Journal
EUROPEAN JOURNAL OF NEUROSCIENCE
Volume 51, Issue 2, Pages 681-694Publisher
WILEY
DOI: 10.1111/ejn.14633
Keywords
insect learning; negative patterning; non-elemental learning; virtual reality; visual cognition; visual learning
Categories
Funding
- European Research Council [835032]
- Human Frontier Science Program [RGP0022/2014]
- Institut Universitaire de France
- Agence Nationale de la Recherche [ANR-13-BSV4-0004-0]
- European Research Council (ERC) [835032] Funding Source: European Research Council (ERC)
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Non-elemental learning constitutes a cognitive challenge because events to be learned are usually ambiguous in terms of reinforcement outcome, contrary to elemental learning, which relies on unambiguous associations. Negative patterning (NP) constitutes a paradigmatic case of non-elemental learning, as subjects have to learn that single elements are reinforced while their simultaneous presentation is not reinforced (A+, B+ vs. AB-). Solving NP requires treating AB as being different from the linear sum of its components in order to overcome the ambiguity of stimulus reinforcement (i.e. A and B are as often reinforced as not reinforced). The honeybee is currently the only insect mastering NP as shown by studies restricted mainly to the olfactory domain. Here, we tested the bees' capacity to solve a NP discrimination in the visual domain and used to this end a virtual reality (VR) environment in which a tethered bee walking stationary on a spherical treadmill faces visual stimuli projected on a semicircular screen. We show that bees learn a composite grating made of alternated green and blue bars in an elemental way, and generalize their response to both a blue and a green grating. Yet, after NP training, one-quarter of the bees inhibited elemental processing and responded significantly more to the single-coloured gratings than to the composite grating. Alternative strategies were used by the other bees, which achieved partial NP learning. These results offer attractive perspectives to study different forms of visual learning in a controlled VR environment, and dissect their underlying mechanisms.
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