Multiple object tracking

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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.

Contents

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

History

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

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. MOT Task figure.jpg
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 ).

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References

Open Access logo PLoS transparent.svg 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. 1 2 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. 1 2 3 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. 1 2 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. 1 2 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. 1 2 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. 1 2 3 4 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. 1 2 3 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 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. 1 2 3 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. 1 2 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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