Minimalistic toy robot to analyze a scenery of speaker–listener condition in autism

Atypical neural architecture causes impairment in communication capabilities and reduces the ability of representing the referential statements of other people in children with autism. During a scenery of “speaker–listener” communication, we have analyzed verbal and emotional expressions in neurotypical children (n = 20) and in children with autism (n = 20). The speaker was always a child, and the listener was a human or a minimalistic robot which reacts to speech expression by nodding only. Although both groups performed the task, everything happens as if the robot could allow children with autism to elaborate a multivariate equation encoding and conceptualizing within his/her brain, and externalizing into unconscious emotion (heart rate) and conscious verbal speech (words). Such a behavior would indicate that minimalistic artificial environments such as toy robots could be considered as the root of neuronal organization and reorganization with the potential to improve brain activity.     

Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case

The problem of penalized maximum likelihood (PML) for an exploratory factor analysis (EFA) model is studied in this paper. An EFA model is typically estimated using maximum likelihood and then the estimated loading matrix is rotated to obtain a sparse representation. Penalized maximum likelihood simultaneously fits the EFA model and produces a sparse loading matrix. To overcome some of the computational drawbacks of PML, an approximation to PML is proposed in this paper. It is further applied to an empirical dataset for illustration. A simulation study shows that the approximation naturally produces a sparse loading matrix and more accurately estimates the factor loadings and the covariance matrix, in the sense of having a lower mean squared error than factor rotations, under various conditions.     

Assessment of generalised Bayesian structural equation models for continuous and binary data

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive $p$-values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17 , 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.