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dc.contributor.author
Narula, Gagan
dc.contributor.author
Herbst, Joshua A.
dc.contributor.author
Rychen, Joerg
dc.contributor.author
Hahnloser, Richard H.R.
dc.date.accessioned
2018-08-24T11:14:35Z
dc.date.available
2018-08-23T06:50:00Z
dc.date.available
2018-08-24T11:14:35Z
dc.date.issued
2018-08-13
dc.identifier.other
10.1038/s41467-018-05422-y
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/283924
dc.identifier.doi
10.3929/ethz-b-000283924
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Learning auditory discriminations from observation is efficient but less robust than learning from experience
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Nature Communications
ethz.journal.volume
9
en_US
ethz.pages.start
3218
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Vocal tuning and sequencing in songbirds and in humans
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
ethz.grant.agreementno
156976
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
ethz.date.deposited
2018-08-23T06:50:20Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-08-24T11:14:39Z
ethz.rosetta.lastUpdated
2019-02-03T05:16:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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