Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences.

Detrended Fluctuation Analysis (DFA) is the most popular fractal analytical technique used to evaluate the strength of long-range correlations in empirical time series in terms of the Hurst exponent, . Specifically, DFA quantifies the linear regression slope in log-log coordinates representing the relationship between the time series' variability and the number of timescales over which this variability is computed. We compared the performance of two methods of fractal analysis-the current gold standard, DFA, and a Bayesian method that is not currently well-known in behavioral sciences: the Hurst-Kolmogorov (HK) method-in estimating the Hurst exponent of synthetic and empirical time series.

Tracking strategy changes using machine learning classifiers.

In complex tasks, high performers often have better strategies than low performers, even with similar amounts of practice. Relatively little research has examined how people form and change strategies in tasks that permit a large set of strategies. One challenge with such research is identifying strategies based on behavior.