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Learning auditory discriminations from observation is efficient but less robust than learning from experience


Narula, Gagan; Herbst, Joshua A.; Rychen, Joerg; Hahnloser, Richard H R (2018). Learning auditory discriminations from observation is efficient but less robust than learning from experience. Nature Communications, 9:3218.

Abstract

Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. Here, we use aversive reinforcement to train “experimenter” zebra finches to discriminate between auditory stimuli in the presence of an “observer” finch. We show that experimenters are slow to successfully discriminate the stimuli, but immediately generalize their ability to a new set of similar stimuli. By contrast, observers subjected to the same task are able to discriminate the initial stimulus set, but require more time for successful generalization. Drawing on concepts from machine learning, we suggest that observer learning has evolved to rapidly absorb sensory statistics without pressure to minimize neural resources, whereas learning from experience is endowed with a form of regularization that enables robust inference.

Abstract

Social learning enables complex societies. However, it is largely unknown how insights obtained from observation compare with insights gained from trial-and-error, in particular in terms of their robustness. Here, we use aversive reinforcement to train “experimenter” zebra finches to discriminate between auditory stimuli in the presence of an “observer” finch. We show that experimenters are slow to successfully discriminate the stimuli, but immediately generalize their ability to a new set of similar stimuli. By contrast, observers subjected to the same task are able to discriminate the initial stimulus set, but require more time for successful generalization. Drawing on concepts from machine learning, we suggest that observer learning has evolved to rapidly absorb sensory statistics without pressure to minimize neural resources, whereas learning from experience is endowed with a form of regularization that enables robust inference.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:August 2018
Deposited On:08 Mar 2019 15:46
Last Modified:25 Sep 2019 00:27
Publisher:Nature Publishing Group
Series Name:Nature Communications
ISSN:2041-1723
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41467-018-05422-y
Official URL:https://www.nature.com/articles/s41467-018-05422-y

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