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Bayesian selection of Markov models for symbol sequences: application to microsaccadic eye movements
Mario Bettenbühl1, Marco Rusconi, Ralf Engbert
1Institute of Mathematics, Focus Area for Dynamics of Complex Systems, University of Potsdam, Potsdam, Germany. mario.bettenbuehl@uni-potsdam.de
Plos One
|September 13, 2012
Summary
Researchers studied Markov order in human microsaccadic eye movements. Most data fit a first-order Markov process, suggesting statistical coupling between successive eye movements.
Area of Science:
- Neuroscience
- Computational Biology
- Statistical Physics
Background:
- Complex biological systems generate event sequences.
- Markov processes model these sequences, with order indicating statistical dependencies.
- Microsaccades are rapid eye movements crucial for visual processing.
Purpose of the Study:
- To determine the Markov order of human microsaccadic eye movement sequences.
- To apply Bayesian inference for statistical analysis of Markov order.
- To explore potential statistical couplings in microsaccade behavior.
Main Methods:
- Calculating integrated likelihood for various Markov process orders.
- Employing Bayesian inference for Markov order estimation.
- Analyzing sequences of human microsaccadic eye movements.
Main Results:
- Data from most participants were best explained by a first-order Markov process.
- This finding aligns with previous research on microsaccade orientation coupling.
- The developed method is broadly applicable to biological sequence analysis.
Conclusions:
- Human microsaccadic eye movements often follow a first-order Markov process.
- This suggests underlying statistical dependencies between consecutive microsaccades.
- The analytical framework can be applied to diverse biological sequence data.
