I am an Associate Professor in Cognitive Psychology at the Department of Psychosocial Science, Faculty of Psychology, University of Bergen. I received my Ph.D. from St. Petersburg University, Russia, in 2015 supervised by Dr. Viktor Allakhverdov. During that time I was studying how the accuracy of perceptual predictions is related to an experience of positive or negative affect, for example, how an ability to discern a figure in a noisy image makes this image look more pleasant. I then left Russia to work at the University of Iceland with Dr. Árni Kristjánsson, where I looked into a representation of probabilistic perceptual ensembles. I developed a simple behavioral technique (coined ‘feature distribution learning’) allowing to infer how observers quickly approximate different kinds of probability distributions from just a few examples of stimuli sets. Later, I moved to the Visual Computation group headed by Dr. Janneke Jehee at Donders Center for Cognitive Neuroimaging (DCCN) where I studied the representation of visual uncertainty in the brain with particular emphasis on uncertainty in motion perception. First as a postdoc, and then as a staff scientist at DCCN, I worked to develop new computational models and tools for simulations and data analyses. For example, we used a Bayesian model to decode the information about the uncertainty from the fMRI BOLD response in the human visual cortex.
Currently, I use a combination of computational modeling and behavioral methods to understand biases in perception and memory (see more on that below). I am also interested in a wide range of problems in cognitive psychology and neuroscience (some examples from my previous work), so get in touch if you want to collaborate.
One of the driving questions in vision science is how our internal models of the world relate to the world itself. Quite often answers to this question are made by studying single stimuli (e.g, green circle) or sets of similar stimuli (a set of green circles). In contrast, we study how people encode perceptual ensembles (such as fields of grass or differently colored marbles). We use a new experimental paradigm based on priming of pop-out in visual search to study ensembles with different probability density functions and analyze the internal representations corresponding to these functions.
This project was the core of my PhD thesis. The idea is quite simple. Most of the cognitive acts can be treated as a prediction-making or inference process. But then what is the role of affect in this process? Why do we need to have a positive or negative experience? The hypothesis that we tested in a number of studies was that affect provides feedback for predictions' accuracy weighted by their prior probability. One of the corollaries was that, for example, errors should result in negative affect proportional to the amount of evidence in favor of the correct response. Crucially, here we are not talking about errors in some complex decision making ("Should I buy this car?") but rather about errors in very simple and to a large extent automatic processing ("Did I see this face before?").
Some other work I did over the years, ranging from tests of RT measument accuracy in online experiments to simulations for transcranial ultrasound neuromodulation to perception of #TheDress. Note: the list here includes only selected papers, check my CV or the Google Scholar page for the full list.
Besides my main work, here are a few other things I take part in:
Think Cognitive Think Science - this is a non-governmental support fund for Russian students studying cognitive science (currently inactive due to the war in Ukraine). We provide grants, organize summer schools, and do other similar things to help the community.
APAstats - if you are using R and Markdown, you might be interested in this package that provides functions for describing the results of common statistical tests in APA style.
circhelp - R package with helper functions for circular (directional) data analysis. Useful if you study things like orientation perception as it includes specialized function to remove cardinal biases, for example.
PRESTUS - a set of functions for ultrasonic simulations including brain preprocessing, extracting data from Localite neuronavigation files, and more