doi:10.1016/0010-0285(92)90007-O. PMID 1582172. S2CID 2688060.
  35. ^ Mitroff, Stephen R.; Scholl, Brian J.; Wynn, Karen (May 2005). "The relationship between object files and conscious perception". Cognition. 96 (1): 67–92. doi:10.1016/j.cognition.2004.03.008. PMID 15833307. S2CID 9043690.
  36. ^ Saiki, J.; Holcombe, A. O. (2012-03-06). "Blindness to a simultaneous change of all elements in a scene, unless there is a change in summary statistics". Journal of Vision. 12 (3): 2. doi:10.1167/12.3.2. ISSN 1534-7362. PMID 22396462.
  37. ^ Suchow, Jordan W.; Alvarez, George A. (January 2011). "Motion Silences Awareness of Visual Change". Current Biology. 21 (2): 140–143. Bibcode:2011CBio...21..140S. doi:10.1016/j.cub.2010.12.019. PMID 21215632. S2CID 10500810.
  38. ^ Blaser, Erik; Pylyshyn, Zenon W.; Holcombe, Alex O. (November 2000). "Tracking an object through feature space". Nature. 408 (6809): 196–199. Bibcode:2000Natur.408..196B. doi:10.1038/35041567. ISSN 0028-0836. PMID 11089972. S2CID 4418346.
  39. ^ a b Howe, Piers D.; Incledon, Natalie C.; Little, Daniel R. (2012-07-30). de Fockert, Jan (ed.). "Can Attention Be Confined to Just Part of a Moving Object? Revisiting Target-Distractor Merging in Multiple Object Tracking". PLOS ONE. 7 (7): e41491. Bibcode:2012PLoSO...741491H. doi:10.1371/journal.pone.0041491. ISSN 1932-6203. PMC 3408494. PMID 22859990.
  40. ^ Scholl, Brian J; Pylyshyn, Zenon W; Feldman, Jacob (June 2001). "What is a visual object? Evidence from target merging in multiple object tracking". Cognition. 80 (1–2): 159–177. doi:10.1016/S0010-0277(00)00157-8. PMID 11245843. S2CID 7053492.
  41. ^ Holcombe 2023, Section 7.4.
  42. ^ Wolfe, Jeremy M.; Bennett, Sara C. (January 1997). "Preattentive Object Files: Shapeless Bundles of Basic Features". Vision Research. 37 (1): 25–43. doi:10.1016/S0042-6989(96)00111-3. PMID 9068829. S2CID 16189579.
  43. ^ Anstis, S. (1990). Imperceptible intersections: The chopstick illusion. In A. Blake and T. Troscianko (Eds.), AI and the Eye. London: Wiley and Sons Ltd., 105-117.
  44. ^ vanMarle, Kristy; Scholl, Brian J. (September 2003). "Attentive Tracking of Objects Versus Substances". Psychological Science. 14 (5): 498–504. doi:10.1111/1467-9280.03451. ISSN 0956-7976. PMID 12930483. S2CID 15083705.
  45. ^ a b Alvarez, George A.; Horowitz, Todd S.; Arsenio, Helga C.; DiMase, Jennifer S.; Wolfe, Jeremy M. (2005). "Do Multielement Visual Tracking and Visual Search Draw Continuously on the Same Visual Attention Resources?". Journal of Experimental Psychology: Human Perception and Performance. 31 (4): 643–667. doi:10.1037/0096-1523.31.4.643. ISSN 1939-1277. PMID 16131240.
  46. ^ Wahn, Basil; König, Peter (2015-07-29). "Audition and vision share spatial attentional resources, yet attentional load does not disrupt audiovisual integration". Frontiers in Psychology. 6: 1084. doi:10.3389/fpsyg.2015.01084. ISSN 1664-1078. PMC 4518141. PMID 26284008.
