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 study demonstrates that learning to reach with mirror-reversed feedback relies on explicit motor and perceptual awareness and generalizes to novel targets, whereas learning with a standard visuomotor rotation occurs implicitly without generalization.
This study demonstrates that in Betta splendens, the divergent behavioral outcomes of social competition arise not from persistent differences in individual gene expression, but from asymmetric, outcome-specific remodeling of whole-brain transcriptomic networks involving immune, neuroendocrine, and purinergic systems.
This study demonstrates that applying human acoustic analysis to zebra finch song reveals distinct vocal biomarkers—specifically non-linear spectral and intensity changes for aging versus increased fundamental frequency variability and intensity regulation deficits for Parkinson's disease—thereby validating the songbird as a translational model for differentiating age-related and disease-related vocal impairments.
This study demonstrates that while learning temporal intervals and spatial line segments follows similar rapid improvement trajectories, spatial learning achieves higher accuracy and precision more quickly and sustains better transfer to untrained magnitudes, with feedback playing a crucial role in enhancing precision and generalization across both domains.
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.