Biography

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.

Featured Publications

Representing Variability: How Do We Process the Heterogeneity in the Visual Environment?

Andrey Chetverikov, Arni Kristjansson

March 2024 Cambridge: Cambridge University Press (Elements in Perception)

The visual world is full of detail. This Element focuses on this variability in perception, asking how it affects performance in visual tasks and how the variability is represented by human observers. The authors highlight different methods for assessing representations of variability and suggest that understanding visual variability can be elusive when straightforward explicit methods are used, while more implicit methods may be better suited to uncovering such processing. The authors conclude that variability is represented in far more detail than previously thought and that this aspect of perception is vital for understanding the complexity of visual consciousness.

PDF 10.1017/9781009396035

Motion direction is represented as a bimodal probability distribution in the human visual cortex

Andrey Chetverikov, Janneke F.M. Jehee

November 2023 Nature Communications

Humans infer motion direction from noisy sensory signals. We hypothesize that to make these inferences more precise, the visual system computes motion direction not only from velocity but also spatial orientation signals – a ‘streak’ created by moving objects. We implement this hypothesis in a Bayesian model, which quantifies knowledge with probability distributions, and test its predictions using psychophysics and fMRI. Using a probabilistic pattern-based analysis, we decode probability distributions of motion direction from trial-by-trial activity in the human visual cortex. Corroborating the predictions, the decoded distributions have a bimodal shape, with peaks that predict the direction and magnitude of behavioral errors. Interestingly, we observe similar bimodality in the distribution of the observers’ behavioral responses across trials. Together, these results suggest that observers use spatial orientation signals when estimating motion direction. More broadly, our findings indicate that the cortical representation of low-level visual features, such as motion direction, can reflect a combination of several qualitatively distinct signals.

PDF Data/Scripts 10.1038/s41467-023-43251-w

Demixing Model: A Normative Explanation for Inter-Item Biases in Memory and Perception

Andrey Chetverikov

March 2023 bioRxiv

Many studies in perception and in the working memory literature demonstrate that human observers systematically deviate from the truth when estimating the features of one item in the presence of another. Such inter-item or contextual biases are well established but lack a coherent explanation at the computational level. Here, I propose a novel normative model showing that such biases exist for any observer striving for optimality when trying to infer the features of multiple similar objects from a mixture of sensory observations. The ‘demixing’ model predicts that bias strength and direction would vary as a function of the amount of sensory noise and the similarity between items. Crucially, these biases exist not because of the prior knowledge in any form, but simply because the biased solutions to this inference problem are more probable than unbiased ones, counter to the common intuition. The model makes novel predictions about the effect of discriminability along the dimension used to select the item to report (e.g., spatial location) and the relative amount of sensory noise. Although the model is consistent with previously reported data from human observers, more carefully controlled studies are needed for a stringent test of its predictions. The strongest point of the `demixing’ model, however, is that it shows that interitem biases are inevitable when observers lack perfect knowledge of which stimuli caused which sensory observations, which is, arguably, always the case.

PDF Data/Scripts 10.1101/2023.03.26.534226

Probabilistic Representations as Building Blocks for Higher-Level Vision

Andrey Chetverikov, Arni Kristjansson

November 2022 Neurons, Behavior, Data analysis, and Theory

Current theories of perception suggest that the brain represents features of the world as probability distributions, but can such uncertain foundations provide the basis for everyday vision? Perceiving objects and scenes requires knowing not just how features (e.g., colors) are distributed but also where they are and which other features they are combined with. Using a Bayesian computational model, we recovered probabilistic representations used by human observers to search for odd stimuli among distractors. Importantly, we found that the brain integrates information between feature dimensions and spatial locations, leading to more precise representations compared to when information integration is not possible. We also uncovered representational asymmetries and biases, showing their spatial organization and explain how this structure argues against ‘summary statistics’ accounts of visual representations. Our results confirm that probabilistically encoded visual features are bound with other features and to particular locations, providing a powerful demonstration of how probabilistic representations can be a foundation for higher-level vision.

PDF Data/Scripts 10.51628/001c.24910

Publications

Feature distribution learning

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.

Hansmann-Roth, S., Chetverikov, A., & Kristjánsson, Á. (2023). Extracting statistical information about shapes in the visual environment. Vision Research, 206, 108190. 10.1016/j.visres.2023.108190 Data/Scripts PDF

Chetverikov, A., & Kristjánsson, Á. (2022). Probabilistic representations as building blocks for higher-level vision. Neurons, Behavior, Data analysis, and Theory, 6 (1), 1-32. 10.51628/001c.24910 Data/Scripts PDF

Hansmann-Roth, S., Kristjansson, A., Whitney, D., & Chetverikov, A. (2021). Limits of perception and richness of behaviour: Dissociating implicit and explicit ensemble representations. Scientific Reports, 11, 3899. 10.1038/s41598-021-83358-y Data/Scripts PDF

