Elemental and non-elemental olfactory learning using PER conditioning in the bumblebee, Bombus terrestris

被引:29
|
作者
Sommerlandt, Frank M. J. [1 ]
Roessler, Wolfgang [1 ]
Spaethe, Johannes [1 ]
机构
[1] Univ Wurzburg, Biozentrum, Dept Behav Physiol & Sociobiol, D-97074 Wurzburg, Germany
关键词
elemental learning; Bombus terrestris; proboscis extension conditioning; bumblebee; configural associations; PROBOSCIS EXTENSION RESPONSE; APIS-MELLIFERA; FLOWER CONSTANCY; HONEY-BEES; BODY-SIZE; MEMORY; ODORS; DISCRIMINATION; INTERFERENCE; HYMENOPTERA;
D O I
10.1007/s13592-013-0227-4
中图分类号
Q96 [昆虫学];
学科分类号
摘要
Learning olfactory stimuli and their implications is essential in bumblebees for orientation and recognition of nest sites and food sources. To evaluate associative learning abilities in bees under controlled environmental conditions, the proboscis extension response (PER) assay is a well-established method used in honeybees and has recently been successfully adapted to bumblebees. In this study, we examined the cognitive abilities of workers of the eusocial bumblebee, Bombus terrestris, by training individuals in different olfactory learning tasks using classical PER conditioning. We compared learning performance for four different floral odors. Individuals were able to solve absolute (A+) and differential (A+ vs. B-) conditioning tasks, and no differences were found between single odors and odor combinations, respectively. Furthermore, bumblebees performed well on a positive pattern discrimination task (A-, B- vs. AB+), but failed to solve the negative pattern discrimination (A+, B+ vs. AB-). Our results indicate that workers of B. terrestris possess elemental olfactory learning abilities, but, in contrast to previous findings in honeybees, fail in more complex tasks, such as negative pattern discrimination. We discuss possible ultimate causes that have led to the difference in learning capabilities between bumblebees and honeybees.
引用
收藏
页码:106 / 115
页数:10
相关论文
共 50 条
  • [41] Simultaneous and sequential Feature Negative discriminations: Elemental learning and occasion setting in human Pavlovian conditioning
    Baeyens, F
    Vervliet, B
    Vansteenwegen, D
    Beckers, T
    Hermans, D
    Eelen, P
    LEARNING AND MOTIVATION, 2004, 35 (02) : 136 - 166
  • [42] Non-destructive Elemental Analysis Using Negative Muon
    Kubo, Michael K.
    JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 2016, 85 (09)
  • [43] Classical olfactory conditioning promotes long-term memory and improves odor-cued flight orientation in the South American native bumblebee Bombus pauloensis
    Nery, Denise
    Palottini, Florencia
    Farina, Walter M.
    CURRENT ZOOLOGY, 2021, 67 (05) : 561 - 563
  • [44] Elemental diffusion coefficient prediction in conventional alloys using machine learning
    Kulathuvayal, Arjun S.
    Rao, Yi
    Su, Yanqing
    CHEMICAL PHYSICS REVIEWS, 2024, 5 (04):
  • [45] Removal mechanism of elemental mercury by using non-thermal plasma
    Byun, Youngchul
    Koh, Dong Jun
    Shin, Dong Nam
    CHEMOSPHERE, 2011, 83 (01) : 69 - 75
  • [46] Analysis of the elemental effects on the surface potential of aluminum alloy using machine learning
    Takara, Yuya
    Ozawa, Takahiro
    Yamaguchi, Masaki
    JAPANESE JOURNAL OF APPLIED PHYSICS, 2022, 61 (SL)
  • [47] Oxidation of elemental mercury using atmospheric pressure non-thermal plasma
    Byun, Youngchul
    Ko, Kyung Bo
    Cho, Moohyun
    Namkung, Won
    Shin, Dong Nam
    Lee, Jin Wook
    Koh, Dong Jun
    Kim, Kyoung Tae
    CHEMOSPHERE, 2008, 72 (04) : 652 - 658
  • [48] Enhanced food authenticity control using machine learning-assisted elemental analysis
    Yang, Ying
    Zhang, Lu
    Qu, Xinquan
    Zhang, Wenqi
    Shi, Junling
    Xu, Xiaoguang
    FOOD RESEARCH INTERNATIONAL, 2024, 198
  • [49] Elemental Design of Alkali-Activated Materials with Solid Wastes Using Machine Learning
    Zhang, Junfei
    Shang, Shenyan
    Huo, Zehui
    Chen, Junlin
    Wang, Yuhang
    MATERIALS, 2024, 17 (18)
  • [50] Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
    Haoyang Xian
    Pinjing He
    Dongying Lan
    Yaping Qi
    Ruiheng Wang
    Fan L
    Hua Zhang
    Jisheng Long
    Frontiers of Environmental Science & Engineering, 2023, 17 (10) : 47 - 60