ReviewNature neuroscience2020
Quantifying behavior to understand the brain.
Review in Nature neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 2 of them syntheses that pooled it.
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
168 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- The evolutionary trajectory of drosophilid walking.Current biology : CB · 2022Pooled it
- Experience-dependent emergence of multi-scale spatial organization during foraging in freely moving marmosets.iScience · 2026Article
- Article
- Linking signal integrity to probabilistic models of behavioral dynamics.Behavior research methods · 2026Article
- Article
- The Inversion in(2L)t Impacts Complex, Temperature-Sensitive Behaviors inEcology and evolution · 2026Article
- Embodied behavioural complexity in a ciliated microorganism.Nature communications · 2026Article
- SqueakPose Studio, an end-to-end platform for pose estimation and real-time edge-AI deployment.eLife · 2026Article
- Automated analysis of C. elegans behavior by LabGym: an open-source, AI-powered platform.G3 (Bethesda, Md.) · 2026Article
- Ecological Visual Processing in the Mouse.Annual review of neuroscience · 2026Review
- Embodied postural dynamics underlie behavioral diversity in a benthic sessile chordate.iScience · 2026Article
- Article
- Beast3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting.ArXiv · 2026Article
- Incentive salience, not psychomotor sensitization or tolerance, drives escalation of cocaine self-administration in heterogeneous stock rats.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- A Machine Learning Framework Integrating DeepLabCut and SimBA for Quantifying Aggressive Behavior in Swimming CrabAnimals : an open access journal from MDPI · 2026Article
- Whole-body 3D kinematics of freely behavingbioRxiv : the preprint server for biology · 2026Article
- Unsupervised transfer learning enables multi-animal tracking without training annotation.Nature methods · 2026Article
- Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation.bioRxiv : the preprint server for biology · 2026Article
- Probabilistic Perceptual Decisions during Navigation Are Driven by a Small Subpopulation of Neurons in a Single Cortical Column of the Primary Somatosensory Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
108 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and 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.