ArticleeLife2021
Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
56 citing papers in PubMed, 107 citations in OpenAlex.
- Family identity is represented within vocal categories by the gerbil auditory cortex.Current biology : CB · 2026Article
- A synaptic locus of song learning.Nature · 2026Article
- Vocal and social impairments in songbirds embryonically exposed to an autism risk factor.iScience · 2026Article
- Investigating the structure of the greylag goose vocal repertoire: what can unsupervised methods tell us?Scientific reports · 2026Article
- Correlating ultrasonic communication with behavior in a small-scale rat colony living in a naturalistic habitat.iScience · 2026Article
- TweetyBERT: Automated parsing of birdsong through self-supervised machine learning.Patterns (New York, N.Y.) · 2026Article
- A species rules syntax model accurately organizes birdsong syllables into songs.Scientific reports · 2026Article
- Dynamic Compression Flows for Neuroscience Data.bioRxiv : the preprint server for biology · 2026Article
- Re-examining hidden fitness: Female preferences for long-path songs in zebra finches.PloS one · 2026Article
- Article
- Model Predictive Control on the Neural Manifold.Neural computation · 2025Article
- Article
- Roar Data: Redefining a Lion's Roar Using Machine Learning.Ecology and evolution · 2025Article
- Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning.bioRxiv : the preprint server for biology · 2025Article
- Hidden in plain sound: the scientific potential of house mouse squeaks.Biology letters · 2025Review
- Infant rat ultrasonic vocalizations in the neurodevelopmental model of schizophrenia.Scientific reports · 2025Article
- Computational Urban Ecology of New York City Rats.bioRxiv : the preprint server for biology · 2025Article
- Article
- Detection of Individual Differences Encoded in Sequential Variations of Elements in Zebra Finch Songs.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Increases in the scale and complexity of behavioral data pose an increasing challenge for data analysis. A common strategy involves replacing entire behaviors with small numbers of handpicked, domain-specific features, but this approach suffers from several crucial limitations. For example, handpicked features may miss important dimensions of variability, and correlations among them complicate statistical testing. Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. The VAE converges on a parsimonious representation that outperforms handpicked features on a variety of common analysis tasks, enables the measurement of moment-by-moment vocal variability on the timescale of tens of milliseconds in the zebra finch, provides strong evidence that mouse ultrasonic vocalizations do not cluster as is commonly believed, and captures the similarity of tutor and pupil birdsong with qualitatively higher fidelity than previous approaches. In all, we demonstrate the utility of modern unsupervised learning approaches to the quantification of complex and high-dimensional vocal behavior.
Indexed as
Identifiers
What OpenQuestion holds
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.