ArticleCommunications biology2024
Unsupervised decomposition of natural monkey behavior into a sequence of motion motifs.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Altered kinship vocal dynamics in marmosets with valproic acid-induced model of autism.iScience · 2026Article
- Primate neuroethology: a new synthesis.Trends in cognitive sciences · 2026Review
- A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson's disease motor symptoms.NPJ Parkinson's disease · 2025Article
- Three-dimensional markerless motion capture of multiple freely behaving monkeys toward automated characterization of social behavior.Science advances · 2025Article
- Imaging-based chemogenetics for dissecting neural circuits in nonhuman primates.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2024Review
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Authors and funding
8 authors.
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Abstract
Nonhuman primates (NHPs) exhibit complex and diverse behavior that typifies advanced cognitive function and social communication, but quantitative and systematical measure of this natural nonverbal processing has been a technical challenge. Specifically, a method is required to automatically segment time series of behavior into elemental motion motifs, much like finding meaningful words in character strings. Here, we propose a solution called SyntacticMotionParser (SMP), a general-purpose unsupervised behavior parsing algorithm using a nonparametric Bayesian model. Using three-dimensional posture-tracking data from NHPs, SMP automatically outputs an optimized sequence of latent motion motifs classified into the most likely number of states. When applied to behavioral datasets from common marmosets and rhesus monkeys, SMP outperformed conventional posture-clustering models and detected a set of behavioral ethograms from publicly available data. SMP also quantified and visualized the behavioral effects of chemogenetic neural manipulations. SMP thus has the potential to dramatically improve our understanding of natural NHP behavior in a variety of contexts.
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