  47. ^ Wahn, Basil; König, Peter (2015). "Vision and Haptics Share Spatial Attentional Resources and Visuotactile Integration Is Not Affected by High Attentional Load". Multisensory Research. 28 (3–4): 371–392. doi:10.1163/22134808-00002482. ISSN 2213-4794. PMID 26288905.
  48. ^ Arrighi, Roberto; Lunardi, Roy; Burr, David (2011). "Vision and Audition Do Not Share Attentional Resources in Sustained Tasks". Frontiers in Psychology. 2: 56. doi:10.3389/fpsyg.2011.00056. ISSN 1664-1078. PMC 3110771. PMID 21734893.
  49. ^ Fougnie, Daryl; Cockhren, Jurnell; Marois, René (August 2018). "A common source of attention for auditory and visual tracking". Attention, Perception, & Psychophysics. 80 (6): 1571–1583. doi:10.3758/s13414-018-1524-9. ISSN 1943-3921. PMC 6061001. PMID 29717471.
  50. ^ Jovicich, Jorge; Peters, Robert J.; Koch, Christof; Braun, Jochen; Chang, Linda; Ernst, Thomas (2001-11-15). "Brain Areas Specific for Attentional Load in a Motion-Tracking Task". Journal of Cognitive Neuroscience. 13 (8): 1048–1058. doi:10.1162/089892901753294347. ISSN 0898-929X. PMID 11784443. S2CID 10836232.
  51. ^ Culham, Jody C; Cavanagh, Patrick; Kanwisher, Nancy G (November 2001). "Attention Response Functions". Neuron. 32 (4): 737–745. doi:10.1016/S0896-6273(01)00499-8. PMID 11719212. S2CID 14414579.
  52. ^ a b Alnaes, D.; Sneve, M. H.; Espeseth, T.; Endestad, T.; van de Pavert, S. H. P.; Laeng, B. (2014-04-01). "Pupil size signals mental effort deployed during multiple object tracking and predicts brain activity in the dorsal attention network and the locus coeruleus". Journal of Vision. 14 (4): 1. doi:10.1167/14.4.1. ISSN 1534-7362. PMID 24692319. S2CID 11688513.
  53. ^ Wahn, Basil; Ferris, Daniel P.; Hairston, W. David; König, Peter (2016-12-15). Price, Nicholas Seow Chiang (ed.). "Pupil Sizes Scale with Attentional Load and Task Experience in a Multiple Object Tracking Task". PLOS ONE. 11 (12): e0168087. Bibcode:2016PLoSO..1168087W. doi:10.1371/journal.pone.0168087. ISSN 1932-6203. PMC 5157994. PMID 27977762.
  54. ^ Holcombe 2023, Section 9.6.
  55. ^ Mesulam, M.-Marsel (1999-07-29). Howseman, A.; Zeki, S. (eds.). "Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events". Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences. 354 (1387): 1325–1346. doi:10.1098/rstb.1999.0482. ISSN 0962-8436. PMC 1692628. PMID 10466154.
  56. ^ Drew, T.; Vogel, E. K. (2008-04-16). "Neural Measures of Individual Differences in Selecting and Tracking Multiple Moving Objects". Journal of Neuroscience. 28 (16): 4183–4191. doi:10.1523/JNEUROSCI.0556-08.2008. ISSN 0270-6474. PMC 2570324. PMID 18417697.
  57. ^ a b Huang, Liqiang; Mo, Lei; Li, Ying (April 2012). "Measuring the interrelations among multiple paradigms of visual attention: An individual differences approach". Journal of Experimental Psychology: Human Perception and Performance. 38 (2): 414–428. doi:10.1037/a0026314. ISSN 1939-1277. PMID 22250865.
  58. ^ Wilbiks, Jonathan M. P.; Beatteay, Annika (October 2020). "Individual differences in multiple object tracking, attentional cueing, and age account for variability in the capacity of audiovisual integration". Attention, Perception, & Psychophysics. 82 (7): 3521–3543. doi:10.3758/s13414-020-02062-7. ISSN 1943-3921. PMID 32529573. S2CID 219606656.
