Understanding attraction in formal mentoring relationships from an affective perspective

Despite the critical role affect can play in mentoring, a type of close relationships in the workplace, affect has not received much attention in existing mentoring research. Drawing upon Byrne's affect-centered model of attraction, we examined the relationships among mentors' moods during their interaction with protégés, mentors' liking of protégés, and mentoring support received by protégés. Moreover, we also examined the moderating effect of protégés' emotional intelligence (EI) on the relationship between mentors' moods and liking. Based on data collected from 237 ongoing formal mentoring dyads, we found that mentors' positive moods were positively related, and negative moods were negatively related, to mentoring support received through mentors' liking. Furthermore, protégés' EI only moderated the positive relationship between mentors' positive moods and liking such that the positive relationship was stronger for protégés low on EI. Theoretical and practical implications of the findings are discussed.     

Job characteristics and burnout: The moderating roles of emotional intelligence,motivation and pay among bank employees

This study investigated the prediction of burnout from job characteristics, emotional intelligence, motivation and pay among bank employees. It also examined the interactions of emotional intelligence, motivation, pay and job characteristics in the prediction of burnout. Data obtained from 230 (Males = 127, Females = 103) bank employees were analysed using Pearson's Product Moment Correlation and multiple regression analysis. Results showed that theses variables jointly and separately negatively predicted burnout components. The results further indicated that emotional intelligence, motivation and pay separately interacted with some job characteristic components to negatively predict some burnout components. The findings imply that emotional intelligence, motivation and pay could be considered by counsellors when designing interventions to reduce burnout among bank employees.     

Factor Structure of the Torrance Tests of Creative Thinking Verbal Form B in a Spanish‐speaking Population

The objective of this study was to compare, through a Confirmatory Factor Analysis, two different theoretical models that explain the operationalized creativity construct with the Verbal Torrance Tests of Creative Thinking (TTCT), Form B. Model 1 is represented by six factors which correspond to each activity and its respective indicators while Model 2 is integrated by three factors which correspond to each TTCT ability (i.e., Fluency, Originality, and Flexibility) and the corresponding indicators for each variable. The study was carried out with a sample consisting of 432 Spanish‐speaking youngsters of both sexes aged 15–26. According to the research findings, the model which showed the most satisfactory fit identifies six correlated factors that correspond to each of the activities proposed (χ² = 414.48; df = 116; χ²/df = 3.57; GFI = .90; NFI = .95; CFI = .96 and RMSEA = .077). These results are discussed according to its psychometric implications for the construct assessment in different fields.     

Can Word Puzzles be Tailored to Improve Different Dimensions of Verbal Fluency? A Report of an Intervention Study

Verbal fluency is commonly used as a proxy measure of executive functioning, as it involves cognitive flexibility, working memory, and inhibitory control. Previous research has demonstrated that crosswords can be a useful means of improving verbal fluency, results consistent with the cognitive reserve hypothesis; the form of verbal fluency affected has, however, differed across studies. The present study sought to assess the extent to which it was possible to target phonemic (PVF) and semantic verbal fluency (SVF) separately through word puzzles designed to focus on semantic/thematic and structural clues respectively. Fifty-three university students were randomly assigned to one of three groups: semantic/thematic, structural, or a daily diary control group. They were assessed on PVF and SVF at baseline, and immediately following a four-week intervention. Age, sex, and depression scores were controlled for. A 2 × 3 mixed ANCOVA showed that the structural group improved significantly more in PVF during the intervention period than did the semantic/thematic or control groups, with the improvement linked to improved switching performance. The effect size was large. No significant difference in improvement in SVF emerged, although the effect size was moderate. The findings support the notion that it is possible to improve specific forms of verbal fluency through tailored brief word-puzzle interventions.     

The Role of Trait Emotional Intelligence in Academic Performance: Theoretical Overview and Empirical Update

Considerable debate still exists among scholars over the role of trait emotional intelligence (TEI) in academic performance. The dominant theoretical position is that TEI should be orthogonal or only weakly related to achievement; yet, there are strong theoretical reasons to believe that TEI plays a key role in performance. The purpose of the current article is to provide (a) an overview of the possible theoretical mechanisms linking TEI with achievement and (b) an update on empirical research examining this relationship. To elucidate these theoretical mechanisms, the overview draws on multiple theories of emotion and regulation, including TEI theory, social-functional accounts of emotion, and expectancy-value and psychobiological model of emotion and regulation. Although these theoretical accounts variously emphasize different variables as focal constructs, when taken together, they provide a comprehensive picture of the possible mechanisms linking TEI with achievement. In this regard, the article redresses the problem of vaguely specified theoretical links currently hampering progress in the field. The article closes with a consideration of directions for future research.     

Promoting the emergence of advanced knowledge: A review of peak relational training system: Direct training module by Mark R. Dixon

Mark Dixon's (2014) manual, PEAK Relational Training System: Direct Training Module, proposes a novel approach to manualized evaluation and curriculum development. Dixon's PEAK system, introduced in the book as the first of four modules, translates derived relational responding methodology into a new verbal‐behavior approach. The PEAK system is firmly rooted in the basic, conceptual, and applied behavior‐analytic tradition; however, it differs substantially from the competition in its unique application of relational frame theory to produce efficient learning. The manual's accessible nature renders it a viable product for many users and readers. The growing empirical support for PEAK’s efficacy, usability, and psychometrics is impressive and provides a robust empirical basis for the system that is not described within the pages of the manual. Behavior analysts may shy away from a manualized system that explicitly omits discussion of scholarship and empirical bases but would be remiss in doing so, given the potential of PEAK to revolutionize the way clinicians and parents apply the verbal behavior approach.     

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness.     

Verbalization of Decision Strategies in Multiple‐Cue Probabilistic Inference

In multiple‐cue probabilistic inference, people choose between alternatives based on several cues, each of which is differentially associated with an alternative's overall value. Various strategies have been proposed for probabilistic inference (e.g., weighted additive, tally, and take‐the‐best). These strategies differ in how many cue values they require to enact and in how they weight each cue. Do decision makers actually use any of these strategies? Ways to investigate this question include analyzing people's choices and the cues that they reveal. However, different strategies often predict the same decisions, and search behavior says nothing about whether or how people use the information that they acquire. In this research, we attempt to elucidate which strategies participants use in a multiple‐cue probabilistic inference task by examining verbal protocols, a high‐density source of process data. The promise of verbal data is in their utility for testing detailed information processing models. To that end, we apply protocol analysis in conjunction with computational simulations. We find converging evidence across outcome measures, search measures, and verbal reports that most participants use simplifying heuristics, namely take‐the‐best. Copyright © 2015 John Wiley & Sons, Ltd.