Free-flying honeybees extrapolate relational size rules to sort successively visited artificial flowers in a realistic foraging situation.
Identifieur interne : 000C06 ( Main/Exploration ); précédent : 000C05; suivant : 000C07Free-flying honeybees extrapolate relational size rules to sort successively visited artificial flowers in a realistic foraging situation.
Auteurs : Scarlett R. Howard [Australie] ; Aurore Avarguès-Weber [France] ; Jair Garcia [Australie] ; Adrian G. Dyer [Australie]Source :
- Animal cognition [ 1435-9456 ] ; 2017.
Abstract
Learning and applying relational concepts to solve novel tasks is considered an indicator of cognitive-like ability. It requires the abstraction of relational concepts to different objects independent to the physical nature of the individual objects. Recent research has revealed the honeybee's ability to rapidly learn and manipulate relations between visual stimuli such as 'same/different', 'above/below', or 'larger/smaller' despite having a miniature-sized brain. While honeybees can solve problems using rule-based relative size comparison, it remains unresolved as to whether bees can apply size rules when stimuli are encountered successively, which requires reliance on working memory for stimuli comparison. Additionally, the potential ability of bees to extrapolate acquired information to novel sizes beyond training sets remains to be investigated. We tested whether individual free-flying honeybees could learn 'larger/smaller' size rules when visual stimuli were presented successively, and whether such rules could then be extrapolated to novel stimulus sizes. Honeybees were individually trained to a set of four sizes such that individual elements might be correct, or incorrect, depending upon the alternative stimulus. In a learning test, bees preferred the correct size relation for their respective learning group. Bees were also able to successfully extrapolate the learnt relation during transfer tests by maintaining the correct size relationships when considering either two smaller, or two larger, novel stimulus sizes. This performance demonstrates that an insect operating in a complex environment has sufficient cognitive capacity to learn rules that can be abstracted to novel problems. We discuss the possible learning mechanisms which allow their success.
DOI: 10.1007/s10071-017-1086-6
PubMed: 28374206
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Learning and applying relational concepts to solve novel tasks is considered an indicator of cognitive-like ability. It requires the abstraction of relational concepts to different objects independent to the physical nature of the individual objects. Recent research has revealed the honeybee's ability to rapidly learn and manipulate relations between visual stimuli such as 'same/different', 'above/below', or 'larger/smaller' despite having a miniature-sized brain. While honeybees can solve problems using rule-based relative size comparison, it remains unresolved as to whether bees can apply size rules when stimuli are encountered successively, which requires reliance on working memory for stimuli comparison. Additionally, the potential ability of bees to extrapolate acquired information to novel sizes beyond training sets remains to be investigated. We tested whether individual free-flying honeybees could learn 'larger/smaller' size rules when visual stimuli were presented successively, and whether such rules could then be extrapolated to novel stimulus sizes. Honeybees were individually trained to a set of four sizes such that individual elements might be correct, or incorrect, depending upon the alternative stimulus. In a learning test, bees preferred the correct size relation for their respective learning group. Bees were also able to successfully extrapolate the learnt relation during transfer tests by maintaining the correct size relationships when considering either two smaller, or two larger, novel stimulus sizes. This performance demonstrates that an insect operating in a complex environment has sufficient cognitive capacity to learn rules that can be abstracted to novel problems. We discuss the possible learning mechanisms which allow their success.</div>
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