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Brain size does not predict learning strategies in a serial reversal learning test

Reversal learning assays are commonly used across a wide range of taxa to investigate associative learning and behavioural flexibility. In serial reversal learning, the reward contingency in a binary discrimination is reversed multiple times. Performance during serial reversal learning varies greatl...

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Detalles Bibliográficos
Autores principales: Boussard, Annika, Buechel, Séverine D., Amcoff, Mirjam, Kotrschal, Alexander, Kolm, Niclas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Company of Biologists Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413604/
https://www.ncbi.nlm.nih.gov/pubmed/32561630
http://dx.doi.org/10.1242/jeb.224741
Descripción
Sumario:Reversal learning assays are commonly used across a wide range of taxa to investigate associative learning and behavioural flexibility. In serial reversal learning, the reward contingency in a binary discrimination is reversed multiple times. Performance during serial reversal learning varies greatly at the interspecific level, as some animals adopt a rule-based strategy that enables them to switch quickly between reward contingencies. A larger relative brain size, generating enhanced learning ability and increased behavioural flexibility, has been proposed to be an important factor underlying this variation. Here, we experimentally tested this hypothesis at the intraspecific level. We used guppies (Poecilia reticulata) artificially selected for small and large relative brain size, with matching differences in neuron number, in a serial reversal learning assay. We tested 96 individuals over 10 serial reversals and found that learning performance and memory were predicted by brain size, whereas differences in efficient learning strategies were not. We conclude that variation in brain size and neuron number is important for variation in learning performance and memory, but these differences are not great enough to cause the larger differences in efficient learning strategies observed at higher taxonomic levels.