  59. ^ a b Treviño, Melissa; Zhu, Xiaoshu; Lu, Yi Yi; Scheuer, Luke S.; Passell, Eliza; Huang, Grace C.; Germine, Laura T.; Horowitz, Todd S. (December 2021). "How do we measure attention? Using factor analysis to establish construct validity of neuropsychological tests". Cognitive Research: Principles and Implications. 6 (1): 51. doi:10.1186/s41235-021-00313-1. ISSN 2365-7464. PMC 8298746. PMID 34292418.
  60. ^ Eayrs, Joshua; Lavie, Nilli (August 2018). "Establishing individual differences in perceptual capacity". Journal of Experimental Psychology: Human Perception and Performance. 44 (8): 1240–1257. doi:10.1037/xhp0000530. ISSN 1939-1277. PMID 29578735. S2CID 4422544.
  61. ^ Meyerhoff, Hauke S.; Papenmeier, Frank (December 2020). "Individual differences in visual attention: A short, reliable, open-source, and multilingual test of multiple object tracking in PsychoPy". Behavior Research Methods. 52 (6): 2556–2566. doi:10.3758/s13428-020-01413-4. ISSN 1554-3528. PMID 32495028. S2CID 256203146.
  62. ^ a b Oksama, Lauri; Hyönä, Jukka (July 2004). "Is multiple object tracking carried out automatically by an early vision mechanism independent of higher-order cognition? An individual difference approach". Visual Cognition. 11 (5): 631–671. doi:10.1080/13506280344000473. ISSN 1350-6285. S2CID 144881546.
  63. ^ Trick, Lana M.; Jaspers-Fayer, Fern; Sethi, Naina (2005-07-01). "Multiple-object tracking in children: The "Catch the Spies" task". Cognitive Development. 20 (3): 373–387. doi:10.1016/j.cogdev.2005.05.009. ISSN 0885-2014. S2CID 655920.
  64. ^ a b Dye, Matthew W. G.; Bavelier, Daphne (2010-02-22). "Differential development of visual attention skills in school-age children". Vision Research. Perceptual Learning Part II. 50 (4): 452–459. doi:10.1016/j.visres.2009.10.010. ISSN 0042-6989. PMC 2824025. PMID 19836409.
  65. ^ a b Koldewyn, Kami; Weigelt, Sarah; Kanwisher, Nancy; Jiang, Yuhong (June 2013). "Multiple Object Tracking in Autism Spectrum Disorders". Journal of Autism and Developmental Disorders. 43 (6): 1394–1405. doi:10.1007/s10803-012-1694-6. ISSN 0162-3257. PMC 3581699. PMID 23104619.
  66. ^ a b O'Hearn, Kirsten; Franconeri, Steven; Wright, Catherine; Minshew, Nancy; Luna, Beatriz (April 2013). "The development of individuation in autism". Journal of Experimental Psychology: Human Perception and Performance. 39 (2): 494–509. doi:10.1037/a0029400. ISSN 1939-1277. PMC 3608798. PMID 22963232.
  67. ^ Mervis, Carolyn B.; Robinson, Byron F.; Pani, John R. (November 1999). "Visuospatial Construction". The American Journal of Human Genetics. 65 (5): 1222–1229. doi:10.1086/302633. PMC 1288273. PMID 10521286.
  68. ^ Ferrara, Katrina; Hoffman, James E.; O’Hearn, Kirsten; Landau, Barbara (2016-08-07). "Constraints on Multiple Object Tracking in Williams Syndrome: How Atypical Development Can Inform Theories of Visual Processing". Journal of Cognition and Development. 17 (4): 620–641. doi:10.1080/15248372.2016.1195389. ISSN 1524-8372. S2CID 4677194.
