The overarching goal of our research is to develop computational models to describe the functioning of the visual system at multiple levels, ranging from behavioral to neuronal. We work on building models that can explain a wide range of phenomena, including the effects of expectation, context, adaptation, and perceptual learning on visual processes. To do so, we conduct behavioral and neuroimaging experiments and build our models based on the results of those experiments. Some of the active projects are listed below.
Predictive Processing in Visual Perception
Sensory information we experience about the hidden states of the world is often incomplete, weak, ambiguous, or noisy. Thus recognition of a stimulus based on only the sensory input may sometimes be very difficult. However, our visual system usually comes up with a useful interpretation of a visual scene pretty quickly because our prior knowledge facilitates perception while we make decisions. In this line of work, we investigate how prior knowledge and expectations affect early visual processes. We use psychophysics, computational modeling, and fMRI to unravel the effect of expectation on visual perception and the computational mechanisms underlying those effects. (This project has been supported by funds from TUBITAK). Here are some relevant readings
Malik, A, Doerschner, K, Boyaci, H (2022). Unmet Expectations About Material Properties Delay Perceptual Decisions, bioRxiv, https://www.biorxiv.org/content/10.1101/2022.07.28.501825v1
Urgen, BM, Boyaci, H (2021). A recurrent cortical model can parsimoniously explain the effect of expectations on sensory processes. bioRxiv, https://doi.org/10.1101/2021.02.05.429913
Urgen, BM, Boyaci, H. (2021). Unmet expectations delay sensory processes. Vision Research, 181, 1-9, https://doi.org/10.1016/j.visres.2020.12.004 (Also bioRxiv, https://doi.org/10.1101/545244)
Center-surround Interaction in Motion Processing
Center-surround interaction is a common working principle in the visual system. It is present in nearly all levels, from the retina to higher-level cortical areas. These interactions lead to suppression or facilitation of neuronal activity. There are, however, still many unanswered questions related to this mechanism. For example, how the extent of spatial attention affects this center-surround interaction in motion perception in humans has not been systematically studied before. In this line of research, using a well-known motion effect and behavioral and fMRI methods, we investigate the interaction of center and surround in the human visual system. (This project has been supported by funds from TUBITAK). For more about this research:
Kiniklioglu, M, Boyaci, H (2022). Increasing spatial extent of attention strengthens surround suppression. Vision Research, 199. https://doi.org/10.1016/j.visres.2022.108074 (Also bioRxiv
Er, G, Pamir, Z, Boyaci, H (2020). Distinct patterns of surround modulation in V1 and hMT+. NeuroImage, 220, 117084, https://doi.org/10.1016/j.neuroimage.2020.117084. (Also bioRxiv, https://doi.org/10.1101/817171)
Turkozer, HB, Pamir, Z, Boyaci, H (2016). Contrast Affects fMRI Activity in Middle Temporal Cortex Related to Center-Surround Interaction in Motion Perception. Frontiers in Psychology, 7:454, 1-8, http://journal.frontiersin.org/article/10.3389/fpsyg.2016.00454/full, doi: 10.3389/fpsyg.2016.00454
Non-local Effects of Adaptation
Prolonged exposure to a certain stimulus affects the perceived features of a subsequently presented stimulus. This phenomenon causes well-known effects such as repulsive size adaptation and the tilt aftereffect. For example, in the size adaptation effect, if the adapter is larger than the test, the test disc is perceived as smaller than its veridical size. Conversely, when the adapter is smaller than the test, the test disc is perceived as larger than its veridical size. In previous studies in literature, the test stimulus has always been presented at the same location as the adapter stimulus. The spatial extent of these aftereffects has not been systematically studied, and what happens in the rest of the visual space is not known. In this line of research, we investigate the effect of adaptation across the entire visual field. Further, we are working on computational models to explain those effects. Here are some relevant readings
Gurbuz, T, Boyaci, H (2022). Tilt aftereffect spreads across the visual field. bioRxiv, https://doi.org/10.1101/2022.06.21.496978
Altan, E, Boyaci, H (2020). Size aftereffect is non-local. Vision Research, 176, pages 40-47, https://doi.org/10.1016/j.visres.2020.07.006. (Also bioRxiv, https://doi.org/10.1101/2020.03.19.998161)
Perceptual Learning & Brain Plasticity
Extensive training on a certain visual feature can improve performance on various tasks related to that feature. For example, a person can become much better at detecting the direction of motion in a noisy and weak stimulus after long hours of training. This is called visual perceptual learning. This kind of learning is often specific to the trained stimulus, for example, its location and feature. In this project, we investigate the effect of training on a visual task and its neuronal correlates using magnetic resonance imaging. (This project has been supported by funds from TUBITAK).
Effect of Context-dependent Lightness on Contrast Perception
Does the perceived contrast of a grating vary with the luminance or lightness of its background? Suppose that we superimpose grating patterns on patches with equal luminance but different lightness (see Figure: in each image, the squares with gratings are identical); what happens to the perceived contrast of those gratings? Is contrast perception affected by context-dependent lightness? In this series of studies, using psychophysical and functional magnetic resonance imaging (fMRI) methods, we seek answers to these questions. (This project has been supported by funds from TUBITAK). Read more