-
Something wrong with this record ?
Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations
J. Lukavský, F. Děchtěrenko,
Language English Country United States
Document type Journal Article
NLK
Free Medical Journals
from 1966
ProQuest Central
from 2011-01-01 to 1 year ago
Medline Complete (EBSCOhost)
from 2009-01-01 to 1 year ago
Health & Medicine (ProQuest)
from 2011-01-01 to 1 year ago
Psychology Database (ProQuest)
from 2011-01-01 to 1 year ago
- MeSH
- Time Factors MeSH
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Fixation, Ocular physiology MeSH
- Attention physiology MeSH
- Models, Psychological * MeSH
- Pattern Recognition, Visual physiology MeSH
- Motion Perception physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
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.
References provided by Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc18017138
- 003
- CZ-PrNML
- 005
- 20180523111241.0
- 007
- ta
- 008
- 180515s2016 xxu f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.3758/s13414-016-1178-4 $2 doi
- 035 __
- $a (PubMed)27460357
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a xxu
- 100 1_
- $a Lukavský, Jiří $u Institute of Psychology, The Czech Academy of Sciences, Hybernská 8, 110 00, Prague, Czech Republic. jirilukavsky@gmail.com.
- 245 10
- $a Gaze position lagging behind scene content in multiple object tracking: Evidence from forward and backward presentations / $c J. Lukavský, F. Děchtěrenko,
- 520 9_
- $a 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.
- 650 _2
- $a dospělí $7 D000328
- 650 _2
- $a pozornost $x fyziologie $7 D001288
- 650 _2
- $a ženské pohlaví $7 D005260
- 650 _2
- $a oční fixace $x fyziologie $7 D005403
- 650 _2
- $a lidé $7 D006801
- 650 _2
- $a mužské pohlaví $7 D008297
- 650 12
- $a psychologické modely $7 D008960
- 650 _2
- $a vnímání pohybu $x fyziologie $7 D009039
- 650 _2
- $a rozpoznávání obrazu $x fyziologie $7 D010364
- 650 _2
- $a časové faktory $7 D013997
- 650 _2
- $a mladý dospělý $7 D055815
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Děchtěrenko, Filip $u Institute of Psychology, The Czech Academy of Sciences, Hybernská 8, 110 00, Prague, Czech Republic. Department of Software and Computer Science Education, Faculty of Mathematics and Physics, Charles University in Prague, Malostranské nám. 25, 118 00, Prague, Czech Republic.
- 773 0_
- $w MED00172775 $t Attention, perception & psychophysics $x 1943-393X $g Roč. 78, č. 8 (2016), s. 2456-2468
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/27460357 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y a $z 0
- 990 __
- $a 20180515 $b ABA008
- 991 __
- $a 20180523111425 $b ABA008
- 999 __
- $a ok $b bmc $g 1300762 $s 1013978
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2016 $b 78 $c 8 $d 2456-2468 $i 1943-393X $m Attention, perception & psychophysics $n Atten Percept Psychophys $x MED00172775
- LZP __
- $a Pubmed-20180515