Running paths to nowhere: repetition of routes shows how navigating ants modulate online the weights accorded to cues

Antoine Wystrach, Sebastian Schwarz, Paul Graham, Ken Cheng*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    30 Citations (Scopus)
    90 Downloads (Pure)


    Ants are expert navigators, keeping track of the vector to home as they travel, through path integration, and using terrestrial panoramas in view-based navigation. Although insect learning has been much studied, the learning processes in navigation have not received much attention. Here, we investigate in desert ants (Melophorus bagoti) the effects of repeating a well-travelled and familiar route segment without success. We find that re-running a homeward route without entering the nest impacted subsequent trips. Over trips, ants showed more meandering from side to side and more scanning behaviour, in which the ant stopped and turned, rotating to a range of directions. In repeatedly re-running their familiar route, ants eventually gave up heading in the nestward direction as defined by visual cues and turned to walk in the opposite direction. Further manipulations showed that the extent and rate of this path degradation depend on (1) the length of the vector accumulated in the direction opposite to the food-to-nest direction, (2) the specific visual experience of the repeated segment of the route that the ants were forced to re-run, and (3) the visual panorama: paths are more degraded in an open panorama, compared with a visually cluttered scene. The results show that ants dynamically modulate the weighting given to route memories, and that fits well with the recent models, suggesting that the mushroom bodies provide a substrate for the reinforcement learning of views for navigation.

    Original languageEnglish
    Pages (from-to)213-222
    Number of pages10
    JournalAnimal Cognition
    Issue number2
    Publication statusPublished - Mar 2019


    • desert ant
    • view-based navigation
    • path integration
    • mushroom bodies
    • cue weighting


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