DOI: 10.5061/dryad.n0b8m
title: Data from: History dependence in insect flight decisions during odor tracking

description: Natural decision-making often involves extended decision sequences in
 response to variable stimuli with complex structure. As an example, many
 animals follow odor plumes to locate food sources or mates, but turbulence
 breaks up the advected odor signal into intermittent filaments and puffs.
 This scenario provides an opportunity to ask how animals use sparse,
 instantaneous, and stochastic signal encounters to generate goal-oriented
 behavioral sequences. Here we examined the trajectories of flying fruit
 flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating
 in controlled plumes of attractive odorants. While it is known that mean
 odor-triggered flight responses are dominated by upwind turns, individual
 responses are highly variable. We asked whether deviations from mean
 responses depended on specific features of odor encounters, and found that
 odor-triggered turns were slightly but significantly modulated by two
 features of odor encounters. First, encounters with higher concentrations
 triggered stronger upwind turns. Second, encounters occurring later in a
 sequence triggered weaker upwind turns. To contextualize the latter
 history dependence theoretically, we examined trajectories simulated from
 three normative tracking strategies. We found that neither a purely
 reactive strategy nor a strategy in which the tracker learned the plume
 centerline over time captured the observed history dependence. In
 contrast, “infotaxis”, in which flight decisions maximized expected
 information gain about source location, exhibited a history dependence
 aligned in sign with the data, though much larger in magnitude. These
 findings suggest that while true plume tracking is dominated by a reactive
 odor response it might also involve a history-dependent modulation of
 responses consistent with the accumulation of information about a source
 over multi-encounter timescales. This suggests that short-term memory
 processes modulating decision sequences may play a role in natural plume
 tracking.
description: Flight trajectories of
 fruit flies and mosquitoes in a wind tunnel.This data file is a
 MySQL database file which must be uploaded to a MySQL database management
 system (DBMS) (e.g., via the MAMP installation:
 http://localhost:8888/MAMP/?language=English, as was used in the
 associated manuscript). Once you have installed a MySQL DBMS on your
 machine, make a new database called “wind_tunnel_db”. To populate this
 database using the data file, first download all of the data files and
 join them together using: cat wind_tunnel_db_* &gt; wind_tunnel_db.sql
 Then run the following command to populate the wind_tunnel_db MySQL
 database with the result. /path/to/mysql -uroot -proot wind_tunnel_db &lt;
 /path/to/wind_tunnel_db.sql replacing the paths and username/passwords as
 appropriate. It will take several minutes since it is a large file. The
 database contains several tables, which are mostly self explanatory. The
 key tables of interest are the “experiment” table, which lists the 4
 experiments contained in this data set, the “timepoint” table, which
 contains the position, velocity, etc., of every fly/mosquito at every
 measured time point, and the “trajectory” table, which indicates which set
 of time points correspond to which individual trajectories. Other useful
 tables that have been pre-populated are the “crossing” table, which
 specifies trajectory segments corresponding to each plume crossing, and
 the “crossing_group” table, which groups sets of crossings together
 according to experiment and crossing identification criteria. The code
 that interacts with this database and recreates the figures in the
 associated manuscript is contained at
 https://github.com/rkp8000/wind_tunnel.wind_tunnel_db_aaPart 2wind_tunnel_db_abPart 3wind_tunnel_db_acPart 4wind_tunnel_db_adPart 5wind_tunnel_db_aePart 6wind_tunnel_db_afPart 7wind_tunnel_db_agPart 8wind_tunnel_db_ahPart 9wind_tunnel_db_aiInfotaxis databaseBase database for running infotaxis simulations. To see how to prepare and populate this database with simulated trajectory data, see the file _paper_auxiliary_code in the GitHub repository http://github.com/rkp8000/wind_tunnel.infotaxis_db.sql