From eye to AI: studying rodent social behavior in the era of machine Learning
- URL: http://arxiv.org/abs/2508.04255v1
- Date: Wed, 06 Aug 2025 09:39:07 GMT
- Title: From eye to AI: studying rodent social behavior in the era of machine Learning
- Authors: Giuseppe Chindemi, Camilla Bellone, Benoit Girard,
- Abstract summary: We discuss the main steps involved and the tools available for analyzing rodent social behavior.<n>We suggest practical solutions to address common hurdles, aiming to guide young researchers in adopting these methods.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The study of rodent social behavior has shifted in the last years from relying on direct human observation to more nuanced approaches integrating computational methods in artificial intelligence (AI) and machine learning. While conventional approaches introduce bias and can fail to capture the complexity of rodent social interactions, modern approaches bridging computer vision, ethology and neuroscience provide more multifaceted insights into behavior which are particularly relevant to social neuroscience. Despite these benefits, the integration of AI into social behavior research also poses several challenges. Here we discuss the main steps involved and the tools available for analyzing rodent social behavior, examining their advantages and limitations. Additionally, we suggest practical solutions to address common hurdles, aiming to guide young researchers in adopting these methods and to stimulate further discussion among experts regarding the evolving requirements of these tools in scientific applications.
Related papers
- The Role of Generative AI in Facilitating Social Interactions: A Scoping Review [0.0]
Reduced social connectedness poses a threat to mental health, life expectancy, and general well-being.<n>Generative AI (GAI) technologies, such as large language models (LLMs) and image generation tools, are increasingly integrated into applications aimed at enhancing human social experiences.<n>Despite their growing presence, little is known about how these technologies influence social interactions.
arXiv Detail & Related papers (2025-06-12T17:37:19Z) - Tinkering Against Scaling [15.060264126253212]
We propose a "tinkering" approach that is inspired by existing works.<n>This method involves engaging with smaller models or components that are manageable for ordinary researchers.<n>We argue that tinkering is both a way of making and knowing for computational social science and a way of knowing for critical studies.
arXiv Detail & Related papers (2025-04-23T09:21:39Z) - Social Genome: Grounded Social Reasoning Abilities of Multimodal Models [61.88413918026431]
Social reasoning abilities are crucial for AI systems to interpret and respond to multimodal human communication and interaction within social contexts.<n>We introduce SOCIAL GENOME, the first benchmark for fine-grained, grounded social reasoning abilities of multimodal models.
arXiv Detail & Related papers (2025-02-21T00:05:40Z) - AlphaChimp: Tracking and Behavior Recognition of Chimpanzees [29.14013458574676]
We develop an end-to-end approach that simultaneously detects chimpanzee positions and estimates behavior categories from videos.
AlphaChimp achieves 10% higher tracking accuracy and a 20% improvement in behavior recognition compared to state-of-the-art methods.
Our approach bridges the gap between computer vision and primatology, enhancing technical capabilities and deepening our understanding of primate communication and sociality.
arXiv Detail & Related papers (2024-10-22T16:08:09Z) - Towards interactive evaluations for interaction harms in human-AI systems [8.989911701384788]
We propose a shift towards evaluation based on textitinteractional ethics, which focuses on textitinteraction harms<n>First, we discuss the limitations of current evaluation methods, which (1) are static, (2) assume a universal user experience, and (3) have limited construct validity.<n>We present practical principles for designing interactive evaluations. These include ecologically valid interaction scenarios, human impact metrics, and diverse human participation approaches.
arXiv Detail & Related papers (2024-05-17T08:49:34Z) - Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - Computer Vision for Primate Behavior Analysis in the Wild [61.08941894580172]
Video-based behavioral monitoring has great potential for transforming how we study animal cognition and behavior.
There is still a fairly large gap between the exciting prospects and what can actually be achieved in practice today.
arXiv Detail & Related papers (2024-01-29T18:59:56Z) - AI for social science and social science of AI: A Survey [47.5235291525383]
Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities.
The increasing human-like capabilities of AI are also attracting attention in social science research.
arXiv Detail & Related papers (2024-01-22T10:57:09Z) - LISBET: a machine learning model for the automatic segmentation of social behavior motifs [0.0]
We introduce LISBET (LISBET Is a Social BEhavior Transformer), a machine learning model for detecting and segmenting social interactions.
Using self-supervised learning on body tracking data, our model eliminates the need for extensive human annotation.
In vivo electrophysiology revealed distinct neural signatures in the Ventral Tegmental Area corresponding to motifs identified by our model.
arXiv Detail & Related papers (2023-11-07T15:35:17Z) - Human-AI Coevolution [48.74579595505374]
Coevolution AI is a process in which humans and AI algorithms continuously influence each other.
This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
arXiv Detail & Related papers (2023-06-23T18:10:54Z) - Machine Psychology [54.287802134327485]
We argue that a fruitful direction for research is engaging large language models in behavioral experiments inspired by psychology.
We highlight theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table.
It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks.
arXiv Detail & Related papers (2023-03-24T13:24:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.