Shape-transitions of a morphing illusory contour can be decoded during multiple-object tracking from the ongoing EEG

TL;DR

The study shows that the brain tracks moving objects by maintaining an abstract shape, and changes in this shape can be detected from brain activity.

Contribution

The novel finding is that topological transitions of an illusory polygon during tracking can be decoded from ongoing EEG signals.

Findings

Topological transitions of a morphing illusory polygon can be decoded from EEG signals.

The brain represents qualitative shape changes during multiple-object tracking.

Abstract configuration changes are reflected in ongoing EEG activity.

Abstract

The human visual system continuously extracts a wealth of dynamic information from the incoming retinal signal. One important task is the simultaneous tracking of multiple items that are moving within the visual environment. Past work has proposed that such multiple-object tracking relies on attentional resources that are location-based, i.e. resources are respectively associated with the individual items spatial positions. However, another possibility is that attentional resources are object-based, i.e. allocated to the combination of all items as an abstract shape configuration. Indeed, recent data suggests that during multiple-object tracking, the visual system continuously maintains a configuration represented by an illusory polygon formed by the shortest closed path connecting all tracked items. Here, we test this hypothesis by comparing signatures in the electroencephalographic (EEG) signal of 38 subjects with topological transitions of the polygon’s shape. The topological transitions are qualitative changes in the polygon shape that go beyond mere quantitative changes in item positions (i.e., polygon corners). During object motion, the shapeshifting polygon can display switches between a convex and a concave shape. Concave shortest-path polygons can exhibit changes in the order in which the tracked objects are connected (‘flips’). We demonstrate that topological transition events can be decoded from the ongoing EEG signal, revealing how the abstract configuration and its qualitative changes are represented throughout the tracking. Taken together, our work demonstrates that an object-based attentional mechanism is crucial during multiple-object tracking.

Tracking multiple moving locations can be facilitated by an encapsulating illusory contour connecting all relevant locations. The ongoing EEG-signal can be used to decode topological transitions within this morphing illusory contour during tracking.

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