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

Gagan Narula, Joshua A. Herbst, Joerg Rychen and Richard H. R. Hahnloser ()
Additional contact information
Gagan Narula: University of Zurich and ETH Zurich
Joshua A. Herbst: University of Zurich and ETH Zurich
Joerg Rychen: University of Zurich and ETH Zurich
Richard H. R. Hahnloser: University of Zurich and ETH Zurich

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-05422-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05422-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-05422-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().