Valence of facial cues influences sheep learning in a visual discrimination task

Lucille Bellegarde, Hans Erhard, Alexander Weiss, Alain Boissy, M. J. Haskell

Research output: Contribution to journalArticlepeer-review


Sheep are one of the most studied farm species in terms of their ability to process information from faces, but little is known about their face-based emotion recognition abilities. We investigated a) whether sheep could use images of sheep faces taken in situation of varying valence as cues in asimultaneous discrimination task and b) whether the valence of the situation affects their learning performance. To accomplish this, we photographed faces of sheep in three situations inducing emotional states of neutral (ruminating in the home pen) or negative valence (social isolation or aggressive interaction). Sheep (n=35) first had to learn a discrimination task with coloured cards. Animals that reached the learning criterion (n=16) were then presented with pairs of images of the face of a single individual taken in the neutral situation and in one of the negative situations. Finally, sheep had to generalise what they had learned to new pairs of images of faces taken in the same situation, but of a different conspecific. All sheep that learned the discrimination task with coloured cards reached the learning criterion with images of faces. Sheep that had to associate a negative image with a food reward learned faster than sheep that had to associate a neutral image with a reward. With the exception of sheep from the aggression-rewarded group, sheep generalised this discrimination to images of faces of different individuals. Our results suggest that sheep can perceive the emotional valence displayed on faces of conspecifics and that this valence affects learning processes.
Original languageEnglish
Article number188
Pages (from-to)1-10
JournalFrontiers in Veterinary Science
Publication statusPublished - 6 Nov 2017


  • sheep
  • faces
  • emotions
  • discrimination task
  • facial expression
  • learning

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