Researchers tested the effects of including cues, anchors, and savings goals in a company email encouraging employee contributions to their 401(k).
Researchers found that providing high contribution rate or savings goal examples, or highlighting high savings thresholds created by the 401(k) plan rules, increased 401(k) contribution rates by 1-2% of income per pay period.
The expert performance framework distinguishes between deliberate practice and less effective practice activities. The current longitudinal study is the first to use this framework to understand how children improve in an academic skill. Specifically, the authors examined the effectiveness and subjective experience of three preparation activities widely recommended to improve spelling skill. Deliberate practice, operationally defined as studying and memorizing words while alone, better predicted performance in the National Spelling Bee than being quizzed by others or reading for pleasure. Rated as the most effortful and least enjoyable type of preparation activity, deliberate practice was increasingly favored over being quizzed as spellers accumulated competition experience. Deliberate practice mediated the prediction of final performance by the personality trait of grit, suggesting that perseverance and passion for long-term goals enable spellers to persist with practice activities that are less intrinsically rewarding—but more effective—than other types of preparation.
Expanding upon Simon's (1955) seminal theory, this investigation compared the choice-making strategies of maximizers and satisficers, finding that maximizing tendencies, although positively correlated with objectively better decision outcomes, are also associated with more negative subjective evaluations of these decision outcomes. Specifically, in the fall of their final year in school, students were administered a scale that measured maximizing tendencies and were then followed over the course of the year as they searched for jobs. Students with high maximizing tendencies secured jobs with 20% higher starting salaries than did students with low maximizing tendencies. However, maximizers were less satisfied than satisficers with the jobs they obtained, and experienced more negative affect throughout the job-search process. These effects were mediated by maximizers' greater reliance on external sources of information and their fixation on realized and unrealized options during the search and selection process.
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In order to produce a beneficial result, professionals must sometimes cause harm to another human being. To capture this phenomenon, we introduce the construct of "necessary evils" and explore the inherent challenges such tasks pose for those who must perform them. Whereas previous research has established the importance of treating victims of necessary evils with interpersonal sensitivity, we focus on the challenges performers face when attempting to achieve this prescribed standard in practice.
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In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.