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DOI 10.1167/17.9.22

Медицинская библиотека · Journal of vision · 2017

Feature-based attentional tuning during biological motion detection measured with SSVEP.

Hasan Rakibul, Srinivasan Ramesh, Grossman Emily D

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Аннотация

Performance in detection tasks can be improved by directing attention to task-relevant features. In this study, we evaluate the direction tuning of selective attention to motion features when observers detect point-light biological motion in noise. Feature-based attention strategy is assessed by capitalizing on the sensitivity of unattended steady-state visual-evoked potential (SSVEP) to the spreading of feature-based attention to unattended regions of space. Participants monitored for the presence of a point-light walker embedded in uniform dynamic noise in the center of the screen. We analyzed the phase-locked electroencephalogram response to a flickering random-dot kinematogram (RDK) in an unattended peripheral annulus for the 1 s prior to the onset of the target. We found the highest SSVEP power to originate from electrodes over posterior parietal cortex (PPC), with power modulated by the direction of motion in the unattended annulus. The SSVEP was strongest on trials in which the unattended motion was opposite the facing direction of the walker, consistent with the backstroke of the feet and with the global direction of perceived background motion from a translating walker. Coherence between electrodes over PPC and other brain regions successfully predicted individual participant's d-prime, with the highest regression coefficients at electrodes over ventrolateral prefrontal cortex (VLPFC). The findings are evidence that functional connectivity between frontal and parietal cortex promote perceptual feature-based attention, and subsequent perceptual sensitivity, when segregating point-light figures from masking surround.

Для цитирования: Hasan Rakibul, Srinivasan Ramesh, Grossman Emily D. Feature-based attentional tuning during biological motion detection measured with SSVEP.. Journal of vision. 2017. DOI: 10.1167/17.9.22.

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