ArticleNature communications2018
Learning auditory discriminations from observation is efficient but less robust than learning from experience.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Social context affects sequence modification learning in birdsong.Frontiers in psychology · 2025Article
- Article
- Unsupervised discovery of family specific vocal usage in the Mongolian gerbil.bioRxiv : the preprint server for biology · 2024Article
- Sensory cortex plasticity supports auditory social learning.Nature communications · 2023Article
- 'Do I know you?' Categorizing individuals on the basis of familiarity in kea (Royal Society open science · 2023Article
- Dopaminergic signaling supports auditory social learning.Scientific reports · 2021Article
- A system for controlling vocal communication networks.Scientific reports · 2021Article
- Surprise response as a probe for compressed memory states.PLoS computational biology · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.