A1 Journal article – refereed
Model of Multiple Identity Tracking (MOMIT) 2.0: Resolving the serial vs parallel controversy in tracking




List of Authors: Li Jie, Oksama Lauri, Hyönä Jukka
Publication year: 2019
Journal: Cognition
Journal name in source: Cognition
Journal acronym: Cognition
Volume number: 182
ISSN: 0010-0277
eISSN: 1873-7838

Abstract
The present study investigated whether during tracking of multiple moving objects with distinct identities only one identity is tracked at each moment (serial tracking) or whether multiple identities can be tracked simultaneously (parallel tracking). By adopting the gaze-contingent display change technique, we manipulated in real time the presence/absence of object identities during tracking. The data on performance accuracy revealed a serial tracking pattern for facial images and a parallel pattern for color discs: when tracking faces, the presence/absence of only the currently foveated identity impacted the performance, whereas when tracking colors, the presence of multiple identities across the visual field led to improved tracking performance. This pattern is consistent with the identifiability of the different types of objects in the visual field. The eye movements during MIT showed a bias towards visiting and dwelling on individual targets when facial identities were present and towards visiting the blank areas between targets when color identities were present. Nevertheless, the eye visits were predominately on individual targets regardless of the type of objects and the presence of object identities. The eye visits to targets were beneficial for target tracking, particularly in face tracking. We propose the Model of Multiple Identity Tracking (MOMIT) 2.0 which accounts for the results and reconcile the serial vs. parallel controversy. The model suggests that observers cooperatively use attention, eye movements, perception, and working memory for dynamic tracking. Tracking appears more serial when high-resolution information needs to be sampled and maintained for discriminating the targets, whereas it appears more parallel when low-resolution information is sufficient.


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Last updated on 2019-20-07 at 10:38