Soothing Sadness: When Sharing Emotions Helps

2018 ◽  
Author(s):  
Hans IJzerman ◽  
Ivan Ropovik ◽  
Marcello Gallucci ◽  
Michael Häfner ◽  
Siegwart Lindenberg
Keyword(s):  
Individual Difference ◽  
Interaction Partner ◽  
Prior Work ◽  
High Quality ◽  
Loved One ◽  
Psychological Literature ◽  
Individual Difference Variables ◽  
Relevant Variables ◽  
Reported Health ◽  
Communal Strength

The psychological literature has shown that sharing one’s emotions with loved ones does not alleviate distress. We challenge this notion. In four studies (N=2581), participants were asked to recall an emotional episode (Studies 1a-2: sadness, fear, affection, joy, anger) and write about this episode. Not replicating prior work, participants shared emotions less frequently than in previous work and not all emotions were shared equally. In Study 3 (N=894), participants also answered relationship-relevant variables (communal strength and liking) and individual difference variables (comfort with touch); we found that people who are comfortable with touch by loved ones and who are in a high-quality relationship with their interaction partner reflect more and ruminate less about the episode and are less stressed. Findings about self-reported health were inconclusive. Our work shows that sharing one’s sad episodes can help, if one can “outsource” the emotion to a loved one.

Individual Difference Variables as Predictors of Error during Multitasking

2007 ◽  
Author(s):  
Elizabeth M. Oberlander ◽  
Frederick L. Oswald ◽  
David Z. Hambrick ◽  
L. Andrew Jones
Keyword(s):  
Individual Difference ◽  
Individual Difference Variables

Individual difference variables associated with the willingness to engage in various minor moral and legal violations

2005 ◽  
Author(s):  
Natalie Brown ◽  
Mark Barnett ◽  
Fred Sanborn ◽  
Jennifer Sperfslage ◽  
Andrea Shane
Keyword(s):  
Individual Difference ◽  
Individual Difference Variables ◽  
Willingness To Engage

Experiencing Instigations and Trait Aggression Contribute to Harsh Parenting Behaviors

2016 ◽  
Author(s):  
Randy J. McCarthy
Keyword(s):  
Individual Difference ◽  
Parenting Behaviors ◽  
Situational Factors ◽  
Harsh Parenting ◽  
Total N ◽  
Individual Difference Variables ◽  
Trait Aggression ◽  
Reported Study

Three studies (total N = 1,777 parents) examined whether harsh parenting behaviors would increase when parents experienced an instigation and whether this increase would be especially pronounced for parents who were high in trait aggression. These predictions were tested both when parents’ experience of an instigation was manipulated (Studies 1 and 2) and when parents’ perceptions of their child’s instigating behavior was reported (Study 3). Further, these predictions were tested across a variety of measures of parents’ harsh behaviors: (1) Asking parents to report their likelihood of behaving harshly (Study 1); (2) using proxy tasks for parents’ inclinations to behave harshly (Study 2); and (3) having parents report their past child-directed behaviors, some of which were harsh (Study 3). Both child instigations and parents’ trait aggression were consistently associated with parents’ child-directed harsh behaviors. However, parents’ trait aggression only moderated the extent to which the instigation was associated with their harsh parenting for self-reported physical harsh behaviors (Study 1). The results of the current studies demonstrate that both situational factors, such as experiencing an instigation, and individual difference variables, such as trait aggression, affect parents’ likelihood to exhibit harsh behaviors, but found little evidence these factors interact.

Analysis Of Factors Influencing Acupuncture Clinical Effects: A Systematic Review

2021 ◽  
Author(s):  
Yali Liu ◽  
Honglin Jiang ◽  
Xiaotong Li ◽  
Xingyue Yang
Keyword(s):  
Systematic Review ◽  
Individual Difference ◽  
Current Status ◽  
Clinical Effects ◽  
High Quality ◽  
Psychological States ◽  
Factors Influencing ◽  
High Quality Evidence ◽  
Stimulus Parameters ◽  
Quality Evidence

