136 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 study identifies a novel class of Drosophila BPI-like TULIP domain proteins (B-TDPs) that are overexpressed in chemosensory organs, secreted into the olfactory lymph, and function as candidate odorant transporters while potentially serving as a barrier against plant-emitted terpenoids.
This study demonstrates that when visibility is degraded, drivers rely on learned local associations between landmarks and upcoming road geometry to guide moment-to-moment steering, rather than depending on a globally coherent mental map of the environment.
Through a combination of experiments and modeling, this study demonstrates that despite intrinsic behavioral differences between rosy and tiger barbs, local speed-matching interactions enable heterogeneous mixed-species groups to achieve collective motion dominated by a single fast-swimming mode, thereby confirming that canonical principles of collective behavior extend to diverse animal assemblages.
FERAL is a supervised, open-source video-understanding system that directly maps raw video to frame-level behavioral labels across diverse species and scales, bypassing traditional keypoint-tracking limitations while outperforming state-of-the-art baselines with significantly less training data.
This study demonstrates that while humans and machine learning models can reliably identify shared acoustic structures in pig vocalizations, human perceptual judgments of emotional valence only align with presumed affective states in highly aversive contexts, suggesting a critical distinction between recoverable acoustic patterns and their biological interpretation in animal welfare research.
By employing a direct experimental method where participants explicitly identified the reasons for their fixations, this study demonstrates that semantic factors, particularly novelty and prior knowledge, generally dominate low-level saliency in gaze control and proposes a framework distinguishing between image-based highlighting processes and scanpath sampling strategies to better interpret the performance of deep learning models like DeepGaze IIE.
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.