The relationship between the Five‐Factor Model personality traits and peptic ulcer disease in a large population‐based adult sample

The current study examined the relationship between the Five‐Factor Model personality traits and physician‐confirmed peptic ulcer disease (PUD) diagnosis in a large population‐based adult sample, controlling for the relevant behavioral and sociodemographic factors. Personality traits were assessed by participants themselves and by knowledgeable informants using the NEO Personality Inventory‐3 (NEO PI‐3). When controlling for age, sex, education, and cigarette smoking, only one of the five NEO PI‐3 domain scales – higher Neuroticism – and two facet scales – lower A1: Trust and higher C1: Competence – made a small, yet significant contribution (p < 0.01) to predicting PUD in logistic regression analyses. In the light of these relatively modest associations, our findings imply that it is certain behavior (such as smoking) and sociodemographic variables (such as age, gender, and education) rather than personality traits that are associated with the diagnosis of PUD at a particular point in time. Further prospective studies with a longitudinal design and multiple assessments would be needed to fully understand if the FFM personality traits serve as risk factors for the development of PUD.     

Observer judgmental accuracy of personality: Benefits related to being a good (normative) judge

The current study tested the hypothesis that accuracy of personality judgment would be positively related to beneficial life outcomes. 189 participants observed targets across 5 dyadic interactions and made judgments of the 10 targets on the Big Five traits. Self and acquaintance ratings were used as the accuracy criteria. Normative accuracy for all traits was related to agreeableness, and normative accuracy for some traits was positively related to interpersonal control, interpersonal support, positive affect, and life satisfaction. Distinctive accuracy was not related to beneficial outcomes. These results imply that normative accuracy based on observation is associated with beneficial outcomes, whereas distinctive accuracy is not.     

State‐Trace Analysis: Dissociable Processes in a Connectionist Network?

Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state‐trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, performance measured by either two dependent variables, or two conditions of the same dependent measure. The resulting analysis is considered to provide evidence that either (a) a single process underlies performance (one function is produced) or (b) there is evidence for more than one process (more than one function is produced). This article reports simulations using the simple recurrent network (SRN; Elman, 1990) in which changes to the learning rate produced state‐trace plots with multiple functions. We also report simulations using a single‐layer error‐correcting network that generate plots with a single function. We argue that the presence of different functions on a state‐trace plot does not necessarily support a dual‐system account, at least as typically defined (e.g. two separate autonomous systems competing to control responding); it can also indicate variation in a single parameter within theories generally considered to be single‐system accounts.     

Perceptual effects of linguistic category priming: The Stapel and Semin (2007) paradigm revisited in twelve experiments

Linguistic category priming is a novel paradigm to examine automatic influences of language on cognition (Semin, 2008). An initial article reported that priming abstract linguistic categories (adjectives) led to more global perceptual processing, whereas priming concrete linguistic categories (verbs) led to more local perceptual processing (Stapel & Semin, 2007). However, this report was compromised by data fabrication by the first author, so that it remains unclear whether or not linguistic category priming influences perceptual processing. To fill this gap in the literature, the present article reports 12 studies among Dutch and US samples examining the perceptual effects of linguistic category priming. The results yielded no evidence of linguistic category priming effects. These findings are discussed in relation to other research showing cultural variations in linguistic category priming effects (IJzerman, Saddlemyer, & Koole, 2014). The authors conclude by highlighting the importance of conducting and publishing replication research for achieving scientific progress.     

Using a metaperspective to clarify the structural-narrative debate in family therapy

The debate in family therapy between structural and narrative therapists often seems irreconcilable. Drawing from work in theoretical sociology that identifies the basic structure of existing social theory, a metaperspective is introduced to clarify a discussion between leading figures in the debate. Disparate views of family interactions are examined in terms of more fundamental disagreements about the center of attention in therapy. Narrative and structural modes of presentation are examined as separate traditions that could not be further apart conceptually. Efforts to identify common ground are discussed in terms of more significant differences that are minimized by drawing out similarities. This analysis provides a framework for standing outside entrenched arguments to see more clearly the unique contributions of each approach.     

Neural modeling and imaging of the cortical interactions underlying syllable production

This paper describes a neural model of speech acquisition and production that accounts for a wide range of acoustic, kinematic, and neuroimaging data concerning the control of speech movements. The model is a neural network whose components correspond to regions of the cerebral cortex and cerebellum, including premotor, motor, auditory, and somatosensory cortical areas. Computer simulations of the model verify its ability to account for compensation to lip and jaw perturbations during speech. Specific anatomical locations of the model's components are estimated, and these estimates are used to simulate fMRI experiments of simple syllable production.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.