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 demonstrates that humans utilize an internalized prior of Earth's gravity to predict their own self-motion, but this effect is only observable under ecologically valid task conditions where body parts serve as reference points rather than eye level.
This paper synthesizes empirical evidence and theoretical models to demonstrate that temporal variations in animal activity traits significantly shape predator-prey interaction strengths and food web stability, highlighting the critical need to understand how human-induced changes to environmental cues may rewire these ecological networks.
The paper introduces a computationally efficient method for quantifying uncertainty in drift diffusion model parameters that accounts for temporal dependence and parameter variability, which is demonstrated through its application to dynamic rat decision-making in a visual task.
This study demonstrates that in the absence of a queen, bumble bee workers exhibit increased behavioral variation and adopt influential roles, driving a reorganization of social networks from centralized to decentralized architectures.
Using BOLD fMRI on juvenile pigs, this study demonstrates that the brains of anesthetized piglets can differentially process positive and negative conspecific vocalizations by activating distinct regions within the auditory, limbic, and reward pathways, with negative sounds uniquely engaging the amygdala, insula, and right hippocampus.
Contrary to the expectation that service quality drives growth in cleaner wrasse, a 11-month study of 540 individuals reveals that fast growth is primarily determined by reduced social control from dominant individuals rather than by cheating behavior or spawning activity.
This study demonstrates that verbal confidence reports are more sensitive than numerical ones in distinguishing correct from incorrect perceptual decisions, while both formats prove equally effective in facilitating accurate judgment revisions during dyadic collaboration.
Autobehaver is an interpretable, AI-driven pipeline that combines a low-cost recording platform with deep learning and machine learning techniques to quantitatively analyze and classify complex Drosophila behaviors, successfully identifying neural, age-related, and intermediate phenotypic changes.
This study demonstrates that song recognition in the parasitoid fly *Ormia ochracea* relies on a multidimensional encoding of independent temporal features (pulse duration and interpulse interval) rather than a single derived parameter like pulse rate, revealing that the fly's preference for 50 pulses/s emerges from an underlying feature space.
This paper introduces the open-source, reproducible `ai-pam-pipeline` framework for CNN-based cetacean vocalization detection, demonstrating through controlled experiments that preprocessing choices like FFT window length significantly impact cross-domain generalization while achieving high performance in both binary and multiclass detection tasks.