↓ Skip to main content

Facilitating recognition of crowded faces with presaccadic attention

Overview of attention for article published in Frontiers in Human Neuroscience, January 2014
Altmetric Badge

Readers on

mendeley
56 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Facilitating recognition of crowded faces with presaccadic attention
Published in
Frontiers in Human Neuroscience, January 2014
DOI 10.3389/fnhum.2014.00103
Pubmed ID
Authors

Benjamin A. Wolfe, David Whitney

Abstract

In daily life, we make several saccades per second to objects we cannot normally recognize in the periphery due to visual crowding. While we are aware of the presence of these objects, we cannot identify them and may, at best, only know that an object is present at a particular location. The process of planning a saccade involves a presaccadic attentional component known to be critical for saccadic accuracy, but whether this or other presaccadic processes facilitate object identification as opposed to object detection-especially with high level natural objects like faces-is less clear. In the following experiments, we show that presaccadic information about a crowded face reduces the deleterious effect of crowding, facilitating discrimination of two emotional faces, even when the target face is never foveated. While accurate identification of crowded objects is possible in the absence of a saccade, accurate identification of a crowded object is considerably facilitated by presaccadic attention. Our results provide converging evidence for a selective increase in available information about high level objects, such as faces, at a presaccadic stage.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Canada 1 2%
Unknown 54 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 25%
Student > Master 8 14%
Other 7 13%
Researcher 6 11%
Student > Bachelor 3 5%
Other 11 20%
Unknown 7 13%
Readers by discipline Count As %
Psychology 25 45%
Neuroscience 9 16%
Engineering 3 5%
Agricultural and Biological Sciences 2 4%
Decision Sciences 2 4%
Other 4 7%
Unknown 11 20%