Global-feature classification can be acquired more rapidly than local-feature classification in both humans and pigeons

被引:0
|
作者
Kazuhiro Goto
A. J. Wills
Stephen E. G. Lea
机构
[1] University of Exeter,School of Psychology, Washington Singer Laboratories
来源
Animal Cognition | 2004年 / 7卷
关键词
Visual perception; Global–local processing; Gestalt perception; Categorization; Pigeons;
D O I
暂无
中图分类号
学科分类号
摘要
When humans process visual stimuli, global information often takes precedence over local information. In contrast, some recent studies have pointed to a local precedence effect in both pigeons and nonhuman primates. In the experiment reported here, we compared the speed of acquisition of two different categorizations of the same four geometric figures. One categorization was on the basis of a local feature, the other on the basis of a readily apparent global feature. For both humans and pigeons, the global-feature categorization was acquired more rapidly. This result reinforces the conclusion that local information does not always take precedence over global information in nonhuman animals.
引用
收藏
页码:109 / 113
页数:4
相关论文
共 50 条
  • [31] Global-local Feature Adaptive Fusion Мethod for Small Sample Classification of Нyperspectral Images
    Zuo X.
    Liu Z.
    Jin F.
    Lin Y.
    Wang S.
    Liu X.
    Li M.
    Journal of Geo-Information Science, 2023, 25 (08) : 1699 - 1716
  • [32] Local and global feature selection for multilabel classification with binary relevance: An empirical comparison on flat and hierarchical problems
    Melo, Andre
    Paulheim, Heiko
    ARTIFICIAL INTELLIGENCE REVIEW, 2019, 51 (01) : 33 - 60
  • [33] Cross-region feature fusion of global and local area for subtype classification prediction in cervical tumour
    He, Jiahui
    Xiao, Zhibo
    Chen, Fuqiang
    Zheng, Boyun
    Tan, Shudong
    Xie, Yaoqin
    He, Xiangjian
    Qin, Wenjian
    JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES, 2024, 17 (04)
  • [34] Global-local feature learning for fine-grained food classification based on Swin Transformer
    Kim, Jun-Hwa
    Kim, Namho
    Won, Chee Sun
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 133
  • [35] A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification
    Zhu, Qiqi
    Zhong, Yanfei
    Liu, Yanfei
    Zhang, Liangpei
    Li, Deren
    REMOTE SENSING, 2018, 10 (04)
  • [36] From Global to Local: A Dual-Branch Structural Feature Extraction Method for Hyperspectral Image Classification
    Zhang, Ying
    Liang, Lianhui
    Mao, Jianxu
    Wang, Yaonan
    Jia, Lin
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 1778 - 1791
  • [37] Local and global feature selection for multilabel classification with binary relevanceAn empirical comparison on flat and hierarchical problems
    André Melo
    Heiko Paulheim
    Artificial Intelligence Review, 2019, 51 : 33 - 60
  • [38] More than one kind of inference: Re-examining what's learned in feature inference and classification
    Sweller, Naomi
    Hayes, Brett K.
    QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY, 2010, 63 (08): : 1568 - 1589
  • [39] HBNet: an integrated approach for resolving class imbalance and global local feature fusion for accurate breast cancer classification
    Abhisheka, Barsha
    Biswas, Saroj Kumar
    Purkayastha, Biswajit
    NEURAL COMPUTING & APPLICATIONS, 2024, 36 (15): : 8455 - 8472
  • [40] ADC-CPANet:A remote sensing image classification method based on local-global feature fusion
    Wang, Wei
    Li, Xijie
    Wang, Xin
    National Remote Sensing Bulletin, 2024, 28 (10) : 2661 - 2672