Interactive Multiple Object Tracking (iMOT)
- Authors
- Thornton, Ian M.; Buelthoff, Heinrich H.; Horowitz, Todd S.; Rynning, Aksel; Lee, Seong-Whan
- Issue Date
- 3-2월-2014
- Publisher
- PUBLIC LIBRARY SCIENCE
- Citation
- PLOS ONE, v.9, no.2
- Indexed
- SCIE
SCOPUS
- Journal Title
- PLOS ONE
- Volume
- 9
- Number
- 2
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/99311
- DOI
- 10.1371/journal.pone.0086974
- ISSN
- 1932-6203
- Abstract
- We introduce a new task for exploring the relationship between action and attention. In this interactive multiple object tracking (iMOT) task, implemented as an iPad app, participants were presented with a display of multiple, visually identical disks which moved independently. The task was to prevent any collisions during a fixed duration. Participants could perturb object trajectories via the touchscreen. In Experiment 1, we used a staircase procedure to measure the ability to control moving objects. Object speed was set to 1 degrees/s. On average participants could control 8.4 items without collision. Individual control strategies were quite variable, but did not predict overall performance. In Experiment 2, we compared iMOT with standard MOT performance using identical displays. Object speed was set to 2 degrees/s. Participants could reliably control more objects (M = 6.6) than they could track (M = 4.0), but performance in the two tasks was positively correlated. In Experiment 3, we used a dual-task design. Compared to single-task baseline, iMOT performance decreased and MOT performance increased when the two tasks had to be completed together. Overall, these findings suggest: 1) There is a clear limit to the number of items that can be simultaneously controlled, for a given speed and display density; 2) participants can control more items than they can track; 3) task-relevant action appears not to disrupt MOT performance in the current experimental context.
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- Appears in
Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
- Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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