Unknown

Dataset Information

0

Competing rhythmic neural representations of orientations during concurrent attention to multiple orientation features.


ABSTRACT: When a feature is attended, all locations containing this feature are enhanced throughout the visual field. However, how the brain concurrently attends to multiple features remains unknown and cannot be easily deduced from classical attention theories. Here, we recorded human magnetoencephalography signals when subjects concurrently attended to two spatially overlapping orientations. A time-resolved multivariate inverted encoding model was employed to track the ongoing temporal courses of the neural representations of the attended orientations. We show that the two orientation representations alternate with each other and undergo a theta-band (~4?Hz) rhythmic fluctuation over time. Similar temporal profiles are also revealed in the orientation discrimination performance. Computational modeling suggests a tuning competition process between the two neuronal populations that are selectively tuned to one of the attended orientations. Taken together, our findings reveal for the first time a rhythm-based, time-multiplexing neural machinery underlying concurrent multi-feature attention.

SUBMITTER: Mo C 

PROVIDER: S-EPMC6868242 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Competing rhythmic neural representations of orientations during concurrent attention to multiple orientation features.

Mo Ce C   Lu Junshi J   Wu Bichan B   Jia Jianrong J   Luo Huan H   Fang Fang F  

Nature communications 20191120 1


When a feature is attended, all locations containing this feature are enhanced throughout the visual field. However, how the brain concurrently attends to multiple features remains unknown and cannot be easily deduced from classical attention theories. Here, we recorded human magnetoencephalography signals when subjects concurrently attended to two spatially overlapping orientations. A time-resolved multivariate inverted encoding model was employed to track the ongoing temporal courses of the ne  ...[more]

Similar Datasets

| S-EPMC6286091 | biostudies-literature
| S-EPMC6505953 | biostudies-literature
| S-EPMC11325722 | biostudies-literature
| S-EPMC3419335 | biostudies-literature
| S-EPMC5885971 | biostudies-literature
| S-EPMC10415262 | biostudies-literature
| S-EPMC4824044 | biostudies-literature
| S-EPMC4822201 | biostudies-literature
| S-EPMC3020592 | biostudies-literature
| S-EPMC7195455 | biostudies-literature