Nikos Gekas PhD

Nikos Gekas


Research Interests

My primary research interest is to identify neural processes that accurately represent uncertainty in the cortex. Using psychophysical experimental procedures, I aim to investigate the properties of induced expectations, in particular the limits of forming priors in complex visual tasks of statistical learning, which involve feature-dependent as well as time-dependent paradigms. Using computational models, I aim to evaluate possible neural mechanisms of learning, and propose a unified framework that incorporates both uncertainty and learning.

Publications:
2015
  Expectations developed over multiple timescales facilitate visual search performance
Gekas, N, Seitz, AR & Seriés, P 2015, 'Expectations developed over multiple timescales facilitate visual search performance' Journal of Vision, vol 15, no. 9, 10. DOI: 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 interaction with the environment. We used a visual search task to investigate how expectations of different timescales (from the last few trials to hours to long-term statistics of natural scenes) interact to alter perception. We presented human observers with low-contrast white dots at 12 possible locations equally spaced on a circle, and we asked them to simultaneously identify the presence and location of the dots while manipulating their expectations by presenting stimuli at some locations more frequently than others. Our findings suggest that there are strong acuity differences between absolute target locations (e.g., horizontal vs. vertical) and preexisting long-term biases influencing observers' detection and localization performance, respectively. On top of these, subjects quickly learned about the stimulus distribution, which improved their detection performance but caused increased false alarms at the most frequently presented stimulus locations. Recent exposure to a stimulus resulted in significantly improved detection performance and significantly more false alarms but only at locations at which it was more probable that a stimulus would be presented. Our results can be modeled and understood within a Bayesian framework in terms of a near-optimal integration of sensory evidence with rapidly learned statistical priors, which are skewed toward the very recent history of trials and may help understanding the time scale of developing expectations at the neural level.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Gekas, Nikos, Seitz, Aaron R. & Seriés, Peggy.
Publication Date: Jul 2015
Publication Information
Category: Article
Journal: Journal of Vision
Volume: 15
Issue number: 9
ISSN: 1534-7362
Original Language: English
DOIs: 10.1167/15.9.10
2013
  Complexity and specificity of experimentally induced expectations in motion perception
Gekas, N, Chalk, M, Seitz, AR & Seriès, P 2013, 'Complexity and specificity of experimentally induced expectations in motion perception' 22nd Annual Computational Neuroscience Meeting: CNS 2013, Paris, France, 13/07/13 - 18/07/13, pp. 355. DOI: 10.1186/1471-2202-14-S1-P355
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Gekas, Nikos, Chalk, Matthew, Seitz, Aaron R & Seriès, Peggy.
Number of pages: 1
Publication Date: 1 Jan 2013
Publication Information
Category: Poster
Original Language: English
DOIs: 10.1186/1471-2202-14-S1-P355
  Complexity and specificity of experimentally-induced expectations in motion perception
Chalk, M, Seitz, AR, Seriès, P & Gekas, N 2013, 'Complexity and specificity of experimentally-induced expectations in motion perception' Journal of Vision, vol 13, no. 4, 8. DOI: 10.1167/13.4.8
Our perceptions are fundamentally altered by our expectations, i.e., priors about the world. In previous statistical learning experiments (Chalk, Seitz, & Seriès, 2010), we investigated how such priors are formed by presenting subjects with white low contrast moving dots on a blank screen and using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. We found that human observers quickly and automatically developed expectations for the most frequently presented directions of motion. Here, we examine the specificity of these expectations. Can one learn simultaneously to expect different motion directions for dots of different colors? We interleaved moving dot displays of two different colors, either red or green, with different motion direction distributions. When one distribution was bimodal while the other was uniform, we found that subjects learned a single bimodal prior for the two stimuli. On the contrary, when both distributions were similarly structured, we found evidence for the formation of two distinct priors, which significantly influenced the subjects' behavior when no stimulus was presented. Our results can be modeled using a Bayesian framework and discussed in terms of a suboptimality of the statistical learning process under some conditions.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Chalk, Matthew, Seitz, Aaron R., Seriès, Peggy & Gekas, Nikos.
Publication Date: Mar 2013
Publication Information
Category: Article
Journal: Journal of Vision
Volume: 13
Issue number: 4
Original Language: English
DOIs: 10.1167/13.4.8
2012
  Investigating the specificity of experimentally induced expectations in motion perception
Seitz, A, Seriès, P & Gekas, N 2012, 'Investigating the specificity of experimentally induced expectations in motion perception' Journal of Vision, vol 12, no. 9, pp. 1137. DOI: 10.1167/12.9.1137
Our perceptions are fundamentally altered by our expectations, a.k.a. "priors" about the world. In previous statistical learning experiments (Chalk et al, 2010), we investigated how such priors are formed by presenting subjects with low contrast moving dots or a blank screen, and asking them to report the direction of motion, and whether the stimulus was present. We manipulated subjects? expectations by using a bimodal distribution of motion directions such that two directions were more frequently presented than the others. We found that human observers quickly, automatically, and implicitly developed expectations for the most frequently presented directions of motion. These expectations induced attractive biases towards the perceived motion direction as well as visual hallucinations in the absence of a stimulus. Here, we examine the specificity of these expectations. Would exposure to green dots lead to particular expectations about the motion of red dots? Can one learn simultaneously to expect different motion directions for dots of different colors? We interleaved moving dot displays of two different colors, either red or green, with different motion direction distributions. When one distribution was bimodal while the other was uniform, we found that subjects learned a single bimodal prior for the two stimuli. On the contrary, when both distributions were similarly structured, we found evidence for the formation of two distinct priors, which were not strong enough to alter estimation behavior, but influenced significantly the subjects? behavior when no stimulus was present. Our results can be modeled using a Bayesian framework and discussed in terms of a sub-optimality of the statistical learning process under some conditions. Understanding the limitations of statistical learning for complex stimuli may help understanding how expectations are learned at the neural level.Meeting abstract presented at VSS 2012
General Information
Organisations: Neuroinformatics DTC.
Authors: Seitz, Aaron, Seriès, Peggy & Gekas, Nikos.
Number of pages: 1
Pages: 1137
Publication Date: 2012
Publication Information
Category: Meeting abstract
Journal: Journal of Vision
Volume: 12
Issue number: 9
Original Language: English
DOIs: 10.1167/12.9.1137

Projects:
Neural representations of uncertainty for perception and learning (PhD)