Eye Movement Entropy in Physics
Eye movement entropy describes how uncertain a gaze path is. It supports visual search, control, attention, and sensor noise measurement. A low value means the eyes stay within a narrow pattern. A high value means gaze visits many zones or changes direction often. This calculator turns streamed fixation labels or coordinate samples into entropy values that can be read during an experiment.
Real Time Measurement Idea
The page accepts a sequence of gaze zones or point samples. Coordinate samples are placed into a screen grid. Each grid cell becomes an area of interest. The tool then counts visits, transitions, and recent window events. The main output is Shannon entropy. It also reports normalized entropy, transition entropy, conditional transition entropy, dominant state, and a simple complexity note. These values help compare trials with different lengths.
Research Use
Entropy is useful when raw traces are too detailed for quick judgment. It reduces a long eye path to a compact measure. During a reading task, low entropy may show focused inspection. During scene search, high entropy may show wider exploration. In cockpit, sports, or human machine tests, transition entropy can reveal how often attention moves between instruments, targets, and background regions.
Practical Notes
Good entropy depends on good binning. Too few bins hide useful motion. Too many bins create sparse counts. Start with a grid that matches the task layout. Use the same bin count for each condition. For short trials, read normalized entropy with care. For noisy data, use a recent window and compare several runs, not one isolated number.
Interpreting Results
The calculator does not diagnose vision or cognition. It gives a physics style signal summary. Report the sampling rule, grid size, entropy base, window size, and sample count with every result. This makes the calculation repeatable and easier to audit.
Live Window Meaning
Live dashboards often need stable numbers before a trial ends. A sliding window helps because it focuses on recent behavior. The full sequence still remains useful for final reporting. If the window entropy rises, the viewer is spreading attention. If it falls, attention is becoming more concentrated. Always compare entropy with the task timeline, because a planned search can also look complex.