Background: Acupuncture therapy has been widely applied all around the world. But there still lacks high-quality evidence to evaluate its clinical effects, safety and cost-effectiveness, which seriously restricts its further promotion and development. Acupuncture, as an operational intervention, is more complex than drug therapy. Factors involved in the acupuncture operation process, such as individual and interventional factors, may affect clinical effects. Purpose:s Through this systematic review, we attempt to analyze the current status of studies about the key factors influencing clinical effects, so as to provide evidence for the design of acupuncture studies. Methods: A literature research was conducted in six databases until May, 2020. Studies were screened and assessed by two authors independently. Data extraction was made after screening. We extracted the basic information of these studies, including publishing years and countries, population and factors influencing clinical effects.Results: Through the initial search in these six database, 6404 records were retrieved. After duplication and screening, a total of 355 studies were finally included in the qualitative analysis. About 88.5% of these studies were conducted in China with increasing number in the past decades, covering neurological disorders, orthopedic disorders, obstetrical and gynecological diseases, digestive diseases and so on; 239 studies focused on stimulus parameters which may influence effects, accounting for 67.3%, and 128, 44, 39, 32, 15, 11 studies discussed acupoints, patients’ pathological states, individual difference, psychological states, deqi, and other factors separately. Conclusion: There are more and more studies focusing on factors influencing acupuncture clinical effects, including stimulus parameters, acupoints, individual difference, patient pathological and psychological states. Based on this systematic review, several ignored factors should be considered for future researches, and more scientifically and objectively high quality evidence should also be proved.

Individual Difference Variables, Ethical Judgments, and Ethical Behavioral Intentions

1999 ◽  
Vol 9 (2) ◽  
pp. 183-205 ◽  
Author(s):  
Kenneth Bass ◽  
Tim Barnett ◽  
Gene Brown
Keyword(s):  
Individual Difference ◽  
Behavioral Intentions ◽  
Structural Equation ◽  
Equation Modeling ◽  
Just World ◽  
Ethical Judgments ◽  
Individual Difference Variables ◽  
The Individual ◽  
Ethical Behavioral Intentions ◽  
The Relationship

Abstract:This study examined the relationship between the individual difference variables of personal moral philosophy, locus of control, Machiavellianism, and just world beliefs and ethical judgments and behavioral intentions. A sample of 602 marketing practitioners participated in the study. Structural equation modeling was used to test hypothesized relationships. The results either fully or partially supported hypothesized direct effects for idealism, relativism, and Machiavellianism. Findings also suggested that Machiavellianism mediated the relationship between individual difference variables and ethical judgments/behavioral intentions.

scholarly journals Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Relationship Quality ◽  
Individual Difference ◽  
Large Scale ◽  
Well Being ◽  
Attachment Anxiety ◽  
Self Report ◽  
Individual Difference Variables ◽  
Partner Satisfaction

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

scholarly journals A Neurocomputational Theory of Nightmares: The role of formal properties of nightmare images

2021 ◽  
Author(s):  
Patrick McNamara ◽  
Wesley J Wildman ◽  
George Hodulik ◽  
David Rohr
Keyword(s):  
Individual Difference ◽  
Parameter Space ◽  
Computational Models ◽  
Computational Simulation ◽  
Time Varying ◽  
Individual Difference Variables ◽  
Image Characteristics ◽  
Nightmare Disorder ◽  
Formal Properties

Abstract Study Objectives To test and extend Levin & Nielsen’s (2007) Affective Network Dysfunction (AND) model with nightmare disorder (ND) image characteristics, and then to implement the extension as a computational simulation, the Disturbed Dreaming Model (DDM). Methods We used AnyLogic V7.2 to computationally implement an extended AND model incorporating quantitative effects of image characteristics including valence, dominance, and arousal. We explored the DDM parameter space by varying parameters, running approximately one million runs, each for one month of model time, varying pathway bifurcation thresholds, image characteristics, and individual-difference variables to quantitively evaluate their combinatory effects on nightmare symptomology. Results The DDM shows that the AND model extended with pathway bifurcations and image properties is computationally coherent. Varying levels of image properties we found that when nightmare images exhibit lower dominance and arousal levels, the ND agent will choose to sleep but then has a traumatic nightmare, whereas, when images exhibit greater than average dominance and arousal levels, the nightmares trigger sleep-avoidant behavior, but lower overall nightmare distress at the price of exacerbating nightmare effects during waking hours. Conclusions Computational simulation of nightmare symptomology within the AND framework suggests that nightmare image properties significantly influence nightmare symptomology. Computational models for sleep and dream studies are powerful tools for testing quantitative effects of variables affecting nightmare symptomology and confirms the value of extending the Levin & Nielsen AND model of disturbed dreaming/ND.

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