Tanrikulu, O. D., Chetverikov, A., Hansmann-Roth, S., & Kristjansson, A. (2021). What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception. Cognition, 217, 104903. Preprint is available at PsyArXiv. 10.1016/j.cognition.2021.104903 PDF

Van de Cruys, S., Lemmens, L., Sapey‐Triomphe, L., Chetverikov, A., Noens, I., & Wagemans, J. (2021). Structural and contextual priors affect visual search in children with and without autism. Autism Research, 1–12. 10.1002/aur.2511 Data/Scripts PDF

Tanrikulu, O. D., Chetverikov, A., & Kristjansson, A. (2021). Testing temporal integration of feature probability distributions using role-reversal effects in visual search. Vision Research, 188, 211–226. Preprint is available at PsyArXiv. 10.1016/j.visres.2021.07.012 Data/Scripts PDF

Tanrikulu, O. D., Chetverikov, A., & Kristjansson, A. (2020). Encoding perceptual ensembles during visual search in peripheral vision. Journal of Vision, 20(8), 20. 10.1167/jov.20.8.20. Data/Scripts PDF

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2020). Probabilistic rejection templates in visual working memory. Cognition, 196, pp. 104075. Preprint is available at PsyArXiv. 10.1016/j.cognition.2019.104075 Data/Scripts PDF

Hansmann-Roth, S., Chetverikov, A., & Kristjánsson, Á. (2019). Representing color and orientation ensembles: Can observers learn multiple feature distributions? Journal of Vision, 19(9), 1-17. 10.1167/19.9.2 PDF Data

Chetverikov, A., Hansmann-Roth, S., Tanrikulu, Ö. D., & Kristjansson, Á. (2019). Feature Distribution Learning (FDL): A New Method for Studying Visual Ensembles Perception with Priming of Attention Shifts. In Neuromethods (pp. 1–21). 10.1007/7657_2019_20 PDF

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017). Learning features in a complex and changing environment: A distribution-based framework for visual attention and vision in general. In Progress in brain research (Vol. 236, pp. 97–120). 10.1016/bs.pbr.2017.07.001 PDF

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017). Rapid learning of visual ensembles. Journal of Vision, 17(21), 1–15. 10.1167/17.2.21 PDF Data

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017). Representing Color Ensembles. Psychological Science, 28(10), 1–8. 10.1177/0956797617713787 PDF Data/Scripts

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017). Set size manipulations reveal the boundary conditions of distractor distribution learning. Vision Research, 140(November), 144–156. 10.1016/j.visres.2017.08.003 PDF Data

Chetverikov, A., Campana, G., & Kristjánsson, Á. (2016). Building ensemble representations: How the shape of preceding distractor distributions affects visual search. Cognition, 153, 196–210. 10.1016/j.cognition.2016.04.018 PDF Data

Affect-as-feedback for predictions

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?").

Ivanchei, I., Begler, A., Iamschinina, P., Filippova, M., Kuvaldina, M., & Chetverikov, A. (2018). A different kind of pain: affective valence of errors and incongruence. Cognition and Emotion, 1–8. 10.1080/02699931.2018.1520077 PDF Data/Scripts

Chetverikov, A., Iamschinina, P., Begler, A., Ivanchei, I., Filippova, M., & Kuvaldina, M. (2017). Blame everyone: Error-related devaluation in Eriksen flanker task. Acta Psychologica, 180(September 2017), 155–159. 10.1016/j.actpsy.2017.09.008 PDF Data/Scripts

Chetverikov, A., & Kristjánsson, Á. (2016). On the joys of perceiving: Affect as feedback for perceptual predictions. Acta Psychologica, 169, 1–10. 10.1016/j.actpsy.2016.05.005 PDF

Chetverikov, A., & Kristjánsson, Á. (2015). History effects in visual search for monsters: Search times, choice biases, and liking. Attention, Perception, & Psychophysics, 77(2), 402–412. 10.3758/s13414-014-0782-4 PDF

Chetverikov, A., Jóhannesson, Ó. I., & Kristjánsson, Á. (2015). Blaming the victims of your own mistakes: How visual search accuracy influences evaluation of stimuli. Cognition and Emotion, 29(6), 1091–1106. 10.1080/02699931.2014.968097 PDF

Chetverikov, A., & Filippova, M. (2014). How to tell a wife from a hat: Affective feedback in perceptual categorization. Acta Psychologica, 151, 206–213. 10.1016/j.actpsy.2014.06.012 PDF

Chetverikov, A. (2014). Warmth of familiarity and chill of error: Affective consequences of recognition decisions. Cognition and Emotion, 28(3), 385–415. 10.1080/02699931.2013.833085 PDF

Other papers

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 the Google Scholar page for the full list.

