Relational rules and extrapolation enables foraging decisions in honeybees — ASN Events

Relational rules and extrapolation enables foraging decisions in honeybees (#15)

Scarlett R Howard 1 , Aurore Avarguès-Weber 2 , Devi M Stuart-Fox 1 , Jair E Garcia 3 , Adrian G Dyer 3
  1. School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
  2. Centre de Recherches sur la Cognition Animale, Université Toulouse III (UPS), Toulouse, France
  3. School of Media and Communication, RMIT University, Melbourne, Victoria, Australia

Honeybees are important pollinators of flowers [1], but must make difficult decisions due to the presence of mimic flowers that offer no rewards but consume time resources [2]. The decision-making process of honeybees during foraging is currently poorly understood despite their importance as pollinators, but recent work has started to provide insights into how complex problems are solved by these animals despite the highly constrained size of their brain [3]. One potential cue bees may use to make decisions is flower size, and bees can learn this type of information and apply it with interpolation to novel objects so long as sizes are within a learning set range [4]. Since the capacity to both interpolate and extrapolate perceptual rules is assumed to require a vertebrate brain [5], we wished to test if bees may possess any capacity to extrapolate learnt size rules to novel stimuli from outside the learning set. Additionally, we tested if such learning and extrapolation might be possible when presented with a successive viewing condition that closely simulated how bees typically encounter flowers in natural conditions. Bees (N=20; 10+10 counterbalance with small/LARGE CS+) were trained for 80 learning events with appetitive-aversive differential conditioning [6] and a custom ‘cube’ arena (Figure). Bees learnt the visual task at a level significant from chance expectation, and were able to extrapolate their decisions to novel stimuli either smaller than, or larger than the training set (P < 0.05). Moreover, we discuss the extent to which such behaviour fits perceptual mechanisms like Weber’s Law. We report that the capacity to use rule based visual problem solving is possible in an insect with a brain size of less than 1 million neurons, suggesting that environment may be a key determining factor in how brains evolved to solve problems [7].


  1. Barth, FG (1985) Insects and flowers: the biology of a partnership. Princeton, NJ: Princeton University Press.
  2. Dyer AG, Dorin A, Reinhardt V, Garcia JE & Rosa MGP (2014) Bee reverse-learning behavior and intra-colony differences: simulations based on behavioural experiments reveal benefits of diversity. Ecological Modelling. 277, 119-131.
  3. Avarguès-Weber A, Dyer AG, Combe M & Giurfa M (2012) Simultaneous mastering of two abstract concepts by the miniature brain of bees. Proc Nat Acad Sci (USA) 109, 7481–7486.
  4. Avarguès-Weber A, d’Amaro D, Metzler M & Dyer AG (2014) Conceptualization of relative size by honeybees. Front. Behav. Neurosci. 8:80. doi: 10.3389/fnbeh.2014.00080
  5. Spetch ML & Friedman A (2003). Recognizing rotated views of objects: Interpolation versus generalization by humans and pigeons. Psychonomic Bulletin & Review, 10(1), 135-140.
  6. Avargue`s-Weber A, de Brito Sanchez MG, Giurfa M & Dyer AG (2010) Aversive reinforcement improves visual discrimination learning in free-flying honeybees. PLoS ONE 5(10): e15370. doi:10.1371/journal.pone.0015370
  7. Chittka L & Niven J (2009). Are Bigger Brains Better? Current Biology, 19: R995-R1008