arrow
返回

Individual Differences in Object Recognition

delete2019-03-01
delete53
delete
OA
AI
J
Jennifer J. Richler
A
Andrew J. Tomarken
M
Mackenzie Sunday
T
Timothy J. Vickery
K
Kaitlin F. Ryan
R
R. Jackie Floyd
D
David L. Sheinberg
A
Alan C.‐N. Wong
G
Gauthier, Isabel *
DOI:10.1037/rev0000129delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
There is substantial evidence for individual differences in personality and cognitive abilities, but we lack clear intuitions about individual differences in visual abilities. Previous work on this topic has typically compared performance with only 2 categories, each measured with only 1 task. This approach is insufficient for demonstration of domain-general effects. Most previous work has used familiar object categories, for which experience may vary between participants and categories, thereby reducing correlations that would stem from a common factor. In Study 1, we adopted a latent variable approach to test for the first time whether there is a domain-general object recognition ability, o. We assessed whether shared variance between latent factors representing performance for each of 5 novel object categories could be accounted for by a single higher-order factor. On average, 89% of the variance of lower-order factors denoting performance on novel object categories could be accounted for by a higher-order factor, providing strong evidence for o. Moreover, o also accounted for a moderate proportion of variance in tests of familiar object recognition. In Study 2, we assessed whether the strong association across categories in object recognition is due to third-variable influences. We find that o has weak to moderate associations with a host of cognitive, perceptual, and personality constructs and that a clear majority of the variance in and covariance between performance on different categories is independent of fluid intelligence. This work provides the first demonstration of a reliable, specific, and domain-general object recognition ability, and suggest a rich framework for future work in this area.
Keyword:
visual abilities
structural equation modeling
latent variable modeling
holistic processing
intelligence

期刊

Psychological Review 封面图
Psychological Review
IF:
5.8
论文数:
1.8K
被引数:
3.2W

机构

B
Brown University
学者数:
2.4W
论文数: 2.2W
被引数: 3.2W
U
University of Delaware
学者数:
1.3W
论文数: 1.3W
被引数: 2.0W
V
vanderbilt university
学者数:
5.1W
论文数: 4.1W
被引数: 59
C
Chinese University of Hong Kong
学者数:
3.4W
论文数: 3.2W
被引数: 5.6W
学者 查看更多机构
引用论文

引用论文

暂无论文信息