The Past Guides the Future: Implementing the Multicultural and Social Justice Counseling Competencies

The authors describe the ways in which the Multicultural and Social Justice Counseling Competencies (MSJCC; Ratts, Singh, Nassar-McMillan, Butler, & McCullough, 2015) can be viewed from a human rights framework and as the latest iteration in the long history of the multicultural and social justice counseling competency movement. MSJCC implementation and integration are explored, and recommendations for innovating the MSJCC are described.     

Embodying the Multicultural and Social Justice Counseling Competency Movement: Voices From the Field

This article illustrates counselors’ embodiment, over time, of the multicultural and social justice counseling competency movement leading to the Multicultural and Social Justice Counseling Competencies (Ratts, Singh, Nassar-McMillan, Butler, & McCullough, 2015). The authors discuss the multicultural and social justice counseling competency movement in the context of relationships as appropriate to the counseling field. Aligned with contemporary research perspectives, the authors focus on the lived experiences of 2 pioneering social justice and multicultural competency advocates, Drs. Patricia Arredondo and Derald Wing Sue. The authors integrate scholarship with these historical and personal perspectives, as well as their own, to demonstrate the ways in which people and movements drive counseling leadership and advocacy.     

关系认知与善意认知对大学生人际信任的影响

采用投资博弈,以大学生为被试探讨关系认知（亲疏关系、阶层关系）和特质认知（以善意为例）对中国人人际信任的影响。结果发现,亲疏关系认知、阶层关系认知和善意认知都会影响中国人的人际信任。关系越近、阶层越高、越善意,信任水平越高。随着亲疏关系下降或善意降低,阶层关系对信任水平的作用之间的差异逐步减弱。亲疏关系认知和阶层关系认知存在交互作用,亲疏、阶层和善意也具有交互作用。人们对上位阶层的信任大于平位大于下位,但是当信任对象存在低善意特质或陌生人身份时,人们对上位阶层的信任会下降至和平位没有差异;当低善意特质和陌生人身份同时存在时,人们对上位、平位和下位的信任无差异。     

The association between risky decision making and attention-deficit/hyperactivity disorder symptoms: A preregistered assessment of need for cognition as underlying mechanism

Attention-Deficit/Hyperactivity Disorder (ADHD) is related to suboptimal decision making in experimental tasks and to real-life risk-taking behavior (RTB) such as substance abuse and unsafe traffic conduct. In this preregistered study, we tested whether these associations are mediated by need for cognition—the extent to which one tends towards, and enjoys, analytical thought. In a large sample of young adults (N = 463, M_age = 19.7 years), we tested whether need for cognition mediated the association between self-reported ADHD symptoms on the one hand and decision-making strategy complexity on an experimental gambling task and self-reported real-life RTB on the other hand. Preregistered confirmatory analyses indicated first that ADHD symptoms were positively associated with real-life RTB, but the association was not mediated by need for cognition. Second, ADHD symptoms were not related to decision-making strategy complexity, and need for cognition was not a significant mediator. Explorative analyses revealed that (a) need for cognition was associated with higher decision-making accuracy and slower reaction time; (b) need for cognition was related to inattentive but not to hyperactive/impulsive ADHD symptoms; (c) need for cognition was associated with health-related RTB but not interpersonal RTB; and (4) only the association between inattention and health-related RTB was mediated by need for cognition. We conclude that need for cognition is not a mediator in the association between ADHD symptoms and RTB. Additionally, we conclude that neither ADHD symptoms nor need for cognition predict decision-making strategy complexity. Implications for both future research and clinical practice are discussed.     

Searching for Prosociality in Qualitative Data: Comparing Manual,Closed-Vocabulary,and Open-Vocabulary Methods

Although most people present themselves as possessing prosocial traits, people differ in the extent to which they actually act prosocially in everyday life. Qualitative data that were not ostensibly collected to measure prosociality might contain information about prosocial dispositions that is not distorted by self-presentation concerns. This paper seeks to characterise charitable donors from qualitative data. We compared a manual approach of extracting predictors from participants' self-described personal strivings to two automated approaches: A summation of words predefined as prosocial and a support vector machine classifier. Although variables extracted by the support vector machine predicted donation behaviour well in the training sample (N = 984), virtually, no variables from any method significantly predicted donations in a holdout sample (N = 496). Raters' attempts to predict donations to charity based on reading participants' personal strivings were also unsuccessful. However, raters' predictions were associated with past charitable involvement. In sum, predictors derived from personal strivings did not robustly explain variation in charitable behaviour, but personal strivings may nevertheless contain some information about trait prosociality. The sparseness of personal strivings data, rather than the irrelevance of open-ended text or individual differences in goal pursuit, likely explains their limited value in predicting prosocial behaviour. © 2020 European Association of Personality Psychology     

