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A Comparison of Individual Learning and Social Learning in Zebra fish Through an Ethorobotics Approach
Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies, Centre for Cultural Evolution. Brooklyn College, United States; The Graduate Center of the City University of New York (CUNY), United States.
Number of Authors: 42019 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 6, article id 71Article in journal (Refereed) Published
Abstract [en]

Social learning is ubiquitous across the animal kingdom, where animals learn from group members about predators, foraging strategies, and so on. Despite its prevalence and adaptive benefits, our understanding of social learning is far from complete. Here, we study observational learning in zebra fish, a popular animal model in neuroscience. Toward fine control of experimental variables and high consistency across trials, we developed a novel robotics-based experimental test paradigm, in which a robotic replica demonstrated to live subjects the correct door to join a group of conspecifics. We performed two experimental conditions. In the individual training condition, subjects learned the correct door without the replica. In the social training condition, subjects observed the replica approaching both the incorrect door, to no effect, and the correct door, which would open after spending enough time close to it. During these observations, subjects could not actively follow the replica. Zebra fish increased their preference for the correct door over the course of 20 training sessions, but we failed to identify evidence of social learning, whereby we did not register significant differences in performance between the individual and social training conditions. These results suggest that zebra fish may not be able to learn a route by observation, although more research comparing robots to live demonstrators is needed to substantiate this claim.

Place, publisher, year, edition, pages
2019. Vol. 6, article id 71
Keywords [en]
behavior, biomimetics, ethorobotics, observational learning, robotics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:su:diva-173147DOI: 10.3389/frobt.2019.00071ISI: 000481663300001OAI: oai:DiVA.org:su-173147DiVA, id: diva2:1358032
Available from: 2019-10-06 Created: 2019-10-06 Last updated: 2019-10-18Bibliographically approved

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