Guilt Enhances the Sense of Control and Drives Risky Judgments

The present studies investigate the hypothesis that guilt influences risk-taking by enhancing one's sense of control. Across multiple inductions of guilt, we demonstrate that experimentally induced guilt enhances optimism about risks for the self (Study 1), preferences for gambles versus guaranteed payoffs (Studies 2, 4, and 6), and the likelihood that one will engage in risk-taking behaviors (Study 5). In addition, we demonstrate that guilt enhances the sense of control over uncontrollable events, an illusory control (Studies 3, 4, and 5), and found that a model with illusory control as a mediator is consistent with the data (Studies 5 and 6). We also found that a model with feelings of guilt as a mediator, but not generalized negative affect, fits the data (Study 4). Finally, we examined the relative explanatory power of different appraisals and found that appraisals of illusory control best explain the influence of guilt on risk-taking (Study 6). These results provide the first empirical demonstration of the influence of guilt on sense of control and risk-taking, extend previous theorizing on guilt, and more generally contribute to our understanding of how specific emotions influence cognition and behavior

Lying Because We Care: Compassion Increases Prosocial Lying

Prosocial lies, or lies intended to benefit others, are ubiquitous behaviors that have important social and economic consequences. Though emotions play a central role in many forms of prosocial behavior, no work has investigated how emotions influence behavior when one has the opportunity to tell a prosocial lie-a situation that presents a conflict between two prosocial ethics: lying to prevent harm to another, and honesty, which might also provide benefits to the target of the lie. Here, we examine whether the emotion of compassion influences prosocial lying, and find that compassion causally increases and positively predicts prosocial lying. In Studies 1 and 2, participants evaluated a poorly written essay and provided feedback to the essay writer. Experimentally induced compassion felt toward the essay writer (Study 1) and individual differences in trait compassion (Study 2) were positively associated with inflated feedback to the essay writer. In both of these studies, the relationship between compassion and prosocial lying was partially mediated by an enhanced importance placed on preventing emotional harm. In Study 3, we found moderation such that experimentally induced compassion increased lies that resulted in financial gains for a charity, but not lies that produced financial gains for the self. This research illuminates the emotional underpinnings of the common yet morally complex behavior of prosocial lying, and builds on work highlighting the potentially harmful effects of compassion-an emotion typically seen as socially beneficial. (PsycINFO Database Recor

Everyday co-presence with a romantic partner is associated with lower C-reactive protein

Social relationships are an important driver of health, and inflammation has been proposed as a key neurobiological mechanism to explain this effect. Behavioral researchers have focused on social relationship quality to further explain the association, yet recent research indicates that relationship quality may not be as robust a predictor as previously thought. Here, building on animal models of social bonds and recent theory on close relationships, we instead investigated merely being in the physical presence of one's romantic partner. Specifically, we tested the hypothesis that spending more time co-present with a loved partner in everyday life would be associated with lower C-reactive protein (CRP). Three times over the course of one month, 100 people in romantic relationships reported how much time they spent in the same physical space as their partner in the prior 24 h, in minutes, and provided a sample of blood for CRP assay (n observations = 296). Results from multi-level models showed that when one reported spending more time in the physical presence of their partner they had lower CRP – an effect that was independent from social relationship quality explanations from the prior literature, including romantic relationship quality, hostility, and loneliness. These findings move past global assessments of social isolation to consider a novel everyday behavior that is of great interest in the non-human animal literature – spending time together – as a potential mechanism linking high-quality relationships and physical health in adult humans. The findings also point to future research on additional behavioral mechanisms that are not dependent on stress pathways: people in high-quality relationships tend to spend enjoyable and affectionate time with one another, which may impact inflammation

Pulmonary Function Affects Language Performance in Aging

Background Good pulmonary function (PF) is associated with preservation of cognitive performance, primarily of executive functions, in aging (Albert et al., 1995; Chyou et al., 1996; Emery, Finkel, & Pedersen, 2012; Yohannes & Gindo, 2013). The contribution of PF to older adults’ language abilities, however, has never been explored, to our knowledge. We addressed this gap by examining the effects of PF on older adults’ language functions, as measured by naming and sentence processing accuracy. We predicted similar effects as found for executive functions, given the positive associations between executive functions and sentence processing in aging (e.g., Goral et al., 2011). Methods Data were collected from 190 healthy adults aged 55 to 84 years (M = 71.1, SD = 8.1), with no history of neurological or psychiatric disorders. Procedure PF was measured prior to language testing. Measures included forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC). Language functions were assessed through performance on computer-administered lexical retrieval and sentence processing tasks. Sentence processing was measured using two auditory comprehension tasks: one, of embedded sentences (ES), the other, of sentences with multiple negatives (MN). Lexical retrieval was measured using the Boston Naming Test (BNT) and Action Naming Test (ANT). Performance was scored for percent accuracy. Additionally, lexical retrieval was evaluated with a phonemic fluency task (FAS), which also taps executive function abilities. Statistical Analyses Multiple regression was used to examine the association between pulmonary and language functions, adjusting for age, education, gender, history of respiratory illness, current level of physical activities, and current and past smoking. Results Better PF was associated with better sentence processing and lexical retrieval on naming tasks, but not with phonemic fluency, after adjusting for covariates. Higher FVC was associated with better ES performance (B = 6.64, SE = 2.43, p = .01). Higher FVC and FEV1 were related to better MN performance, but this did not reach statistical significance (FVC: B = 3.68, SE = 2.16, p = .09; FEV1: B = 4.92, SE = 2.64, p = .06). Higher FVC (B = 3.98, SE = 1.44, p = .01) and FEV1 (B = 4.79, SE = 1.75, p = .01) were associated with better ANT performance. The positive association between PF and BNT performance was marginally significant (FVC: B = 4.19, SE = 2.18, p = .06; FEV1: B = 3.51, SE = 2.66, p = .10). Discussion and Conclusion Better PF was associated with higher accuracy on sentence processing and naming-based lexical retrieval tasks, consistent with the conclusion that pulmonary function affects older adults’ language performance. Our findings support the emerging thesis that language changes in aging are influenced by health-related physiological and neural mechanisms (e.g., Albert et al., 2009; Cahana-Amitay et al., 2013). From a clinical perspective, these findings highlight the promise of targeting PF as an intervention for improving language abilities among the elderly

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms