Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2023.10.10.561519). Abstract Invasive alien species are a major and growing problem, devastating ecosystems and costing billions of euros in damage and control efforts. Argentine ants, Linepithema humile, are particularly concerning, with control efforts often falling short likely due to a lack of sufficient bait consumption. Using neuroactives to manipulate ant navigation and learning could increase recruitment and consumption, ultimately leading to more efficient control strategies. Caffeine is naturally occurring, cheap, and has been found to cause motivational and cognitive improvements in bees. Here, we subject L. humile to a wide range of caffeine concentrations and a complex but ecologically relevant task: an open landscape foraging experiment. Without caffeine, we find no effect of consecutive foraging visits on the time the ants take to reach a reward, suggesting a failure to learn the reward's location. However, low (25ppm) to intermediate (250ppm) concentrations of caffeine lead to a decrease of up to 38% in the time taken to find the reward during each consecutive visit, implying that caffeine boosts learning. Interestingly, such improvements are lost at high (2000ppm) doses. In contrast, caffeine appears to have no impact on the ants' homing behaviour, as the time required to reach the nest was similar across treatments. The effect of caffeine is thus not only dose-dependent, but also differentially targets neurologically distinct navigational mechanisms. Adding moderate levels of caffeine to baits could be a simple way to improve ant's ability to learn its location, potentially leading to increased recruitment to, and consumption of, the toxicant. sample_videos.zip: A subset of the videos used for data extraction. The complete collection of videos is not publicly accessible primarily due to their considerable size (105.35GB). Requests for access to the entire video set are encouraged. OpLan_D1_metadata.csv: Manually collected metadata pertaining to experimental conditions, subjects, and treatments. OpLan_D2_DLC_coordinates.zip: Cartesian coordinates obtained from DeepLabCut for each of the videos analysed. OpLan_C1_reproject_coordinates.py: Python code used to standardise the ants' coordinates by ensuring the same corner of the A4 platform was used as the origin of the cartesian referential of all videos. The known dimensions of the A4 were further used to convert coordinates from pixels to millimetres. OpLan_C2_remove_impossibilities.py: Python code used to account for DeepLabCut tracking errors, with any ant movement exceeding two millimetres per frame being considered implausible and subsequently removed. OpLan_C3_find_changepoints.py: Python code used to automatically derive the times at which an ant reached and left the reward from the tracking data. OpLan_C4_inward_outward_data.py: Python code used to calculate relevant measures for the foodward (inward) and nestward (outward) journey such as journey duration, mean instantaneous speed and path tortuosity. OpLan_C5_Figure_2.R: R code used to produce the raw elements of Figure 2. OpLan_C6_Figure_4.R: R code used to produce the raw elements of Figure 4. OpLan_C7_Statistical_Analysis.html: Complete statistical analysis and code for the manuscript.