Language knowledge, age of acquisition (AoA), and stimulus intelligibility all affect gaze behavior for reading print, but it is unknown how these factors affect "sign-watching" among signers. This study investigated how these factors affect gaze behavior during sign language comprehension in 52 adult signers who acquired American Sign Language (ASL) at different ages. We examined gaze patterns and story comprehension in four subject groups who differ in hearing status and when they learned ASL (i.e. Deaf Early, Deaf Late, Hearing Late, and Hearing Novice). Participants watched signed stories in normal (high intelligibility) and video-reversed (low intelligibility) conditions. This video manipulation was used because it distorts word order and thus disrupts the syntax and semantic content of narratives, while preserving most surface phonological features of individual signs. Video reversal decreased story comprehension accuracy, and this effect was greater for those who learned ASL later in life. Reversal also was associated with more dispersed gaze behavior. Although each subject group had unique gaze patterns, the effect of video reversal on gaze measures was similar across all groups. Among fluent signers, gaze behavior was not correlated with AoA, suggesting that "efficient" sign watching can be quickly learnt even among signers exposed to signed language later in life.
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http://dx.doi.org/10.1093/deafed/enaa007 | DOI Listing |
Psych J
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Department of Psychology, Keio University, Tokyo, Japan.
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Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India. Electronic address:
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View Article and Find Full Text PDFSci Rep
January 2025
College of Computer Sciences, Anhui University, Hefei, 230039, China.
Decoding the semantic categories of complex sceneries is fundamental to numerous artificial intelligence (AI) infrastructures. This work presents an advanced selection of multi-channel perceptual visual features for recognizing scenic images with elaborate spatial structures, focusing on developing a deep hierarchical model dedicated to learning human gaze behavior. Utilizing the BING objectness measure, we efficiently localize objects or their details across varying scales within scenes.
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Pro Vice Chancellor, Staffordshire University, UK.
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Institute of Informatics, Federal University of Goiás, GO, Brazil.
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