Validation

Upon clicking Validate Eye Tracker button, the window.py script runs the validation paradigm. The user follows a moving dot, and pyETA crunches the gaze data against target positions. The validate.py script checks how well your tracker performed at time t to target positions on a 3x3 grid. The table generated provides with metrics for each grid position (0-8), plus percentiles and mean.

Metrics

The table lists 6 metrics per grid position (0-8), with a summary stat (25%, 50%, 75%, mean) below the separator denoted by --- that shows the cumulative description of grid 3x3 (group 0-8). Here’s what each of the 6 metric column means:

validation metric

Columns	Description
group	Grid, top-left is 0 and bottom-right is 8
target_position	Screen coordinate, in pixels
distance (target to gaze points)	Average distance from target to all gaze points (left + right eyes averaged).
spread (target to gaze points)	Standard deviation of those distances
mean gaze point	Average x/y of gaze points per group.
distance (mean to target)	Distance from mean gaze point to target measures accuracy
distance (mean to gaze points)	Average distance from mean gaze point to individual gaze points. measures precision
spread (mean to gaze points)	Standard deviation of distances from mean to gaze points

Metric Calculation Algorithm

The algorithm in validate.py does the following:

1. Load Data: Pull gaze and validation JSONs.

2. Preprocess: Filter gaze data to match validation timestamps, group by target position.

3. Calculate Per Group:

   • Compute mean gaze point (average x/y for left + right eyes).

   • Calculate distances: target to each gaze point, mean to target, mean to each gaze point.

   • Calculate spreads: standard deviations for target-to-gaze and mean-to-gaze distances.

4. Summarize: Compute percentiles (25%, 50%, 75%) and mean across groups.