Hi, I am Alexander (Sasha) Pastukhov.
I am a behavioral neuroscientist / scientific programmer / data scientist keenly interested in visual perception, consciousness, attention, and decision making. My favorite psychophysical tool: multistable displays, like the rotating head (yes, that is me!) on the left. Currently, I teach courses on Bayesian statistics, machine learning, data science using R, programming using Python, and linear algebra.
Education |
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2000–2001 | Ph.D. (Kandidat Technicheskih Nauk) in Computer Science at Volgograd State Technical University, Russia |
1998–2000 | M. Sc. in Computer Science at Volgograd State Technical University, Russia |
1994–1998 | B.Sc. in Computer Science at Volgograd State Technical University, Russia |
Estimates cumulative history for time-series for continuously viewed bistable perceptual rivalry displays. Computes cumulative history via a homogeneous first order differential process. I.e., it assumes exponential growth/decay of the history as a function time and perceptually dominant state. Supports Gamma, log normal, and normal distribution families. A package to compute a cumulative history for time-series of perceptual dominance in bistable displays.
R package to import eye tracking recording generated by SR Research Eyelink eye tracker from EDF-files. It includes options to import events and/or recorded samples and extract individual events such as saccades, fixations, blinks, and recorded variables.
The package uses an ensemble of methods approach to label individual samples and then applies a majority vote approach to identify saccades. It uses several methods to label individual samples as belonging to a saccade, classifies a sample as a potential saccade if its proportion of votes exceeds a preset threshold, and then identifies saccades based on minimal saccade duration and minimal time between the saccades. Currently, the library implements saccade detection using methods proposed in Engbert and Kliegl (2003), Otero-Millan et al. (2014), and Nyström and Holmqvist (2010). For binocular data, 1) samples can be averaged before velocity computation, 2) votes can be merged so that function returns binocular saccades, or 3) saccades are extracted for each eye separately. The package can be extended via custom methods and it also uses a modular approach to compute velocity and acceleration from noisy samples with the possibility of using custom differentiation methods. Finally, you can obtain methods votes per gaze sample instead of saccades.
Package to calculate the bidimensional and tridimensional regression between two 2D/3D configurations. Uses Stan engine to provide posterior distribution of fits. Individual fits can be evaluated based on Bayesian R2 and compared via widely applicable information criteria (WAIC) or leave-one out cross-validation criteria (LOO).
Package to calculate the bidimensional regression between two 2D configurations following the approach by Tobler (1965). Provides fits and statistics for Eucledian, affine, and projective transformation. Individual fits can be compared via ANOVA.
The library provides a simple interface to import contents of the EDF files generated by Eyelink eye-tracker into Matlab. It imports events and/or samples, automatically parsing them into separate trials. In addition to that, several post-processing functions can be used to extract selected events (fixations, saccades and blinks), variable values (TRIAL_VAR events) and microsaccades.
A two-semester introductory course on programming and Python aimed at undergraduate psychology students. The aim is to learn how to program psychological experiments using Python and PsychoPy by writing computer games (because experiments are simply boring computer games). The course assumes no prior knowledge or programming skills. The first semester covers basics including conditional statements, lists, dictionaries and use of PsychoPy. The second semester covers topics such as classes, generators, etc. You will program classic games such as "Guess the Number", "Whack-a-Mole", "Memory", "Snake", "Life", "Guitar Hero", etc.
https://alexander-pastukhov.github.io/writing-games-with-python-and-psychopy/
An introductory course on how to use R to analyze a typical psychophysical and social psychology research data. The course will walk you through all the analysis stages from importing a raw data to compiling a nice looking final report that automatically incorporates all the figures and statistics. Although I will introduce base, the main focus is on using Tidyverse family of packages that make data wrangling easy.
https://alexander-pastukhov.github.io/data-analysis-using-r-for-psychology/
Currently, a haphazard collection of notes on statistics. The primaryaim is to clarify or expand on topics mentioned but not fully explained in the "Statistical Rethinking" book by Richard McElreath but also now include topics from frequentist statistics. The topics include detailed explanation of information criteria, loss functions, (hidden) collider bias, DAGs, multicolliniarity, etc.
A two-semester introductory course on programming and Python aimed at undergraduate psychology students. The aim is to learn how to program psychological experiments using Python and PsychoPy by writing computer games (because experiments are simply boring computer games). The course assumes no prior knowledge or programming skills. The first semester covers basics including conditional statements, lists, dictionaries and use of PsychoPy. The second semester covers topics such as classes, generators, NumPy and Pandas libraries, programming online experiments, etc. You will program classic games such as "Guess the Number", "Hunt the Wumpus", "Memory", "Snake", "Life", "Guitar Hero", etc.
https://alexander-pastukhov.github.io/python-for-experimental-psychology/