Family still matters: Human social motivation across 42 countries during a global pandemic.

The COVID-19 pandemic caused drastic social changes for many people, including separation from friends and coworkers, enforced close contact with family, and reductions in mobility. Here we assess the extent to which people's evolutionarily-relevant basic motivations and goals-fundamental social motives such as Affiliation and Kin Care-might have been affected. To address this question, we gathered data on fundamental social motives in 42 countries (N = 15,915) across two waves, including 19 countries (N = 10,907) for which data were gathered both before and during the pandemic (pre-pandemic wave: 32 countries, N = 8998; 3302 male, 5585 female; M age  = 24.43, SD = 7.91; mid-pandemic wave: 29 countries, N = 6917; 2249 male, 4218 female; M age  = 28.59, SD = 11.31). Samples include data collected online (e.g., Prolific, MTurk), at universities, and via community sampling. We found that Disease Avoidance motivation was substantially higher during the pandemic, and that most of the other fundamental social motives showed small, yet significant, differences across waves. Most sensibly, concern with caring for one's children was higher during the pandemic, and concerns with Mate Seeking and Status were lower. Earlier findings showing the prioritization of family motives over mating motives (and even over Disease Avoidance motives) were replicated during the pandemic. Finally, well-being remained positively associated with family-related motives and negatively associated with mating motives during the pandemic, as in the pre-pandemic samples. Our results provide further evidence for the robust primacy of family-related motivations even during this unique disruption of social life.

Family Matters: Rethinking the Psychology of Human Social Motivation.

What motives do people prioritize in their social lives? Historically, social psychologists, especially those adopting an evolutionary perspective, have devoted a great deal of research attention to sexual attraction and romantic-partner choice (mate seeking). Research on long-term familial bonds (mate retention and kin care) has been less thoroughly connected to relevant comparative and evolutionary work on other species, and in the case of kin care, these bonds have been less well researched. Examining varied sources of data from 27 societies around the world, we found that people generally view familial motives as primary in importance and mate-seeking motives as relatively low in importance. Compared with other groups, college students, single people, and men place relatively higher emphasis on mate seeking, but even those samples rated kin-care motives as more important. Furthermore, motives linked to long-term familial bonds are positively associated with psychological well-being, but mate-seeking motives are associated with anxiety and depression. We address theoretical and empirical reasons why there has been extensive research on mate seeking and why people prioritize goals related to long-term familial bonds over mating goals. Reallocating relatively greater research effort toward long-term familial relationships would likely yield many interesting new findings relevant to everyday people's highest social priorities.

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors' entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing in Cardiology Conference. APPROACH: Our approach involves extracting features from the ECG waveform and training a machine learning classifier. In feature extraction, we calculate several statistical features related to the ECG signal and fiduciary points. We also used a disciplined method of feature selection to reduce the dimensionality of the feature space. We also employ sparse coding as an unsupervised feature extraction tool. The classifier we use is a decision tree-based ensemble learning classifier. MAIN RESULTS: When applied to the hidden test data reserved by the PhysioNet Challenge organizers, our classifier reports F1 scores of 0.91, 0.78, and 0.71 for the Normal, AF, and Other classes, respectively. The overall test score is 0.80, and is obtained by averaging the F1 scores for these three classes. SIGNIFICANCE: This work demonstrates that feature selection and ensemble learning can be used to improve the performance of ECG-based classification of AF.