Examining the correlations between Individual Non-conscious Prioritization Speed (NPS) and visual perception

My Cognitive Psychology Thesis involved the following:

Non-conscious prioritization speed (NPS) is an individual trait describing the speed in which visual stimuli tend to break suppression and enter consciousness. In two experiments (involving more than 200 participants) I demonstrated, for the first time, that individual NPS can account for differences between individuals in the way they perceive their visual environment. The full thesis disertation can be found Here.

• Developed and deployed online experiments using HTML and JavaScript. A shortened demonstration can be found Here
• Prepared, cleaned, and tranformed raw data using R.
• Computed individual perception ability parameters using curve fitting (of inverse exponential function) to each individual's behavioral data.
• Explored and visualized data using descriptive statistical analysis.
• Found a significant correlation between NPS and various perception ability parameters.
• Visualized findings in support of conclusions, using ggplot2 (R statistical package). Example visualizations are shown below.

Examining gender differences in face prioritization for consciousness

My BSc final project in cognitive sciences involved the following:

Because we become aware to only a small fraction of external stimuli, understanding the determinants of conscious experience is extremely interesting. Here I used a novel data-driven approach to explore gender differences in the determinants for consciousness of human faces. In a 100 participants study I uncovered physical facial dimensions capable of explaining the speed in which faces will reach conscious awareness. I showed strong evidence for a distinct difference in these dimensions between the two genders. The full paper can be found Here.

• Developed and deployed online experiments using HTML, JavaScript and AWS.
• Prepared, cleaned, and tranformed raw data using R.
• Explored and visualized data using descriptive statistical analysis.
• Deployed a data-driven approach for analysis. Used reverse correlation to model the facial-properties dimensions predictive of prioritization speed.
• Found statistically significant differences between males and females in their priority dimension.
• Visualized findings in support of conclusions, using ggplot2 (R statistical package) and more. Example visualizations are shown below.