Multiple-object tracking in displays where each item is unique: Do competing organizations impair the ability to track targets that share a common colour or shape?

Multiple-object tracking (MOT) involves monitoring positions of multiple independent targets as they move among distractors. Standard MOT involves identical items but Erlikhman et al. (2013) showed better performance when targets and distractors differed in colour or shape. They attributed this to automatic featural grouping processes that facilitated MOT when they kept targets and distractors distinct, which interfered otherwise. However, if featural grouping is fully automatic, grouping benefits should disappear in displays that give rise to competing groupings (i.e., colour vs. shape). In this study, participants tracked four targets in 16 item displays. MOT performance with identical items was compared to that when each of the 16 items was a unique combination of four colours and shapes, which produced four colour and four shape groupings per display. In the Colour Share condition, the four targets had the same colour but differed in shape. In the Shape Share condition, the four targets had the same shape but differed in colour. In the No Share condition, the four targets shared neither a common colour nor a common shape. We found that even in the face of competing groupings, MOT was better when targets shared a common feature (Colour or Shape Share conditions) than when they did not (the No Share condition). Nonetheless, performance in the No Share condition was significantly superior to that for identical items. Thus, featural grouping is not the full story; item uniqueness benefits persist even in multidimensional displays where unique items are defined by a combination of different features.

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Publication Details

Journal
Vision Research
Published
2026-03-23
DOI
https://doi.org/10.1016/j.visres.2026.108820
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
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article

Multiple-object tracking in displays where each item is unique: Do competing organizations impair the ability to track targets that share a common colour or shape?

Rachel A. Eng, Lana M. Trick, Jessica Swayze
Vision Research
Human-Automation Interaction and Safety
article

Multiple-object tracking in displays where each item is unique: Do competing organizations impair the ability to track targets that share a common colour or shape?

Rachel A. Eng, Lana M. Trick, Jessica Swayze
article en

Abstract

Multiple-object tracking (MOT) involves monitoring positions of multiple independent targets as they move among distractors. Standard MOT involves identical items but Erlikhman et al. (2013) showed better performance when targets and distractors differed in colour or shape. They attributed this to automatic featural grouping processes that facilitated MOT when they kept targets and distractors distinct, which interfered otherwise. However, if featural grouping is fully automatic, grouping benefits should disappear in displays that give rise to competing groupings (i.e., colour vs. shape). In this study, participants tracked four targets in 16 item displays. MOT performance with identical items was compared to that when each of the 16 items was a unique combination of four colours and shapes, which produced four colour and four shape groupings per display. In the Colour Share condition, the four targets had the same colour but differed in shape. In the Shape Share condition, the four targets had the same shape but differed in colour. In the No Share condition, the four targets shared neither a common colour nor a common shape. We found that even in the face of competing groupings, MOT was better when targets shared a common feature (Colour or Shape Share conditions) than when they did not (the No Share condition). Nonetheless, performance in the No Share condition was significantly superior to that for identical items. Thus, featural grouping is not the full story; item uniqueness benefits persist even in multidimensional displays where unique items are defined by a combination of different features.

Vision ResearchVol. 244
University of Guelph (CA)
Openalex Percentile: Top 18%
Human-Automation Interaction and Safety
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