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Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations

Overview of attention for article published in Attention, Perception, & Psychophysics, July 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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Title
Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations
Published in
Attention, Perception, & Psychophysics, July 2016
DOI 10.3758/s13414-016-1178-4
Pubmed ID
Authors

Jiří Lukavský, Filip Děchtěrenko

Abstract

In everyday life, people often need to track moving objects. Recently, a topic of discussion has been whether people rely solely on the locations of tracked objects, or take their directions into account in multiple object tracking (MOT). In the current paper, we pose a related question: do people utilise extrapolation in their gaze behaviour, or, in more practical terms, should the mathematical models of gaze behaviour in an MOT task be based on objects' current, past or anticipated positions? We used a data-driven approach with no a priori assumption about the underlying gaze model. We repeatedly presented the same MOT trials forward and backward and collected gaze data. After reversing the data from the backward trials, we gradually tested different time adjustments to find the local maximum of similarity. In a series of four experiments, we showed that the gaze position lagged by approximately 110 ms behind the scene content. We observed the lag in all subjects (Experiment 1). We further experimented to determine whether tracking workload or predictability of movements affect the size of the lag. Low workload led only to a small non-significant shortening of the lag (Experiment 2). Impairing the predictability of objects' trajectories increased the lag (Experiments 3a and 3b). We tested our observations with predictions of a centroid model: we observed a better fit for a model based on the locations of objects 110 ms earlier. We conclude that mathematical models of gaze behaviour in MOT should account for the lags.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Czechia 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 25%
Student > Master 5 18%
Student > Postgraduate 2 7%
Student > Ph. D. Student 2 7%
Student > Bachelor 2 7%
Other 2 7%
Unknown 8 29%
Readers by discipline Count As %
Psychology 11 39%
Engineering 3 11%
Computer Science 1 4%
Linguistics 1 4%
Neuroscience 1 4%
Other 1 4%
Unknown 10 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2020.
All research outputs
#2,589,783
of 24,003,070 outputs
Outputs from Attention, Perception, & Psychophysics
#89
of 1,773 outputs
Outputs of similar age
#48,142
of 371,366 outputs
Outputs of similar age from Attention, Perception, & Psychophysics
#2
of 31 outputs
Altmetric has tracked 24,003,070 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,773 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 371,366 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.