Foraging Ants as Liquid Brains: Movement Heterogeneity Shapes Collective Efficiency
Fernandez-Lopez, P.; Oro, D.; Lloret-Cabot, R.; Genovart, M.; Garriga, J.; Bartumeus, F.
Show abstract
Liquid brains conceptualize living systems operating without central control, where collective outcomes emerge from local but dynamic interactions. Therefore, movement is expected to shape the connectivity among individuals, allowing the system to optimize its efficiency. We empirically measured ant movement behavior across large spatiotemporal scales, closely reflecting the ecology of our model species, Aphaenogaster senilis. We then incorporated this into a liquid brain framework, enabling a quantitative replication of ant foraging efficiency and their spatiotemporal dynamics. Our results highlight that a simple feedback mechanism explains the foraging patterns of this species. Indeed, such feedback is modulated by adjusting the proportion of two coexisting movement behaviors: while the recruits facilitated information transfer and food exploitation by aggregating closely to the nest, the scouts mostly bypassed this feedback, enabling the discovery of alternative food sources. These findings underscore how complex systems frameworks can benefit from empirical insights, enhancing our understanding of the mechanisms underlying collective intelligence in biological systems.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Effects of local versus global competition on reproductive skew and sex differences in social dominance behaviour 96%
- Leaderless consensus decision-making determines cooperative transport direction in weaver ants 95%
- The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.