On the relationship between unprompted thought and affective well-being: A systematic review and meta-analysis.

There is a growing recognition that thoughts often arise independently of external demands. These thoughts can span from reminiscing your last vacation to contemplating career goals to fantasizing about meeting your favorite musician. Often referred to as mind wandering, such frequently occurring unprompted thoughts have widespread impact on our daily functions, with the dominant narrative converging on a negative relationship between unprompted thought and affective well-being. In this systematic review of 76 studies, we implemented a meta-analysis and qualitative review to elucidate if and when unprompted thought is indeed negatively associated with affective well-being in adults. Using a multilevel mixed-model approach on 386 effect sizes from 23,168 participants across 64 studies, our meta-analyses indicated an overall relationship between unprompted thought and worse affective well-being (r¯ = -.18, 95% CI [-.23, -.14]); however, the magnitude and direction of this relationship changed when considering specific aspects of the phenomenon (including thought content and intentionality) and methodological approaches (including questionnaires vs. experience sampling). The qualitative review further contextualizes this relationship by revealing the nuances of how and when unprompted thought is associated with affective well-being. Taken together, our meta-analysis and qualitative review indicate that the commonly reported relationship between unprompted thought and affective well-being is contingent upon the content and conceptualization of unprompted thought, as well as the methodological and analytic approaches implemented. Based on these findings, we propose emerging directions for future empirical and theoretical work that highlight the importance of accounting for when, how, and for whom unprompted thought is associated with affective well-being. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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, H. 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. Simulations demonstrate that the HK method consistently outperforms DFA in three important ways. The HK method: (i) accurately assesses long-range correlations when the measurement time series is short, (ii) shows minimal dispersion about the central tendency, and (iii) yields a point estimate that does not depend on the length of the measurement time series or its underlying Hurst exponent. Comparing the two methods using empirical time series from multiple settings further supports these findings. We conclude that applying DFA to synthetic time series and empirical time series during brief trials is unreliable and encourage the systematic application of the HK method to assess the Hurst exponent of empirical time series in behavioral sciences.

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. Three algorithms were developed that track the task features people use in their strategies while performing a complex task. Two of these algorithms were based on task-general, machine-learning classifiers: a support vector machine and a decision tree algorithm. The third was a task-specific algorithm. Data from several strategies in a complex task were simulated, and the algorithms were tested to see how well they identified the underlying features of the simulated strategy. The two machine-learning classifiers performed better than the task-specific algorithm. However, the two classifiers differed on how well they identified different types of strategies. The first two studies show that the ability of these algorithms to recover the underlying strategy depends on the complexity of the strategy relative to the quantity of performance data available. If the underlying strategy changes too frequently, then the performance of the algorithms suffers. However, results from the third study show that it is possible to use these algorithms to track strategy changes that occur in a task. The fourth study examines performance on data from human participants. This approach to tracking strategy exploration may enable further development of theories about how people search for and select effective strategies.