  69. ^ O’Hearn, Kirsten; Hoffman, James E.; Landau, Barbara (May 2010). "Developmental profiles for multiple object tracking and spatial memory: typically developing preschoolers and people with Williams syndrome: Multiple object tracking in preschool children and WS". Developmental Science. 13 (3): 430–440. doi:10.1111/j.1467-7687.2009.00893.x. PMC 2927133. PMID 20443964.
  70. ^ Sekuler, Robert; McLaughlin, Chris; Yotsumoto, Yuko (June 2008). "Age-Related Changes in Attentional Tracking of Multiple Moving Objects". Perception. 37 (6): 867–876. doi:10.1068/p5923. ISSN 0301-0066. PMID 18686706. S2CID 879560.
  71. ^ Kennedy, G. J.; Tripathy, S. P.; Barrett, B. T. (2009-02-01). "Early age-related decline in the effective number of trajectories tracked in adult human vision". Journal of Vision. 9 (2): 21.1–10. doi:10.1167/9.2.21. ISSN 1534-7362. PMID 19271931.
  72. ^ Scialfa, C. T.; Cordazzo, S.; Bubric, K.; Lyon, J. (2013-07-01). "Aging and Visual Crowding". The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 68 (4): 522–528. doi:10.1093/geronb/gbs086. ISSN 1079-5014. PMID 23009956.
  73. ^ Green, C. S.; Bavelier, D. (2006-08-01). "Enumeration versus multiple object tracking: the case of action video game players". Cognition. 101 (1): 217–245. doi:10.1016/j.cognition.2005.10.004. ISSN 0010-0277. PMC 2896820. PMID 16359652.
  74. ^ Hilgard, Joseph; Sala, Giovanni; Boot, Walter R.; Simons, Daniel J. (2019-01-01). "Overestimation of Action-Game Training Effects: Publication Bias and Salami Slicing". Collabra: Psychology. 5 (1). doi:10.1525/collabra.231. ISSN 2474-7394. S2CID 198617728.
  75. ^ Fortenbaugh, Francesca C.; DeGutis, Joseph; Germine, Laura; Wilmer, Jeremy B.; Grosso, Mallory; Russo, Kathryn; Esterman, Michael (September 2015). "Sustained Attention Across the Life Span in a Sample of 10,000: Dissociating Ability and Strategy". Psychological Science. 26 (9): 1497–1510. doi:10.1177/0956797615594896. ISSN 0956-7976. PMC 4567490. PMID 26253551.
  76. ^ Horowitz, Todd S.; Birnkrant, Randall S.; Fencsik, David E.; Tran, Linda; Wolfe, Jeremy M. (June 2006). "How do we track invisible objects?". Psychonomic Bulletin & Review. 13 (3): 516–523. doi:10.3758/BF03193879. ISSN 1069-9384. PMID 17048740. S2CID 9749474.
  77. ^ Redick, Thomas S.; Engle, Randall W. (July 2006). "Working memory capacity and attention network test performance". Applied Cognitive Psychology. 20 (5): 713–721. doi:10.1002/acp.1224. ISSN 0888-4080.
  78. ^ Mashburn, Cody A.; Tsukahara, Jason S.; Engle, Randall W. (2020-11-05). Individual Differences in Attention Control: Implications for the Relationship Between Working Memory Capacity and Fluid Intelligence. Oxford University Press. pp. 175–211. doi:10.1093/oso/9780198842286.003.0007. ISBN 978-0-19-884228-6.
  79. ^ a b Schonbrun, Zach (2017-01-04). "Keep Your Eye on the Balls to Become a Better Athlete". The New York Times. ISSN 0362-4331. Retrieved 2022-10-06.
  80. ^ Vater, Christian; Gray, Rob; Holcombe, Alex O. (October 2021). "A critical systematic review of the Neurotracker perceptual-cognitive training tool". Psychonomic Bulletin & Review. 28 (5): 1458–1483. doi:10.3758/s13423-021-01892-2. ISSN 1069-9384. PMC 8500884. PMID 33821464.
