In my research I use neuroimaging and computational techniques to study behaviour, cognition and the brain in the healthy population and across the psychosis continuum. The motivation behind my research is to understand why some people experience psychosis, and to improve the diagnostic methods and therapies for people diagnosed with a psychotic disorder.
I use human neuroimaging methods, such as Electroencephalography (EEG), functional Magnetic Resonance Imaging (fMRI) and diffusion-weighted Magnetic Resonance Imaging (DW-MRI). I employ modelling techniques, such as Dynamic Causal Modelling and the Hierarchical Gaussian Filter. These techniques are based on the Predictive Coding theory of the brain as an inference machine, which continually generates, and updates predictive models of the world based on prior expectations and sensory experience. In my research, I am interested in the aberrant predictive processes that underlie symptoms in schizophrenia. Forming a predictive model of the world (and about others) depends on regularity learning. Regularity learning is the process used to learn the statistics of the environment. I am exploring if regularity learning is impaired across the psychosis continuum, rendering the environment chaotic and surprising, and if this leads to delusional beliefs and precepts.
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Displaying the 1 most recent scholarly work by Ilvana Dzafic.
Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis
Ilvana Dzafic, Roshini Randeniya, Clare D Harris, Moritz Bammel, Marta Garrido
Journal article | 2020 | The Journal of Neuroscience
Our perceptions result from the brain's ability to make inferences, or predictive models, of sensory information. Recently, it has..
Postdoctoral Research Fellow In Psychology
Melbourne School Of Psychological Sciences
Doctor of Philosophy
The University of Queensland
Bachelor of Psychological Sciences with Honours
University of Queensland