Grouping Effects and Its Additivity in Multiple Object Tracking
Reducing the consumption of attentional resources and improving human performance in dynamic visual sustained attention tasks is a key issue in sustained attention research.The multiple object tracking(MOT)task is a widely used paradigm for studying individual sustained attention.In a classic MOT paradigm,observers need to maintain their attention on specific targets among a set of distractors and track their movement.To further utilize attentional resources and improve tracking performance,researchers have proposed studying the additivity problem of grouping effects in attention tracking.Grouping effects during MOT is the phenomenon that moving items can be perceived into larger moving units based on featural cues of themselves or task requirements.This article reviewed previous studies about attention resources,classification,additivity,and neural mechanisms of grouping effects in MOT.Based on previous research,we concluded that grouping effects in MOT can be classified into three categories,i.e.,spatiotemporal-based grouping,object-based grouping,and feature-based grouping,according to different grouping cues(spatiotemporal continuity,global perception and organization of objects,and surface featural similarity).Grouping based on multiple cues will produce greater effects compared with one cue,this is the additive effect.The study of additivity is important for understanding the cognitive mechanisms of different grouping effects,the attentional mechanisms,and resource allocation in individual dynamic visual tracking.This study summarized previous behavioral and neuroimaging research and systematically explored the non-additivity based on different surface features and the additivity based on surface features and specific spatiotemporal features.Exploring the mechanism of additivity effects provides us with new insight into understanding grouping effects.For future studies,researchers need to thoroughly investigate the neural mechanisms of different kinds of groupings.This can not only provide explanations for the additivity of groupings but also provide substantial evidence for the classification of groupings.
NETL
NSTL
万方数据
维普
