Highlights d Open-source Python toolkit for 3D animal pose estimation, with DeepLabCut support d Enables camera calibration, filtering of trajectories, and visualization of tracked data d Tracking evaluation on calibration board, fly, mouse, and human datasets d Identifies a role for joint rotation in motor control of fly walking
Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial constraints, and (4) a pipeline to structure processing of large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. Because Anipose is built on popular 2D tracking methods (e.g., DeepLabCut), users can expand their existing experimental setups to incorporate robust 3D tracking. We hope this opensource software and accompanying tutorials (anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.
Disparities in outcomes across social groups pervade human societies and are of central interest to the social sciences. How people treat others is known to depend on a multitude of factors (e.g., others' gender, ethnicity, appearance) even when these should be irrelevant. However, despite substantial progress, much remains unknown regarding () the set of mechanisms shaping people's behavior toward members of different social groups and () the extent to which these mechanisms can explain the structure of existing societal disparities. Here, we show in a set of experiments the important interplay between social perception and social valuation processes in explaining how people treat members of different social groups. Building on the idea that stereotypes can be organized onto basic, underlying dimensions, we first found using laboratory economic games that quantitative variation in stereotypes about different groups' warmth and competence translated meaningfully into resource allocation behavior toward those groups. Computational modeling further revealed that these effects operated via the interaction of social perception and social valuation processes, with warmth and competence exerting diverging effects on participants' preferences for equitable distributions of resources. This framework successfully predicted behavior toward members of a diverse set of social groups across samples and successfully generalized to predict societal disparities documented in labor and education settings with substantial precision and accuracy. Together, these results highlight a common set of mechanisms linking social group information to social treatment and show how preexisting, societally shared assumptions about different social groups can produce and reinforce societal disparities.
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