کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7285003 | 1474086 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Greater reliance on the eye region predicts better face recognition ability
ترجمه فارسی عنوان
وابستگی بیشتر به منطقه چشم، توانایی تشخیص چهره بهتر را پیش بینی می کند
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
تفاوتهای فردی، تشخیص چهره، ادراک چهره، حباب ها،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
Interest in using individual differences in face recognition ability to better understand the perceptual and cognitive mechanisms supporting face processing has grown substantially in recent years. The goal of this study was to determine how varying levels of face recognition ability are linked to changes in visual information extraction strategies in an identity recognition task. To address this question, fifty participants completed six tasks measuring face and object processing abilities. Using the Bubbles method (Gosselin & Schyns, 2001), we also measured each individual's use of visual information in face recognition. At the group level, our results replicate previous findings demonstrating the importance of the eye region for face identification. More importantly, we show that face processing ability is related to a systematic increase in the use of the eye area, especially the left eye from the observer's perspective. Indeed, our results suggest that the use of this region accounts for approximately 20% of the variance in face processing ability. These results support the idea that individual differences in face processing are at least partially related to the perceptual extraction strategy used during face identification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognition - Volume 181, December 2018, Pages 12-20
Journal: Cognition - Volume 181, December 2018, Pages 12-20
نویسندگان
Jessica Royer, Caroline Blais, Isabelle Charbonneau, Karine Déry, Jessica Tardif, Brad Duchaine, Frédéric Gosselin, Daniel Fiset,