Nikos Gkekas
nikos gkekas

Dr Nikos Gkekas

Lecturer

Biography

I initially trained in Informatics (Aristotle University of Thessaloniki, Greece) and obtained a Masters in Machine Learning and Data Mining (University of Bristol). After working in the industry for some years, I obtained a Masters and PhD in Neuroinformatics from the University of Edinburgh. After the completion of my PhD, I worked as a post-doctoral researcher at the École normale supérieure in Paris and at the University of Nottingham. I joined Edinburgh Napier University in December 2021 as a Lecturer.

My main research interest is understanding how humans learn from the statistical properties of our environment in a process that appears to be seamless and automatic. I have looked at the role of prior expectations in perception, the limits of complexity of what can be learned, and how learning mechanisms of different temporal and structural properties interact.

A complimentary research interest is understanding the neural mechanisms of low- to mid-level vision. Specifically, the investigation of canonical neural computations (normalization, lateral inhibition, probabilistic formulations) that are potentially repeated across different levels of the cortex and are responsible for organizing incoming information into increasingly complex perceptual representations.

Current research topics include adaptation and learning at different timescales, motion and speed perception, serial effects and probabilistic models of perception.

Research Groups

Research Areas

Date


11 results

Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

Journal Article
Meso, A. I., Gekas, N., Mamassian, P., & Masson, G. S. (2022)
Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions. eNeuro, 9(3), https://doi.org/10.1523/ENEURO.0511-21.2022
Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probe...

Adaptation to one perceived motion direction can generate multiple velocity aftereffects

Journal Article
Gekas, N., & Mamassian, P. (2021)
Adaptation to one perceived motion direction can generate multiple velocity aftereffects. Journal of Vision, 21(5), https://doi.org/10.1167/jov.21.5.17
Sensory adaptation is a useful tool to identify the links between perceptual effects and neural mechanisms. Even though motion adaptation is one of the earliest and most docum...

Disambiguating serial effects of multiple timescales

Journal Article
Gekas, N., McDermott, K. C., & Mamassian, P. (2019)
Disambiguating serial effects of multiple timescales. Journal of Vision, 19(6), https://doi.org/10.1167/19.6.24
What has been previously experienced can systematically affect human perception in the present. We designed a novel psychophysical experiment to measure the perceptual effects...

History Effects on Perception of Noisy Stimuli

Journal Article
Gekas, N., & Mamassian, P. (2019)
History Effects on Perception of Noisy Stimuli. Perception, 48(1_suppl), 1-233. https://doi.org/10.1177/0301006618824879
Human perception is partially affected by what has been previously experienced. These history effects presumably help tackle current sensory uncertainty by tracking past stimu...

Perceptual effects of adaptation over multiple timescales

Journal Article
Gekas, N., McDermott, K., & Mamassian, P. (2017)
Perceptual effects of adaptation over multiple timescales. Journal of Vision, 17(10), 489. https://doi.org/10.1167/17.10.489
It is well known that adaptation to a visual stimulus leads to a negative correlation between the current percept and previous percepts. However, there are diverging views on ...

A Normalization Mechanism for Estimating Visual Motion across Speeds and Scales

Journal Article
Gekas, N., Meso, A. I., Masson, G. S., & Mamassian, P. (2017)
A Normalization Mechanism for Estimating Visual Motion across Speeds and Scales. Current Biology, 27(10), 1514-1520.e3. https://doi.org/10.1016/j.cub.2017.04.022
Interacting with the natural environment leads to complex stimulations of our senses. Here we focus on the estimation of visual speed, a critical source of information for the...

Speed channel interactions in naturalistic motion stimuli

Journal Article
Gekas, N., Meso, A., Masson, G., & Mamassian, P. (2016)
Speed channel interactions in naturalistic motion stimuli. Journal of Vision, 16(12), 1131. https://doi.org/10.1167/16.12.1131
Human observers are very sensitive to speeds of moving stimuli. However little is known about how such sensitivity arises independently upon the spatiotemporal properties of t...

Effect of orthogonal adaptation on the perceived velocity of multidirectional random dot stimuli at different speeds

Journal Article
Gekas, N., & Mamassian, P. (2016)
Effect of orthogonal adaptation on the perceived velocity of multidirectional random dot stimuli at different speeds. Perception, 45(2_suppl), 101-101. https://doi.org/10.1177/0301006616671273
Motion adaptation generates strong aftereffects in the opposite direction. Here, we investigated the effect of orthogonal adaptation on perceived velocity across different spe...

Expectations developed over multiple timescales facilitate visual search performance

Journal Article
Gekas, N., Seitz, A. R., & Seriès, P. (2015)
Expectations developed over multiple timescales facilitate visual search performance. Journal of Vision, 15(9), https://doi.org/10.1167/15.9.10
Our perception of the world is strongly influenced by our expectations, and a question of key importance is how the visual system develops and updates its expectations through...

Complexity and specificity of experimentally-induced expectations in motion perception

Journal Article
Gekas, N., Chalk, M., Seitz, A., & Series, P. (2013)
Complexity and specificity of experimentally-induced expectations in motion perception. Journal of Vision, 13(4), https://doi.org/10.1167/13.4.8
Abstract Our perceptions are fundamentally altered by our expectations, i.e., priors about the world. In previous statistical learning experiments (Chalk, Seitz, & Seriès...