  81. ^ Simons, Daniel J.; Boot, Walter R.; Charness, Neil; Gathercole, Susan E.; Chabris, Christopher F.; Hambrick, David Z.; Stine-Morrow, Elizabeth A. L. (October 2016). "Do "Brain-Training" Programs Work?". Psychological Science in the Public Interest. 17 (3): 103–186. doi:10.1177/1529100616661983. ISSN 1529-1006. PMID 27697851. S2CID 13729927.
  82. ^ Bowers, Alex R.; Anastasio, R. Julius; Sheldon, Sarah S.; O’Connor, Margaret G.; Hollis, Ann M.; Howe, Piers D.; Horowitz, Todd S. (October 2013). "Can we improve clinical prediction of at-risk older drivers?". Accident Analysis & Prevention. 59: 537–547. doi:10.1016/j.aap.2013.06.037. PMC 3769510. PMID 23954688.
  83. ^ Mackenzie, Andrew K.; Harris, Julie M. (February 2017). "A link between attentional function, effective eye movements, and driving ability". Journal of Experimental Psychology: Human Perception and Performance. 43 (2): 381–394. doi:10.1037/xhp0000297. ISSN 1939-1277. PMC 5279462. PMID 27893270.
  84. ^ Mackenzie, Andrew K.; Vernon, Mike L.; Cox, Paul R.; Crundall, David; Daly, Rosie C.; Guest, Duncan; Muhl-Richardson, Alexander; Howard, Christina J. (June 2022). "The Multiple Object Avoidance (MOA) task measures attention for action: Evidence from driving and sport". Behavior Research Methods. 54 (3): 1508–1529. doi:10.3758/s13428-021-01679-2. ISSN 1554-3528. PMC 9170642. PMID 34786653.
  85. ^ Holcombe 2023, Section 12.
  86. ^ a b Oksama, Lauri; Hyönä, Jukka (January 2016). "Position tracking and identity tracking are separate systems: Evidence from eye movements". Cognition. 146: 393–409. doi:10.1016/j.cognition.2015.10.016. PMID 26529194. S2CID 14749878.
  87. ^ a b Li, Jie; Oksama, Lauri; Hyönä, Jukka (January 2019). "Model of Multiple Identity Tracking (MOMIT) 2.0: Resolving the serial vs. parallel controversy in tracking". Cognition. 182: 260–274. doi:10.1016/j.cognition.2018.10.016. PMID 30384128. S2CID 53181791.
  88. ^ a b c Lovett, Andrew; Bridewell, Will; Bello, Paul (2019-12-23). "Selection enables enhancement: An integrated model of object tracking". Journal of Vision. 19 (14): 23. doi:10.1167/19.14.23. ISSN 1534-7362. PMID 31868894. S2CID 209446017.
  89. ^ a b Kazanovich, Yakov; Borisyuk, Roman (June 2006). "An Oscillatory Neural Model of Multiple Object Tracking". Neural Computation. 18 (6): 1413–1440. doi:10.1162/neco.2006.18.6.1413. ISSN 0899-7667. PMID 16764509. S2CID 13947567.
  90. ^ Alvarez, George A.; Franconeri, Steven L. (2007-10-30). "How many objects can you track?: Evidence for a resource-limited attentive tracking mechanism". Journal of Vision. 7 (13): 14.1–10. doi:10.1167/7.13.14. ISSN 1534-7362. PMID 17997642.
  91. ^ Vul, E.; Frank, M.; Tenenbaum, J.; Alvarez, G. A. (2009). "Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model". In Bengio, Y.; Schuurmans, D.; Lafferty, J.; Williams, C.; Culotta, A. (eds.). Advances in Neural Information Processing Systems (PDF). Vol. 22. Neural Information Processing Systems. pp. 1955–1963. ISBN 9781615679119.
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