A central motive of my work is the interplay between higher level visual processes and low level feature encoding. I study this by combining behavioural and neural measurements with computational modelling and quantitative characterizations of the environment in which an...
In the Fall term 2019, I am teaching "Sensation and Perception" (PSYC 2220B) and "Intermediate Research Methods" (PSYCH 3010). In the Winter term 2020, I will teach a graduate course on computational neuroscience under the title "Principles of Neural Coding"....
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A central motive of my work is the interplay between higher level visual processes and low level feature encoding. I study this by combining behavioural and neural measurements with computational modelling and quantitative characterizations of the environment in which an organism acts.
When performing image manipulations in natural images, the images typically become less natural, making it difficult to relate results to natural behaviour. We solve this by using generative adversarial nets, which allows us to perform experimental manipulations that are constrained to remain within the manifold of natural images (or at least an approximation thereof).
Shape is one of the fundamental qualities of our visual experience, but little is known about the mechanisms that underly shape representation in the visual system. In a number of recent studies, we tried to shed light on the mechanisms that mediate human perception of planar shape.
In the Fall term 2019, I am teaching "Sensation and Perception" (PSYC 2220B) and "Intermediate Research Methods" (PSYCH 3010). In the Winter term 2020, I will teach a graduate course on computational neuroscience under the title "Principles of Neural Coding".
In "Sensation and Perception" we study how our mind creates the experience of a coherent world from the physical phenomena that arrive at our receptors. After a brief introduction to perception in general, the course focusses on vision from both a behavioural as well as a neuroscientific perspective.
Assessment of learning progress is achieved through a combination of brief, weekly online tests and a written final exam in which students summarize their learning outcomes.
In "intermediate research methods", we address questions about operationalization of theoretical concepts and the formulation of a research question and we discuss aspects of planning an experiment, proper experimental control, and appropriate embedding of a study in the context of prior research. Students practise these skills by designing and executing a small research study of their own.
Assessment of learning progress is achieved through a series of written assignments that cover the study's motivation, design, critique of other students' writing, and discussion of results and limitations.
What are the rules that allow our brain the process sensory input, make decisions, and form actions? How can populations of neurons represent and process information? In this course, we study these questions from a computational perspective, asking questions about information processing and computational principles. Students practise these concepts in a series of analytical and programming exercises and they read and discuss recent papers from the field.
I am an Assistant Professor for Computational Neuroscience as York University, Toronto, ON. I am also a member of the Centre for Vision Research at York University, Toronto, ON and an associate member of the Vision: Science To Applications (VISTA) program at York.
I accept students from the graduate programs in Psychology (PhD, MSc) and Computer Science (MSc).
My research combines behavioural and neural data with computational modeling to understand how high level visual processes interact with low level feature encoding.
I received my PhD in Christoph Herrmann's lab and then went on to do postdoctoral work with Felix Wichmann and James Elder. Before taking on my current position at York, I worked as a Data Scientist at Zalando and later as an A.I. engineer at Twenty Billion Neurons.
I study human vision from a modeling perspective. I use various mathematical techniques, from signal processing to deep learning, to attempt to describe how humans see.
I am a Masters student in Psychology at York University. My interest in research of perception stems from a background in photography, as well as a curiosity of visual perception.
I'm a fourth year cognitive science student interested in the interplay between top-down and bottom up mechanisms in cognitive processing. I am currently working on a neural population model for midlevel visual processing.
I'm in the BSc specialized honours psychology program at York. I'm interested in the discrimination VS recognition process in visual perception and I'm also very interested in studying/modelling consciousness. Previous research experience involved eye-tracking during my summer NSERC USRA.
I am a fourth year Undergraduate Psychology student. I'm interested in how different types of performance feedback impact visual perception thresholds.