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology     

Decision-makers use social information to update their preferences but choose for others as they do for themselves

People's risky decisions are susceptible to the social context in which they take place. Across three experiments using different paradigms, we investigated the influence of three social factors upon participants' decisions: the recipient of the decision-making outcome (self, other, or joint), the nature of the relationship with the other agent (friend, stranger, or teammate), and the type of information that participants received about others' preferences: none at all, general information about how previous participants had decided, or information about a specific partner's preference. We found that participants' decisions about risk did not differ according to whether the outcome at stake was their own, another agent's, or a joint outcome, nor according to the type of information available. Participants did, however, adjust their preferences for risky options in light of social information.     

The influence of biased exposure to forgone outcomes

After making decisions, we often get feedback concerning forgone outcomes (what would have happened had we chosen differently). Yet, many times, our exposure to such feedback is systematically biased. For example, your friends are more likely to tell you about a party you missed if it was fun than if it was boring. Despite its prevalence, the effects of biased exposure to forgone outcomes on future choice have not been directly studied. In three studies (five experiments) using a simplified learning task, we study the basic influence of biased exposure to forgone outcomes in the extreme case in which decision makers can easily infer the missing information such that the biased exposure carries almost no informational value. The results in all studies suggest that nevertheless, the biased exposure to forgone outcomes affected choice. Exposure to forgone outcomes only when they were better than the obtained outcomes (Only-Better-Forgone) increased selections of the forgone option compared with exposure to forgone outcomes only when they were worse than the obtained outcome (Only-Worse-Forgone). Moreover, relative to an unbiased exposure to all forgone outcomes, the effect of exposure to Only-Worse-Forgone was larger than the effect of exposure to Only-Better-Forgone feedback. However, these effects were not universal: In environments that include rare negative events (“disasters”), biased exposure to forgone outcomes had very little effect. We raise potential explanations and further discuss implications for marketing and risk awareness.     

Adding versus averaging: Evaluability theory applied to job choice decisions

While the unique roles of individual job attributes (e.g., salary and benefits) in job and organizational attraction have received extensive research attention, research examining the mechanisms through which an overall evaluation of a job option is made by combining evaluations of individual attributes is scarce. The current study examined the process through which job choice decisions are made under three conditions: when evaluating a single job offer, when comparing two job offers, and when evaluating more than two job offers. In Study 1, it was found that when a single job offer is evaluated, the average of perceived values of attributes in an offer (e.g., the perceived attractiveness of a salary) drives the choice, whereas the difference between jobs is what matters when two jobs are evaluated simultaneously, potentially leading to a preference reversal between conditions when the same two jobs are evaluated. In Study 2, it was found that average values of attributes across options (e.g., average salary in all job offers received) influence job choice when more than two job offers are evaluated simultaneously. These findings indicate that in all three conditions, job choice decisions are influenced by the evaluability of the choice set, which becomes low when a single job offer is evaluated without any context, or when more than two job offers are evaluated simultaneously, and becomes high when two jobs are compared with each other. When evaluability is low, candidates resort to averaging as the decision rule, whereas adding is used when evaluability is high.     

Providing more or less detailed information in job advertisements—Does it matter?

Job advertising is a common and useful recruitment marketing method that is available to a wide range of candidates and offers a practical way to widen the applicant pool. Frequently, retail advertisements only briefly introduce job requirements, but others provide more detailed information. Existing message studies, however, are inconclusive about the effectiveness of message specificity. A scenario‐based experiment that included 164 participants revealed that the type of decision maker (maximizer or satisficer) moderates the specificity effects in recruitment messages. After receiving a detailed job message, more satisficers stop their search process, but only maximizers perceive the company as more attractive and increase intentions toward the advertised job. A follow‐up qualitative study involving 30 participants provided further insights.