Eye Movements

One of the goals of PsychoPy was to generate stimuli in real-time, that is to update the character of a stimulus on a frame-by-frame basis as needed without losing temporal precision. For static stimuli this is an unnecessary benefit, but for moving stimuli, where the alternative is to pre-compute a movie sequence it makes for much cleaner experimental code, with fewer delays (some experiments would previously require several seconds or even minutes before running where they computed the stimulus movies). The possibility of real-time stimulus manipulations also allows experiments to alter based on input form the participant such that, for example, a stimulus might be moved fluidly under mouse (or even eye-movement) control, or the next stimulus can be generated based on the previous response.
In order to achieve good temporal precision, while updating stimuli in real-time from an interpreted language like Python or Matlab, it has been essential to make good use of the hardware accelerated graphics capabilities of modern computers. Most modern machines have very powerful graphics processing units that can perform a lot of the calculations necessary to present stimuli at a precise point in space and time and to update that stimulus frequently. The OpenGL specification determines, fairly precisely, what a graphics card should do given various commands, such that platform independence is largely maintained (there are certain aspects, such as the synchronisation of drawing with the screen vertical refresh that are graphics card and/or platform dependent). PsychoPy 0.95 is fully compatible with the OpenGL 1.5 specification but makes use of further facilities that were added to OpenGL 2.0 on graphics cards and drivers where these are available. Nearly all modern graphics cards are capable of using OpenGL (although they may need updated drivers) and perfectly adequate cards from nVidia or ATI, that support the OpenGL 2.0 extensions, can be currently purchased and added to a desktop computer of any platform for roughly £30.

For the influenza hemagglutinin trimer benchmark, raw movie data and pre-extracted particles from EMPIAR-10097 were downloaded. The movies were processed with the full Warp pipeline using the following settings: motion correction with a temporal resolution of 40 for the global motion, and 5x5 spatial resolution for the local motion, using the 0.03–0.25 Nyquist range and a B-factor of -400 A²; CTF estimation with 6x6 spatial resolution, using the 0.1–0.35 Nyquist range; particle picking with a BoxNet model retrained on particles from 3 micrographs, using the default 0.95 threshold. Quality filters were applied in Warp as follows: defocus between 0.3 and 5.0 µm, resolution better than 8 Å, intra-frame motion of at most 1.5 Å, particle count above 120. Particles were extracted from the micrographs meeting these filters and subjected to processing in cryoSPARC: no 2D classification was performed; ab initio refinement was performed with 6 classes and no symmetry; the 6 classes were then refined heterogeneously, with no symmetry imposed; the only class showing the expected Hemagglutinin structure was refined with C3 symmetry. The original particle set from EMPIAR-10097 was subjected to 3 different processing strategies. First, the full set was refined in cryoSPARC with C3 symmetry using the original CTF estimates. Second, the full set was subjected to the same classification and refinement as the particles from Warp, using the original CTF estimates. Third, particles from the Hemagglutinin class obtained in the second processing branch were updated with local CTF estimates from Warp, and refined again with C3 symmetry. Resolution estimates were obtained for all maps using the respective masks automatically generated by cryoSPARC.
For the β-galactosidase benchmarking studies, raw data from EMPIAR-10061 were downloaded. The movies were processed with the full Warp pipeline using the following settings: motion correction with a temporal resolution of 38 for the global motion, and a 5x5 spatial resolution for the local motion, using the 0.03–0.60 Nyquist range and a B-factor of -160 Ų; CTF estimation with 5x5 spatial resolution, using the 0.08–0.60 Nyquist range; particle picking with a BoxNet model retrained on particles from 5 micrographs, using a threshold of 0.30. No quality filters were used as the data already represent a high-quality subset curated for the initial publication. Picked and extracted particles were subjected to 2D and 3D classification with C1 symmetry in RELION 2.1 to remove incomplete particles. The remaining particles were refined with D2 symmetry. The final half-maps were then used to refine beam tilt and per-particle defocus in RELION 3.0. Global motion tracks for all movies were exported from Warp to RELION 3.0 to perform Bayesian particle polishing.
To assess the frame alignment accuracy in Warp independently of downstream map refinement, β-galactosidase movies were aligned in Warp as described above, and using the default settings in MotionCor2. CTF fitting was performed with 5x5 spatial resolution, using the 0.08–50 Nyquist range. Frequency-dependent fit quality was calculated as described in the ‘Resolution estimation’ section, and all resulting curves averaged. The resolution was then estimated at a cut-off value of 0.3.