期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 19, 页码 7481-7486出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1202576109
关键词
concept learning; vision; insect; Apis mellifera
资金
- French Research Council (Centre National de la Recherche Scientifique)
- University Paul Sabatier
- National Research Agency
- Travelling Fellowship of The Journal of Experimental Biology
- University Paul Sabatier (ATUPS)
- Australian Research Council [DP0878968, DP0987989]
- Australian Research Council [DP0878968, DP0987989] Funding Source: Australian Research Council
Sorting objects and events into categories and concepts is a fundamental cognitive capacity that reduces the cost of learning every particular situation encountered in our daily lives. Relational concepts such as same, different, better than, or larger than-among others-are essential in human cognition because they allow highly efficient classifying of events irrespective of physical similarity. Mastering a relational concept involves encoding a relationship by the brain independently of the physical objects linked by the relation and is, therefore, consistent with abstraction capacities. Processing several concepts at a time presupposes an even higher level of cognitive sophistication that is not expected in an invertebrate. We found that the miniature brains of honey bees rapidly learn to master two abstract concepts simultaneously, one based on spatial relationships (above/below and right/left) and another based on the perception of difference. Bees that learned to classify visual targets by using this dual concept transferred their choices to unknown stimuli that offered a best match in terms of dual-concept availability: their components presented the appropriate spatial relationship and differed from one another. This study reveals a surprising facility of brains to extract abstract concepts from a set of complex pictures and to combine them in a rule for subsequent choices. This finding thus provides excellent opportunities for understanding how cognitive processing is achieved by relatively simple neural architectures.
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