ArticleProceedings of the National Academy of Sciences of the United States of America2018
Optimal multiguidance integration in insect navigation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Latent learning improves foraging efficiency in ants.Die Naturwissenschaften · 2026Article
- A deep reinforcement learning account of competition and integration of visual and goal vector signals for spatial navigation.Scientific reports · 2026Article
- Updating an allocentric goal from lateralised egocentric visual memories.Nature communications · 2026Article
- A Vector-Based Computational Model of Multimodal Insect Learning Walks.Biomimetics (Basel, Switzerland) · 2025Article
- Ant visual route navigation: How the fine details of behaviour promote successful route performance and convergence.PLoS computational biology · 2025Article
- Path integration and optic flow in flying insects: a review of current evidence.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2025Review
- Contrast and luminance dependence of target choice and visual orientation in walking stick insects.Scientific reports · 2025Article
- Vector-based navigation in desert ants: the significance of path-integration vectors.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2025Article
- Coming home: how visually navigating ants (Myrmecia spp.) pinpoint their nest.The Journal of experimental biology · 2025Article
- Adaptive Route Memory Sequences for Insect-Inspired Visual Route Navigation.Biomimetics (Basel, Switzerland) · 2024Article
- Desert Ant (Insects · 2024Article
- Synaptic ring attractor: A unified framework for attractor dynamics and multiple cues integration.Heliyon · 2024Article
- Parallel vector memories in the brain of a bee as foundation for flexible navigation.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Effect of repetition of vertical and horizontal routes on navigation performance in Australian bull ants.Learning & behavior · 2024Article
- Trail using ants follow idiosyncratic routes in complex landscapes.Learning & behavior · 2024Article
- Unpacking the navigation toolbox: insights from comparative cognition.Proceedings. Biological sciences · 2024Review
- Route retracing: way pointing and multiple vector memories in trail-following ants.The Journal of experimental biology · 2024Article
- Not seeing the forest for the trees: combination of path integration and landmark cues in human virtual navigation.Frontiers in behavioral neuroscience · 2024Article
- Reinforcement learning as a robotics-inspired framework for insect navigation: from spatial representations to neural implementation.Frontiers in computational neuroscience · 2024Article
- Emergent spatial goals in an integrative model of the insect central complex.PLoS computational biology · 2023Article
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2 authors.
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Abstract
In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with "optimal" meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator's optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.
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