The BMD algorithm works best on fixational eye movement time series with inferred measurement noise of 0.01 deg or higher. Chances are high data that has been filtered online by the eye tracker will have substantially lower noise. With low noise, the discrepancy between the assumptions of the generative model and data tends to cause mistakes in the inference such as detecting multiple small microsaccades instead of one (Figure A7).

• To run this, you will have to make sure you have a C compiler installed on your computer. If not, install it.

  • MacOS: Install Xcode Command Line Tools by entering in Terminal: xcode-select --install
  • Windows: Install Windows SDK or MinGW

• Install the boost C++ package. If this step is troublesome, use the already compiled bmd executable available here.

• Compile the bmd.cpp code into the bmd executable in the Command Prompt.

  • g++ -std=c++11 -O3 -I$BOOST_INC -fexpensive-optimizations -Wall -Wextra -o bmd bmd.cpp

• If the starting eye traces do not contain a continuous recording (i.e - they have excluded parts of trials or trials with broken fixation), it is necessary to translocate them such that they form a continuous time series. For a sample such translation, see prep_for_BMD_and_preprocess.m in Microsacc (includes preprocessing)

• xi.mat do contain continuous recordings. Preprocess raw eye traces xi.mat with preprocess_data.m to match the isotropy assumption in our generative model (see paper). This writes the output to xi.txt.

• Run BMD inference algorithm, either on the command line or on batch with ./run_BMD_all.sh

  • ./bmd x1.txt integral_table.txt params1.txt changepoints1.txt >output1.txt

• Analyze and plot the output.

  • Run the Matlab script BMD_vis.m, which reads in the change points from the changepointsi.txt file, converts them to the eye state time series C with the helper function tau_to_C.m and averages across these C samples to give the probability of the eye being in a microsaccade state across the whole eye position time series xi.txt. This can be visualized in microsaccades_inferred_BMDi.pdf.

• changepoints.txt

  • N total number of 1’s
  • n total number of change points
  • t01 indices in the time series for 0 (drift) to 1 (microsaccade) eye state transitions
  • t10 indices in the time series for 1 to 0 eye state transitions

• params.txt has the following parameters (columns). Each row represents the values across N iterations of the BMD algorithm.

• sigmaz (deg/ sqrt(ms))

• sigmax (deg)

• d0 (unitless, fixed to 1)

• sigma0(deg/ms)

• d1 (unitless)

• sigma1 (deg/ms)

The user might need params.txt when running the BMD algorithm on simulated data and checking parameter recovery. Also, the BMD variant BMD reduced plus threshold takes as input the inferred values for sigmaz and sigmax.

• output.txt: logposterior values across iterations and estimated parameter values.

BMD might need additional constraints in order to give reasonable inferences on particular datasets. Instead of allowing sigma0 to take any value in between 0.0001 and 1 (function estimate_sigma_down in bmd.cpp), it can be constrained to [0.0001, 0.0013], which is plausible since this upper limit corresponds (see our parametrization of the generalized gamma distribution) to a drift velocity distribution with mean 1.5 deg/s (head fixed mean drift velocities are typically even lower). Inference might improve further by also also lowering the upper limit on sigma1, for instance to 0.1.

• BMD with parallel tempering: BMD_pt.cpp with BMD_pt.h
• ./bmd_pt x1.txt integral_table.txt params_pt1.txt changepoints_pt1.txt paral_temp_pt1.txt chain_swaps_pt1.txt >output_pt1.txt
• BMD reduced plus threshold: function bmd_reduced_thresh.m. To visualize the output, set the appropriate flags in the BMD_vis.m script
• simulated data generation script according to our generative model with several values of motor and measurement noise: sim_data_create.m and sim_data_write.m