Visual learning in multiple-object tracking.

• Main Authors: Tal Makovski, Gustavo A Vázquez, Yuhong V Jiang
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2008-05-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC2375111?pdf=render

Tracking moving objects in space is important for the maintenance of spatiotemporal continuity in everyday visual tasks. In the laboratory, this ability is tested using the Multiple Object Tracking (MOT) task, where participants track a subset of moving objects with attention over an extended period of time. The ability to track multiple objects with attention is severely limited. Recent research has shown that this ability may improve with extensive practice (e.g., from action videogame playing). However, whether tracking also improves in a short training session with repeated trajectories has rarely been investigated. In this study we examine the role of visual learning in multiple-object tracking and characterize how varieties of attention interact with visual learning.Participants first conducted attentive tracking on trials with repeated motion trajectories for a short session. In a transfer phase we used the same motion trajectories but changed the role of tracking targets and nontargets. We found that compared with novel trials, tracking was enhanced only when the target subset was the same as that used during training. Learning did not transfer when the previously trained targets and nontargets switched roles or mixed up. However, learning was not specific to the trained temporal order as it transferred to trials where the motion was played backwards.These findings suggest that a demanding task of tracking multiple objects can benefit from learning of repeated motion trajectories. Such learning potentially facilitates tracking in natural vision, although learning is largely confined to the trajectories of attended objects. Furthermore, we showed that learning in attentive tracking relies on relational coding of all target trajectories. Surprisingly, learning was not specific to the trained temporal context, probably because observers have learned motion paths of each trajectory independently of the exact temporal order.