Previously, I analyzed this batch of eye tracking data using R, but the results, especially regarding the areas of interest (AOIs), weren’t as striking as I hoped. So, I turned to Python, eager to unearth more information from the data and, of course, to buff up my Python data handling skills.
The Toolbox 🛠️
My preprocessing toolkit was pretty standard yet powerful, featuring the likes of:
numpymatplotlibpandas
Since my results included both categorical data (like word segmentation choices in different conditions) and eye movement data, I had to approach the visualization of each type differently.
Visualizing the Data 📊
With Matplotlib and Seaborn, I embarked on a visual exploration to tease out patterns and anomalies. Here’s a sneak peek into my approach:
- For categorical data: I grouped the data based on experimental conditions or other variables to analyze the distribution of categorical outcomes.
- For continuous data: I focused on grouping by experimental conditions and calculating the distribution of outcomes within these groups.
The Power of Plots
I employed box plots and bar charts, which were incredibly helpful in spotting trends and outliers in fixation durations and saccadic movements. These insights are like gold dust for understanding the cognitive processes in language comprehension.
The Twist in the Tale 🌀
Despite my efforts, hypothesis testing and linear mixed models analysis of AOIs didn’t reveal significant differences in eye movement patterns. So, it seems I’ve got more digging to do, either by delving deeper into the preprocessed data or by taking the raw data for a fresh spin in Python.
Until next time, keep exploring and stay awesome! 💫