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.     

A Guide to Conducting a Meta-Analysis

Meta-analysis is widely accepted as the preferred method to synthesize research findings in various disciplines. This paper provides an introduction to when and how to conduct a meta-analysis. Several practical questions, such as advantages of meta-analysis over conventional narrative review and the number of studies required for a meta-analysis, are addressed. Common meta-analytic models are then introduced. An artificial dataset is used to illustrate how a meta-analysis is conducted in several software packages. The paper concludes with some common pitfalls of meta-analysis and their solutions. The primary goal of this paper is to provide a summary background to readers who would like to conduct their first meta-analytic study.     

Probabilities,causation, and logic programming in conditional reasoning: reply to Stenning and Van Lambalgen (2016)

Oaksford and Chater (2014 Oaksford, M., &; Chater, N. (2014). Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning. Thinking and Reasoning, 20, 269–295. doi:10.1080/13546783.2013.877401[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar], Thinking and Reasoning, 20, 269–295) critiqued the logic programming (LP) approach to nonmonotonicity and proposed that a Bayesian probabilistic approach to conditional reasoning provided a more empirically adequate theory. The current paper is a reply to Stenning and van Lambalgen's rejoinder to this earlier paper entitled ‘Logic programming, probability, and two-system accounts of reasoning: a rejoinder to Oaksford and Chater’ (2016) in Thinking and Reasoning. It is argued that causation is basic in human cognition and that explaining how abnormality lists are created in LP requires causal models. Each specific rejoinder to the original critique is then addressed. While many areas of agreement are identified, with respect to the key differences, it is concluded the current evidence favours the Bayesian approach, at least for the moment.     

Gesture Supports Spatial Thinking in STEM

The present article describes two studies that examine the impact of teaching students to use gesture to support spatial thinking in the Science, Technology, Engineering, and Mathematics (STEM) discipline of chemistry. In Study 1 we compared the effectiveness of instruction that involved either watching gesture, reproducing gesture, or reading text. The results indicate that students in the reproducing gesture condition produced significantly more gestures while problem solving than students in the other two groups and significantly outperformed the other groups on study measures. In Study 2 we compared the effectiveness of gesture to an instructional approach that involved manually handling concrete models without gesture. Students performed equally well in both conditions; however, students taught with concrete models performed significantly worse if concrete models were not available during assessment. These studies show that gesture is an effective strategy for supporting spatial thinking in STEM disciplines and that this benefit may result from physically simulating spatial transformations.     

Passport Checks: Interactions Between Matching Faces and Biographical Details

Matching unfamiliar faces is known to be a difficult task. However, most research has tested viewers' ability to match pairs of faces presented in isolation. In real settings, professionals are commonly required to examine photo ID that contains other biographical information too. In three experiments, we present faces embedded in passport frames and ask viewers to make face matching decisions and to check biographical information. We find that the inclusion of a passport frame reduces viewers' ability to detect a face mismatch. Furthermore, the nature of the face match influences biographical data checking—true matches lead to fewer detections of invalid data. In general, viewers were poor at spotting errors in biographical information. This pattern suggests that detection of fraudulent photo ID is even harder than current experimental studies suggest. Possible mechanisms for these effects are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Mortality salience increases language style matching and well-being

The present research examined whether people imitate the language style of others (i.e., the use of function words) as a form of liking when mortality concerns are salient. In Study 1, participants answered questions about death or public speaking and then engaged in an instant messaging conversation with a confederate. In Study 2, participant pairs verbally discussed a news article about increasing homicide rates or the rise in academic pressure. Next, everyone completed measures of self-esteem, life satisfaction, and relationship need satisfaction. The results revealed that, in comparison to the control conditions, participants exhibited greater language style matching (LSM) following reminders of death (Studies 1 & 2). Further, mediational analyses showed that higher LSM after mortality salience was associated with better psychological and social well-being (Study 2). Although the threat of death has been shown to make people more hostile and disparaging toward dissimilar others, the present work suggests that individuals, even strangers, may feel closer through language coordination following thoughts of mortality.     

From Pixels to People: A Model of Familiar Face Recognition

Research in face recognition has largely been divided between those projects concerned with front-end image processing and those projects concerned with memory for familiar people. These perceptual and cognitive programmes of research have proceeded in parallel, with only limited mutual influence. In this paper we present a model of human face recognition which combines both a perceptual and a cognitive component. The perceptual front-end is based on principal components analysis of face images, and the cognitive back-end is based on a simple interactive activation and competition architecture. We demonstrate that this model has a much wider predictive range than either perceptual or cognitive models alone, and we show that this type of combination is necessary in order to analyse some important effects in human face recognition. In sum, the model takes varying images of “known” faces and delivers information about these people.     

Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.     

InterFace: A software package for face image warping,averaging, and principal components analysis

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the “face space” produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.