Use of random time-intervals (RTIs) generation for biometric verification
Pattern Recognition
A new model for explaining long-range correlations in human time interval production
Computational Statistics & Data Analysis
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The Hurst exponent (H) was estimated for series of 256 time intervals produced by human participants, collected in 5 sessions performed on different days. Each series was obtained during the continuation phase following synchronization with 25 isochronous intervals generated by a computer and presented through headphones. Dispersional analysis yielded estimates of H 0.5. These were sufficiently stable to yield statistically significant differences between participants and between each target interval duration (0.5, 0.8, 1.1, and 1.5 s). The results indicate that variability in isochronous serial interval production (ISIP) can be modeled as fractional Gaussian noise, which corroborates and qualifies previous research indicating positive serial dependency or long memory in ISIP data in terms of drift and 1/f noise characteristics. It is concluded that ISIP is a more complex process than is assumed by influential timing models and theories, and that realistic modeling of human timing must account for nonlinear variability patterns.