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Data for paper: Wood ants learn the magnetic direction of a route but express uncertainty because of competing directional cues

dataset
posted on 05.09.2022, 10:25 authored by Collett, Thomas, Andy PhilippidesAndy Philippides

Data for paper published in Journal of Experimental Biology July 2022


The data for each Ant in the experiments described in all but Figure 7 is held in matlab files with the name as follows:

AntU_LN22WESTtest_1522_31072019_Published.mat

The data for the experiments with two triangles is held in the zip file TrianglesData.zip which has individual files in the same format as above

There is a detailed description of the variables in the file  ants_magnets_philippides_dataset_description.pdf


Abstract

Wood ants were trained indoors to follow a magnetically specified route that went from the centre of an arena to a drop of sucrose at the edge. The arena, placed in a white cylinder, was in the centre of a 3D coil system generating an inclined Earth-strength magnetic field in any horizontal direction. The specified direction was rotated between each trial. The ants’ knowledge of the route was tested in trials without food. Tests given early in the day, before any training, show that ants remember the magnetic route direction overnight. During the first 2 seconds of a test, ants mostly faced in the specified direction, but thereafter were often misdirected, with a tendency to face briefly in the opposite direction. Uncertainty about the correct path to take may stem in part from competing directional cues linked to the room. In addition to facing along the route, there is evidence that ants develop magnetically directed home and food vectors dependent upon path integration. A second experiment asked whether ants can use magnetic information contextually. In contrast to honeybees given a similar task, ants failed this test. Overall, we conclude that magnetic directional cues can be sufficient for route learning. 

Funding

Brains on Board: Neuromorphic Control of Flying Robots

Engineering and Physical Sciences Research Council

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ActiveAI - active learning and selective attention for robust, transparent and efficient AI

Engineering and Physical Sciences Research Council

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