AI Insight
Researchers developed a novel experimental approach to study how laboratory mice learn to hunt for hidden resources in a large arena using ambiguous sound cues. Without food restriction, mice learned to efficiently locate one correct location out of 157 possibilities by reorganizing their existing behaviors into structured patterns of sampling and site-checking, rather than simply increasing any single behavior. This reorganization of behavioral sequences emerged as the mice gained expertise in the foraging task.
Why it matters
This work provides a new experimental framework for studying naturalistic decision-making in laboratory settings that bridges the gap between wild behavior and controlled experiments. The approach could enable researchers to investigate the neural and molecular mechanisms underlying complex, self-paced decision-making that more closely resembles real-world conditions.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
In the wild, a mouse must flexibly perform multiple computations at once, rapidly navigating, sampling its environment, forming and using memories, and balancing internal needs. Capturing the self-paced, low-repetition, complex nature of decision making in the wild while preserving the experimental control needed to interpret its process remains a challenge. Here we show that lab mice, without food restriction, learn to forage for hidden resources cued by ambiguous sounds in a dynamic environment by spatiotemporally reorganizing their existing behavioral repertoire. Using a novel closed-loop assay, we required mice to sit still to receive informative but hard-to-localize sound cues emanating from one correct location they must find, out of 157 possible locations in a large arena. With experience, mice become efficient hunters. Efficient hunting cannot be explained by an increase in a particular behavioral module, but instead, mice reorganize existing behaviors into clustered bouts of high-quality sampling and site-checking. Looking forward, this work establishes an approach for studying the neural, molecular, and evolutionary basis of naturalistic decision-making in mice.