Bumblebees Learn a Relational Rule but Switch to a Win-Stay/Lose-Switch Heuristic After Extensive Training.

abstract concepts adaptive decision-making animal cognition behavioral analyses cognitive flexibility cognitive offloading the law of least effort

Journal

Frontiers in behavioral neuroscience
ISSN: 1662-5153
Titre abrégé: Front Behav Neurosci
Pays: Switzerland
ID NLM: 101477952

Informations de publication

Date de publication:
2020
Historique:
received: 21 05 2020
accepted: 16 07 2020
entrez: 9 9 2020
pubmed: 10 9 2020
medline: 10 9 2020
Statut: epublish

Résumé

Mapping animal performance in a behavioral task to underlying cognitive mechanisms and strategies is rarely straightforward, since a task may be solvable in more than one manner. Here, we show that bumblebees perform well on a concept-based visual discrimination task but spontaneously switch from a concept-based solution to a simpler heuristic with extended training, all while continually increasing performance. Bumblebees were trained in an arena to find rewards on displays with shapes of different sizes where they could not use low-level visual cues. One group of bees was rewarded at displays with larger shapes and another group at displays with smaller shapes. Analysis of total choices shows bees increased their performance over 30 bouts to above chance. However, analyses of first and sequential choices suggest that after approximately 20 bouts, bumblebees changed to a win-stay/lose-switch strategy. Comparing bees' behavior to a probabilistic model based on a win-stay/lose-switch strategy further supports the idea that bees changed strategies with extensive training. Analyses of unrewarded tests indicate that bumblebees learned and retained the concept of relative size even after they had already switched to a win-stay, lost-shift strategy. We propose that the reason for this strategy switching may be due to cognitive flexibility and efficiency.

Identifiants

pubmed: 32903410
doi: 10.3389/fnbeh.2020.00137
pmc: PMC7434978
doi:

Types de publication

Journal Article

Langues

eng

Pagination

137

Subventions

Organisme : European Research Council
ID : 339347
Pays : International

Informations de copyright

Copyright © 2020 MaBouDi, Solvi and Chittka.

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Auteurs

HaDi MaBouDi (H)

School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom.
Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.

Cwyn Solvi (C)

School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom.
Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia.

Lars Chittka (L)

School of Biological and Chemical Sciences, Queen Mary University of London, London, United Kingdom.

Classifications MeSH