Consoling touch is a powerful form of social support that has been repeatedly demonstrated to reduce the experience of physical pain. However, it remains unknown whether touch reduces emotional pain in the same way that it reduces physical pain. The present research sought to understand how handholding with a romantic partner shapes experiences of emotional pain and comfort during emotional recollection, as well as how it shapes lasting emotional pain associated with emotional experiences. Participants recalled emotionally painful memories or neutral memories with their partners, while holding their partner’s hand or holding a squeeze-ball. They additionally completed a follow-up survey to report how much emotional pain they associated with the emotional experiences after recalling them in the lab with their partners. Although consoling touch did not reduce emotional pain during the task, consoling touch increased feelings of comfort. Moreover, participants later recalled emotional memories that were paired with touch as being less emotionally painful than those that were not paired with touch. These findings suggest that touch does not decrease the immediate experience of emotional pain and may instead support adaptive processing of emotional experiences over time.

Skeptics of Programme for International Student Assessment (PISA) and Trend for International Math and Science Study (TIMSS) argue that while US elementary and high school students are behind their peers in other nations, the US workforce is still excellent because of the high quality post-secondary educational institutions in the US. However, the Programme for the International Assessment of Adult Competencies (PIAAC) indicates that US adults are in fact far behind their international peers in literacy, numeracy, and technology-based problem solving. Through the use of data mining, this study explored the possible association between PIAAC scores and several constructs. Since the US, Canada, and New Zealand were considered culturally similar nations, according to cluster analysis, patterns between PIAAC scores and selected constructs were analyzed by a variety of big data analytical methods, including cluster analysis, bootstrap forest, boosted tree, and data visualization. Given that PIAAC used multiple computerized adaptive testing, the consequential plausible values were randomly selected when the ensemble approach was used. Additionally, model comparison was utilized to decide between bagging and boosting in order to select the optimal model for each sample. In these samples, cultural engagement, readiness to learn, and social trust, respectively emerged as strong predictors for learning outcomes as they were assessed by PIAAC.

No abstract