ArticleFrontiers in behavioral neuroscience2026
Development of an automated approach for investigating social learning in mice.
Article in Frontiers in behavioral neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mice have been shown to learn from each other through social interactions. However, the extent and strategies of social learning in mice remain largely unknown, beyond spatially and temporally limited tests of social memory retention. Here, we present a method that integrates (1) the IntelliCage, a commercially available tool for automated behavioral testing, and (2) the Live Mouse Tracker (LMT), an open-source solution for 24/7 live animal tracking. This approach allows for the investigation of learning behavior in semi-naturalistic group settings while minimizing experimenter interference. In this study, we report on the development of the method, evaluate its accuracy, and identify current limitations. While automated presentation of spatial learning tasks and behavior annotation were effective, identifying individual animals proved unreliable in a highly enriched environment. In response, we provide a rationale for identifying the reliable portion of tracking data, to which we confine the exemplary behavioral analysis. We acknowledge imperfect animal identification as a clear limitation of the method in its current configuration. However, we outline a path to mitigate this and are confident in presenting a promising tool that, after straightforward optimization, may prove useful for various research questions, including the investigation of social learning behavior in mice. The proof-of-principle experiment in this study did not indicate that place learning is facilitated by co-learning over individual learning, and we could not establish a clear association between social interactions and learning performance. While we observed some sporadic differences in interaction rates between co-learning and individually learning animals, we emphasize that these results do not support any conclusions about the mechanisms of social learning in mice. Rather, we present a tool for simultaneously studying learning and tracking behavior with minimal experimenter interference, which, after refinement, may aid future studies of social learning in mice alongside other research questions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.