Research Projects

Our visual system is sensitive to the statistical properties of complex scenes. It can encode a distribution of features (e.g., orientation, color, size, facial expression) across many objects as ensembles and can accurately and efficiently extract the summary statistics of these ensembles. Our research aims to understand the computational mechanisms involved in constructing ensemble representations. We investigate the process involved in developing such representations.

Collaborators: Arni Kristjansson, University of Iceland, Iceland ; Andrey Chetverikov, University of Bergen, Norway

Relevant publications:

Tanrıkulu, Ö.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. https://doi.org/10.1016/j.visres.2021.07.012

Tanrıkulu, Ö.D., Chetverikov, A. & Kristjansson, A. (2020). Encoding perceptual ensembles during visual search in peripheral vision. Journal of Vision, 20(8):20. https://doi.org/10.1167/jov.20.8.20

Chetverikov, A., Hansmann-Roth S., Tanrıkulu, Ö. D. & Kristjansson, A. (2019) Feature Distribution Learning (FDL): A New Method for Studying Visual Ensembles Perception with Priming of Attention Shifts. In: Neuromethods. Humana Press. https://doi.org/10.1007/7657_2019_20

Our visual system has the capacity to build rich 3-D representations of our environment from complex 2D retinal images. Even after contours and regions on an image are computed, the visual system must organize these seemingly unstructured contours and regions. Therefore, one of the crucial steps of visual processing is to determine what regions and contours in the image belong together as distinct surfaces, objects, and other useful perceptual units. An important part of this perceptual grouping involves segmenting images into figural and ground regions. We investigate how our visual system combines border geometry and texture motion in determining the figural and ground regions. 

Collaborators: Manish Singh, Rutgers University, New Brunswick, New Jersey, US ; Jacob Feldman, Rutgers University, New Brunswick, New Jersey, US ; Vicky Froyen, Rutgers University, New Brunswick, New Jersey, US

Relevant publications:

Tanrıkulu, Ö.D., Froyen, V., Feldman, J. & Singh, M. (2024). Interaction of contour geometry and optic flow in determining relative depth of surfaces Attention, Perception & Psychophysics, 10.3758/s13414-023-02807-0. https://doi.org/10.3758/s13414-023-02807-0

Tanrıkulu, Ö.D., Froyen, V., Feldman, J. & Singh, M. (2022). The interpretation of dynamic occlusion: Combining contour geometry and accretion/deletion of texture. Vision Research, 199, 108075 https://doi.org/10.1016/j.visres.2022.108075

Tanrıkulu, Ö.D., Froyen, V., Feldman, J. & Singh, M. (2018). When Is Accreting/Deleting Texture Seen as In Front? Interpretation of Depth From Texture Motion. Perception, 47(7), 694 - 721. doi.org/10.1177/0301006618776119

Tanrıkulu, Ö.D., Froyen, V., Feldman, J. & Singh, M. (2016). Geometric figure-ground cues override standard depth from accretion-deletion. Journal of Vision, 16(5), 15. https://doi.org/10.1167/16.5.15

Probabilistic approaches have been successful in building computational models of perception and cognition. This has led researchers to propose that the brain represents information probabilistically. This theoretical assumption underlies most recent computational studies in vision science. Although the theoretical basis for this assumption is convincing, there is currently inadequate empirical evidence supporting it in the field. We investigate questions like: 1) what kind of empirical evidence is needed for probabilistic representation in perception or cognition? 2) what does it mean to represent perceptual information in a probabilistic format?

Collaborators: Arni Kristjansson, University of Iceland, Iceland ; Andrey Chetverikov, University of Bergen, Norway

Relevant publications:

Tanrıkulu, Ö.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, https://doi.org/10.1016/j.cognition.2021.104903

Chetverikov, A., Hansmann-Roth S., Tanrıkulu, Ö. D. & Kristjansson, A. (2019) Feature Distribution Learning (FDL): A New Method for Studying Visual Ensembles Perception with Priming of Attention Shifts. In: Neuromethods. Humana Press. https://doi.org/10.1007/7657_2019_20

Philosophy of cognitive science and perceptual psychology

What is the explanatory power of probabilistic models in psychology? How should we interpret the results of Bayesian models of perception in understanding the computations occurring within the visual system?

Collaborators: Eyup Kucuk, University of New Hampshire;  Clauidi Brink University of New Hampshire

Relevant Publications: coming soon..

Visual estimates of stimulus features are systematically biased toward the features of previously encountered stimuli. This phenomenon is referred to as visual serial dependence. For example, when observers are asked to reproduce a visual feature, such as the orientation of an object, their judgments are systematically biased toward the orientation of the object seen in the previous trials. This bias has been demonstrated in a variety of visual tasks, suggesting that this is a general principle of visual processing. We investigate the computational nature of this phenomenon to understand how this bias allows our brains to maintain perceptual continuity in an ever-changing and dynamic stream of visual input. 

Collaborators:Arni Kristjansson, University of Iceland, Iceland ; David Pascucci, École polytechnique fédérale de Lausanne, (EPFL), Switzerland

Relevant publications:

Pascucci, D., Tanrıkulu, Ö.D., Chetverikov, A., Ozkirli A., Houborg, C., Ceylan, G., Zerr, P., Rafiei, M. & Krist-jansson, A. (2023). Serial dependence in visual perception: A review. Journal of Vision, 23(1):9. https://doi.org/10.1167/jov.23.1.9

Houborg, C., Kristjansson, A., Tanrıkulu, Ö.D. & Pascucci, D. (2023). The effects of visual distractors on serial dependence Journal of Vision, 2023;23(12):1. https://doi.org/10.1167/jov.23.12.1.

Tanrıkulu, Ö.D. Pascucci, D. & Kristjansson, A. (2023) Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: Evidence from virtual reality. Journal of Vision, 23(5):20 https://doi.org/10.1167/jov.23.5.20.

Houborg, C., Kristjansson, A., Tanrıkulu, Ö.D. & Pascucci, D. (2023). The role of secondary features in serial dependence. Journal of Vision, 23(5):21. https://doi.org/10.1167/jov.23.5.21.

Testing the assumptions of mind-based models in the traditional Cognitive Science of Religion 

The traditional mind-based model in the Cognitive Science of Religion (CSR) suggests that humans understand the world through ontological categories and make socially strategic inferences to mitigate threats and maximize utility. We are developing visual tasks and statistical methods to empirically test these assumptions.

Collaborators: Shannon Fleming, University of New Hampshire, Paul Robertson, University of New Hampshire; Daniel Durek, Lafayette College, Pennsylvania, US

Relevant publications:

Fleming, S., Robertson, P., Turek, D., & Tanrikulu, Ö. D. (2025). Testing the Relative Influence of Three Key Factors in Mind-Based Models of Religion: Template Categories, Utility, and Threat. Journal of Cognition and Culture, 25(1-2), 159-181. https://doi.org/10.1163/15685373-12340205

Effects of violation-of-expectancy on long-term visual memory

More details coming soon!

Collaborators: Kimele Persaud, Rutgers University, Newark, New Jersey, US ; Shannon Fleming, University of New Hampshire, Durham.