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.     

Responding to social and symbolic extrafoveal cues: cue shape trumps biological relevance

Psychological Research - Social cues presented at visual fixation have been shown to strongly influence an observer’s attention and response selection. Here we ask whether the same holds for...     

Forgiveness in Younger,Middle-Aged and Older Adults: Age and Gender Matters

The present study investigated age and gender differences in forgiveness of real-life transgressions. Emerging and young, middle-aged, and older adults recalled the most recent and serious interpersonal transgression and then completed the Transgression-Related Interpersonal Motivations Inventory (TRIM-18), which measured their avoidance, revenge, and benevolence motivation toward an offender and indicated to what extent they are generally concerned with the subject of forgiveness. The results revealed a trend among middle-aged adults to express more avoidance than younger adults. Moreover, young men had a greater motivation to seek revenge than middle-aged and older men. No such age differences were apparent for women. Additionally, forgiveness was a more manifest subject in everyday life for middle-aged adults and women. These findings emphasize the importance of age and gender when investigating forgiveness.     

Cognitive culture: theoretical and empirical insights into social learning strategies

Research into social learning (learning from others) has expanded significantly in recent years, not least because of productive interactions between theoretical and empirical approaches. This has been coupled with a new emphasis on learning strategies, which places social learning within a cognitive decision-making framework. Understanding when, how and why individuals learn from others is a significant challenge, but one that is critical to numerous fields in multiple academic disciplines, including the study of social cognition.     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

Learning new faces in typically developing children and children on the autistic spectrum

As faces become familiar, recognition becomes easier but the style of processing also changes. Here, twenty-one typically developing (TD) children and twenty-one children with autism spectrum disorder (ASD) were familiarised with 6 identities over 3 days. Next, they completed a 4-alternative forced-choice matching test in which targets were the 6 familiarised faces and 6 unfamiliar faces. The TD group showed a significant advantage for familiarised faces when matching whole faces and both internal and external facial regions. The ASD group showed similar familiarisation effects for whole and external faces, but not for internal regions. The ASD group was also impaired at matching eyes and mouths of familiarised faces. Results suggest the process of acquiring familiarity with faces differs from ASD and TD children.