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Ventral motion parallax enhances fruit fly steering to visual sideslip


Ruiz, Carlos; Theobald, Jamie (2020), Ventral motion parallax enhances fruit fly steering to visual sideslip, Dryad, Dataset,


Flies and other insects use incoherent motion (parallax) to the front and sides to measure distances and identify obstacles during translation. Although additional depth information could be drawn from below, there is no experimental proof that they use it. The finding that blowflies encode motion disparities in their ventral visual fields suggests this may be an important region for depth information. We used a virtual flight arena to measure fruit fly responses to optic flow. The stimuli appeared below (n=51) or above the fly (n=44), at different speeds, with or without motion parallax cues. Dorsal parallax does not affect responses, and similar motion disparities in rotation have no effect anywhere in the visual field. But responses to strong ventral sideslip (206 deg/s) change drastically depending on the presence or absence of parallax. Ventral parallax could help resolve ambiguities in cluttered motion fields, and enhance corrective responses to nearby objects.


We recorded the steering responses of Drosophila melanogaster when presented with ventral and dorsal visual stimuli in a rigid tether virtual arena. Tethered flies were illuminated from above with an infrared light, while photodiodes below measured the shadow produced by each wing beat. Since flies steer by changing the relative amplitudes of left and right wing beats, attempts to turn produce a differential voltage by the sensor pair, and is reported as the voltage difference in wing beat amplitude, ΔWBA. This information is saved in a binary file per experiment and per fly. The data stored here has not been processed. We provide a Python code that can be used to read it, analyze it and reproduce the figures we present in our paper.

Usage Notes

The files are compressed as a RAR file. Once unpacked they provide the file structure required by the script to read them. Feel free to contact C. Ruiz if you have any questions regarding the dataset.

Detailed instructions

The analysis is written as a Python script that can be called from the command line or imported on an interpreter such as Spyder. It is necessary to have Python installed already in the system. Follow these instructions in order to generate the plots and examine the data:

Unpack the compressed ZIP file preserving the folder structure.

Open a terminal window or an interpreter such as Spyder and run the code
The code will load the binary files, process the data and generate the plots presented in the manuscript for publication.
It will also show some lines of text at the end offering the results of the T-tests ran on the data.


National Science Foundation, Award: IOS-1750833