Employees High in Personal Intelligence Differ From Their Colleagues in Workplace Perceptions and Behavior

Personal intelligence (PI) involves the ability to recognize, reason, and use information about personality to understand oneself and other people. Employees in two studies (Ns = 394, 482) completed the Test of Personal Intelligence (TOPI; e.g., Mayer, Panter, & Caruso, 2017a) and assessments of workplace perception and behavior. Higher PI was associated with higher perceived workplace support and lower counterproductive work behavior. These relationships continued to hold after controlling for other key variables. The results indicate the TOPI, although still in research trials, shows promise as a screening device for selecting employees and targeting individuals for training.     

Parent–Child Informant Discrepancy is Associated with Poorer Treatment Outcome

In child clinical psychology, parent and child reports are typically used to make treatment decisions and determine the effectiveness of treatment. However, there are often moderate to large discrepancies between parent and child reports, and these discrepancies may reflect meaningful information about the parent, the child, and the parent–child relationship. Additionally, parent–child discrepancy may predict treatment outcome. This study examined parent–child discrepancy in a sample of 62 children (10.15±1.26 years old) with prominent social competence deficits and mixed diagnoses who were treated with a resilience-based, cognitive–behavioral group therapy program (the Resilience Builder Program) in a private clinical setting. Further analyses were conducted to investigate whether parent–child discrepancy related to treatment outcome. Consistent with the literature, prominent parent–child discrepancy was found across domains, with parents generally reporting more severe symptomatology. Treatment with the Resilience Builder Program resulted in significant improvement in parent report of multiple domains of functioning, including resilience, social skills, and emotion and behavior regulation. Importantly, larger parent–child discrepancy at the start of therapy was predictive of poorer overall treatment response. Given its impact on therapeutic effectiveness, these results suggest that parent–child disagreement regarding the child’s impairment at the onset of therapy is worthy of assessment prior to treatment, and may itself be a topic worthy of targeting in treatment.     

The Role of Negative Information in Distributional Semantic Learning

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations.     

Attention Problems and Restlessness as Transdiagnostic Markers of Severity and Treatment Response in Youth with Internalizing Problems

Research on Child and Adolescent Psychopathology - Transdiagnostic models of psychopathology suggest that disorders may share common features that could influence their severity. Attention problems...     

Effects of aging on the relation between episodic simulation and prosocial intentions

Imagining helping a person in need can facilitate prosocial intentions. Here we investigated how this effect can change with aging. We found that, similar to young adults, older adults were more willing to help a person in need when they imagined helping that person compared to a baseline condition that did not involve helping, but not compared to a conceptual helping control condition. Controlling for heightened emotional concern in older adults revealed an age-related difference in the effect of imagining on willingness to help. While we observed age-related condition effects, we also found that the subjective vividness of scene imagery predicted willingness to help for both age groups. Our findings provide insight into the relations among episodic simulation, healthy aging, emotion, and prosociality. Implications for effects of episodic memory and aging on social decision-making are discussed.     

Using experiential optimization to build lexical representations

To account for natural variability in cognitive processing, it is standard practice to optimize a model’s parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model’s fit to representative data. We fit language-based behavioral data using experiential optimization, a method that optimizes the materials that a model is given while retaining the learning and processing mechanisms of standard practice. Rather than using default materials, experiential optimization selects the optimal linguistic sources to create a memory representation that maximizes task performance. We demonstrate performance on multiple benchmark tasks by optimizing the experience on which a model’s representation is based.

     

Causation and melanoma classification

In this article, I begin by giving a brief history of melanoma causation. I then discuss the current manner in which malignant melanoma is classified. In general, these systems of classification do not take account of the manner of tumour causation. Instead, they are based on phenomenological features of the tumour, such as size, spread, and morphology. I go on to suggest that misclassification of melanoma is a major problem in clinical practice. I therefore outline an alternative means of classifying these tumours based on causal factors. By analogy with similar systems that have recently emerged for other cancers, I suggest that this causal classification is likely to be both workable and helpful, even in the absence of a full causal-mechanistic understanding of the aetiology of the tumour.