Malik, Amna2023-09-272023-09-272023-092023-092023-09-19https://hdl.handle.net/11693/113902Cataloged from PDF version of article.Thesis (Ph.D.): Bilkent University, Graduate Program in Neuroscience, İhsan Doğramacı Bilkent University, 2023.Includes bibliographical references (leaves 110-138).Living organisms rely on the information they receive through their senses to survive and function in an environment that is constantly changing. However, incoming information from the senses is often ambiguous, noisy, or incomplete. Context, either spatial, temporal, or cognitive, is fundamental, especially in visual perception, to disambiguate and complete this noisy input and optimize behavior. The mechanisms by which contextual information influences visual perception, however, are not fully understood. Studies in the current thesis aim to add to the understanding of those mechanisms. It comprises two lines of work. The first line of work focuses on the spatial context, where using fMRI, we investigate the neural correlates of spatiotemporal properties of context-dependent lightness perception. Results show that activity in the primary visual cortex (V1) correlates with context-dependent lightness perception, providing evidence for low-level mechanisms underlying the contextual effects. The second line of work focuses on the cognitive context, where we systematically study the effect of expectations about dynamic material properties on perceptual decisions. To do so, we used behavioral methods, where we manipulated participants’ long-term and short-term expectations about material properties. Results show that expectations about material properties can impact relatively low-level perceptual decision-making processes. Furthermore, we found an interplay between long-term and newly learned expectations. In conclusion, the current thesis broadens our understanding of how context influences visual processes, particularly by pro-viding evidence that low-level processes are affected by the visual context. This knowledge has the potential to help develop more accurate models of visual perception, which in turn can have implications in clinical neuroscience, artificial intelligence, computer vision, and marketing.xviii, 138 leaves : color illustrations, charts ; 30 cm.Englishinfo:eu-repo/semantics/openAccessContextual effectsLightness inductionVisual perceptionExpectationsPerception of material propertiesThesisB162529