110 papers
Animal behavior and cognition explores the fascinating inner lives of creatures big and small, from how a crow solves a puzzle to why dogs seem to understand human emotions. This field investigates the mental processes and social interactions that drive the natural world, revealing that intelligence and awareness take many forms across species.
At Gist.Science, we bring these discoveries directly from bioRxiv to your screen. Our team processes every new preprint in this category from bioRxiv, ensuring you have access to the latest research through both detailed technical summaries and easy-to-understand plain-language explanations. Whether you are a researcher or a curious reader, you can dive deep into the data or grasp the core concepts without the barrier of dense academic jargon.
Below are the most recent papers exploring the minds and behaviors of animals, freshly processed and ready for you to explore.
This paper presents and validates a proof-of-concept system using Hall-effect sensors and a magnetic tag to enable reliable, non-invasive, real-time monitoring of queen bee presence and activity for precision apiculture.
This study establishes an automated, non-invasive home-cage platform for marmosets and utilizes reinforcement learning modeling to demonstrate that reward predictability reorganizes behavioral states, sharpening error correction and enhancing exploitative control during decision-making.
This paper extends the information-greedy "infotaxis" algorithm to model echolocation as an active pursuit of information, demonstrating that an infotaxis-based agent achieves more efficient and robust target localization under sensory uncertainty compared to traditional maximum a posteriori approaches.
This review argues that the translational gap in psychedelic research stems from systematic methodological flaws in animal studies—specifically the neglect of "set and setting" factors, the use of inadequate behavioral assays, and the failure to account for individual differences and social context—necessitating a fundamental shift toward longitudinal, socially enriched, and individually tracked experimental designs.
This paper introduces the Successor Representation with Importance Sampling (SR-IS), a novel model that constructs policy-independent predictive maps to enable efficient, flexible planning and better explains human replanning biases compared to existing theories.
Through a series of drawing experiments with adults and children, this study demonstrates that human spatial memory is fundamentally structured around preserved topological relations, such as junctions and holes, while metric details like angles and lengths are systematically distorted and degraded over time.
This study challenges the hypothesis that human speech evolved through an expansion of vocal acoustic range, demonstrating that speech and song actually occupy a significantly smaller and statistically indistinguishable acoustic feature space compared to human non-linguistic vocalizations and the calls of close primate relatives.
Based on long-term data from wild chimpanzees, this study demonstrates that individuals proactively reorganize their social networks by increasing the breadth and efficiency of their affiliative interactions prior to risky collective territorial defense, suggesting a shared evolutionary mechanism for preparing hominid groups for social dilemmas.
This paper introduces a Distributed Model that incorporates dimensional outcome representations into associative learning, demonstrating through four experiments that it significantly outperforms traditional binary models in predicting continuous outcomes and better reflects how humans encode causal relationships in complex environments.
This paper presents a comprehensive multimodal dataset that enables the 3D reconstruction of common marmoset body-environment interactions by integrating CT-derived meshes, rigged models, synchronized multi-view behavioral videos, and environmental models to support advanced behavioral analysis and digital-twin applications.