Chetverikov, A., & Kristjánsson, Á. (2024). Representing Variability: How Do We Process the Heterogeneity in the Visual Environment?. Cambridge: Cambridge University Press (Elements in Perception) 10.1017/9781009396035 PDF

Chetverikov, A., & Jehee, J. F. M. (2023). Motion direction is represented as a bimodal probability distribution in the human visual cortex. Nature Communications, 14(7634). https://doi.org/10.1038/s41467-023-43251-w PDF Data/Scripts

Kop, B. R., ... , Chetverikov, A., Ouden, H. E. M. den, Bergmann, T. O., Chen, R., & Verhagen, L. (2023). Auditory confounds can drive online effects of transcranial ultrasonic stimulation in humans. eLife, 12:RP88762. 10.7554/eLife.88762.2 PDF

Rafiei, M., Chetverikov, A., Hansmann-Roth, S., & Kristjansson, A. (2023). The influence of the tested item on serial dependence in perceptual decisions. Perception, 52 (4), 255-265. 10.1177/03010066231157582 PDF

Lyakhovetskii, V., Chetverikov, A., Zelenskaya, I., Tomilovskaya, E., & Karpinskaia, V. (2023). Perception of length and orientation in dry immersion. _Frontiers in Neural Circuits_, 17, 1157228. 10.3389/fncir.2023.1157228 PDF

Bertana^*, A., Chetverikov^*, A., van Bergen^*, R. S., Ling, S., & Jehee, J. F. M. (2021). Dual strategies in human confidence judgments. Journal of Vision, 21, 21. Preprint is available at BioRxiv. 10.1167/jov.21.5.21 PDF
^* - shared first authorship.

Rafiei, M., Chetverikov, A., Hansmann-Roth, S., & Kristjansson, A. (2021). You see what you look for: perceptual biases induced by targets and distractors in visual search. Journal of Vision, 21, 3. Preprint is available at PsyArXiv. 10.1167/jov.21.10.3 Data/Scripts PDF

Rafiei, M., Hansmann-Roth, S., Whitney, D., Kristjansson, A., & Chetverikov, A. (2021). Optimizing perception: Attended and ignored stimuli create opposing perceptual biases. Attention, Perception, & Psychophysics. 83, 1230–1239. Preprint is available at PsyArXiv. 10.3758/s13414-020-02030-1 Data/Scripts PDF

MacInnes, J., Jóhannesson, Ó. I., Chetverikov, A., & Kristjansson, A. (2020). No advantage for separating overt and covert attention in visual search. Vision, 4(2), 28. Preprint is available at PsyArXiv. https://doi.org/10.3390/vision4020028 Data

Rahnev, D., …, Chetverikov, A., … Zylberberg, A. (2020). The confidence database. Nature Human Behaviour, 4, 317–325. Preprint is available at PsyArXiv: https://doi.org/10.31234/osf.io/h8tju 10.1038/s41562-019-0813-1 Data

Kristjánsson, T., Thornton, I. M., Chetverikov, A., & Kristjánsson, Á. (2020). Dynamics of visual attention revealed in foraging tasks. Cognition, 194, 104032. 10.1016/j.cognition.2019.104032 PDF

Shurygina, O., Kristjánsson, Á., Tudge, L., & Chetverikov, A. (2019). Expectations and perceptual priming in a visual search task: Evidence from eye movements and behavior. Journal of Experimental Psychology: Human Perception and Performance, 45(4), 489–499. 10.1037/xhp0000618 PDF Data/Scripts

Chetverikov, A., Kuvaldina, M., Macinnes, W. J., Jóhannesson, Ó. I., & Kristjánsson, Á. (2018). Implicit processing during change blindness revealed with mouse-contingent and gaze-contingent displays. Attention, Perception, & Psychophysics, 80(4), 844–859. 10.3758/s13414-017-1468-5 PDF

Geurts, L. S., Chetverikov, A., Bergen, R. S. van, Zhou, Y. J., Bertana, A., & Jehee, J. F. (2018). Optimality is critical when it comes to testing computation-level hypotheses. Behavioral and Brain Sciences, 41, e231. 10.1017/S0140525X18001450

Kristjánsson, Á., Chetverikov, A., & Brinkhuis, M. (2017). How functional are functional viewing fields? Behavioral and Brain Sciences, 40, e143. 10.1017/S0140525X16000133 PDF

Chetverikov, A., & Ivanchei, I. (2016). Seeing “the Dress” in the Right Light: Perceived Colors and Inferred Light Sources. Perception, 45(8), 910–930. 10.1177/0301006616643664 PDF Data

Johannesson, O., Thornton, I. M., Smith, I. J., Chetverikov, A., & Kristjánsson, Á. (2016). Visual Foraging With Fingers and Eye Gaze. I-Perception, 7(2). 10.1177/2041669516637279 PDF

Marshev, V., Chetverikov, A., & Kuvaldina, M. (2016). Timing makes a difference: A study of change blindness with secondary task interference. Cognitie, Creier, Comportament/Cognition, Brain, Behavior, 20(5), 373–385. URL

Chetverikov, A., & Upravitelev, P. (2016). Online versus offline: The Web as a medium for response time data collection. Behavior Research Methods, 48(3), 1086–1099. 10.3758/s13428-015-0632-x PDF Data/Scripts